National Library of Energy BETA

Sample records for household characteristics census

  1. " of Supplier, Census Region, Census Division, and Economic Characteristics"

    U.S. Energy Information Administration (EIA) (indexed site)

    Quantity of Purchased Electricity and Steam by Type" " of Supplier, Census Region, Census Division, and Economic Characteristics" " of the Establishment, 1994" " (Estimates in Btu or Physical Units)" ," Electricity",," Steam" ," (million kWh)",," (billion Btu)" ,,,,,"RSE" " ","Utility","Nonutility","Utility","Nonutility","Row" "Economic

  2. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    9a. Household Characteristics by Northeast Census Region, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.2 1.5 Total .............................................................. 107.0 20.3 14.8 5.4 NE Household Size 1 Person ...................................................... 28.2 6.0 4.4 1.6 3.5 2 Persons

  3. 1999 Commercial Buildings Characteristics--Census Region

    U.S. Energy Information Administration (EIA) (indexed site)

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  4. 1999 Commercial Building Characteristics--Detailed Tables--Census...

    U.S. Energy Information Administration (EIA) (indexed site)

    Census Region > Detailed Tables-Census Region Complete Set of 1999 CBECS Detailed Tables Detailed Tables-Census Region Table B3. Census Region, Number of Buildings and Floorspace...

  5. Table HC1-3a. Housing Unit Characteristics by Household Income,

    U.S. Energy Information Administration (EIA) (indexed site)

    3a. Housing Unit Characteristics by Household Income, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factors Less than $14,999 $15,000 to $29,999 $30,000 to $49,999 $50,000 or More 0.6 1.3 1.1 1.0 0.9 1.4 1.0 Total ............................................... 107.0 18.7 22.9 27.1 38.3 15.0 33.8 3.3 Census Region and Division Northeast

  6. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2001 2 HC2-12a. Household Characteristics by West Census Region, Million U.S. Households, 2001 2 These data are from the 2001 Residential Energy Consumption Survey ...

  7. Estimating household fuel oil/kerosine, natural gas, and LPG prices by census region

    SciTech Connect (OSTI)

    Poyer, D.A.; Teotia, A.P.S.

    1994-08-01

    The purpose of this research is to estimate individual fuel prices within the residential sector. The data from four US Department of Energy, Energy Information Administration, residential energy consumption surveys were used to estimate the models. For a number of important fuel types - fuel oil, natural gas, and liquefied petroleum gas - the estimation presents a problem because these fuels are not used by all households. Estimates obtained by using only data in which observed fuel prices are present would be biased. A correction for this self-selection bias is needed for estimating prices of these fuels. A literature search identified no past studies on application of the selectivity model for estimating prices of residential fuel oil/kerosine, natural gas, and liquefied petroleum gas. This report describes selectivity models that utilize the Dubin/McFadden correction method for estimating prices of residential fuel oil/kerosine, natural gas, and liquefied petroleum gas in the Northeast, Midwest, South, and West census regions. Statistically significant explanatory variables are identified and discussed in each of the models. This new application of the selectivity model should be of interest to energy policy makers, researchers, and academicians.

  8. Table 2.4 Household Energy Consumption by Census Region, Selected...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... NANot available. 2See Appendix C for map of Census regions. Notes: * Data are estimates, and are for major energy sources only. * For years not shown, there are no data available. ...

  9. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    0a. Household Characteristics by Midwest Census Region, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.7 Total .............................................................. 107.0 24.5 17.1 7.4 NE Household Size 1 Person ...................................................... 28.2 6.7 4.7 2.0 6.2 2 Persons

  10. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    1a. Household Characteristics by South Census Region, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.1 1.5 1.6 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Household Size 1 Person ...................................................... 28.2 9.9 5.0 1.8 3.1 6.3 2 Persons

  11. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Household Characteristics by West Census Region, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.5 1.0 1.8 1.1 Total .............................................................. 107.0 23.3 6.7 16.6 NE Household Size 1 Person ...................................................... 28.2 5.6 1.8 3.8 5.4 2 Persons .................................................... 35.1 7.3 1.9 5.5

  12. "Table HC11.1 Housing Unit Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    Housing Unit Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units" ,,,"Census Division" ,,"Total Northeast" "Housing Unit Characteristics",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Urban/Rural Location (as Self-Reported)" "City",47.1,6.9,4.7,2.2 "Town",19,6,4.2,1.9

  13. "Table HC11.11 Home Electronics Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Home Electronics Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Home Electronics Characteristics",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Personal Computers" "Do Not Use a Personal Computer ",35.5,6.9,5.3,1.6 "Use a

  14. "Table HC11.2 Living Space Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Living Space Characteristics",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Floorspace (Square Feet)" "Total Floorspace1" "Fewer than 500",3.2,0.9,0.5,0.4

  15. "Table HC11.4 Space Heating Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Space Heating Characteristics",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Do Not Have Space Heating Equipment",1.2,"Q","Q","Q" "Have Main

  16. "Table HC11.6 Air Conditioning Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Air Conditioning Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Air Conditioning Characteristics",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Do Not Have Cooling Equipment",17.8,4,2.4,1.7 "Have Coolling

  17. "Table HC11.8 Water Heating Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    8 Water Heating Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Water Heating Characteristics",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Number of Water Heaters" "1.",106.3,19.6,14.4,5.2 "2 or

  18. "Table HC11.9 Home Appliances Characteristics by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    1.9 Home Appliances Characteristics by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Home Appliances Characteristics",,,"Middle Atlantic","New England" "Total U.S.",111.1,20.6,15.1,5.5 "Cooking Appliances" "Conventional Ovens" "Use an

  19. "Table HC12.1 Housing Unit Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    Housing Unit Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Housing Unit Characteristics",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Urban/Rural Location (as Self-Reported)" "City",47.1,9.7,7.3,2.4

  20. "Table HC12.11 Home Electronics Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Home Electronics Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Home Electronics Characteristics",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Personal Computers" "Do Not Use a Personal Computer ",35.5,8.1,5.6,2.5 "Use a

  1. "Table HC12.2 Living Space Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Living Space Characteristics",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Floorspace (Square Feet)" "Total Floorspace1" "Fewer than

  2. "Table HC12.4 Space Heating Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Space Heating Characteristics",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Do Not Have Space Heating Equipment",1.2,"Q","Q","N" "Have Main

  3. "Table HC12.6 Air Conditioning Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Air Conditioning Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Air Conditioning Characteristics",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Do Not Have Cooling Equipment",17.8,2.1,1.8,0.3 "Have Cooling

  4. "Table HC12.9 Home Appliances Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2.9 Home Appliances Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Home Appliances Characteristics",,,"East North Central","West North Central" "Total U.S.",111.1,25.6,17.7,7.9 "Cooking Appliances" "Conventional Ovens" "Use an

  5. "Table HC13.1 Housing Unit Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    Housing Unit Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Housing Unit Characteristics",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Urban/Rural Location (as Self-Reported)"

  6. "Table HC13.11 Home Electronics Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Home Electronics Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Home Electronics Characteristics",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Personal Computers" "Do Not Use a Personal Computer

  7. "Table HC13.2 Living Space Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Living Space Characteristics",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Floorspace (Square Feet)" "Total Floorspace1" "Fewer than

  8. "Table HC13.4 Space Heating Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Space Heating Characteristics",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Do Not Have Space Heating

  9. "Table HC13.6 Air Conditioning Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Air Conditioning Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Air Conditioning Characteristics",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Do Not Have Cooling Equipment",17.8,1.4,0.8,0.2,0.3 "Have

  10. "Table HC13.8 Water Heating Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    8 Water Heating Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Water Heating Characteristics",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Number of Water Heaters" "1.",106.3,39,21.1,6.6,11.3 "2

  11. "Table HC13.9 Home Appliances Characteristics by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    3.9 Home Appliances Characteristics by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Home Appliances Characteristics",,,"South Atlantic","East South Central","West South Central" "Total U.S.",111.1,40.7,21.7,6.9,12.1 "Cooking Appliances" "Conventional Ovens" "Use

  12. "Table HC14.1 Housing Unit Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    Housing Unit Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Housing Unit Characteristics",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Urban/Rural Location (as Self-Reported)" "City",47.1,12.8,3.2,9.6 "Town",19,3,1.1,1.9

  13. "Table HC14.11 Home Electronics Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Home Electronics Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Home Electronics Characteristics",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Personal Computers" "Do Not Use a Personal Computer ",35.5,6.4,2.2,4.2 "Use a Personal

  14. "Table HC14.2 Living Space Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Living Space Characteristics",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Floorspace (Square Feet)" "Total Floorspace1" "Fewer than 500",3.2,1,0.2,0.8 "500 to

  15. "Table HC14.4 Space Heating Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Space Heating Characteristics",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Do Not Have Space Heating Equipment",1.2,0.7,"Q",0.7 "Have Main Space Heating Equipment",109.8,23.4,7.5,16

  16. "Table HC14.6 Air Conditioning Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Air Conditioning Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Air Conditioning Characteristics",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Do Not Have Cooling Equipment",17.8,10.3,3.1,7.3 "Have Cooling Equipment",93.3,13.9,4.5,9.4 "Use Cooling

  17. "Table HC14.8 Water Heating Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    8 Water Heating Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Water Heating Characteristics",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Number of Water Heaters" "1.",106.3,23.2,7.1,16.1 "2 or More",3.7,1,0.4,0.6 "Do Not Use Hot

  18. "Table HC14.9 Home Appliances Characteristics by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    4.9 Home Appliances Characteristics by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Home Appliances Characteristics",,,"Mountain","Pacific" "Total U.S.",111.1,24.2,7.6,16.6 "Cooking Appliances" "Conventional Ovens" "Use an Oven",109.6,23.7,7.5,16.2

  19. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    9a. Air Conditioning by Northeast Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.2 1.8 Households With Electric Air-Conditioning Equipment ...................... 82.9 14.5 11.3 3.2 3.3 Air Conditioners Not Used ........................... 2.1 0.3 0.3 Q 28.3 Households Using Electric Air-Conditioning 1

  20. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    0a. Air Conditioning by Midwest Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 20.5 13.6 6.8 2.2 Air Conditioners Not Used ........................... 2.1 0.3 Q Q 27.5 Households Using Electric Air-Conditioning 1

  1. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    1a. Air Conditioning by South Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.2 1.3 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 37.2 19.3 6.4 11.5 1.5 Air Conditioners Not Used ........................... 2.1 0.4 Q Q Q 28.2 Households Using Electric Air-Conditioning 1

  2. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Air Conditioning by West Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.4 1.2 1.7 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 10.7 3.4 7.2 7.1 Air Conditioners Not Used ........................... 2.1 1.1 0.2 0.9 15.5 Households Using Electric Air-Conditioning 1 ........................................ 80.8 9.6 3.2

  3. http://www.census.gov/

    National Nuclear Security Administration (NNSA)

    People & Households American Community Survey * Estimates * Projections Income | State Median Income * Poverty * Health Insurance International * Genealogy * Census 2000 * More ...

  4. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Appliances by West Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.5 1.0 1.7 1.2 Total .............................................................. 107.0 23.3 6.7 16.6 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 22.1 6.6 15.5 1.1 1

  5. "Table HC12.8 Water Heating Characteristics by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    8 Water Heating Characteristics by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Water Heating Characteristics",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Number of Water Heaters" "1.",106.3,24.5,17.1,7.4 "2 or More",3.7,0.9,0.5,0.4

  6. Table HC11.1 Housing Unit Characteristics by Northeast Census Region, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    1.1 Housing Unit Characteristics by Northeast Census Region, 2005 Total......................................................................... 111.1 20.6 15.1 5.5 Urban/Rural Location (as Self-Reported) City....................................................................... 47.1 6.9 4.7 2.2 Town..................................................................... 19.0 6.0 4.2 1.9 Suburbs................................................................ 22.7 4.4 4.0 0.5

  7. "Table HC10.1 Housing Unit Characteristics by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0.1 Housing Unit Characteristics by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Housing Unit Characteristics",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Census Region and Division" "Northeast",20.6,20.6,"N","N","N" "New

  8. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    0a. Appliances by Midwest Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.5 Total .............................................................. 107.0 24.5 17.1 7.4 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 23.8 16.6 7.2 NE 1

  9. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    1a. Appliances by South Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.1 1.4 1.5 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 36.2 19.4 6.4 10.3 1.5 1

  10. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    9a. Appliances by Northeast Census Region, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.3 1.6 Total .............................................................. 107.0 20.3 14.8 5.4 NE Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 19.6 14.5 5.2 1.1 1

  11. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Space Heating by West Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.6 1.0 1.6 1.2 Total .............................................................. 107.0 23.3 6.7 16.6 NE Heat Home .................................................... 106.0 22.6 6.7 15.9 NE Do Not Heat Home ....................................... 1.0 0.7 Q 0.7 10.6 No Heating Equipment

  12. usage_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    Usage Indicators by South Census Region, Million U.S. Households, 2001 5 HC6-12a. Usage Indicators by West Census Region, Million U.S. Households, 2001 5 These data are from the ...

  13. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    Home Office Equipment by South Census Region, Million U.S. Households, 2001 1 HC7-12a. Home Office Equipment by West Census Region, Million U.S. Households, 2001 1 These data are ...

  14. "Table HC10.11 Home Electronics Characteristics by U.S. Census Regions, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Home Electronics Characteristics by U.S. Census Regions, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Home Electronics Characteristics",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Personal Computers" "Do Not Use a Personal Computer ",35.5,6.9,8.1,14.2,6.4 "Use a Personal

  15. "Table HC10.2 Living Space Characteristics by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Living Space Characteristics",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Floorspace (Square Feet)" "Total Floorspace1" "Fewer than 500",3.2,0.9,0.5,0.9,1 "500 to 999",23.8,4.6,3.9,9,6.3

  16. "Table HC10.4 Space Heating Characteristics by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Space Heating Characteristics",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Do Not Have Space Heating Equipment",1.2,"Q","Q","Q",0.7 "Have Main Space Heating

  17. "Table HC10.6 Air Conditioning Characteristics by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Air Conditioning Characteristics by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Air Conditioning Characteristics",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Do Not Have Cooling Equipment",17.8,4,2.1,1.4,10.3 "Have Cooling Equipment",93.3,16.5,23.5,39.3,13.9 "Use Cooling

  18. "Table HC10.8 Water Heating Characteristics by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    8 Water Heating Characteristics by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Water Heating Characteristics",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Number of Water Heaters" "1.",106.3,19.6,24.5,39,23.2 "2 or More",3.7,0.3,0.9,1.5,1 "Do Not Use Hot

  19. "Table HC10.9 Home Appliances Characteristics by U.S. Census Regions, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0.9 Home Appliances Characteristics by U.S. Census Regions, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Home Appliances Characteristics",,"Northeast","Midwest","South","West" "Total U.S.",111.1,20.6,25.6,40.7,24.2 "Cooking Appliances" "Conventional Ovens" "Use an Oven",109.6,20.3,25.3,40.2,23.7

  20. Household and environmental characteristics related to household energy-consumption change: A human ecological approach

    SciTech Connect (OSTI)

    Guerin, D.A.

    1988-01-01

    This study focused on the family household as an organism and on its interaction with the three environments of the human ecosystem (natural, behavioral, and constructed) as these influence energy consumption and energy-consumption change. A secondary statistical analysis of data from the US Department of Energy Residential Energy Consumption Surveys (RECS) was completed. The 1980 and 1983 RECS were used as the data base. Longitudinal data, including household, environmental, and energy-consumption measures, were available for over 800 households. The households were selected from a national sample of owner-occupied housing units surveyed in both years. Results showed a significant( p = <.05) relationship between the dependent-variable energy-consumption change and the predictor variables heating degree days, addition of insulation, addition of a wood-burning stove, year the housing unit was built, and weighted number of appliances. A significant (p = <.05) relationship was found between the criterion variable energy-consumption change and the discriminating variables of age of the head of the household, cooling degree days, heating degree days, year the housing unit was built, and number of stories in the housing unit.

  1. Ventilation Behavior and Household Characteristics in NewCalifornia Houses

    SciTech Connect (OSTI)

    Price, Phillip N.; Sherman, Max H.

    2006-02-01

    A survey was conducted to determine occupant use of windows and mechanical ventilation devices; barriers that inhibit their use; satisfaction with indoor air quality (IAQ); and the relationship between these factors. A questionnaire was mailed to a stratified random sample of 4,972 single-family detached homes built in 2003, and 1,448 responses were received. A convenience sample of 230 houses known to have mechanical ventilation systems resulted in another 67 completed interviews. Some results are: (1) Many houses are under-ventilated: depending on season, only 10-50% of houses meet the standard recommendation of 0.35 air changes per hour. (2) Local exhaust fans are under-utilized. For instance, about 30% of households rarely or never use their bathroom fan. (3) More than 95% of households report that indoor air quality is ''very'' or ''somewhat'' acceptable, although about 1/3 of households also report dustiness, dry air, or stagnant or humid air. (4) Except households where people cook several hours per week, there is no evidence that households with significant indoor pollutant sources get more ventilation. (5) Except households containing asthmatics, there is no evidence that health issues motivate ventilation behavior. (6) Security and energy saving are the two main reasons people close windows or keep them closed.

  2. Improving Demographic Components of Integrated Assessment Models: The Effect of Changes in Population Composition by Household Characteristics

    SciTech Connect (OSTI)

    Brian C. O'Neill

    2006-08-09

    This report describes results of the research project on "Improving Demographic Components of Integrated Assessment Models: The Effect of Changes in Population Composition by Household Characteristics". The overall objective of this project was to improve projections of energy demand and associated greenhouse gas emissions by taking into account demographic factors currently not incorporated in Integrated Assessment Models (IAMs) of global climate change. We proposed to examine the potential magnitude of effects on energy demand of changes in the composition of populations by household characteristics for three countries: the U.S., China, and Indonesia. For each country, we planned to analyze household energy use survey data to estimate relationships between household characteristics and energy use; develop a new set of detailed household projections for each country; and combine these analyses to produce new projections of energy demand illustrating the potential importance of consideration of households.

  3. Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay

    SciTech Connect (OSTI)

    Oribe-Garcia, Iraia Kamara-Esteban, Oihane; Martin, Cristina; Macarulla-Arenaza, Ana M.; Alonso-Vicario, Ainhoa

    2015-05-15

    Highlights: • We have modelled household waste generation in Biscay municipalities. • We have identified relevant characteristics regarding household waste generation. • Factor models are used in order to identify the best subset of explicative variables. • Biscay’s municipalities are grouped by means of hierarchical clustering. - Abstract: The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.

  4. Table HC6.2 Living Space Characteristics by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by Number of Household Members, 2005 Total...................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Floorspace (Square Feet) Total Floorspace 1 Fewer than 500............................................... 3.2 1.7 0.8 0.4 0.3 Q 500 to 999....................................................... 23.8 10.2 6.4 3.4 2.3 1.5 1,000 to 1,499................................................. 20.8 5.5 6.3 3.0 3.3 2.6 1,500 to

  5. Table HC6.9 Home Appliances Characteristics by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    HC6.9 Home Appliances Characteristics by Number of Household Members, 2005 Total U.S.............................................................. 111.1 30.0 34.8 18.4 15.9 12.0 Cooking Appliances Conventional Ovens Use an Oven.................................................. 109.6 29.5 34.4 18.2 15.7 11.8 1................................................................. 103.3 28.4 32.0 17.3 14.7 11.0 2 or More.................................................... 6.2 1.1 2.5 1.0 0.9 0.8 Do Not

  6. Table HC6.4 Space Heating Characteristics by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by Number of Household Members, 2005 Total..................................................................... 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Space Heating Equipment............ 1.2 0.3 0.3 Q 0.2 0.2 Have Main Space Heating Equipment............... 109.8 29.7 34.5 18.2 15.6 11.8 Use Main Space Heating Equipment................. 109.1 29.5 34.4 18.1 15.5 11.6 Have Equipment But Do Not Use It................... 0.8 Q Q Q Q Q Main Heating Fuel and

  7. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    0a. Home Office Equipment by Midwest Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.6 Total .............................................................. 107.0 24.5 17.1 7.4 NE Households Using Office Equipment ......................................... 96.2 22.4 15.7 6.7 1.3 Personal Computers 1 ................................. 60.0

  8. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    1a. Home Office Equipment by South Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.2 1.3 1.6 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Households Using Office Equipment ......................................... 96.2 34.6 18.4 6.0 10.1 1.2 Personal Computers 1

  9. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Home Office Equipment by West Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total Census Division Mountain Pacific 0.5 1.0 1.6 1.2 Total .............................................................. 107.0 23.3 6.7 16.6 NE Households Using Office Equipment ......................................... 96.2 21.4 6.2 15.2 1.0 Personal Computers 1 ................................. 60.0 14.3 4.0 10.4 3.7 Number of

  10. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    9a. Home Office Equipment by Northeast Census Region, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.1 1.4 1.2 Total .............................................................. 107.0 20.3 14.8 5.4 NE Households Using Office Equipment ......................................... 96.2 17.9 12.8 5.0 1.3 Personal Computers 1 ................................. 60.0 10.9

  11. 1997 Housing Characteristics Tables Home Office Equipment Tables

    Annual Energy Outlook

    Home Office Equipment by South Census Region, Percent of U.S. Households, 1997 1 HC7-12b. Home Office Equipment by West Census Region, Percent of U.S. Households, 1997 1 These data ...

  12. " Electricity Generation by Census Region...

    U.S. Energy Information Administration (EIA) (indexed site)

    A6. Total Inputs of Selected Byproduct Energy for Heat, Power, and" " Electricity Generation by Census Region, Census Division, Industry Group, and" " Selected Industries, 1994" " ...

  13. Table A26. Components of Total Electricity Demand by Census Region, Census Di

    U.S. Energy Information Administration (EIA) (indexed site)

    Components of Total Electricity Demand by Census Region, Census Division, and" " Economic Characteristics of the Establishment, 1994" " (Estimates in Million Kilowatthours)" " "," "," "," ","Sales/"," ","RSE" " "," ","Transfers","Onsite","Transfers"," ","Row" "Economic

  14. National Solar Jobs Census 2014

    Office of Energy Efficiency and Renewable Energy (EERE)

    The Solar Foundation’s National Solar Jobs Census 2014 is the fifth annual update of current employment, trends, and projected growth in the U.S. solar industry. Data for Census 2014 is derived...

  15. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    4a. Household Characteristics by Type of Housing Unit, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Homes Two to Four Units Five or More Units 0.4 0.5 1.6 1.4 2.0 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.3 Household Size 1 Person ....................................... 28.2 15.0 3.3 7.9 1.9 5.9 2 Persons

  16. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    6a. Household Characteristics by Type of Rented Housing Unit, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.8 1.1 0.9 2.5 Total Rented Units ........................ 34.3 10.5 7.4 15.2 1.1 6.9 Household Size 1 Person ....................................... 12.3 2.5 2.6 7.0 0.3 10.0 2 Persons

  17. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    8a. Household Characteristics by Urban/Rural Location, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total Urban/Rural Location 1 RSE Row Factors City Town Suburbs Rural 0.5 0.8 1.4 1.3 1.4 Total .............................................................. 107.0 49.9 18.0 21.2 17.9 4.1 Household Size 1 Person ...................................................... 28.2 14.6 5.3 4.8 3.6 6.4 2 Persons .................................................... 35.1 15.7 5.7

  18. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    3a. Household Characteristics by Household Income, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factors Less than $14,999 $15,000 to $29,999 $30,000 to $49,999 $50,000 or More 0.6 1.3 1.1 1.0 0.9 1.4 1.0 Total ............................................... 107.0 18.7 22.9 27.1 38.3 15.0 33.8 3.3 Household Size 1 Person ....................................... 28.2 9.7 --

  19. "Table A33. Total Quantity of Purchased Energy Sources by Census Region, Census Division,"

    U.S. Energy Information Administration (EIA) (indexed site)

    Quantity of Purchased Energy Sources by Census Region, Census Division," " and Economic Characteristics of the Establishment, 1994" " (Estimates in Btu or Physical Units)" ,,,,,"Natural",,,"Coke" " ","Total","Electricity","Residual","Distillate","Gas(c)"," ","Coal","and Breeze","Other(d)","RSE" "

  20. 2015 National Solar Jobs Census

    Energy.gov [DOE]

    The Solar Foundation's National Solar Jobs Census 2015 is the sixth annual edition of current employment, trends, and projected growth in the U.S. solar industry. Given this industry's rapid...

  1. Household energy consumption and expenditures 1987

    SciTech Connect (OSTI)

    Not Available

    1990-01-22

    This report is the third in the series of reports presenting data from the 1987 Residential Energy Consumption Survey (RECS). The 1987 RECS, seventh in a series of national surveys of households and their energy suppliers, provides baseline information on household energy use in the United States. Data from the seven RECS and its companion survey, the Residential Transportation Energy Consumption Survey (RTECS), are made available to the public in published reports such as this one, and on public use data files. This report presents data for the four Census regions and nine Census divisions on the consumption of and expenditures for electricity, natural gas, fuel oil and kerosene (as a single category), and liquefied petroleum gas (LPG). Data are also presented on consumption of wood at the Census region level. The emphasis in this report is on graphic depiction of the data. Data from previous RECS surveys are provided in the graphics, which indicate the regional trends in consumption, expenditures, and uses of energy. These graphs present data for the United States and each Census division. 12 figs., 71 tabs.

  2. Household magnets

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Household magnets Chances are very good that you have experimented with magnets. People have been fascinated with magnetism for thousands of years. As familiar to us as they may be, magnets still have some surprises for us. Here is a small collection of some of our favorite magnet experiments. What happens when we break a magnet in half? Radio Shack sells cheap ceramic magnets in several shapes. Get a ring shaped magnet and break it with pliers or a tap with a hammer. Try to put it back

  3. ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Average Square Footage of Single-Family Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Single-Family",78.6,2422,2002,1522,880,727,553 "Census

  4. ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Average Square Footage of Multi-Family Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Multi-Family",28.1,930,807,535,453,393,261 "Census Region"

  5. ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Average Square Footage of Mobile Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total Mobile Homes",6.9,1087,985,746,413,375,283 "Census Region"

  6. ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member"

    U.S. Energy Information Administration (EIA) (indexed site)

    9 Average Square Footage of U.S. Homes, by Housing Characteristics, 2009" " Final" ,"Housing Units1","Average Square Footage Per Housing Unit",,,"Average Square Footage Per Household Member" "Housing Characteristics","Millions","Total2","Heated","Cooled","Total2","Heated","Cooled" "Total",113.6,1971,1644,1230,766,639,478 "Census Region"

  7. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    3a. Appliances by Household Income, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factors Less than $14,999 $15,000 to $29,999 $30,000 to $49,999 $50,000 or More 0.5 1.4 1.1 1.0 0.8 1.6 1.0 Total ............................................... 107.0 18.7 22.9 27.1 38.3 15.0 33.8 3.2 Kitchen Appliances Cooking Appliances Oven

  8. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    4a. Air Conditioning by Type of Housing Unit, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.6 1.5 1.4 1.8 Households With Electric Air-Conditioning Equipment ........ 82.9 58.7 6.5 12.4 5.3 4.9 Air Conditioners Not Used ............ 2.1 1.1 Q 0.6 Q 21.8 Households Using Electric Air-Conditioning 1

  9. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    a. Household Characteristics by Climate Zone, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total Climate Zone 1 RSE Row Factors Fewer than 2,000 CDD and -- 2,000 CDD or More and Fewer than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Fewer than 4,000 HDD 0.4 1.9 1.1 1.1 1.2 1.0 Total ............................................... 107.0 9.2 28.6 24.0 21.0 24.1 7.8 Household Size 1 Person ....................................... 28.2 2.5 8.1 6.5

  10. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Household Characteristics by Year of Construction, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.6 1.2 1.0 1.2 1.2 0.9 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.2 Household Size 1 Person ....................................... 28.2 2.5 4.5 5.1 4.0 3.7 8.3 7.5 2 Persons

  11. char_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    5a. Household Characteristics by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Household Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Homes Two to Four Units Five or More Units 0.3 0.4 2.0 2.9 1.3 Total Owner-Occupied Units ....... 72.7 63.2 2.1 1.8 5.7 6.7 Household Size 1 Person ....................................... 15.8 12.5 0.8 0.9 1.6 10.3 2 Persons

  12. Household vehicles energy consumption 1994

    SciTech Connect (OSTI)

    1997-08-01

    Household Vehicles Energy Consumption 1994 reports on the results of the 1994 Residential Transportation Energy Consumption Survey (RTECS). The RTECS is a national sample survey that has been conducted every 3 years since 1985. For the 1994 survey, more than 3,000 households that own or use some 6,000 vehicles provided information to describe vehicle stock, vehicle-miles traveled, energy end-use consumption, and energy expenditures for personal vehicles. The survey results represent the characteristics of the 84.9 million households that used or had access to vehicles in 1994 nationwide. (An additional 12 million households neither owned or had access to vehicles during the survey year.) To be included in then RTECS survey, vehicles must be either owned or used by household members on a regular basis for personal transportation, or owned by a company rather than a household, but kept at home, regularly available for the use of household members. Most vehicles included in the RTECS are classified as {open_quotes}light-duty vehicles{close_quotes} (weighing less than 8,500 pounds). However, the RTECS also includes a very small number of {open_quotes}other{close_quotes} vehicles, such as motor homes and larger trucks that are available for personal use.

  13. Household energy consumption and expenditures, 1990

    SciTech Connect (OSTI)

    Not Available

    1993-03-02

    This report, Household Energy Consumption and Expenditures 1990, is based upon data from the 1990 Residential Energy Consumption Survey (RECS). Focusing on energy end-use consumption and expenditures of households, the 1990 RECS is the eighth in a series conducted since 1978 by the Energy Information Administration (EIA). Over 5,000 households were surveyed, providing information on their housing units, housing characteristics, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information provided represents the characteristics and energy consumption of 94 million households nationwide.

  14. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    3a. Air Conditioning by Household Income, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factors Less than $14,999 $15,000 to $29,999 $30,000 to $49,999 $50,000 or More 0.5 1.4 1.1 1.0 0.9 1.5 0.9 Households With Electric Air-Conditioning Equipment ........ 82.9 12.3 17.4 21.5 31.7 9.6 23.4 3.9 Air Conditioners Not Used ............ 2.1 0.4 0.7 0.5 0.5 0.4 0.9 20.8

  15. Transferring 2001 National Household Travel Survey

    SciTech Connect (OSTI)

    Hu, Patricia S; Reuscher, Tim; Schmoyer, Richard L; Chin, Shih-Miao

    2007-05-01

    Policy makers rely on transportation statistics, including data on personal travel behavior, to formulate strategic transportation policies, and to improve the safety and efficiency of the U.S. transportation system. Data on personal travel trends are needed to examine the reliability, efficiency, capacity, and flexibility of the Nation's transportation system to meet current demands and to accommodate future demand. These data are also needed to assess the feasibility and efficiency of alternative congestion-mitigating technologies (e.g., high-speed rail, magnetically levitated trains, and intelligent vehicle and highway systems); to evaluate the merits of alternative transportation investment programs; and to assess the energy-use and air-quality impacts of various policies. To address these data needs, the U.S. Department of Transportation (USDOT) initiated an effort in 1969 to collect detailed data on personal travel. The 1969 survey was the first Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990, 1995, and 2001. Data on daily travel were collected in 1969, 1977, 1983, 1990 and 1995. In 2001, the survey was renamed the National Household Travel Survey (NHTS) and it collected both daily and long-distance trips. The 2001 survey was sponsored by three USDOT agencies: Federal Highway Administration (FHWA), Bureau of Transportation Statistics (BTS), and National Highway Traffic Safety Administration (NHTSA). The primary objective of the survey was to collect trip-based data on the nature and characteristics of personal travel so that the relationships between the characteristics of personal travel and the demographics of the traveler can be established. Commercial and institutional travel were not part of the survey. Due to the survey's design, data in the NHTS survey series were not recommended for estimating travel statistics for categories smaller than the combination of Census division (e.g., New England, Middle

  16. National Solar Schools Census 2014

    Energy.gov [DOE]

    With support from the SunShot Initiative, TSF’s national solar schools census, “Brighter Future: A Study on Solar in U.S. Schools” serves as a starting point for sharing ideas and best practices between schools experienced with solar energy and those seeking to join their ranks. Each solar school has its own unique story to tell on how their systems were financed and installed and how (and whether) solar has been integrated into class curricula.

  17. Table A28. Components of Onsite Electricity Generation by Census Region, Cens

    U.S. Energy Information Administration (EIA) (indexed site)

    Components of Onsite Electricity Generation by Census Region, Census Division, and" " Economic Characteristics of the Establishment, 1994" " (Estimates in Million Kilowatthours)" ,,,"Renewables" ,,,"(excluding Wood",,"RSE" " "," "," ","and"," ","Row" "Economic Characteristics(a)","Total","Cogeneration(b)","Other

  18. EIA - Household Transportation report: Household Vehicles Energy...

    U.S. Energy Information Administration (EIA) (indexed site)

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1994 August 1997 Release Next Update: EIA has discontinued this series....

  19. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    6a. Air Conditioning by Type of Rented Housing Unit, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.8 0.5 1.4 1.2 1.6 Households With Electric Air-Conditioning Equipment ........ 23.4 58.7 6.5 12.4 5.3 6.1 Air Conditioners Not Used ............ 0.9 1.1 Q 0.6 Q 23.0 Households Using Electric Air-Conditioning

  20. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Air Conditioning by Year of Construction, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.6 1.2 1.1 1.2 1.1 0.9 Households With Electric Air-Conditioning Equipment ........ 82.9 13.6 16.0 14.7 10.4 10.5 17.6 4.7 Air Conditioners Not Used ............ 2.1 Q 0.3 0.5 0.3 0.4 0.5 27.2 Households Using Electric Air-Conditioning 2

  1. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    5a. Air Conditioning by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.5 1.5 1.4 1.8 Households With Electric Air-Conditioning Equipment ........ 59.5 58.7 6.5 12.4 5.3 5.2 Air Conditioners Not Used ............ 1.2 1.1 Q 0.6 Q 23.3 Households Using

  2. "Table A37. Total Expenditures for Purchased Energy Sources by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    7. Total Expenditures for Purchased Energy Sources by Census Region," " Census Division, and Economic Characteristics of the Establishment, 1994" " (Estimates in Million Dollars)" " "," "," "," ",," "," "," "," "," ","RSE" " "," "," ","Residual","Distillate","Natural"," ","

  3. Table HC1.1.2 Housing Unit Characteristics by Average Floorspace, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Housing Unit Characteristics by Average Floorspace, 2005 " ,,"Average Square Feet per--" ," Housing Units (millions)" ,,"Housing Unit",,,"Household Member" "Housing Unit Characteristics",,"Total1","Heated","Cooled","Total","Heated","Cooled" "Total",111.1,2171,1618,1031,845,630,401 "Census Region and Division" "Northeast",20.6,2334,1664,562,911,649,220

  4. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    a. Air Conditioning by Climate Zone, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Climate Zone 1 RSE Row Factors Fewer than 2,000 CDD and -- 2,000 CDD or More and Fewer than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Fewer than 4,000 HDD 0.4 2.1 1.0 0.9 1.5 1.0 Total Households With Air-Conditioning ........................... 82.9 5.4 20.9 20.2 14.2 22.1 8.1 Air Conditioners Not Used ............ 2.1 Q 0.4 0.3 0.8 0.4 23.2

  5. Household energy consumption and expenditures, 1990. [Contains Glossary

    SciTech Connect (OSTI)

    Not Available

    1993-03-02

    This report, Household Energy Consumption and Expenditures 1990, is based upon data from the 1990 Residential Energy Consumption Survey (RECS). Focusing on energy end-use consumption and expenditures of households, the 1990 RECS is the eighth in a series conducted since 1978 by the Energy Information Administration (EIA). Over 5,000 households were surveyed, providing information on their housing units, housing characteristics, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information provided represents the characteristics and energy consumption of 94 million households nationwide.

  6. Table HC1-5a. Housing Unit Characteristics by Type of Owner-Occupied Housing Unit,

    U.S. Energy Information Administration (EIA) (indexed site)

    5a. Housing Unit Characteristics by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Homes Two to Four Units Five or More Units 0.4 0.4 1.8 2.1 1.4 Total ............................................... 72.7 63.2 2.1 1.8 5.7 6.7 Census Region and Division Northeast ......................................

  7. Characteristics RSE Column Factor: Households with Children Households...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... 7.6 2.1 3.3 2.2 11.5 Q Q Q 1.4 6.9 2.8 18.8 Below Poverty Line 100 Percent ... 6.6 1.6 3.6 1.3 5.8 0.3 0.7...

  8. Hanford Site Regional Population - 2010 Census

    SciTech Connect (OSTI)

    Hamilton, Erin L.; Snyder, Sandra F.

    2011-08-12

    The U.S. Department of Energy conducts radiological operations in south-central Washington State. Population dose estimates must be performed to provide a measure of the impact from site radiological releases. Results of the U.S. 2010 Census were used to determine counts and distributions for the residential population located within 50-miles of several operating areas of the Hanford Site. Year 2010 was the first census year that a 50-mile population of a Hanford Site operational area exceeded the half-million mark.

  9. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    4a. Appliances by Type of Housing Unit, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.5 1.7 1.6 1.9 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.2 Kitchen Appliances Cooking Appliances Oven ........................................... 101.7 69.1 9.4 16.7 6.6 4.3 1

  10. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    5a. Appliances by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.3 0.4 2.1 3.1 1.3 Total ............................................... 72.7 63.2 2.1 1.8 5.7 6.7 Kitchen Appliances Cooking Appliances Oven ...........................................

  11. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    6a. Appliances by Type of Rented Housing Unit, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.8 1.1 0.9 2.5 Total ............................................... 34.3 10.5 7.4 15.2 1.1 6.9 Kitchen Appliances Cooking Appliances Oven ........................................... 33.4 10.1 7.3 14.9 1.1

  12. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    8a. Appliances by Urban/Rural Location, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Urban/Rural Location 1 RSE Row Factors City Town Suburbs Rural 0.5 0.9 1.4 1.2 1.3 Total .............................................................. 107.0 49.9 18.0 21.2 17.9 4.1 Kitchen Appliances Cooking Appliances Oven ......................................................... 101.7 47.5 17.5 19.9 16.8 4.2 1

  13. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    5a. Space Heating by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Owner- Occupied Units Type of Owner-Occupied Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.4 0.4 1.9 3.0 1.3 Total ............................................... 72.7 63.2 2.1 1.8 5.7 6.7 Heat Home ..................................... 72.4 63.0 2.0 1.7 5.7 6.7 Do Not Heat Home

  14. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    6a. Space Heating by Type of Rented Housing Unit, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Rented Units Type of Rented Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.8 1.1 0.9 2.5 Total ............................................... 34.3 10.5 7.4 15.2 1.1 6.9 Heat Home ..................................... 33.7 10.4 7.4 14.8 1.1 6.9 Do Not Heat Home

  15. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    Air Conditioning Tables (Million U.S. Households; 24 pages, 138 kb) Contents Pages HC4-1a. Air Conditioning by Climate Zone, Million U.S. Households, 2001 2 HC4-2a. Air Conditioning by Year of Construction, Million U.S. Households, 2001 2 HC4-3a. Air Conditioning by Household Income, Million U.S. Households, 2001 2 HC4-4a. Air Conditioning by Type of Housing Unit, Million U.S. Households, 2001 2 HC4-5a. Air Conditioning by Type of Owner-Occupied Housing Unit, Million U.S. Households, 2001 2

  16. " by Census Region, Census Division, Industry Group, Selected Industries, and"

    U.S. Energy Information Administration (EIA) (indexed site)

    Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Census Region, Census Division, Industry Group, Selected Industries, and" " Presence of Cogeneration Technologies, 1994: Part 1" " (Estimates in Trillion Btu)",," ",,,,,,," "," "," " ,,,"Steam Turbines",,,,"Steam Turbines" ,," ","Supplied by Either","Conventional",,,"Supplied by","One

  17. " Census Region, Census Division, Industry Group, and Selected Industries, 1994"

    U.S. Energy Information Administration (EIA) (indexed site)

    Quantity of Purchased Electricity and Steam by Type of Supplier," " Census Region, Census Division, Industry Group, and Selected Industries, 1994" " (Estimates in Btu or Physical Units)" ,," Electricity",," Steam" ,," (million kWh)",," (billion Btu)" ,,,,,,"RSE" "SIC",,"Utility","Nonutility","Utility","Nonutility","Row" "Code(a)","Industry Group

  18. " by Type of Supplier, Census Region, Census Division, Industry Group,"

    U.S. Energy Information Administration (EIA) (indexed site)

    3. Average Prices of Purchased Electricity and Steam" " by Type of Supplier, Census Region, Census Division, Industry Group," " and Selected Industries, 1994" " (Estimates in Dollars per Physical Units)" ,," Electricity",," Steam" ,," (kWh)",," (million Btu)" ,,,,,,"RSE" "SIC",,"Utility","Nonutility","Utility","Nonutility","Row"

  19. Try This: Household Magnets

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Household Magnets Household Magnets Chances are very good that you have experimented with magnets. People have been fascinated with magnetism for thousands of years. As familiar to us as they may be, magnets still have some surprises for us. Here is a small collection of some of our favorite magnet experiments. What happens when we break a magnet in half? Radio Shack sells cheap ceramic magnets in several shapes. Get a ring shaped magnet and break it with pliers or a tap with a hammer. Try to

  20. Commercial Buildings Characteristics 1995 - Index Page

    U.S. Energy Information Administration (EIA) (indexed site)

    >Commercial Buildings Home > 1995 Characteristics Data 1995 Data Executive Summary Table of Contents Overview to Detailed Tables Detailed Tables 1995 national and Census region...

  1. 45th annual Reed rig census

    SciTech Connect (OSTI)

    Stokes, T.A.; Rodriguez, M.R.

    1997-10-01

    Since 1983, Reed Tool Co.`s annual rotary rig census has reported 14 consecutive annual reductions in the U.S. rig fleet. This year, the downward trend has reversed and more rigs have been added to the available fleet than have left. Robust drilling activity has also spurred higher rig utilization in 1997. Utilization climbed to 86.9% this year, more than ten percentage points higher than a year ago and the highest since 1981. Data and trends are discussed.

  2. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  3. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    a. Appliances by Climate Zone, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Climate Zone 1 RSE Row Factors Fewer than 2,000 CDD and -- 2,000 CDD or More and Fewer than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Fewer than 4,000 HDD 0.4 1.9 1.1 1.1 1.2 1.1 Total .................................................. 107.0 9.2 28.6 24.0 21.0 24.1 7.8 Kitchen Appliances Cooking Appliances Oven

  4. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Appliances by Year of Construction, Million U.S. Households, 2001 Appliance Types and Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.5 1.2 1.1 1.2 1.1 0.9 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.2 Kitchen Appliances Cooking Appliances Oven ........................................... 101.7 14.3 17.2 17.8 12.9 13.7 25.9 4.2 1

  5. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Space Heating by Year of Construction, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.5 1.5 1.1 1.1 1.1 1.1 0.9 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.3 Heat Home ..................................... 106.0 15.4 18.2 18.6 13.6 13.9 26.4 4.3 Do Not Heat Home ........................

  6. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    4a. Space Heating by Type of Housing Unit, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total Type of Housing Unit RSE Row Factors Single-Family Apartments in Buildings With Mobile Home Two to Four Units Five or More Units 0.5 0.5 1.5 1.4 1.7 Total ............................................... 107.0 73.7 9.5 17.0 6.8 4.4 Heat Home ..................................... 106.0 73.4 9.4 16.4 6.8 4.5 Do Not Heat Home ........................ 1.0 0.3 Q 0.6 Q 19.0 No

  7. 1997 Housing Characteristics Tables Housing Unit Tables

    U.S. Energy Information Administration (EIA) (indexed site)

    Million U.S. Households; 45 pages, 128 kb) Contents Pages HC1-1a. Housing Unit Characteristics by Climate Zone, Million U.S. Households, 1997 4 HC1-2a. Housing Unit Characteristics by Year of Construction, Million U.S. Households, 1997 4 HC1-3a. Housing Unit Characteristics by Household Income, Million U.S. Households, 1997 4 HC1-4a. Housing Unit Characteristics by Type of Housing Unit, Million U.S. Households, 1997 3 HC1-5a. Housing Unit Characteristics by Type of Owner-Occupied Housing Unit,

  8. 1997 Housing Characteristics Tables Housing Unit Tables

    U.S. Energy Information Administration (EIA) (indexed site)

    Percent of U.S. Households; 45 pages, 121 kb) Contents Pages HC1-1b. Housing Unit Characteristics by Climate Zone, Percent of U.S. Households, 1997 4 HC1-2b. Housing Unit Characteristics by Year of Construction, Percent of U.S. Households, 1997 4 HC1-3b. Housing Unit Characteristics by Household Income, Percent of U.S. Households, 1997 4 HC1-4b. Housing Unit Characteristics by Type of Housing Unit, Percent of U.S. Households, 1997 3 HC1-5b. Housing Unit Characteristics by Type of Owner-Occupied

  9. " by Census Region, Census Division, Industry Group, Selected Industries, and"

    U.S. Energy Information Administration (EIA) (indexed site)

    Total Inputs of Energy for Heat, Power, and Electricity Generation" " by Census Region, Census Division, Industry Group, Selected Industries, and" " Presence of General Technologies, 1994: Part 1" " (Estimates in Trillion Btu)" ,,,,"Computer Control" ,," "," ","of Processes"," "," ",," "," "," "," " ,," ","Computer Control","or

  10. " and Electricity Generation by Census Region, Census Division, Industry Group,"

    U.S. Energy Information Administration (EIA) (indexed site)

    3. Total Inputs of Selected Wood and Wood-Related Products for Heat, Power," " and Electricity Generation by Census Region, Census Division, Industry Group," " and Selected Industries, 1994" " (Estimates in Billion Btu)" ,,,,"Selected Wood and Wood-Related Products" ,,,,,"Biomass" " "," ",," "," "," ","Wood Residues","Wood-Related"," " " ","

  11. Table C12. Electricity Expenditures by Census Region, 1999

    U.S. Energy Information Administration (EIA) (indexed site)

    Electricity Expenditures by Census Region, 1999" ,"Total Electricity Expenditures (million dollars)",,,,"Electricity Expenditures (dollars)" ,,,,,"per kWh",,,,"per Square Foot"...

  12. Table A19. Components of Total Electricity Demand by Census...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... Division, Form EIA-846, '1991" "Manufacturing Energy Consumption Survey,' and Bureau of the Census, Industry" "Division, data files for the '1991 Annual Survey of Manufactures.'

  13. Wade Hampton Census Area, Alaska: Energy Resources | Open Energy...

    Open Energy Information (Open El) [EERE & EIA]

    Wade Hampton Census Area, Alaska: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 62.1458336, -162.8919191 Show Map Loading map......

  14. Housing characteristics 1993

    SciTech Connect (OSTI)

    1995-06-01

    This report, Housing Characteristics 1993, presents statistics about the energy-related characteristics of US households. These data were collected in the 1993 Residential Energy Consumption Survey (RECS) -- the ninth in a series of nationwide energy consumption surveys conducted since 1978 by the Energy Information Administration of the US Department of Energy. Over 7 thousand households were surveyed, representing 97 million households nationwide. A second report, to be released in late 1995, will present statistics on residential energy consumption and expenditures.

  15. ac_household2001.pdf

    Annual Energy Outlook

    ... those households were treated as if the fuel was electricity. 3 The 2001 RECS reported ... Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  16. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    or commercial trucks (See Table 1). Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 5 The 1991 RTECS count includes vehicles that were owned or used...

  17. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1991 December 1993 Release Next Update: August 1997. Based on the 1991...

  18. Perceptions of risk among households in two Australian coastal communities

    SciTech Connect (OSTI)

    Elrick-Barr, Carmen E.; Smith, Timothy F.; Thomsen, Dana C.; Preston, Benjamin L.

    2015-04-20

    There is limited knowledge of risk perceptions in coastal communities despite their vulnerability to a range of risks including the impacts of climate change. A survey of 400 households in two Australian coastal communities, combined with semi-structured interviews, provides insight into household perceptions of the relative importance of climatic and non-climatic risks and the subsequent risk priorities that may inform household adaptive action. In contrast to previous research, the results demonstrated that geographic location and household characteristics might not affect perceptions of vulnerability to environmental hazards. However, past experience was a significant influence, raising the priority of environmental concerns. Overall, the results highlight the priority concerns of coastal households (from finance, to health and environment) and suggest to increase the profile of climate issues in coastal communities climate change strategies need to better demonstrate links between climate vulnerability and other household concerns. Moreover, promoting generic capacities in isolation from understanding the context in which households construe climate risks is unlikely to yield the changes required to decrease the vulnerability of coastal communities.

  19. Perceptions of risk among households in two Australian coastal communities

    DOE PAGES-Beta [OSTI]

    Elrick-Barr, Carmen E.; Smith, Timothy F.; Thomsen, Dana C.; Preston, Benjamin L.

    2015-04-20

    There is limited knowledge of risk perceptions in coastal communities despite their vulnerability to a range of risks including the impacts of climate change. A survey of 400 households in two Australian coastal communities, combined with semi-structured interviews, provides insight into household perceptions of the relative importance of climatic and non-climatic risks and the subsequent risk priorities that may inform household adaptive action. In contrast to previous research, the results demonstrated that geographic location and household characteristics might not affect perceptions of vulnerability to environmental hazards. However, past experience was a significant influence, raising the priority of environmental concerns. Overall,more » the results highlight the priority concerns of coastal households (from finance, to health and environment) and suggest to increase the profile of climate issues in coastal communities climate change strategies need to better demonstrate links between climate vulnerability and other household concerns. Moreover, promoting generic capacities in isolation from understanding the context in which households construe climate risks is unlikely to yield the changes required to decrease the vulnerability of coastal communities.« less

  20. S:\\VM3\\RX97\\TBL_LIST.WPD

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Household Characteristics by West Census Region, Percent of U.S. Households, 1997 1 These data are from the 1997 Residential Energy Consumption Survey (RECS) which provides ...

  1. Strategies for Collecting Household Energy Data | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Collecting Household Energy Data Strategies for Collecting Household Energy Data Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for ...

  2. Household Vehicles Energy Use Cover Page

    U.S. Energy Information Administration (EIA) (indexed site)

    Energy Use Cover Page Glossary Home > Households, Buildings & Industry >Transportation Surveys > Household Vehicles Energy Use Cover Page Contact Us * Feedback * PrivacySecurity *...

  3. Shared Solar Projects Powering Households Throughout America...

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Shared Solar Projects Powering Households Throughout America Shared Solar Projects Powering Households Throughout America January 31, 2014 - 2:30pm Addthis Shared solar projects ...

  4. Next Generation Household Refrigerator | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Next Generation Household Refrigerator Next Generation Household Refrigerator Embraco's high efficiency, oil-free linear compressor.
    Credit: Whirlpool Embraco's high ...

  5. Table 38. Coal Stocks at Coke Plants by Census Division

    Gasoline and Diesel Fuel Update

    Coal Stocks at Coke Plants by Census Division (thousand short tons) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2014 Table 38. Coal Stocks at Coke Plants by Census Division (thousand short tons) U.S. Energy Information Administration | Quarterly Coal Report, April - June 2014 Census Division June 30, 2014 March 31, 2014 June 30, 2013 Percent Change (June 30) 2014 versus 2013 Middle Atlantic 547 544 857 -36.2 East North Central 1,130 963 1,313 -13.9 South

  6. Table HC1-1a. Housing Unit Characteristics by Climate Zone,

    U.S. Energy Information Administration (EIA) (indexed site)

    a. Housing Unit Characteristics by Climate Zone, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total Climate Zone 1 RSE Row Factors Fewer than 2,000 CDD and -- 2,000 CDD or More and Fewer than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Fewer than 4,000 HDD 0.4 1.8 1.0 1.1 1.2 1.1 Total ............................................... 107.0 9.2 28.6 24.0 21.0 24.1 8.0 Census Region and Division Northeast

  7. Table HC1-2a. Housing Unit Characteristics by Year of Construction,

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Housing Unit Characteristics by Year of Construction, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.5 1.6 1.2 1.0 1.1 1.1 0.8 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.3 Census Region and Division Northeast ...................................... 20.3 1.5 2.4 2.1 2.8 3.0 8.5 8.8 New

  8. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    16.8 17.4 18.6 18.9 1.7 2.2 0.6 1.5 Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 15 Vehicle Miles Traveled per Vehicle (Thousand) . . . . . . . . ....

  9. Cover Page of Household Vehicles Energy Use: Latest Data & Trends

    Gasoline and Diesel Fuel Update

    Household Vehicles Energy Use Cover Page Cover Page of Household Vehicles Energy Use: Latest Data & Trends...

  10. Table A9. Total Primary Consumption of Energy for All Purposes by Census

    U.S. Energy Information Administration (EIA) (indexed site)

    A9. Total Primary Consumption of Energy for All Purposes by Census" " Region and Economic Characteristics of the Establishment, 1991" " (Estimates in Btu or Physical Units)" ,,,,,,,,"Coke" " "," ","Net","Residual","Distillate","Natural Gas(d)"," ","Coal","and Breeze"," ","RSE" " ","Total","Electricity(b)","Fuel

  11. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... 29.1 5.3 22.7 3.8 1 Below 150 percent of poverty line or 60 percent of median State income

  12. ac_household2001.pdf

    Annual Energy Outlook

    RSE Row Factors New York California Texas Florida 0.4 1.1 1.7 1.2 1.2 Households With ... RSE Row Factors New York California Texas Florida 0.4 1.1 1.7 1.2 1.2 Pays for Electricity ...

  13. char_household2001.pdf

    Annual Energy Outlook

    RSE Row Factors New York California Texas Florida 0.4 1.1 1.0 1.5 1.5 Total ... RSE Row Factors New York California Texas Florida 0.4 1.1 1.0 1.5 1.5 Household Owns or ...

  14. "Table A25. Components of Total Electricity Demand by Census Region, Census Division, Industry"

    U.S. Energy Information Administration (EIA) (indexed site)

    Components of Total Electricity Demand by Census Region, Census Division, Industry" " Group, and Selected Industries, 1994" " (Estimates in Million Kilowatthours)" " "," "," "," "," "," "," "," " " "," "," "," "," ","Sales and/or"," ","RSE" "SIC"," ","

  15. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Home Office Equipment by Year of Construction, Million U.S. Households, 2001 Home Office Equipment RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to 2001 1 1980 to 1989 1970 to 1979 1960 to 1969 1950 to 1959 1949 or Before 0.4 1.4 1.1 1.1 1.2 1.2 1.0 Total ............................................... 107.0 15.5 18.2 18.8 13.8 14.2 26.6 4.2 Households Using Office Equipment .......................... 96.2 14.9 16.7 17.0 12.2 13.0 22.4 4.4 Personal Computers 2

  16. Energy-efficient housing alternatives: a predictive model of factors affecting household perceptions

    SciTech Connect (OSTI)

    Schreckengost, R.L.

    1985-01-01

    The major purpose of this investigation was to assess the impact of household socio-economic factors, dwelling characteristics, energy conservation behavior, and energy attitudes on the perceptions of energy-efficient housing alternatives. Perceptions of passive solar, active solar, earth sheltered, and retrofitted housing were examined. Data used were from the Southern Regional Research Project, S-141, Housing for Low and Moderate Income Families. Responses from 1804 households living in seven southern states were analyzed. A conceptual model was proposed to test the hypothesized relationships which were examined by path analysis. Perceptions of energy efficient housing alternatives were found to be a function of selected household and dwelling characteristics, energy attitude, household economic factors, and household conservation behavior. Age and education of the respondent, family size, housing-income ratio, utility income ratio, energy attitude, and size of the dwelling unit were found to have direct and indirect effects on perceptions of energy-efficient housing alternatives. Energy conservation behavior made a significant direct impact with behavioral energy conservation changes having the most profound influence. Conservation behavior was influenced by selected household and dwelling characteristics, energy attitude, and household economic factors.

  17. Strategies for Collecting Household Energy Data

    Energy.gov [DOE]

    Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for Collecting Household Energy Data, Call Slides and Discussion Summary, July 19, 2012.

  18. Table 2: U.S. Geographic Areas and Census Regions | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    2: U.S. Geographic Areas and Census Regions Table 2: U.S. Geographic Areas and Census Regions Table 2: U.S. Geographic Areas and Census Regions (10.42 KB) More Documents & Publications Memorandum Summarizing Ex Parte Communication An Assessment of Heating Fuels And Electricity Markets During the Winters of 2013-2014 and 2014-2015 Before the House Subcommittee on Energy and Power - Committee on Energy and Commerce

  19. Fact #565: April 6, 2009 Household Gasoline Expenditures by Income |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 5: April 6, 2009 Household Gasoline Expenditures by Income Fact #565: April 6, 2009 Household Gasoline Expenditures by Income In the annual Consumer Expenditure Survey, household incomes are grouped into five equal parts called quintiles (each quintile is 20%). Households in the second and third quintiles consistently have a higher share of spending on gasoline each year than households in the other quintiles. Household Gasoline Expenditures by Income Quintile Bar graph

  20. Statement from Energy Secretary Ernest Moniz on the 2014 Solar Job Census |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Energy Secretary Ernest Moniz on the 2014 Solar Job Census Statement from Energy Secretary Ernest Moniz on the 2014 Solar Job Census January 15, 2015 - 9:50am Addthis News Media Contact 202-586-4940 Statement from Energy Secretary Ernest Moniz on the 2014 Solar Job Census WASHINGTON, D.C. - Secretary Ernest Moniz issued the following statement today on the 2014 National Solar Jobs Census: "Solar power is a key component of our all-of-the-above approach to American

  1. Table A26. Total Quantity of Purchased Energy Sources by Census...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... Division, Form EIA-846, '1991" "Manufacturing Energy Consumption Survey,' and Bureau of the Census, Industry" "Division, data files for the '1991 Annual Survey of Manufactures.'

  2. ASSESSMENT OF HOUSEHOLD CARBON FOOTPRINT REDUCTION POTENTIALS

    SciTech Connect (OSTI)

    Kramer, Klaas Jan; Homan, Greg; Brown, Rich; Worrell, Ernst; Masanet, Eric

    2009-04-15

    The term ?household carbon footprint? refers to the total annual carbon emissions associated with household consumption of energy, goods, and services. In this project, Lawrence Berkeley National Laboratory developed a carbon footprint modeling framework that characterizes the key underlying technologies and processes that contribute to household carbon footprints in California and the United States. The approach breaks down the carbon footprint by 35 different household fuel end uses and 32 different supply chain fuel end uses. This level of end use detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the carbon footprint of California households. The modeling framework was applied to estimate the annual home energy and supply chain carbon footprints of a prototypical California household. A preliminary assessment of parameter uncertainty associated with key model input data was also conducted. To illustrate the policy-relevance of this modeling framework, a case study was conducted that analyzed the achievable carbon footprint reductions associated with the adoption of energy efficient household and supply chain technologies.

  3. Loan Programs for Low- and Moderate-Income Households | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Programs for Low- and Moderate-Income Households Loan Programs for Low- and Moderate-Income Households Better Buildings Residential Network Multifamily and Low-Income Housing Peer ...

  4. Energy Information Administration/Household Vehicles Energy Consumptio...

    U.S. Energy Information Administration (EIA) (indexed site)

    , Energy Information AdministrationHousehold Vehicles Energy Consumption 1994 ix Household Vehicles Energy Consumption 1994 presents statistics about energy-related...

  5. Kingston Creek Hydro Project Powers 100 Households | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Kingston Creek Hydro Project Powers 100 Households Kingston Creek Hydro Project Powers 100 Households August 21, 2013 - 12:00am Addthis Nevada-based contracting firm Nevada ...

  6. Household Response To Dynamic Pricing Of Electricity: A Survey...

    Open Energy Information (Open El) [EERE & EIA]

    Household Response To Dynamic Pricing Of Electricity: A Survey Of The Experimental Evidence Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Household Response To Dynamic...

  7. Microsoft Word - Household Energy Use CA

    U.S. Energy Information Administration (EIA) (indexed site)

    0 20 40 60 80 100 US PAC CA Site Consumption million Btu $0 $500 $1,000 $1,500 $2,000 $2,500 US PAC CA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 2,000 4,000 6,000 8,000 10,000 12,000 US PAC CA Site Consumption kilowatthours $0 $250 $500 $750 $1,000 $1,250 $1,500 US PAC CA Expenditures dollars ELECTRICITY ONLY average per household  California households use 62 million Btu of energy per home, 31% less than the U.S. average. The lower than average site

  8. Characterizing Walk Trips in communities by Using Data from 2009 National Household Travel Survey, American Community Survey, and Other Sources

    SciTech Connect (OSTI)

    Hwang, Ho-Ling; Reuscher, Tim; Wilson, Daniel W; Murakami, Elaine

    2013-01-01

    Non-motorized travel (i.e. walking and bicycling) are of increasing interest to the transportation profession, especially in context with energy consumption, reducing vehicular congestion, urban development patterns, and promotion of healthier life styles. This research project aimed to identify factors impacting the amount of travel for both walk and bike trips at the Census block group or tract level, using several public and private data sources. The key survey of travel behavior is the 2009 National Household Travel Survey (NHTS) which had over 87,000 walk trips for persons 16 and over, and over 6000 bike trips for persons 16 and over. The NHTS, in conjunction with the Census Bureau s American Community Survey, street density measures using Census Bureau TIGER, WalkScore , Nielsen Claritas employment estimates, and several other sources were used for this study. Stepwise Logistic Regression modeling techniques as well as Discriminant Analysis were applied using the integrated data set. While the models performed reasonably well for walk trips, travel by bike was abandoned due to sparseness of data. This paper discusses data sources utilized and modeling processes conducted under this study. It also presents a summary of findings and addresses data challenges and lesson-learned from this research effort.

  9. Household energy consumption and expenditures 1993

    SciTech Connect (OSTI)

    1995-10-05

    This presents information about household end-use consumption of energy and expenditures for that energy. These data were collected in the 1993 Residential Energy Consumption Survey; more than 7,000 households were surveyed for information on their housing units, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information represents all households nationwide (97 million). Key findings: National residential energy consumption was 10.0 quadrillion Btu in 1993, a 9% increase over 1990. Weather has a significant effect on energy consumption. Consumption of electricity for appliances is increasing. Houses that use electricity for space heating have lower overall energy expenditures than households that heat with other fuels. RECS collected data for the 4 most populous states: CA, FL, NY, TX.

  10. A GSC Global Genome Census (GSC8 Meeting)

    ScienceCinema (OSTI)

    Kyrpides, Nikos [DOE JGI

    2016-07-12

    The Genomic Standards Consortium was formed in September 2005. It is an international, open-membership working body which promotes standardization in the description of genomes and the exchange and integration of genomic data. The 2009 meeting was an activity of a five-year funding "Research Coordination Network" from the National Science Foundation and was organized held at the DOE Joint Genome Institute with organizational support provided by the JGI and by the University of California - San Diego. Nikos Kyrpides of the DOE Joint Genome Institute discusses the notion of a global genome census at the Genomic Standards Consortium's 8th meeting at the DOE JGI in Walnut Creek, Calif. on Sept. 9, 2009.

  11. A GSC Global Genome Census (GSC8 Meeting)

    SciTech Connect (OSTI)

    Kyrpides, Nikos

    2009-09-09

    The Genomic Standards Consortium was formed in September 2005. It is an international, open-membership working body which promotes standardization in the description of genomes and the exchange and integration of genomic data. The 2009 meeting was an activity of a five-year funding "Research Coordination Network" from the National Science Foundation and was organized held at the DOE Joint Genome Institute with organizational support provided by the JGI and by the University of California - San Diego. Nikos Kyrpides of the DOE Joint Genome Institute discusses the notion of a global genome census at the Genomic Standards Consortium's 8th meeting at the DOE JGI in Walnut Creek, Calif. on Sept. 9, 2009.

  12. 1992 National census for district heating, cooling and cogeneration

    SciTech Connect (OSTI)

    Not Available

    1993-07-01

    District energy systems are a major part of the energy use and delivery infrastructure of the United States. With nearly 6,000 operating systems currently in place, district energy represents approximately 800 billion BTU per hour of installed thermal production capacity, and provides over 1.1 quadrillion BTU of energy annually -- about 1.3% of all energy used in the US each year. Delivered through more that 20,000 miles of pipe, this energy is used to heat and cool almost 12 billion square feet of enclosed space in buildings that serve a diverse range of office, education, health care, military, industrial and residential needs. This Census is intended to provide a better understanding of the character and extent of district heating, cooling and cogeneration in the United States. It defines a district energy system as: Any system that provides thermal energy (steam, hot water, or chilled water) for space heating, space cooling, or process uses from a central plant, and that distributes the energy to two or more buildings through a network of pipes. If electricity is produced, the system is a cogenerating facility. The Census was conducted through surveys administered to the memberships of eleven national associations and agencies that collectively represent the great majority of the nation`s district energy system operators. Responses received from these surveys account for about 11% of all district systems in the United States. Data in this report is organized and presented within six user sectors selected to illustrate the significance of district energy in institutional, community and utility settings. Projections estimate the full extent of district energy systems in each sector.

  13. " East North Central",198,216,263,296,335,385

    U.S. Energy Information Administration (EIA) (indexed site)

    Vehicle-Miles Traveled, Selected Survey Years (Billions) " ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",1215,1353,1511,1602,1793,2287 "Household Characteristics" "Census...

  14. " East North Central",627,"NA",550,553,574,585.28553

    U.S. Energy Information Administration (EIA) (indexed site)

    Fuel Consumption per Vehicle, Selected Survey Years (Gallons) " ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",621,611,559,548,578,592 "Household Characteristics" "Census...

  15. A Method for Modeling Household Occupant Behavior to Simulate Residential Energy Consumption

    SciTech Connect (OSTI)

    Johnson, Brandon J; Starke, Michael R; Abdelaziz, Omar; Jackson, Roderick K; Tolbert, Leon M

    2014-01-01

    This paper presents a statistical method for modeling the behavior of household occupants to estimate residential energy consumption. Using data gathered by the U.S. Census Bureau in the American Time Use Survey (ATUS), actions carried out by survey respondents are categorized into ten distinct activities. These activities are defined to correspond to the major energy consuming loads commonly found within the residential sector. Next, time varying minute resolution Markov chain based statistical models of different occupant types are developed. Using these behavioral models, individual occupants are simulated to show how an occupant interacts with the major residential energy consuming loads throughout the day. From these simulations, the minimum number of occupants, and consequently the minimum number of multiple occupant households, needing to be simulated to produce a statistically accurate representation of aggregate residential behavior can be determined. Finally, future work will involve the use of these occupant models along side residential load models to produce a high-resolution energy consumption profile and estimate the potential for demand response from residential loads.

  16. "Table HC11.13 Lighting Usage Indicators by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    3 Lighting Usage Indicators by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Lighting Usage Indicators",,,"Middle Atlantic","New England" "Total U.S. Housing Units",111.1,20.6,15.1,5.5 "Indoor Lights Turned On During Summer" "Number of Lights Turned On" "Between

  17. "Table HC12.13 Lighting Usage Indicators by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    3 Lighting Usage Indicators by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Lighting Usage Indicators",,,"East North Central","West North Central" "Total U.S. Housing Units",111.1,25.6,17.7,7.9 "Indoor Lights Turned On During Summer" "Number of Lights Turned On"

  18. "Table HC13.13 Lighting Usage Indicators by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    3 Lighting Usage Indicators by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Lighting Usage Indicators",,,"South Atlantic","East South Central","West South Central" "Total U.S. Housing Units",111.1,40.7,21.7,6.9,12.1 "Indoor Lights Turned On During Summer" "Number of Lights

  19. "Table HC14.13 Lighting Usage Indicators by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    3 Lighting Usage Indicators by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Lighting Usage Indicators",,,"Mountain","Pacific" "Total U.S. Housing Units",111.1,24.2,7.6,16.6 "Indoor Lights Turned On During Summer" "Number of Lights Turned On" "Between 1 and 4 Hours per

  20. CENSUS OF BLUE STARS IN SDSS DR8

    SciTech Connect (OSTI)

    Scibelli, Samantha; Newberg, Heidi Jo; Carlin, Jeffrey L.; Yanny, Brian

    2015-01-01

    We present a census of the 12,060 spectra of blue objects ((g – r){sub 0} < –0.25) in the Sloan Digital Sky Survey (SDSS) Data Release 8 (DR8). As part of the data release, all of the spectra were cross-correlated with 48 template spectra of stars, galaxies, and QSOs to determine the best match. We compared the blue spectra by eye to the templates assigned in SDSS DR8. 10,856 of the objects matched their assigned template, 170 could not be classified due to low signal-to-noise ratio, and 1034 were given new classifications. We identify 7458 DA white dwarfs, 1145 DB white dwarfs, 273 rarer white dwarfs (including carbon, DZ, DQ, and magnetic), 294 subdwarf O stars, 648 subdwarf B stars, 679 blue horizontal branch stars, 1026 blue stragglers, 13 cataclysmic variables, 129 white dwarf-M dwarf binaries, 36 objects with spectra similar to DO white dwarfs, 179, quasi-stellar objects (QSOs), and 10 galaxies. We provide two tables of these objects, sample spectra that match the templates, figures showing all of the spectra that were grouped by eye, and diagnostic plots that show the positions, colors, apparent magnitudes, proper motions, etc., for each classification. Future surveys will be able to use templates similar to stars in each of the classes we identify to automatically classify blue stars, including rare types.

  1. The changing character of household waste in the Czech Republic between 1999 and 2009 as a function of home heating methods

    SciTech Connect (OSTI)

    Doležalová, Markéta; Benešová, Libuše; Závodská, Anita

    2013-09-15

    Highlights: • The character of household waste in the three different types of households were assesed. • The quantity, density and composition of household waste were determined. • The physicochemical characteristics were determined. • The changing character of household waste during past 10 years was described. • The potential of energy recovery of household waste in Czech republic was assesed. - Abstract: The authors of this paper report on the changing character of household waste, in the Czech Republic between 1999 and 2009 in households differentiated by their heating methods. The data presented are the result of two projects, financed by the Czech Ministry of Environment, which were undertaken during this time period with the aim of focusing on the waste characterisation and complete analysis of the physicochemical properties of the household waste. In the Czech Republic, the composition of household waste varies significantly between different types of households based on the methods of home heating employed. For the purposes of these studies, the types of homes were divided into three categories – urban, mixed and rural. Some of the biggest differences were found in the quantities of certain subsample categories, especially fine residue (matter smaller than 20 mm), between urban households with central heating and rural households that primarily employ solid fuel such coal or wood. The use of these solid fuels increases the fraction of the finer categories because of the higher presence of ash. Heating values of the residual household waste from the three categories varied very significantly, ranging from 6.8 MJ/kg to 14.2 MJ/kg in 1999 and from 6.8 MJ/kg to 10.5 MJ/kg in 2009 depending on the type of household and season. The same factors affect moisture of residual household waste which varied from 23.2% to 33.3%. The chemical parameters also varied significantly, especially in the quantities of Tl, As, Cr, Zn, Fe and Mn, which were higher in

  2. Solar Census - Perfecting the Art of Automated, Remote Solar Shading Assessments (Fact Sheet)

    SciTech Connect (OSTI)

    Not Available

    2014-04-01

    To validate the work completed by Solar Census as part of the Department of Energy SunShot Incubator 8 award, NREL validated the performanec of the Solar Census Surveyor tool against the industry standard Solmetric SunEye measurements for 4 residential sites in California who experienced light to heavy shading. Using the a two one-sided test (TOST) of statistical equivalence, NREL found that the mean differences between the Solar Census and SunEye mean solar access values for Annual, Summer, and Winter readings fall within the 95% confidence intervals and the confidence intervals themselves fall within the tolerances of +/- 5 SAVs, the Solar Census calculations are statistically equivalent to the SunEye measurements.

  3. A stellar census of the Tucana-Horologium moving group

    SciTech Connect (OSTI)

    Kraus, Adam L.; Shkolnik, Evgenya L.; Allers, Katelyn N.; Liu, Michael C.

    2014-06-01

    We report the selection and spectroscopic confirmation of 129 new late-type (SpT = K3-M6) members of the Tucana-Horologium moving group, a nearby (d ∼ 40 pc), young (τ ∼ 40 Myr) population of comoving stars. We also report observations for 13 of the 17 known Tuc-Hor members in this spectral type range, and that 62 additional candidates are likely to be unassociated field stars; the confirmation frequency for new candidates is therefore 129/191 = 67%. We have used radial velocities, Hα emission, and Li{sub 6708} absorption to distinguish between contaminants and bona fide members. Our expanded census of Tuc-Hor increases the known population by a factor of ∼3 in total and by a factor of ∼8 for members with SpT ≥ K3, but even so, the K-M dwarf population of Tuc-Hor is still markedly incomplete. Our expanded census allows for a much more detailed study of Tuc-Hor than was previously feasible. The spatial distribution of members appears to trace a two-dimensional sheet, with a broad distribution in X and Y, but a very narrow distribution (±5 pc) in Z. The corresponding velocity distribution is very small, with a scatter of ±1.1 km s{sup –1} about the mean UVW velocity for stars spanning the entire 50 pc extent of Tuc-Hor. We also show that the isochronal age (τ ∼ 20-30 Myr) and the lithium depletion boundary age (τ ∼ 40 Myr) disagree, following the trend in other pre-main-sequence populations for isochrones to yield systematically younger ages. The Hα emission line strength follows a trend of increasing equivalent width with later spectral type, as is seen for young clusters. We find that moving group members have been depleted of measurable lithium for spectral types of K7.0-M4.5. None of our targets have significant infrared excesses in the WISE W3 band, yielding an upper limit on warm debris disks of F < 0.7%. Finally, our purely kinematic and color-magnitude selection procedure allows us to test the efficiency and completeness for activity

  4. Table HC1-12a. Housing Unit Characteristics by West Census Region...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  5. Table HC1-11a. Housing Unit Characteristics by South Census...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... 2.9 1.4 1.0 0.3 Q 18.9 Solar ......Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  6. Table HC1-10a. Housing Unit Characteristics by Midwest Census...

    U.S. Energy Information Administration (EIA) (indexed site)

    ......... 2.9 0.5 0.4 Q 15.3 Solar ......Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  7. Table HC1-9a. Housing Unit Characteristics by Northeast Census...

    Gasoline and Diesel Fuel Update

    ... 0.8 0.3 0.2 Q 33.1 Solar ...... Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  8. "Table A32. Total Quantity of Purchased Energy Sources by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    Quantity of Purchased Energy Sources by Census Region," " Census Division, Industry Group, and Selected Industries, 1994" " (Estimates in Btu or Physical Units)" ,,,,,,"Natural",,,"Coke" " "," ","Total","Electricity","Residual","Distillate","Gas(c)"," ","Coal","and Breeze"," ","RSE" "SIC","

  9. "Table A36. Total Expenditures for Purchased Energy Sources by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    6. Total Expenditures for Purchased Energy Sources by Census Region," " Census Division, Industry Group, and Selected Industries, 1994" " (Estimates in Million Dollars)" ,,,,,,,,,,,"RSE" "SIC"," "," "," ","Residual","Distillate ","Natural"," "," ","Coke"," ","Row" "Code(a)","Industry Group and

  10. S:\\VM3\\RX97\\TBL_LIST.WPD

    Annual Energy Outlook

    1997 1 HC2-12a. Household Characteristics by West Census Region, Million U.S. Households, 1997 1 These data are from the 1997 Residential Energy Consumption Survey ...

  11. Competition Helps Kids Learn About Energy and Save Their Households...

    Energy Savers

    Competition Helps Kids Learn About Energy and Save Their Households Some Money Competition Helps Kids Learn About Energy and Save Their Households Some Money May 21, 2013 - 2:40pm ...

  12. Determinants of Household Use of Selected Energy Star Appliances - Energy

    Gasoline and Diesel Fuel Update

    Information Administration Determinants of Household Use of Selected Energy Star Appliances Release date: May 25, 2016 Introduction According to the 2009 Residential Energy Consumption Survey (RECS), household appliances1accounted for 35% of U.S. household energy consumption, up from 24% in 1993. Thus, improvements in the energy performance of residential appliances as well as increases in the use of more efficient appliances can be effective in reducing household energy consumption and

  13. Comparison of energy expenditures by elderly and non-elderly households: 1975 and 1985

    SciTech Connect (OSTI)

    Siler, A.

    1980-05-01

    The relative position of the elderly in the population is examined and their characteristic use of energy in relation to the total population and their non-elderly counterparts is observed. The 1985 projections are based on demographic, economic, and socio-economic, and energy data assumptions contained in the 1978 Annual Report to Congress. The model used for estimating household energy expenditure is MATH/CHRDS - Micro-Analysis of Transfers to Households/Comprehensive Human Resources Data System. Characteristics used include households disposable income, poverty status, location by DOE region and Standard Metropolitan Statistical Area (SMSA), and race and sex of the household head as well as age. Energy use by fuel type will be identified for total home fuels, including electricity, natural gas, bottled gas and fuel oil, and for all fuels, where gasoline use is also included. Throughout the analysis, both income and expenditure-dollar amounts for 1975 and 1985 are expressed in constant 1978 dollars. Two appendices contain statistical information.

  14. Interfacing 1990 US Census TIGER map files with New S graphics software

    SciTech Connect (OSTI)

    Rizzardi, M.; Mohr, M.S.; Merrill, D.W.; Selvin, S. |

    1992-07-01

    In 1990, the United States Bureau of the Census released detailed geographic base files known as TIGER/Line (Topologically Integrated Geographic Encoding and Referencing) which contain detail on the physical features and census tract boundaries of every county in the United States. The TIGER database is attractive for two reasons. First, it is publicly available through the Bureau of the Census on tape or cd-rom for a minimal fee. Second, it contains 24 billion characters of data which describe geographic features of interest to the Census Bureau such as coastlines, hydrography, transportation networks, political boundaries, etc. Unfortunately, the large TIGER database only provides raw alphanumeric data; no utility software, graphical or otherwise, is included. On the other hand New S, a popular statistical software package by AT&T, has easily operated functions that permit advanced graphics in conjunction with data analysis. New S has the ability to plot contours, lines, segments, and points. However, of special interest is the New S function map and its options. Using the map function, which requires polygons as input, census tracts can be quickly selected, plotted, shaded, etc. New S graphics combined with the TIGER database has obvious potential. This paper reports on our efforts to use the TIGER map files with New S, especially to construct census tract maps of counties. While census tract boundaries are inherently polygonal, they are not organized as such in the TIGER database. This conversion of the TIGER ``line`` format into New S ``polygon/polyline`` format is one facet of the work reported here. Also we discuss the selection and extraction of auxiliary geographic information from TIGER files for graphical display using New S.

  15. Household Energy Consumption Segmentation Using Hourly Data

    SciTech Connect (OSTI)

    Kwac, J; Flora, J; Rajagopal, R

    2014-01-01

    The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.

  16. Household energy consumption and expenditures, 1987

    SciTech Connect (OSTI)

    Not Available

    1989-10-10

    Household Energy Consumption and Expenditures 1987, Part 1: National Data is the second publication in a series from the 1987 Residential Energy Consumption Survey (RECS). It is prepared by the Energy End Use Division (EEUD) of the Office of Energy Markets and End Use (EMEU), Energy Information Administration (EIA). The EIA collects and publishes comprehensive data on energy consumption in occupied housing units in the residential sector through the RECS. 15 figs., 50 tabs.

  17. Developing Poultry Facility Type Information from USDA Agricultural Census Data for Use in Epidemiological and Economic Models

    SciTech Connect (OSTI)

    Melius, C

    2007-12-05

    The epidemiological and economic modeling of poultry diseases requires knowing the size, location, and operational type of each poultry type operation within the US. At the present time, the only national database of poultry operations that is available to the general public is the USDA's 2002 Agricultural Census data, published by the National Agricultural Statistics Service, herein referred to as the 'NASS data'. The NASS data provides census data at the county level on poultry operations for various operation types (i.e., layers, broilers, turkeys, ducks, geese). However, the number of farms and sizes of farms for the various types are not independent since some facilities have more than one type of operation. Furthermore, some data on the number of birds represents the number sold, which does not represent the number of birds present at any given time. In addition, any data tabulated by NASS that could identify numbers of birds or other data reported by an individual respondent is suppressed by NASS and coded with a 'D'. To be useful for epidemiological and economic modeling, the NASS data must be converted into a unique set of facility types (farms having similar operational characteristics). The unique set must not double count facilities or birds. At the same time, it must account for all the birds, including those for which the data has been suppressed. Therefore, several data processing steps are required to work back from the published NASS data to obtain a consistent database for individual poultry operations. This technical report documents data processing steps that were used to convert the NASS data into a national poultry facility database with twenty-six facility types (7 egg-laying, 6 broiler, 1 backyard, 3 turkey, and 9 others, representing ducks, geese, ostriches, emus, pigeons, pheasants, quail, game fowl breeders and 'other'). The process involves two major steps. The first step defines the rules used to estimate the data that is suppressed

  18. "Table A27. Components of Onsite Electricity Generation by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    Components of Onsite Electricity Generation by Census Region," " Census Division, Industry Group, and Selected Industries, 1994" " (Estimates in Million Kilowatthours)" ," "," "," "," " " "," "," "," ",," ","RSE" "SIC"," "," "," ",," ","Row" "Code(a)","Industry Group and

  19. Fact #727: May 14, 2012 Nearly Twenty Percent of Households Own...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    its Major Metropolitan Area, 1960-1990, Cambridge, MA, 1994, p. 2-2. 2000 data - U.S. Bureau of the Census, American Fact Finder, factfinder.census.gov, Table QT-04, August 2001. ...

  20. Appliance Commitment for Household Load Scheduling

    SciTech Connect (OSTI)

    Du, Pengwei; Lu, Ning

    2011-06-30

    This paper presents a novel appliance commitment algorithm that schedules thermostatically-controlled household loads based on price and consumption forecasts considering users comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem was described in the paper using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load was modeled by physical models; random hot water consumption was modeled with statistical methods. The models were used to predict the appliance operation over the scheduling time horizon. User comfort was transformed to a set of linear constraints. Then, a novel linear, sequential, optimization process was used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.

  1. Barriers to household investment in residential energy conservation: preliminary assessment

    SciTech Connect (OSTI)

    Hoffman, W.L.

    1982-12-01

    A general assessment of the range of barriers which impede household investments in weatherization and other energy efficiency improvements for their homes is provided. The relationship of similar factors to households' interest in receiving a free energy audits examined. Rates of return that underly household investments in major conservation improvements are assessed. A special analysis of household knowledge of economically attractive investments is provided that compares high payback improvements specified by the energy audit with the list of needed or desirable conservation improvements identified by respondents. (LEW)

  2. Loan Programs for Low- and Moderate-Income Households

    Energy.gov [DOE]

    Better Buildings Residential Network Multifamily and Low-Income Housing Peer Exchange Call Series: Loan Programs for Low- and Moderate-Income Households, March 13, 2014.

  3. Delivering Energy Efficiency to Middle Income Single Family Households

    SciTech Connect (OSTI)

    none,

    2011-12-01

    Provides state and local policymakers with information on successful approaches to the design and implementation of residential efficiency programs for households ineligible for low-income programs.

  4. Characterization of household hazardous waste from Marin County, California, and New Orleans, Louisiana

    SciTech Connect (OSTI)

    Rathje, W.L.; Wilson, D.C.; Lambou, V.W.; Herndon, R.C.

    1987-09-01

    There is a growing concern that certain constituents of common household products, that are discarded in residential garbage, may be potentially harmful to human health and the environment by adversely affecting the quality of ground and surface water. A survey of hazardous wastes in residential garbage from Marin County, California, and New Orleans, Louisiana, was conducted in order to determine the amount and characteristics of such wastes that are entering municipal landfills. The results of the survey indicate that approximately 642 metric tons of hazardous waste are discarded per year for the New Orleans study area and approximately 259 metric tons are discarded per year for the Marin County study area. Even though the percent of hazardous household waste in the garbage discarded in both study areas was less than 1%, it represents a significant quantity of hazardous waste because of the large volume of garbage involved.

  5. Testsite

    Gasoline and Diesel Fuel Update

    0a. Housing Unit Characteristics by Midwest Census Region, Million U.S. Households, 2001 Housing Unit Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.8 Total .............................................................. 107.0 24.5 17.1 7.4 NE Census Region and Division Northeast ..................................................... 20.3 -- -- -- NF New England

  6. "Table HC11.10 Home Appliances Usage Indicators by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ," U.S. Housing Units (millions) " ,,,"Census Division" ,,"Total Northeast" "Home Appliances Usage Indicators",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Cooking Appliances" "Frequency of Hot Meals Cooked" "3 or More Times A

  7. "Table HC11.12 Home Electronics Usage Indicators by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Home Electronics Usage Indicators by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Home Electronics Usage Indicators",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Personal Computers" "Do Not Use a Personal Computer",35.5,6.9,5.3,1.6 "Use a

  8. "Table HC11.5 Space Heating Usage Indicators by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Space Heating Usage Indicators by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Space Heating Usage Indicators",,,"Middle Atlantic","New England" "Total U.S. Housing Units",111.1,20.6,15.1,5.5 "Do Not Have Heating Equipment",1.2,"Q","Q","Q"

  9. "Table HC11.7 Air-Conditioning Usage Indicators by Northeast Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by Northeast Census Region, 2005" " Million U.S. Housing Units" ,,"Northeast Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Northeast" "Air Conditioning Usage Indicators",,,"Middle Atlantic","New England" "Total",111.1,20.6,15.1,5.5 "Do Not Have Cooling Equipment",17.8,4,2.4,1.7 "Have Cooling

  10. "Table HC12.10 Home Appliances Usage Indicators by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Home Appliances Usage Indicators",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Cooking Appliances" "Frequency of Hot Meals Cooked" "3 or More Times A

  11. "Table HC12.12 Home Electronics Usage Indicators by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Home Electronics Usage Indicators by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Home Electronics Usage Indicators",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Personal Computers" "Do Not Use a Personal Computer",35.5,8.1,5.6,2.5 "Use

  12. "Table HC12.5 Space Heating Usage Indicators by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Space Heating Usage Indicators by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Space Heating Usage Indicators",,,"East North Central","West North Central" "Total U.S. Housing Units",111.1,25.6,17.7,7.9 "Do Not Have Heating Equipment",1.2,"Q","Q","N"

  13. "Table HC12.7 Air-Conditioning Usage Indicators by Midwest Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by Midwest Census Region, 2005" " Million U.S. Housing Units" ,,"Midwest Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total Midwest" "Air Conditioning Usage Indicators",,,"East North Central","West North Central" "Total",111.1,25.6,17.7,7.9 "Do Not Have Cooling Equipment",17.8,2.1,1.8,0.3 "Have Cooling

  14. "Table HC13.10 Home Appliances Usage Indicators by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Home Appliances Usage Indicators",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Cooking Appliances" "Frequency of Hot Meals Cooked"

  15. "Table HC13.12 Home Electronics Usage Indicators by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Home Electronics Usage Indicators by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Home Electronics Usage Indicators",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Personal Computers" "Do Not Use a Personal

  16. "Table HC13.5 Space Heating Usage Indicators by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Space Heating Usage Indicators by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Space Heating Usage Indicators",,,"South Atlantic","East South Central","West South Central" "Total U.S. Housing Units",111.1,40.7,21.7,6.9,12.1 "Do Not Have Heating

  17. "Table HC13.7 Air-Conditioning Usage Indicators by South Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by South Census Region, 2005" " Million U.S. Housing Units" ,,"South Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total South" "Air Conditioning Usage Indicators",,,"South Atlantic","East South Central","West South Central" "Total",111.1,40.7,21.7,6.9,12.1 "Do Not Have Cooling Equipment",17.8,1.4,0.8,0.2,0.3 "Have

  18. "Table HC14.10 Home Appliances Usage Indicators by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Home Appliances Usage Indicators",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Cooking Appliances" "Frequency of Hot Meals Cooked" "3 or More Times A Day",8.2,2.6,0.7,1.9 "2

  19. "Table HC14.12 Home Electronics Usage Indicators by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Home Electronics Usage Indicators by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Home Electronics Usage Indicators",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Personal Computers" "Do Not Use a Personal Computer",35.5,6.4,2.2,4.2 "Use a Personal

  20. "Table HC14.5 Space Heating Usage Indicators by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Space Heating Usage Indicators by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Space Heating Usage Indicators",,,"Mountain","Pacific" "Total U.S. Housing Units",111.1,24.2,7.6,16.6 "Do Not Have Heating Equipment",1.2,0.7,"Q",0.7 "Have Space Heating

  1. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-01-01

    Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

  2. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-12-31

    Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

  3. Reconstructing householder vectors from Tall-Skinny QR

    DOE PAGES-Beta [OSTI]

    Ballard, Grey Malone; Demmel, James; Grigori, Laura; Jacquelin, Mathias; Knight, Nicholas; Nguyen, Hong Diep

    2015-08-05

    The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstratemore » the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.« less

  4. Reconstructing householder vectors from Tall-Skinny QR

    SciTech Connect (OSTI)

    Ballard, Grey Malone; Demmel, James; Grigori, Laura; Jacquelin, Mathias; Knight, Nicholas; Nguyen, Hong Diep

    2015-08-05

    The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstrate the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.

  5. TOWARD A SPECTROSCOPIC CENSUS OF WHITE DWARFS WITHIN 40 pc OF THE SUN

    SciTech Connect (OSTI)

    Limoges, M.-M.; Bergeron, P.; Lepine, S. E-mail: bergeron@astro.umontreal.ca

    2013-05-15

    We present the preliminary results of a survey aimed at significantly increasing the range and completeness of the local census of spectroscopically confirmed white dwarfs. The current census of nearby white dwarfs is reasonably complete only to about 20 pc of the Sun, a volume that includes around 130 white dwarfs, a sample too small for detailed statistical analyses. This census is largely based on follow-up investigations of stars with very large proper motions. We describe here the basis of a method that will lead to a catalog of white dwarfs within 40 pc of the Sun and north of the celestial equator, thus increasing by a factor of eight the extent of the northern sky census. White dwarf candidates are identified from the SUPERBLINK proper motion database, allowing us to investigate stars down to a proper motion limit {mu} > 40 mas yr{sup -1}, while minimizing the kinematic bias for nearby objects. The selection criteria and distance estimates are based on a combination of color-magnitude and reduced proper motion diagrams. Our follow-up spectroscopic observation campaign has so far uncovered 193 new white dwarfs, among which we identify 127 DA (including 9 DA+dM and 4 magnetic), 1 DB, 56 DC, 3 DQ, and 6 DZ stars. We perform a spectroscopic analysis on a subsample of 84 DAs, and provide their atmospheric parameters. In particular, we identify 11 new white dwarfs with spectroscopic distances within 25 pc of the Sun, including five candidates to the D < 20 pc subset.

  6. "Table A22. Total Quantity of Purchased Energy Sources by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    2. Total Quantity of Purchased Energy Sources by Census Region," " Industry Group, and Selected Industries, 1991" " (Estimates in Btu or Physical Units)" ,,,,,,"Natural",,,"Coke" " "," ","Total","Electricity","Residual","Distillate","Gas(c)"," ","Coal","and Breeze"," ","RSE" "SIC","

  7. "Table A24. Total Expenditures for Purchased Energy Sources by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    4. Total Expenditures for Purchased Energy Sources by Census Region," " Industry Group, and Selected Industries, 1991" " (Estimates in Million Dollars)" ,,,,,,,,,,,"RSE" "SIC"," "," "," ","Residual","Distillate ","Natural"," "," ","Coke"," ","Row" "Code(a)","Industry Groupsc and

  8. "Table A25. Average Prices of Selected Purchased Energy Sources by Census"

    U.S. Energy Information Administration (EIA) (indexed site)

    . Average Prices of Selected Purchased Energy Sources by Census" " Region, Industry Group, and Selected Industries, 1991: Part 1" " (Estimates in Dollars per Physical Unit)" ,,,,," " " "," "," ","Residual","Distillate","Natural Gas(c)"," "," ","RSE" "SIC"," ","Electricity","Fuel Oil","Fuel

  9. "Table A40. Average Prices of Selected Purchased Energy Sources by Census"

    U.S. Energy Information Administration (EIA) (indexed site)

    Region, Census Division, Industry Group, and Selected Industries, 1994: Part 2" " (Estimates in Dollars per Million Btu)" ,,,,,,,,"RSE" "SIC"," "," ","Residual","Distillate"," "," "," ","Row" "Code(a)","Industry Group and Industry","Electricity","Fuel Oil","Fuel Oil(b)","Natural

  10. Fact #618: April 12, 2010 Vehicles per Household and Other Demographic...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    per Household and Other Demographic Statistics Fact 618: April 12, 2010 Vehicles per Household and Other Demographic Statistics Since 1969, the number of vehicles per ...

  11. "Table HC7.5 Space Heating Usage Indicators by Household Income...

    U.S. Energy Information Administration (EIA) (indexed site)

    ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ... for 2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ...

  12. "Table HC7.12 Home Electronics Usage Indicators by Household...

    U.S. Energy Information Administration (EIA) (indexed site)

    ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ... for 2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ...

  13. Fact #748: October 8, 2012 Components of Household Expenditures on

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Transportation, 1984-2010 | Department of Energy 8: October 8, 2012 Components of Household Expenditures on Transportation, 1984-2010 Fact #748: October 8, 2012 Components of Household Expenditures on Transportation, 1984-2010 The overall share of annual household expenditures for transportation was lower in 2010 than it was in 1984, reaching its lowest point in 2009 at 15.5%. In the early to mid-1980s when oil prices were high, gasoline and motor oil made up a larger share of transportation

  14. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Home Electronics Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Home Electronics Characteristics"

  15. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Living Space Characteristics"

  16. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Space Heating Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Space Heating Characteristics"

  17. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    6 Air Conditioning Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Air Conditioning Characteristics"

  18. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    HC7.9 Home Appliances Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Home Appliances Characteristics" "Total

  19. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    8 Water Heating Characteristics by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Water Heating Characteristics"

  20. Microsoft Word - Household Energy Use CA

    Gasoline and Diesel Fuel Update

    ... None Yes Yes No No 0% 20% 40% 60% 80% 100% US CA No Car CAR IS PARKED WITHIN 20 FT OF ELECTRICAL OUTLET More highlights from RECS on housing characteristics and energy-related ...

  1. Table 2. Percent of Households with Vehicles, Selected Survey...

    U.S. Energy Information Administration (EIA) (indexed site)

    Percent of Households with Vehicles, Selected Survey Years " ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",85.5450237,89.00343643,88.75545852,89.42917548,87.25590956,92.08...

  2. Household energy use in non-OPEC developing countries

    SciTech Connect (OSTI)

    Fernandez, J.C.

    1980-05-01

    Energy use in the residential sector in India, Brazil, Mexico, the Republic of Korea, the Sudan, Pakistan, Malaysia, and Guatemala is presented. Whenever possible, information is included on the commercial fuels (oil, gas, coal, and electricity) and on what are termed noncommercial fuels (firewood, animal dung, and crop residues). Of special interest are the differences in the consumption patterns of urban and rural areas, and of households at different income levels. Where the data allow, the effect of household size on energy consumption is discussed. Section II is an overview of the data for all eight countries. Section III examines those areas (India, Brazil, Mexico City) for which data exist on the actual quantity of energy consumed by households. Korea, the Sudan, and Pakistan, which collect data on household expenditures on fuels, are discussed in Section IV. The patterns of ownership of energy-using durables in Malaysia and Guatemala are discussed in Section V. (MCW)

  3. Travel Patterns And Characteristics Of Transit Users In New York State

    SciTech Connect (OSTI)

    Hwang, Ho-Ling; Wilson, Daniel W.; Reuscher, Tim; Chin, Shih-Miao; Taylor, Rob D.

    2015-12-01

    This research is a detailed examination of the travel behaviors and patterns of transit users within New York State (NYS), primarily based on travel data provided by the National Household Travel Survey (NHTS) in 2009 and the associated Add-on sample households purchased by the New York State Department of Transportation (NYSDOT). Other data sources analyzed in this study include: NYS General Transit Feed Specification (GTFS) to assist in analyzing spatial relationships for access to transit and the creation of Transit Shed geographic areas of 1, 2.5, and 5 miles from transit stop locations, LandScan population database to understand transit coverage, and Census Bureau s American Community Survey (ACS) data to examine general transit patterns and trends in NYS over time. The majority of analyses performed in this research aimed at identifying transit trip locations, understanding differences in transit usage by traveler demographics, as well as producing trip/mode-specific summary statistics including travel distance, trip duration, time of trip, and travel purpose of transit trips made by NYS residents, while also analyzing regional differences and unique travel characteristics and patterns. The analysis was divided into two aggregated geographic regions: New York Metropolitan Transportation Council (NYMTC) and NYS minus NYMTC (Rest of NYS). The inclusion of NYMTC in all analysis would likely produce misleading conclusions for other regions in NYS. TRANSIT COVERAGE The NYS transit network has significant coverage in terms of transit stop locations across the state s population. Out of the 19.3 million NYS population in 2011, about 15.3 million (or 79%) resided within the 1-mile transit shed. This NYS population transit coverage increased to 16.9 million (or 88%) when a 2.5-mile transit shed was considered; and raised to 17.7 million (or 92%) when the 5-mile transit shed was applied. KEY FINDINGS Based on 2009 NHTS data, about 40% of NYMTC households used transit

  4. Household waste disposal in Mekelle city, Northern Ethiopia

    SciTech Connect (OSTI)

    Tadesse, Tewodros Ruijs, Arjan; Hagos, Fitsum

    2008-07-01

    In many cities of developing countries, such as Mekelle (Ethiopia), waste management is poor and solid wastes are dumped along roadsides and into open areas, endangering health and attracting vermin. The effects of demographic factors, economic and social status, waste and environmental attributes on household solid waste disposal are investigated using data from household survey. Household level data are then analyzed using multinomial logit estimation to determine the factors that affect household waste disposal decision making. Results show that demographic features such as age, education and household size have an insignificant impact over the choice of alternative waste disposal means, whereas the supply of waste facilities significantly affects waste disposal choice. Inadequate supply of waste containers and longer distance to these containers increase the probability of waste dumping in open areas and roadsides relative to the use of communal containers. Higher household income decreases the probability of using open areas and roadsides as waste destinations relative to communal containers. Measures to make the process of waste disposal less costly and ensuring well functioning institutional waste management would improve proper waste disposal.

  5. Interfacing 1990 US Census TIGER map files with New S graphics software. [Topologically Integrated Geographic Encoding and Referencing (TIGER)

    SciTech Connect (OSTI)

    Rizzardi, M.; Mohr, M.S.; Merrill, D.W.; Selvin, S. California Univ., Berkeley, CA . School of Public Health)

    1992-07-01

    In 1990, the United States Bureau of the Census released detailed geographic base files known as TIGER/Line (Topologically Integrated Geographic Encoding and Referencing) which contain detail on the physical features and census tract boundaries of every county in the United States. The TIGER database is attractive for two reasons. First, it is publicly available through the Bureau of the Census on tape or cd-rom for a minimal fee. Second, it contains 24 billion characters of data which describe geographic features of interest to the Census Bureau such as coastlines, hydrography, transportation networks, political boundaries, etc. Unfortunately, the large TIGER database only provides raw alphanumeric data; no utility software, graphical or otherwise, is included. On the other hand New S, a popular statistical software package by AT T, has easily operated functions that permit advanced graphics in conjunction with data analysis. New S has the ability to plot contours, lines, segments, and points. However, of special interest is the New S function map and its options. Using the map function, which requires polygons as input, census tracts can be quickly selected, plotted, shaded, etc. New S graphics combined with the TIGER database has obvious potential. This paper reports on our efforts to use the TIGER map files with New S, especially to construct census tract maps of counties. While census tract boundaries are inherently polygonal, they are not organized as such in the TIGER database. This conversion of the TIGER line'' format into New S polygon/polyline'' format is one facet of the work reported here. Also we discuss the selection and extraction of auxiliary geographic information from TIGER files for graphical display using New S.

  6. " by Census Region, Census Division...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... W ",15,129," W ",0," W ",15,1," W "," W ",117,0," W ",19.6 ," Gasification of Biomass Feedstocks"," * ",0,0," * ",0,0,0,0,0,0," * ",0,0,0,20.7 ," Fast Pyrolysis of ...

  7. "Table A16. Components of Total Electricity Demand by Census Region, Industry"

    U.S. Energy Information Administration (EIA) (indexed site)

    6. Components of Total Electricity Demand by Census Region, Industry" " Group, and Selected Industries, 1991" " (Estimates in Million Kilowatthours)" " "," "," "," "," "," "," "," " " "," "," "," "," ","Sales and/or"," ","RSE" "SIC"," "," ","Transfers","Total

  8. "Table A17. Components of Onsite Electricity Generation by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    7. Components of Onsite Electricity Generation by Census Region," " Industry Group, and Selected Industries, 1991" " (Estimates in Million Kilowatthours)" " "," "," "," "," "," "," "," " " "," "," "," "," "," ","RSE" "SIC"," "," "," "," "," ","Row"

  9. "Table A25 Average Prices of Selected Purchased Energy Sources by Census"

    U.S. Energy Information Administration (EIA) (indexed site)

    Average Prices of Selected Purchased Energy Sources by Census" " Region, Industry Group, and Selected Industries, 1991: Part 2" " (Estimates in Dollars per Million Btu)" ,,,,,,,,"RSE" "SIC"," "," ","Residual","Distillate"," "," "," ","Row" "Code(a)","Industry Groups and Industry","Electricity","Fuel Oil","Fuel

  10. "Table A40. Average Prices of Selected Purchased Energy Sources by Census"

    U.S. Energy Information Administration (EIA) (indexed site)

    Region, Census Division, Industry Group, and Selected Industries, 1994: Part 1" " (Estimates in Dollars per Physical Units)" ,,,,," " " "," "," ","Residual","Distillate","Natural Gas(c)"," "," ","RSE" "SIC"," ","Electricity","Fuel Oil","Fuel Oil(b)","(1000","LPG","Coal","Row"

  11. Fact #618: April 12, 2010 Vehicles per Household and Other Demographic

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Statistics | Department of Energy 8: April 12, 2010 Vehicles per Household and Other Demographic Statistics Fact #618: April 12, 2010 Vehicles per Household and Other Demographic Statistics Since 1969, the number of vehicles per household has increased by 66% and the number of vehicles per licensed driver has increased by 47%. The number of workers per household has changed the least of the statistics shown here. There has been a decline in the number of persons per household from 1969 to

  12. How are coastal households responding to climate change?

    DOE PAGES-Beta [OSTI]

    Elrick-Barr, Carmen E.; Smith, Timothy F.; Preston, Benjamin L.; Thomsen, Dana C.; Baum, Scott

    2016-06-13

    In Australia, shared responsibility is a concept advocated to promote collective climate change adaptation by multiple actors and institutions. However, a shared response is often promoted in the absence of information regarding actions currently taken; in particular, there is limited knowledge regarding action occurring at the household scale. To address this gap, we examine household actions taken to address climate change and associated hazards in two Australian coastal communities. Mixed methods research is conducted to answer three questions: (1) what actions are currently taken (mitigation, actions to lobby for change or adaptation to climate impacts)? (2) why are these actionsmore » taken (e.g. are they consistent with capacity, experience, perceptions of risk); and (3) what are the implications for adaptation? We find that households are predominantly mitigating greenhouse gas emissions and that impact orientated adaptive actions are limited. Coping strategies are considered sufficient to mange climate risks, proving a disincentive for additional adaptive action. Influencing factors differ, but generally, risk perception and climate change belief are associated with action. Furthermore, the likelihood of more action is a function of homeownership and a tendency to plan ahead. Addressing factors that support or constrain household adaptive decision-making and action, from the physical (e.g. homeownership) to the social (e.g. skills in planning and a culture of adapting to change) will be critical in increasing household participation in adaptation.« less

  13. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 1 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total 83.1 66.1 144.2 37 17 29.1 10 678 0.31 539 192 Census Region and Division

  14. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 2 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total 83.7 66.0 142.2 36 16 28.0 10 708 0.33 558 204 Census Region and Division

  15. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 4 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total 86.3 67.4 144.3 37 17 28.8 11 808 0.38 632 234 Census Region and Division

  16. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 7 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total 90.5 70.4 156.8 39 18 30.5 12 875 0.39 680 262 Census Region and Division

  17. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 97 Average Electricity Residential Buildings Consumption Expenditures Total per Floor- per Square per per per Total Total space (1) Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total 101.4 83.2 168.8 42 21 35.0 13 1,061 0.52 871 337 Census Region and

  18. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 2001 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total 107.0 85.2 211.2 46 18 36.0 14 1,178 0.48 938 366 Census Region and Division

  19. Modeling patterns of hot water use in households

    SciTech Connect (OSTI)

    Lutz, J.D.; Liu, Xiaomin; McMahon, J.E.

    1996-11-01

    This report presents a detailed model of hot water use patterns in individual household. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies. 21 refs., 3 figs., 10 tabs.

  20. Modeling patterns of hot water use in households

    SciTech Connect (OSTI)

    Lutz, James D.; Liu, Xiaomin; McMahon, James E.; Dunham, Camilla; Shown, Leslie J.; McCure, Quandra T.

    1996-01-01

    This report presents a detailed model of hot water use patterns in individual households. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies.

  1. "Table HC14.7 Air-Conditioning Usage Indicators by West Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by West Census Region, 2005" " Million U.S. Housing Units" ,,"West Census Region" ,"U.S. Housing Units (millions)" ,,,"Census Division" ,,"Total West" "Air Conditioning Usage Indicators",,,"Mountain","Pacific" "Total",111.1,24.2,7.6,16.6 "Do Not Have Cooling Equipment",17.8,10.3,3.1,7.3 "Have Cooling Equipment",93.3,13.9,4.5,9.4 "Use Cooling

  2. Household heating bills expected to be lower this winter

    U.S. Energy Information Administration (EIA) (indexed site)

    Household heating bills expected to be lower this winter U.S. consumers are expected to pay less this winter on their home heating bills because of lower oil and natural gas prices and projected milder temperatures than last winter. In its new forecast, the U.S. Energy Information Administration said households that rely on heating oil which are mainly located in the Northeast will pay the lowest heating expenditures in 9 years down 25% from last winter as consumers are expected to save about

  3. New York Household Travel Patterns: A Comparison Analysis

    SciTech Connect (OSTI)

    Hu, Patricia S; Reuscher, Tim

    2007-05-01

    In 1969, the U. S. Department of Transportation began collecting detailed data on personal travel to address various transportation planning issues. These issues range from assessing transportation investment programs to developing new technologies to alleviate congestion. This 1969 survey was the birth of the Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990 and 1995. Longer-distance travel was collected in 1977 and 1995. In 2001, the survey was renamed to the National Household Travel Survey (NHTS) and collected both daily and longer-distance trips in one survey. In addition to the number of sample households that the national NPTS/NHTS survey allotted to New York State (NYS), the state procured an additional sample of households in both the 1995 and 2001 surveys. In the 1995 survey, NYS procured an addition sample of more than 9,000 households, increasing the final NY NPTS sample size to a total of 11,004 households. Again in 2001, NYS procured 12,000 additional sample households, increasing the final New York NHTS sample size to a total of 13,423 households with usable data. These additional sample households allowed NYS to address transportation planning issues pertinent to geographic areas significantly smaller than for what the national NPTS and NHTS data are intended. Specifically, these larger sample sizes enable detailed analysis of twelve individual Metropolitan Planning Organizations (MPOs). Furthermore, they allowed NYS to address trends in travel behavior over time. In this report, travel data for the entire NYS were compared to those of the rest of the country with respect to personal travel behavior and key travel determinants. The influence of New York City (NYC) data on the comparisons of the state of New York to the rest of the country was also examined. Moreover, the analysis examined the relationship between population density and travel patterns, and the similarities and differences among New

  4. A Glance at China’s Household Consumption

    SciTech Connect (OSTI)

    Shui, Bin

    2009-10-01

    Known for its scale, China is the most populous country with the world’s third largest economy. In the context of rising living standards, a relatively lower share of household consumption in its GDP, a strong domestic market and globalization, China is witnessing an unavoidable increase in household consumption, related energy consumption and carbon emissions. Chinese policy decision makers and researchers are well aware of these challenges and keen to promote green lifestyles. China has developed a series of energy policies and programs, and launched a wide-range social marketing activities to promote energy conservation.

  5. "Table HC10.10 Home Appliances Usage Indicators by U.S. Census Regions, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by U.S. Census Regions, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Home Appliances Usage Indicators",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Cooking Appliances" "Frequency of Hot Meals Cooked" "3 or More Times A Day",8.2,1.2,1.4,3,2.6 "2 Times A

  6. "Table HC10.12 Home Electronics Usage Indicators by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Home Electronics Usage Indicators by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Home Electronics Usage Indicators",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Personal Computers" "Do Not Use a Personal Computer",35.5,6.9,8.1,14.2,6.4 "Use a Personal

  7. "Table HC10.13 Lighting Usage Indicators by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    3 Lighting Usage Indicators by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Lighting Usage Indicators",,"Northeast","Midwest","South","West" "Total U.S. Housing Units",111.1,20.6,25.6,40.7,24.2 "Indoor Lights Turned On During Summer" "Number of Lights Turned On" "Between 1 and 4 Hours per

  8. "Table HC10.5 Space Heating Usage Indicators by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Space Heating Usage Indicators by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Space Heating Usage Indicators",,"Northeast","Midwest","South","West" "Total U.S. Housing Units",111.1,20.6,25.6,40.7,24.2 "Do Not Have Heating Equipment",1.2,"Q","Q","Q",0.7 "Have Space Heating

  9. "Table HC10.7 Air-Conditioning Usage Indicators by U.S. Census Region, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by U.S. Census Region, 2005" " Million U.S. Housing Units" ,"Housing Units (millions)","U.S. Census Region" "Air Conditioning Usage Indicators",,"Northeast","Midwest","South","West" "Total",111.1,20.6,25.6,40.7,24.2 "Do Not Have Cooling Equipment",17.8,4,2.1,1.4,10.3 "Have Cooling Equipment",93.3,16.5,23.5,39.3,13.9 "Use Cooling

  10. " East North Central",21.3,"NA",26,27.6,29,32.4

    U.S. Energy Information Administration (EIA) (indexed site)

    Number of Vehicles, Selected Survey Years (Millions)" ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",129.3,137.3,147.5,151.2,156.8,191 "Household Characteristics" "Census...

  11. "Table 11. Fuel Economy, Selected Survey Years (Miles Per Gallon...

    U.S. Energy Information Administration (EIA) (indexed site)

    Fuel Economy, Selected Survey Years (Miles Per Gallon)" ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",15.1,16.1,18.3,19.3,19.8,20.2 "Household Characteristics" "Census...

  12. table6.xls

    U.S. Energy Information Administration (EIA) (indexed site)

    .4 9.9 10.2 10.6 11.4 12.0 Household Characteristics Census Region and Division Northeast... 9.5 NA 10.3 10.9 11.3 11.9...

  13. " East North Central",9.3,"NA",10.1,10.7,11.6,11.85822

    U.S. Energy Information Administration (EIA) (indexed site)

    (Thousands) " ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",9.4,9.9,10.2,10.6,11.4,12 "Household Characteristics" "Census Region and Division" " Northeast",9.5,"NA",10.3...

  14. PHYSICAL PROPERTIES OF THE CURRENT CENSUS OF NORTHERN WHITE DWARFS WITHIN 40 pc OF THE SUN

    SciTech Connect (OSTI)

    Limoges, M.-M.; Bergeron, P.; Lépine, S. E-mail: bergeron@astro.umontreal.ca

    2015-08-15

    We present a detailed description of the physical properties of our current census of white dwarfs within 40 pc of the Sun, based on an exhaustive spectroscopic survey of northern hemisphere candidates from the SUPERBLINK proper motion database. Our method for selecting white dwarf candidates is based on a combination of theoretical color–magnitude relations and reduced proper motion diagrams. We reported in an earlier publication the discovery of nearly 200 new white dwarfs, and we present here the discovery of an additional 133 new white dwarfs, among which we identify 96 DA, 3 DB, 24 DC, 3 DQ, and 7 DZ stars. We further identify 178 white dwarfs that lie within 40 pc of the Sun, representing a 40% increase of the current census, which now includes 492 objects. We estimate the completeness of our survey at between 66% and 78%, allowing for uncertainties in the distance estimates. We also perform a homogeneous model atmosphere analysis of this 40 pc sample and find a large fraction of massive white dwarfs, indicating that we are successfully recovering the more massive, and less luminous objects often missed in other surveys. We also show that the 40 pc sample is dominated by cool and old white dwarfs, which populate the faint end of the luminosity function, although trigonometric parallaxes will be needed to shape this part of the luminosity function more accurately. Finally, we identify 4 probable members of the 20 pc sample, 4 suspected double degenerate binaries, and we also report the discovery of two new ZZ Ceti pulsators.

  15. The census of complex organic molecules in the solar-type protostar IRAS16293-2422

    SciTech Connect (OSTI)

    Jaber, Ali A.; Ceccarelli, C.; Kahane, C.; Caux, E.

    2014-08-10

    Complex organic molecules (COMs) are considered to be crucial molecules, since they are connected with organic chemistry, at the basis of terrestrial life. More pragmatically, they are molecules which in principle are difficult to synthesize in harsh interstellar environments and, therefore, are a crucial test for astrochemical models. Current models assume that several COMs are synthesized on lukewarm grain surfaces (≳30-40 K) and released in the gas phase at dust temperatures of ≳100 K. However, recent detections of COMs in ≲20 K gas demonstrate that we still need important pieces to complete the puzzle of COMs formation. Here, we present a complete census of the oxygen- and nitrogen-bearing COMs, previously detected in different Interstellar Medium (ISM) regions, toward the solar-type protostar IRAS16293-2422. The census was obtained from the millimeter-submillimeter unbiased spectral survey TIMASSS. Of the 29 COMs searched for, 6 were detected: methyl cyanide, ketene, acetaldehyde, formamide, dimethyl ether, and methyl formate. Multifrequency analysis of the last five COMs provides clear evidence that they are present in the cold (≲30 K) envelope of IRAS16293-2422, with abundances of 0.03-2 × 10{sup –10}. Our data do not allow us to support the hypothesis that the COMs abundance increases with increasing dust temperature in the cold envelope, as expected if COMs were predominately formed on lukewarm grain surfaces. Finally, when also considering other ISM sources, we find a strong correlation over five orders of magnitude between methyl formate and dimethyl ether, and methyl formate and formamide abundances, which may point to a link between these two couples of species in cold and warm gas.

  16. Heating oil and propane households bills to be lower this winter...

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Heating oil and propane households bills to be lower this winter despite recent cold spell Despite the recent cold weather, households that use heating oil or propane as their main ...

  17. Fact #727: May 14, 2012 Nearly Twenty Percent of Households Own Three or More Vehicles

    Office of Energy Efficiency and Renewable Energy (EERE)

    Household vehicle ownership has changed over the last six decades. In 1960, over twenty percent of households did not own a vehicle, but by 2010, that number fell to less than 10%. The number of...

  18. Buildings Energy Data Book: 3.2 Commercial Sector Characteristics

    Buildings Energy Data Book

    4 Share of Commercial Floorspace, by Census Region and Vintage, as of 2003 (Percent) Region Prior to 1960 1960 to 1989 1990 to 2003 Total Northeast 9% 8% 3% 20% Midwest 8% 11% 6% 25% South 5% 18% 14% 37% West 3% 9% 5% 18% 100% Source(s): EIA, 2003 Commercial Buildings Energy Consumption Survey: Building Characteristics Tables, Oct. 2006, Table A2, p. 3-4

  19. Developing Livestock Facility Type Information from USDA Agricultural Census Data for Use in Epidemiological and Economic Models

    SciTech Connect (OSTI)

    Melius, C; Robertson, A; Hullinger, P

    2006-10-24

    The epidemiological and economic modeling of livestock diseases requires knowing the size, location, and operational type of each livestock facility within the US. At the present time, the only national database of livestock facilities that is available to the general public is the USDA's 2002 Agricultural Census data, published by the National Agricultural Statistics Service, herein referred to as the 'NASS data.' The NASS data provides facility data at the county level for various livestock types (i.e., beef cows, milk cows, cattle on feed, other cattle, total hogs and pigs, sheep and lambs, milk goats, and angora goats). However, the number and sizes of facilities for the various livestock types are not independent since some facilities have more than one type of livestock, and some livestock are of more than one type (e.g., 'other cattle' that are being fed for slaughter are also 'cattle on feed'). In addition, any data tabulated by NASS that could identify numbers of animals or other data reported by an individual respondent is suppressed by NASS and coded with a 'D.'. To be useful for epidemiological and economic modeling, the NASS data must be converted into a unique set of facility types (farms having similar operational characteristics). The unique set must not double count facilities or animals. At the same time, it must account for all the animals, including those for which the data has been suppressed. Therefore, several data processing steps are required to work back from the published NASS data to obtain a consistent database for individual livestock operations. This technical report documents data processing steps that were used to convert the NASS data into a national livestock facility database with twenty-eight facility types. The process involves two major steps. The first step defines the rules used to estimate the data that is suppressed within the NASS database. The second step converts the NASS livestock types into the operational facility

  20. Fact #729: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy 9: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles Fact #729: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles When a household has more than one vehicle, the secondary vehicles travel fewer miles than the primary vehicle. In a two-vehicle household, the second vehicle travels less than half of the miles that the primary vehicle travels in a day. In a six-vehicle household, the sixth vehicle travels fewer than five miles a day. Daily Vehicle

  1. Sizing wind/photovoltaic hybrids for households in inner Mongolia

    SciTech Connect (OSTI)

    Barley, C.D.; Lew, D.J.; Flowers, L.T.

    1997-12-31

    Approximately 140,000 wind turbines currently provide electricity to about one-third of the non-grid-connected households in Inner Mongolia. However, these households often suffer from a lack of power during the low-wind summer months. This report describes an analysis of hybrid wind/photovoltaic (PV) systems for such households. The sizing of the major components is based on a subjective trade-off between the cost of the system and the percent unmet load, as determined by the Hybrid2 software in conjunction with a simplified time-series model. Actual resource data (wind speed and solar radiation) from the region are processed so as to best represent the scenarios of interest. Small wind turbines of both Chinese and U.S. manufacture are considered in the designs. The results indicate that combinations of wind and PV are more cost-effective than either one alone, and that the relative amount of PV in the design increases as the acceptable unmet load decreases and as the average wind speed decreases.

  2. Table HC6.11 Home Electronics Characteristics by Number of Household...

    Gasoline and Diesel Fuel Update

    ... Video Cassette Recorders (VCR)...... 89.4 22.5 28.5 15.3 13.3 9.9 ... Digital Video Disc Players (DVD)...... 89.3 19.4 27.9 16.3 14.8 10.9 ...

  3. "Table HC15.3 Household Characteristics by Four Most Populated...

    U.S. Energy Information Administration (EIA) (indexed site)

    "Income Relative to Poverty Line" "Below 100 Percent",16.6,1.5,1,1.5,... " 1. Below 150 percent of poverty line or 60 percent of median State ...

  4. Buildings Energy Data Book: 2.2 Residential Sector Characteristics

    Buildings Energy Data Book

    4 Characteristics of U.S. Housing by Census Division and Region, as of 2005 Census Division Northeast 19% 2,423 1,664 New England 5% 2,552 1,680 Middle Atlantic 14% 2,376 1,658 Midwest 23% 2,566 1,927 East North Central 16% 2,628 1,926 West North Central 7% 2,424 1,930 South 37% 2,295 1,551 South Atlantic 20% 2,370 1,607 East South Central 6% 2,254 1,544 West South Central 11% 2,184 1,455 West 22% 1,963 1,366 Mountain 7% 2,149 1,649 Pacific 15% 1,878 1,238 Total 100% 2,309 1,618 Note(s):

  5. Table HC1.1.1 Housing Unit Characteristics by

    U.S. Energy Information Administration (EIA) (indexed site)

    1 Housing Unit Characteristics by" " Total, Heated, and Cooled Floorspace, 2005" ,,,"Total Square Footage" ,"Housing Units",,"Total",,"Heated",,"Cooled" "Housing Unit Characteristics","Millions","Percent","Billions","Percent","Billions","Percent","Billions","Percent" "Total",111.1,100,256.5,100,179.8,100,114.5,100 "Census Region

  6. A HERSCHEL AND APEX CENSUS OF THE REDDEST SOURCES IN ORION: SEARCHING FOR THE YOUNGEST PROTOSTARS

    SciTech Connect (OSTI)

    Stutz, Amelia M.; Robitaille, Thomas; Henning, Thomas; Krause, Oliver; Tobin, John J.; Stanke, Thomas; Megeath, S. Thomas; Fischer, William J.; Ali, Babar; Furlan, Elise; Hartmann, Lee; Osorio, Mayra; Wilson, Thomas L.; Allen, Lori; Manoj, P.

    2013-04-10

    We perform a census of the reddest, and potentially youngest, protostars in the Orion molecular clouds using data obtained with the PACS instrument on board the Herschel Space Observatory and the LABOCA and SABOCA instruments on APEX as part of the Herschel Orion Protostar Survey (HOPS). A total of 55 new protostar candidates are detected at 70 {mu}m and 160 {mu}m that are either too faint (m{sub 24} > 7 mag) to be reliably classified as protostars or undetected in the Spitzer/MIPS 24 {mu}m band. We find that the 11 reddest protostar candidates with log {lambda}F{sub {lambda}}70/{lambda}F{sub {lambda}}24 > 1.65 are free of contamination and can thus be reliably explained as protostars. The remaining 44 sources have less extreme 70/24 colors, fainter 70 {mu}m fluxes, and higher levels of contamination. Taking the previously known sample of Spitzer protostars and the new sample together, we find 18 sources that have log {lambda}F{sub {lambda}}70/{lambda}F{sub {lambda}}24 > 1.65; we name these sources 'PACS Bright Red sources', or PBRs. Our analysis reveals that the PBR sample is composed of Class 0 like sources characterized by very red spectral energy distributions (SEDs; T{sub bol} < 45 K) and large values of sub-millimeter fluxes (L{sub smm}/L{sub bol} > 0.6%). Modified blackbody fits to the SEDs provide lower limits to the envelope masses of 0.2-2 M{sub Sun} and luminosities of 0.7-10 L{sub Sun }. Based on these properties, and a comparison of the SEDs with radiative transfer models of protostars, we conclude that the PBRs are most likely extreme Class 0 objects distinguished by higher than typical envelope densities and hence, high mass infall rates.

  7. Fact #614: March 15, 2010 Average Age of Household Vehicles | Department of

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Energy 4: March 15, 2010 Average Age of Household Vehicles Fact #614: March 15, 2010 Average Age of Household Vehicles The average age of household vehicles has increased from 6.6 years in 1977 to 9.2 years in 2009. Pickup trucks have the oldest average age in every year listed. Sport utility vehicles (SUVs), first reported in the 1995 survey, have the youngest average age. Average Vehicle Age by Vehicle Type Graph showing the average vehicle age by type (car, van, pickup, SUV, all household

  8. Effect of Income on Appliances in U.S. Households, The

    Reports and Publications

    2004-01-01

    Entails how people live, the factors that cause the most differences in home lifestyle, including energy use in geographic location, socioeconomics and household income.

  9. Forum on Enhancing the Delivery of Energy Efficiency to Middle Income Households: Discussion Summary

    SciTech Connect (OSTI)

    none,

    2012-09-20

    Summarizes discussions and recommendations from a forum for practitioners and policymakers aiming to strengthen residential energy efficiency program design and delivery for middle income households.

  10. THE BARYON CENSUS IN A MULTIPHASE INTERGALACTIC MEDIUM: 30% OF THE BARYONS MAY STILL BE MISSING

    SciTech Connect (OSTI)

    Shull, J. Michael; Danforth, Charles W.; Smith, Britton D. E-mail: smit1685@msu.edu

    2012-11-01

    Although galaxies, groups, and clusters contain {approx}10% of the baryons, many more reside in the photoionized and shocked-heated intergalactic medium (IGM) and in the circumgalactic medium (CGM). We update the baryon census in the (H I) Ly{alpha} forest and warm-hot IGM (WHIM) at 10{sup 5-6} K traced by O VI {lambda}1032, 1038 absorption. From Enzo cosmological simulations of heating, cooling, and metal transport, we improve the H I and O VI baryon surveys using spatially averaged corrections for metallicity (Z/Z {sub Sun }) and ionization fractions (f {sub HI}, f {sub OVI}). Statistically, the O VI correction product correlates with column density, (Z/Z {sub Sun })f {sub OVI} Almost-Equal-To (0.015)(N {sub OVI}/10{sup 14} cm{sup -2}){sup 0.70}, with an N {sub OVI}-weighted mean of 0.01, which doubles previous estimates of WHIM baryon content. We also update the Ly{alpha} forest contribution to baryon density out to z = 0.4, correcting for the (1 + z){sup 3} increase in absorber density, the (1 + z){sup 4.4} rise in photoionizing background, and cosmological proper length dl/dz. We find substantial baryon fractions in the photoionized Ly{alpha} forest (28% {+-} 11%) and WHIM traced by O VI and broad-Ly{alpha} absorbers (25% {+-} 8%). The collapsed phase (galaxies, groups, clusters, CGM) contains 18% {+-} 4%, leaving an apparent baryon shortfall of 29% {+-} 13%. Our simulations suggest that {approx}15% reside in hotter WHIM (T {>=} 10{sup 6} K). Additional baryons could be detected in weaker Ly{alpha} and O VI absorbers. Further progress requires higher-precision baryon surveys of weak absorbers, down to minimum column densities N {sub HI} {>=} 10{sup 12.0} cm{sup -2}, N {sub OVI} {>=} 10{sup 12.5} cm{sup -2}, N {sub OVII} {>=} 10{sup 14.5} cm{sup -2}, using high signal-to-noise data from high-resolution UV and X-ray spectrographs.

  11. Solar Census„Perfecting the Art of Automated, Remote Solar Shading Assessments (Fact Sheet), NREL (National Renewable Energy Laboratory)

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Census Surveyor is an online tool that performs remote shading analysis and creates a fully articulated three-dimensional (3-D) model of the site. Surveyor uses state-of-the-art software and patented algorithms to provide a solar access value (SAV) for every one-foot- by-one-foot section of the roof. The 3-D data, high-resolution imagery, and shade data are all preprocessed and stored in a database that can be instantly accessed by the solar community given the address of the property. Figure 1

  12. "Table A3. Total Primary Consumption of Combustible Energy for Nonfuel Purposes by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    Nonfuel Purposes by Census Region," " Industry Group, and Selected Industries, 1991: Part 1 " " (Estimates in Btu or Physical Units)" " "," "," "," "," "," "," "," ","Coke"," "," " " "," "," ","Residual","Distillate","Natural Gas(c)"," ","Coal","and Breeze","

  13. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 0 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 94.0 74.2 169.2 124 54 98.1 38 1,485 0.65 1,172 450 Census

  14. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 3 Average Electricity Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 96.6 76.4 181.2 43 18 34.0 13 1,061 0.45 840 321 Census Region

  15. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 0 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 15.4 11.6 29.7 131 51 99.0 36 1,053 0.41 795 287 Census

  16. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 1 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 14.6 11.0 28.9 116 44 87.9 32 1,032 0.39 781 283 Census

  17. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 2 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 15.5 12.2 30.0 98 40 77.1 27 829 0.34 650 231 Census

  18. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 4 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 17.5 13.8 32.0 91 39 71.9 27 697 0.30 550 203 Census

  19. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 7 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 17.4 14.0 33.3 87 37 70.3 27 513 0.22 414 156 Census

  20. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 90 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 16.3 13.5 33.2 77 31 63.9 23 609 0.25 506 181 Census

  1. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 3 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures per Total per Square per per per Total Total Floorspace Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 13.8 11.6 29.8 92 36 77.5 28 604 0.23 506 186 Census

  2. Residential Buildings Historical Publications reports, data and housing

    Gasoline and Diesel Fuel Update

    questionnaires 7 Average Fuel Oil/Kerosene Residential Buildings Consumption Expenditures Total per Floor- per Square per per per Total Total space (1) Building Foot per Household per Square per Household Households Number (billion (million (thousand Household Member Building Foot Household Member Characteristics (million) (million) sq. ft.) Btu) Btu) (million Btu) (million Btu) (dollars) (dollars) (dollars) (dollars) Total U.S. Households 13.2 11.0 23.2 97 46 81.1 31 694 0.33 578 224 Census

  3. Enclosures Standing Technical Committee Strategic Plan report

    Energy Savers

    ... Consumption Data ...... 2 Figure 2: Total Btu consumption per household (US Census Bureau 2001) ...

  4. A material flow analysis on current electrical and electronic waste disposal from Hong Kong households

    SciTech Connect (OSTI)

    Lau, Winifred Ka-Yan; Chung, Shan-Shan; Zhang, Chan

    2013-03-15

    Highlights: ► Most household TWARC waste is sold directly to private e-waste collectors in HK. ► The current e-waste recycling network is popular with HK households. ► About 80% of household generated TWARC is exported overseas each year. ► Over 7000 tonnes/yr of household generated TWARC reach landfills. ► It is necessary to upgrade safety and awareness in HK’s e-waste recycling industry. - Abstract: A material flow study on five types of household electrical and electronic equipment, namely television, washing machine, air conditioner, refrigerator and personal computer (TWARC) was conducted to assist the Government of Hong Kong to establish an e-waste take-back system. This study is the first systematic attempt on identifying key TWARC waste disposal outlets and trade practices of key parties involved in Hong Kong. Results from two questionnaire surveys, on local households and private e-waste traders, were used to establish the material flow of household TWARC waste. The study revealed that the majority of obsolete TWARC were sold by households to private e-waste collectors and that the current e-waste collection network is efficient and popular with local households. However, about 65,000 tonnes/yr or 80% of household generated TWARC waste are being exported overseas by private e-waste traders, with some believed to be imported into developing countries where crude recycling methods are practiced. Should Hong Kong establish a formal recycling network with tight regulatory control on imports and exports, the potential risks of current e-waste recycling practices on e-waste recycling workers, local residents and the environment can be greatly reduced.

  5. Commercial viability of hybrid vehicles : best household use and cross national considerations.

    SciTech Connect (OSTI)

    Santini, D. J.; Vyas, A. D.

    1999-07-16

    Japanese automakers have introduced hybrid passenger cars in Japan and will soon do so in the US. In this paper, we report how we used early computer simulation model results to compare the commercial viability of a hypothetical near-term (next decade) hybrid mid-size passenger car configuration under varying fuel price and driving patterns. The fuel prices and driving patterns evaluated are designed to span likely values for major OECD nations. Two types of models are used. One allows the ''design'' of a hybrid to a specified set of performance requirements and the prediction of fuel economy under a number of possible driving patterns (called driving cycles). Another provides an estimate of the incremental cost of the hybrid in comparison to a comparably performing conventional vehicle. In this paper, the models are applied to predict the NPV cost of conventional gasoline-fueled vehicles vs. parallel hybrid vehicles. The parallel hybrids are assumed to (1) be produced at high volume, (2) use nickel metal hydride battery packs, and (3) have high-strength steel bodies. The conventional vehicle also is assumed to have a high-strength steel body. The simulated vehicles are held constant in many respects, including 0-60 time, engine type, aerodynamic drag coefficient, tire rolling resistance, and frontal area. The hybrids analyzed use the minimum size battery pack and motor to meet specified 0-60 times. A key characteristic affecting commercial viability is noted and quantified: that hybrids achieve the most pronounced fuel economy increase (best use) in slow, average-speed, stop-and-go driving, but when households consistently drive these vehicles under these conditions, they tend to travel fewer miles than average vehicles. We find that hours driven is a more valuable measure than miles. Estimates are developed concerning hours of use of household vehicles versus driving cycle, and the pattern of minimum NPV incremental cost (or benefit) of selecting the hybrid over

  6. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

    Sparn, B.; Jin, X.; Earle, L.

    2013-10-01

    With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses. The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

  7. Laboratory Testing of Demand-Response Enabled Household Appliances

    SciTech Connect (OSTI)

    Sparn, B.; Jin, X.; Earle, L.

    2013-10-01

    With the advent of the Advanced Metering Infrastructure (AMI) systems capable of two-way communications between the utility's grid and the building, there has been significant effort in the Automated Home Energy Management (AHEM) industry to develop capabilities that allow residential building systems to respond to utility demand events by temporarily reducing their electricity usage. Major appliance manufacturers are following suit by developing Home Area Network (HAN)-tied appliance suites that can take signals from the home's 'smart meter,' a.k.a. AMI meter, and adjust their run cycles accordingly. There are numerous strategies that can be employed by household appliances to respond to demand-side management opportunities, and they could result in substantial reductions in electricity bills for the residents depending on the pricing structures used by the utilities to incent these types of responses.The first step to quantifying these end effects is to test these systems and their responses in simulated demand-response (DR) conditions while monitoring energy use and overall system performance.

  8. Table HC1.2.2 Living Space Characteristics by Average Floorspace

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Living Space Characteristics by Average Floorspace, " " Per Housing Unit and Per Household Member, 2005" ,,"Average Square Feet" ," Housing Units (millions)" ,,"Per Housing Unit",,,"Per Household Member" "Living Space Characteristics",,"Total1","Heated","Cooled","Total1","Heated","Cooled" "Total",111.1,2033,1618,1031,791,630,401 "Total Floorspace (Square

  9. Household heating bills expected to be higher than last winter, mainly driven by colder weather

    U.S. Energy Information Administration (EIA) (indexed site)

    Household heating bills expected to be higher than last winter, mainly driven by colder weather U.S. households are expected to pay higher heating bills this winter compared to last winter mainly because the weather is forecast to be colder than the relatively mild weather seen last winter and fuel prices are forecast to be higher. In its new forecast, the U.S. Energy Information Administration said households using heating oil will see a 38% jump in their heating fuel expenditures while propane

  10. Fact #616: March 29, 2010 Household Vehicle-Miles of Travel by Trip Purpose

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    | Department of Energy 6: March 29, 2010 Household Vehicle-Miles of Travel by Trip Purpose Fact #616: March 29, 2010 Household Vehicle-Miles of Travel by Trip Purpose In 2009, getting to and from work accounted for about 27% of household vehicle-miles of travel (VMT). Work-related business was 8.4% of VMT in 2001, but declined to 6.7% in 2009, possibly due to advancements in computing technology making it possible for more business to be handled electronically. VMT for shopping was almost

  11. Targeted Marketing and Program Design for Low- and Moderate-Income Households

    Energy.gov [DOE]

    Better Buildings Neighborhood Program Low- / Moderate-Income Peer Exchange Call: Targeted Marketing and Program Design for Low- and Moderate-Income Households, Call Slides and Discussion Summary, October 11, 2011.

  12. How Do You Encourage Everyone in Your Household to Save Energy?

    Energy.gov [DOE]

    Anyone who has decided to save energy at home knows that the entire household needs to be involved if you really want to see savings. Some people—be they roommates, spouses, children, or maybe even...

  13. EPA Webinar: Bringing Energy Efficiency and Renewable Housing to Low-Income Households

    Energy.gov [DOE]

    Hosted by the U.S. Environmental Protection Agency, this webinar will explore the topic of linking and leveraging energy efficiency and renewable energy programs for limited-income households, including the need to coordinate with other energy assistance programs.

  14. Drivers of U.S. Household Energy Consumption, 1980-2009

    U.S. Energy Information Administration (EIA) (indexed site)

    Drivers of U.S. Household Energy Consumption, 1980-2009 February 2015 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy ...

  15. "Table A29. Average Prices of Selected Purchased Energy Sources by Census"

    U.S. Energy Information Administration (EIA) (indexed site)

    1" " (Estimates in Dollars per Physical Unit)" " "," ","Residual","Distillate ","Natural"," "," ","RSE" " ","Electricity","Fuel Oil","Fuel Oil(b)","Gas(c)","LPG","Coal","Row" "Economic Characteristics(a)","(kWh)","(gallon)","(gallon)","(1000 cu

  16. Residential Network Members Impact More Than 42,000 Households | Department

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    of Energy Impact More Than 42,000 Households Residential Network Members Impact More Than 42,000 Households Photo of a row of townhomes. Eligible Better Buildings Residential Network members reported completing 27,563 home energy upgrades during 2013 as part of the Residential Network's first reporting cycle. In addition, 13 Better Buildings Neighborhood Program partners completed 12,166 home energy upgrades, and six Home Performance with ENERGY STAR® Sponsors completed 2,540 home energy

  17. Competition Helps Kids Learn About Energy and Save Their Households Some

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Money | Department of Energy Competition Helps Kids Learn About Energy and Save Their Households Some Money Competition Helps Kids Learn About Energy and Save Their Households Some Money May 21, 2013 - 2:40pm Addthis Students can register now to save energy and win prizes with the Home Energy Challenge. Students can register now to save energy and win prizes with the Home Energy Challenge. Eric Barendsen Energy Technology Program Specialist, Office of Energy Efficiency and Renewable Energy

  18. A Mixed Nordic Experience: Implementing Competitive Retail Electricity Markets for Household Customers

    SciTech Connect (OSTI)

    Olsen, Ole Jess; Johnsen, Tor Arnt; Lewis, Philip

    2006-11-15

    Although the Nordic countries were among the first to develop competition in the electricity industry, it took a long time to make retail competition work. In Norway and Sweden a considerable number of households are actively using the market but very few households are active in Finland and Denmark. One problem has been institutional barriers involving metering, limited unbundling of distribution and supply, and limited access to reliable information on contracts and prices. (author)

  19. Commercial Buildings Characteristics 1992 - Publication and Tables

    U.S. Energy Information Administration (EIA) (indexed site)

    floorspace by census region, 1992 separater bar To View andor Print Reports (requires Adobe Acrobat Reader) - Download Adobe Acrobat Reader If you experience any difficulties,...

  20. Buildings Energy Data Book: 2.2 Residential Sector Characteristics

    Buildings Energy Data Book

    3 Share of Total U.S. Households, by Census Region, Division, and Vintage, as of 2005 Prior to 1950 to 1970 to 1980 to 1990 to 2000 to Region 1950 1969 1979 1989 1999 2005 Northeast 6.7% 5.2% 2.4% 2.1% 1.3% 0.8% 18.5% New England 2.1% 1.2% 0.5% 0.5% 0.3% 0.3% 4.9% Middle Atlantic 4.6% 4.0% 1.9% 1.6% 1.0% 0.5% 13.6% Midwest 5.7% 5.8% 3.6% 2.5% 3.7% 1.7% 23.0% East North Central 4.3% 3.9% 2.7% 1.8% 2.1% 1.1% 16.0% West North Central 1.4% 1.9% 0.9% 0.7% 1.6% 0.6% 7.1% South 4.0% 6.9% 6.4% 7.5% 7.5%

  1. Development of the household sample for furnace and boilerlife-cycle cost analysis

    SciTech Connect (OSTI)

    Whitehead, Camilla Dunham; Franco, Victor; Lekov, Alex; Lutz, Jim

    2005-05-31

    Residential household space heating energy use comprises close to half of all residential energy consumption. Currently, average space heating use by household is 43.9 Mbtu for a year. An average, however, does not reflect regional variation in heating practices, energy costs, or fuel type. Indeed, a national average does not capture regional or consumer group cost impacts from changing efficiency levels of heating equipment. The US Department of Energy sets energy standards for residential appliances in, what is called, a rulemaking process. The residential furnace and boiler efficiency rulemaking process investigates the costs and benefits of possible updates to the current minimum efficiency regulations. Lawrence Berkeley National Laboratory (LBNL) selected the sample used in the residential furnace and boiler efficiency rulemaking from publically available data representing United States residences. The sample represents 107 million households in the country. The data sample provides the household energy consumption and energy price inputs to the life-cycle cost analysis segment of the furnace and boiler rulemaking. This paper describes the choice of criteria to select the sample of houses used in the rulemaking process. The process of data extraction is detailed in the appendices and is easily duplicated. The life-cycle cost is calculated in two ways with a household marginal energy price and a national average energy price. The LCC results show that using an national average energy price produces higher LCC savings but does not reflect regional differences in energy price.

  2. Household energy use in urban Venezuela: Implications from surveys in Maracaibo, Valencia, Merida, and Barcelona-Puerto La Cruz

    SciTech Connect (OSTI)

    Figueroa, M.J.; Sathaye, J.

    1993-08-01

    This report identifies the most important results of a comparative analysis of household commercial energy use in Venezuelan urban cities. The use of modern fuels is widespread among all cities. Cooking consumes the largest share of urban household energy use. The survey documents no use of biomass and a negligible use of kerosene for cooking. LPG, natural gas, and kerosene are the main fuels available. LPG is the fuel choice of low-income households in all cities except Maracaibo, where 40% of all households use natural gas. Electricity consumption in Venezuela`s urban households is remarkably high compared with the levels used in households in comparable Latin American countries and in households of industrialized nations which confront harsher climatic conditions and, therefore, use electricity for water and space heating. The penetration of appliances in Venezuela`s urban households is very high. The appliances available on the market are inefficient, and there are inefficient patterns of energy use among the population. Climate conditions and the urban built form all play important roles in determining the high level of energy consumption in Venezuelan urban households. It is important to acknowledge the opportunities for introducing energy efficiency and conservation in Venezuela`s residential sector, particularly given current economic and financial constraints, which may hamper the future provision of energy services.

  3. NYSERDA's Green Jobs-Green New York Program: Extending Energy Efficiency Financing To Underserved Households

    SciTech Connect (OSTI)

    Zimring, Mark; Fuller, Merrian

    2011-01-24

    The New York legislature passed the Green Jobs-Green New York (GJGNY) Act in 2009. Administered by the New York State Energy Research and Development Authority (NYSERDA), GJGNY programs provide New Yorkers with access to free or low-cost energy assessments,1 energy upgrade services,2 low-cost financing, and training for various 'green-collar' careers. Launched in November 2010, GJGNY's residential initiative is notable for its use of novel underwriting criteria to expand access to energy efficiency financing for households seeking to participate in New York's Home Performance with Energy Star (HPwES) program.3 The GJGNY financing program is a valuable test of whether alternatives to credit scores can be used to responsibly expand credit opportunities for households that do not qualify for traditional lending products and, in doing so, enable more households to make energy efficiency upgrades.

  4. Average household expected to save $675 at the pump in 2015

    U.S. Energy Information Administration (EIA) (indexed site)

    Average household expected to save $675 at the pump in 2015 Although retail gasoline prices have risen in recent weeks U.S. consumers are still expected to save about $675 per household in motor fuel costs this year. In its new monthly forecast, the U.S. Energy Information Administration says the average pump price for regular grade gasoline in 2015 will be $2.43 per gallon. That's about 93 cents lower than last year's average. The savings for consumers will be even bigger during the

  5. Households to pay more than expected to stay warm this winter

    U.S. Energy Information Administration (EIA) (indexed site)

    Households to pay more than expected to stay warm this winter Following a colder-than-expected November, U.S. households are forecast to consume more heating fuels than previously expected....resulting in higher heating bills. Homeowners that rely on natural gas will see their total winter expenses rise nearly 13 percent from last winter....while users of electric heat will see a 2.6 percent increase in costs. That's the latest forecast from the U.S. Energy Information Administration. Propane

  6. EERE Success Story-Kingston Creek Hydro Project Powers 100 Households |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Kingston Creek Hydro Project Powers 100 Households EERE Success Story-Kingston Creek Hydro Project Powers 100 Households August 21, 2013 - 12:00am Addthis Nevada-based contracting firm Nevada Controls, LLC used a low-interest loan from the Nevada State Office of Energy's Revolving Loan Fund to help construct a hydropower project in the small Nevada town of Kingston. The Kingston Creek Project-benefitting the Young Brothers Ranch-is a 175-kilowatt hydro generation plant

  7. "Table HC7.10 Home Appliances Usage Indicators by Household Income, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Home Appliances Usage Indicators"

  8. A Multi Agent-Based Framework for Simulating Household PHEV Distribution and Electric Distribution Network Impact

    SciTech Connect (OSTI)

    Cui, Xiaohui; Liu, Cheng; Kim, Hoe Kyoung; Kao, Shih-Chieh; Tuttle, Mark A; Bhaduri, Budhendra L

    2011-01-01

    The variation of household attributes such as income, travel distance, age, household member, and education for different residential areas may generate different market penetration rates for plug-in hybrid electric vehicle (PHEV). Residential areas with higher PHEV ownership could increase peak electric demand locally and require utilities to upgrade the electric distribution infrastructure even though the capacity of the regional power grid is under-utilized. Estimating the future PHEV ownership distribution at the residential household level can help us understand the impact of PHEV fleet on power line congestion, transformer overload and other unforeseen problems at the local residential distribution network level. It can also help utilities manage the timing of recharging demand to maximize load factors and utilization of existing distribution resources. This paper presents a multi agent-based simulation framework for 1) modeling spatial distribution of PHEV ownership at local residential household level, 2) discovering PHEV hot zones where PHEV ownership may quickly increase in the near future, and 3) estimating the impacts of the increasing PHEV ownership on the local electric distribution network with different charging strategies. In this paper, we use Knox County, TN as a case study to show the simulation results of the agent-based model (ABM) framework. However, the framework can be easily applied to other local areas in the US.

  9. Material World: Forecasting Household Appliance Ownership in a Growing Global Economy

    SciTech Connect (OSTI)

    Letschert, Virginie; McNeil, Michael A.

    2009-03-23

    Over the past years the Lawrence Berkeley National Laboratory (LBNL) has developed an econometric model that predicts appliance ownership at the household level based on macroeconomic variables such as household income (corrected for purchase power parity), electrification, urbanization and climate variables. Hundreds of data points from around the world were collected in order to understand trends in acquisition of new appliances by households, especially in developing countries. The appliances covered by this model are refrigerators, lighting fixtures, air conditioners, washing machines and televisions. The approach followed allows the modeler to construct a bottom-up analysis based at the end use and the household level. It captures the appliance uptake and the saturation effect which will affect the energy demand growth in the residential sector. With this approach, the modeler can also account for stock changes in technology and efficiency as a function of time. This serves two important functions with regard to evaluation of the impact of energy efficiency policies. First, it provides insight into which end uses will be responsible for the largest share of demand growth, and therefore should be policy priorities. Second, it provides a characterization of the rate at which policies affecting new equipment penetrate the appliance stock. Over the past 3 years, this method has been used to support the development of energy demand forecasts at the country, region or global level.

  10. Residential Energy Consumption Survey: Housing Characteristics...

    Gasoline and Diesel Fuel Update

    either air or liquid as the working fluid. It does not refer :<: passive collection of solar thermal energy. Fuel Oil Paid by Household: The household paid directly to the fuel...

  11. Mitigating Carbon Emissions: the Potential of Improving Efficiencyof Household Appliances in China

    SciTech Connect (OSTI)

    Lin, Jiang

    2006-07-10

    China is already the second's largest energy consumer in the world after the United States, and its demand for energy is expected to continue to grow rapidly in the foreseeable future, due to its fast economic growth and its low level of energy use per capita. From 2001 to 2005, the growth rate of energy consumption in China has exceeded the growth rate of its economy (NBS, 2006), raising serious concerns about the consequences of such energy use on local environment and global climate. It is widely expected that China is likely to overtake the US in energy consumption and greenhouse gas (GHG) emissions during the first half of the 21st century. Therefore, there is considerable interest in the international community in searching for options that may help China slow down its growth in energy consumption and GHG emissions through improving energy efficiency and adopting more environmentally friendly fuel supplies such as renewable energy. This study examines the energy saving potential of three major residential energy end uses: household refrigeration, air-conditioning, and water heating. China is already the largest consumer market in the world for household appliances, and increasingly the global production base for consumer appliances. Sales of household refrigerators, room air-conditioners, and water heaters are growing rapidly due to rising incomes and booming housing market. At the same time, the energy use of Chinese appliances is relatively inefficient compared to similar products in the developed economies. Therefore, the potential for energy savings through improving appliance efficiency is substantial. This study focuses particularly on the impact of more stringent energy efficiency standards for household appliances, given that such policies are found to be very effective in improving the efficiency of household appliances, and are well established both in China and around world (CLASP, 2006).

  12. Longwall census '82

    SciTech Connect (OSTI)

    Sprouls, M.W.

    1982-12-01

    Surveys the number of operating longwall systems in the US in 1982. Active longwall systems totalled 112, with 22 companies operating faces in 11 states. Tables list the number of longwalls for each of the top 12 longwall mining companies, and the number of longwalls in each state. The most sophisticated type of longwall roof supports, shields, now comprise 93 of US longwall installations. The most sophisticated type of cutting machines, the double-ended-rangingdrum shearer (DERS) dominates. The survey also shows a trend for operators to purchase stage loaders with crusher/breakers. The crushers eliminate problems with chunks of material at conveyor transfer points. Concludes that the longwall's future in the US looks bright.

  13. "Table HC15.1 Housing Unit Characteristics by Four Most Populated States, 2005"

    U.S. Energy Information Administration (EIA) (indexed site)

    Housing Unit Characteristics by Four Most Populated States, 2005" " Million Housing Units" ,"U.S. Housing Units (millions)","Four Most Populated States" "Housing Unit Characteristics",,"New York","Florida","Texas","California" "Total",111.1,7.1,7,8,12.1 "Census Region and Division" "Northeast",20.6,7.1,"N","N","N" "New

  14. Report for Development of a Census Array and Evaluation of the Array to Detect Biothreat Agents and Environmental Samples for DHS

    SciTech Connect (OSTI)

    Jaing, C; Jackson, P

    2011-04-14

    The objective of this project is to provide DHS a comprehensive evaluation of the current genomic technologies including genotyping, Taqman PCR, multiple locus variable tandem repeat analysis (MLVA), microarray and high-throughput DNA sequencing in the analysis of biothreat agents from complex environmental samples. This report focuses on the design, testing and results of samples on the Census Array. We designed a Census/Detection Array to detect all sequenced viruses (including phage), bacteria (eubacteria), and plasmids. Family-specific probes were selected for all sequenced viral and bacterial complete genomes, segments, and plasmids. Probes were designed to tolerate some sequence variation to enable detection of divergent species with homology to sequenced organisms, and to be unique relative to the human genome. A combination of 'detection' probes with high levels of conservation within a family plus 'census' probes targeting strain/isolate specific regions enabled detection and taxonomic classification from the level of family down to the strain. The array has wider coverage of bacterial and viral targets based on more recent sequence data and more probes per target than other microbial detection/discovery arrays in the literature. We tested the array with purified bacterial and viral DNA/RNA samples, artificial mixes of known bacterial/viral samples, spiked DNA against complex background including BW aerosol samples and soil samples, and environmental samples to evaluate the array's sensitivity and forensic capability. The data were analyzed using our novel maximum likelihood software. For most of the organisms tested, we have achieved at least species level discrimination.

  15. Household-level dynamics of food waste production and related beliefs, attitudes, and behaviours in Guelph, Ontario

    SciTech Connect (OSTI)

    Parizeau, Kate; Massow, Mike von; Martin, Ralph

    2015-01-15

    Highlights: • We combined household waste stream weights with survey data. • We examine relationships between waste and food-related practices and beliefs. • Families and large households produced more total waste, but less waste per capita. • Food awareness and waste awareness were related to reduced food waste. • Convenience lifestyles were differentially associated with food waste. - Abstract: It has been estimated that Canadians waste $27 billion of food annually, and that half of that waste occurs at the household level (Gooch et al., 2010). There are social, environmental, and economic implications for this scale of food waste, and source separation of organic waste is an increasingly common municipal intervention. There is relatively little research that assesses the dynamics of household food waste (particularly in Canada). The purpose of this study is to combine observations of organic, recyclable, and garbage waste production rates to survey results of food waste-related beliefs, attitudes, and behaviours at the household level in the mid-sized municipality of Guelph, Ontario. Waste weights and surveys were obtained from 68 households in the summer of 2013. The results of this study indicate multiple relationships between food waste production and household shopping practices, food preparation behaviours, household waste management practices, and food-related attitudes, beliefs, and lifestyles. Notably, we observed that food awareness, waste awareness, family lifestyles, and convenience lifestyles were related to food waste production. We conclude that it is important to understand the diversity of factors that can influence food wasting behaviours at the household level in order to design waste management systems and policies to reduce food waste.

  16. The importance of China's household sector for black carbon emissions - article no. L12708

    SciTech Connect (OSTI)

    Streets, D.G.; Aunan, K.

    2005-06-30

    The combustion of coal and biofuels in Chinese households is a large source of black carbon (BC), representing about 10-15% of total global emissions during the past two decades, depending on the year. How the Chinese household sector develops during the next 50 years will have an important bearing on future aerosol concentrations, because the range of possible outcomes (about 550 Gg yr{sup -1}) is greater than total BC emissions in either the United States or Europe (each about 400-500 Gg yr{sup -1}). In some Intergovernmental Panel on Climate Change scenarios biofuels persist in rural China for at least the next 50 years, whereas in other scenarios a transition to cleaner fuels and technologies effectively mitigates BC emissions. This paper discusses measures and policies that would help this transition and also raises the possibility of including BC emission reductions as a post-Kyoto option for China and other developing countries.

  17. h:prjq496 ext intext.pdf

    Gasoline and Diesel Fuel Update

    0a. Usage Indicators by Midwest Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Midwest Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | East North| West North| Usage Indicators | U.S. | Total | Central | Central | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 |

  18. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-10a

    U.S. Energy Information Administration (EIA) (indexed site)

    0a. Usage Indicators by Midwest Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Midwest Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | East North| West North| Usage Indicators | U.S. | Total | Central | Central | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 |

  19. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-11a

    U.S. Energy Information Administration (EIA) (indexed site)

    1a. Usage Indicators by South Census Region, Million U.S. Households, 2001 ______________________________________________________________________________________________ | | | | | South Census Region | | |_______________________________________| | | | | | | | Census Division | | | |_____________________________| | | | | | | | | | | East | West | | Total | | South | South | South | Usage Indicators | U.S. | Total | Atlantic| Central | Central | |_________|_________|_________|_________|_________|

  20. steoxxxx1

    Gasoline and Diesel Fuel Update

    0a. Space Heating by Midwest Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.6 Total .............................................................. 107.0 24.5 17.1 7.4 NE Heat Home .................................................... 106.0 24.5 17.1 7.4 NE Do Not Heat Home ....................................... 1.0 Q Q Q 19.8 No

  1. S:\\VM3\\RX97\\TBL_LIST.WPD [PFP#201331587

    Gasoline and Diesel Fuel Update

    Home Office Equipment by South Census Region, Million U.S. Households, 1997 1 HC7-12a. Home Office Equipment by West Census Region, Million U.S. Households, 1997 1 These data are ...

  2. S:\\VM3\\RX97\\TBL_LIST.WPD [PFP#201331587

    Annual Energy Outlook

    Air Conditioning by South Census Region, Million U.S. Households, 1997 2 HC4-12a. Air Conditioning by West Census Region, Million U.S. Households, 1997 2 These data are from the ...

  3. S:\\VM3\\RX97\\TBL_LIST.WPD [PFP#201331587

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Air Conditioning by South Census Region, Percent of U.S. Households, 1997 2 HC4-12b. Air Conditioning by West Census Region, Percent of U.S. Households, 1997 2 These data are from ...

  4. S:\\VM3\\RX97\\TBL_LIST.WPD [PFP#201331587

    Gasoline and Diesel Fuel Update

    Usage Indicators by South Census Region, Million U.S. Households, 1997 4 HC6-12a. Usage Indicators by West Census Region, Million U.S. Households, 1997 4 These data are from the ...

  5. S:\\VM3\\RX97\\TBL_LIST.WPD [PFP#201331587

    U.S. Energy Information Administration (EIA) (indexed site)

    Usage Indicators by South Census Region, Percent of U.S. Households, 1997 4 HC6-12b. Usage Indicators by West Census Region, Percent of U.S. Households, 1997 4 These data are from ...

  6. Evaluation of bulk paint worker exposure to solvents at household hazardous waste collection events

    SciTech Connect (OSTI)

    Cameron, M.

    1995-09-01

    In fiscal year 93/94, over 250 governmental agencies were involved in the collection of household hazardous wastes in the State of California. During that time, over 3,237,000 lbs. of oil based paint were collected in 9,640 drums. Most of this was in lab pack drums, which can only hold up to 20 one gallon cans. Cost for disposal of such drums is approximately $1000. In contrast, during the same year, 1,228,000 lbs. of flammable liquid were collected in 2,098 drums in bulk form. Incineration of bulked flammable liquids is approximately $135 per drum. Clearly, it is most cost effective to bulk flammable liquids at household hazardous waste events. Currently, this is the procedure used at most Temporary Household Hazardous Waste Collection Facilities (THHWCFs). THHWCFs are regulated by the Department of Toxic Substances Control (DTSC) under the new Permit-by Rule Regulations. These regulations specify certain requirements regarding traffic flow, emergency response notifications and prevention of exposure to the public. The regulations require that THHWCF operators bulk wastes only when the public is not present. [22 CCR, section 67450.4 (e) (2) (A)].Santa Clara County Environmental Health Department sponsors local THHWCF`s and does it`s own bulking. In order to save time and money, a variance from the regulation was requested and an employee monitoring program was initiated to determine actual exposure to workers. Results are presented.

  7. Service Report Energy Information Administration Office of Energy...

    U.S. Energy Information Administration (EIA) (indexed site)

    (Millions of Households) ... 43 Table 12. Insulation and Air Infiltration Protection By Year House Was Built and Census Region...

  8. Household`s choices of efficiency levels for appliances: Using stated- and revealed-preference data to identify the importance of rebates and financing arrangements

    SciTech Connect (OSTI)

    Train, K.; Atherton, T.

    1994-11-01

    We examine customers` choice between standard and high-efficiency equipment, and the impact of utility incentives such as rebates and loans on this decision. Using data from interviews with 400 households, we identify the factors that customers consider in their choice of efficiency level for appliances and the relative importance of these factors. We build a model that describes customers` choices and can be used to predict choices in future situations under changes in the attributes of appliances and in the utility`s DSM and as part of the appliance-choice component of utilities` end-use forecasting systems. As examples, the model is used to predict the impacts of: doubling the size of rebates, replacing rebates with financing programs, and offering loans and rebates as alternative options for customers.

  9. 1999 Commercial Buildings Characteristics

    U.S. Energy Information Administration (EIA) (indexed site)

    Data Reports > 2003 Building Characteristics Overview 1999 Commercial Buildings Energy Consumption SurveyCommercial Buildings Characteristics Released: May 2002 Topics: Energy...

  10. The Impact of Carbon Control on Low-Income Household Electricity and Gasoline Expenditures

    SciTech Connect (OSTI)

    Eisenberg, Joel Fred

    2008-06-01

    In July of 2007 The Department of Energy's (DOE's) Energy Information Administration (EIA) released its impact analysis of 'The Climate Stewardship And Innovation Act of 2007,' known as S.280. This legislation, cosponsored by Senators Joseph Lieberman and John McCain, was designed to significantly cut U.S. greenhouse gas emissions over time through a 'cap-and-trade' system, briefly described below, that would gradually but extensively reduce such emissions over many decades. S.280 is one of several proposals that have emerged in recent years to come to grips with the nation's role in causing human-induced global climate change. EIA produced an analysis of this proposal using the National Energy Modeling System (NEMS) to generate price projections for electricity and gasoline under the proposed cap-and-trade system. Oak Ridge National Laboratory integrated those price projections into a data base derived from the EIA Residential Energy Consumption Survey (RECS) for 2001 and the EIA public use files from the National Household Transportation Survey (NHTS) for 2001 to develop a preliminary assessment of impact of these types of policies on low-income consumers. ORNL will analyze the impacts of other specific proposals as EIA makes its projections for them available. The EIA price projections for electricity and gasoline under the S.280 climate change proposal, integrated with RECS and NHTS for 2001, help identify the potential effects on household electric bills and gasoline expenditures, which represent S.280's two largest direct impacts on low-income household budgets in the proposed legislation. The analysis may prove useful in understanding the needs and remedies for the distributive impacts of such policies and how these may vary based on patterns of location, housing and vehicle stock, and energy usage.

  11. Association of pediatric asthma severity with exposure to common household dust allergens

    SciTech Connect (OSTI)

    Gent, Janneane F.; Belanger, Kathleen; Triche, Elizabeth W.; Beckett, William S.; Leaderer, Brian P.

    2009-08-15

    Background: Reducing exposure to household dust inhalant allergens has been proposed as one strategy to reduce asthma. Objective: To examine the dose-response relationships and health impact of five common household dust allergens on disease severity, quantified using both symptom frequency and medication use, in atopic and non-atopic asthmatic children. Methods: Asthmatic children (N=300) aged 4-12 years were followed for 1 year. Household dust samples from two indoor locations were analyzed for allergens including dust mite (Der p 1, Der f 1), cat (Fel d 1), dog (Can f 1), cockroach (Bla g 1). Daily symptoms and medication use were collected in monthly telephone interviews. Annual disease severity was examined in models including allergens, specific IgE sensitivity and adjusted for age, gender, atopy, ethnicity, and mother's education. Results: Der p 1 house dust mite allergen concentration of 2.0 {mu}g/g or more from the main room and the child's bed was related to increased asthma severity independent of allergic status (respectively, OR 2.93, 95% CI 1.37, 6.30 for 2.0-10.0 {mu}g/g and OR 2.55 95% CI 1.13, 5.73 for {>=}10.0 {mu}g/g). Higher pet allergen levels were associated with greater asthma severity, but only for those sensitized (cat OR 2.41 95% CI 1.19, 4.89; dog OR 2.06 95% CI 1.01, 4.22). Conclusion: Higher levels of Der p 1 and pet allergens were associated with asthma severity, but Der p 1 remained an independent risk factor after accounting for pet allergens and regardless of Der p 1 specific IgE status.

  12. Assessment of lead contamination in Bahrain environment. I. Analysis of household paint

    SciTech Connect (OSTI)

    Madany, I.M.; Ali, S.M.; Akhter, M.S.

    1987-01-01

    The analysis of lead in household paint collected from various old buildings in Bahrain is reported. The atomic absorption spectrophotometric method, both flame and flameless (graphite furnace) techniques, were used for the analysis. The concentrations of lead in paint were found in the range 200 to 5700 mg/kg, which are low compared to the limit of 0.5% in UK and 0.06% in USA. Nevertheless, these are hazardous. Recommendations are reported in order to avoid paint containing lead. 17 references, 1 table.

  13. Table HC6.10 Home Appliances Usage Indicators by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    0 Home Appliances Usage Indicators by Number of Household Members, 2005 Total.............................................................................. 111.1 30.0 34.8 18.4 15.9 12.0 Cooking Appliances Frequency of Hot Meals Cooked 3 or More Times A Day........................................... 8.2 1.4 1.9 1.4 1.0 2.4 2 Times A Day........................................................ 24.6 4.3 7.6 4.3 4.8 3.7 Once a Day............................................................ 42.3 9.9

  14. Table HC6.12 Home Electronics Usage Indicators by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    2 Home Electronics Usage Indicators by Number of Household Members, 2005 Total................................................................................ 111.1 30.0 34.8 18.4 15.9 12.0 Personal Computers Do Not Use a Personal Computer............................. 35.5 16.3 9.4 4.0 2.7 3.2 Use a Personal Computer.......................................... 75.6 13.8 25.4 14.4 13.2 8.8 Most-Used Personal Computer Type of PC Desk-top Model.....................................................

  15. Table HC6.7 Air-Conditioning Usage Indicators by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by Number of Household Members, 2005 Total........................................................................ 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Cooling Equipment.......................... 17.8 5.4 5.3 2.7 2.5 2.0 Have Cooling Equipment...................................... 93.3 24.6 29.6 15.7 13.4 10.0 Use Cooling Equipment....................................... 91.4 24.0 29.1 15.5 13.2 9.7 Have Equipment But Do Not Use it......................

  16. WEEE and portable batteries in residual household waste: Quantification and characterisation of misplaced waste

    SciTech Connect (OSTI)

    Bigum, Marianne; Petersen, Claus; Scheutz, Charlotte

    2013-11-15

    Highlights: • We analyse 26.1 Mg of residual waste from 3129 Danish households. • We quantify and characterise misplaced WEEE and portable batteries. • We compare misplaced WEEE and batteries to collection through dedicated schemes. • Characterisation showed that primarily small WEEE and light sources are misplaced. • Significant amounts of misplaced batteries were discarded as built-in WEEE. - Abstract: A total of 26.1 Mg of residual waste from 3129 households in 12 Danish municipalities was analysed and revealed that 89.6 kg of Waste Electrical and Electronic Equipment (WEEE), 11 kg of batteries, 2.2 kg of toners and 16 kg of cables had been wrongfully discarded. This corresponds to a Danish household discarding 29 g of WEEE (7 items per year), 4 g of batteries (9 batteries per year), 1 g of toners and 7 g of unidentifiable cables on average per week, constituting 0.34% (w/w), 0.04% (w/w), 0.01% (w/w) and 0.09% (w/w), respectively, of residual waste. The study also found that misplaced WEEE and batteries in the residual waste constituted 16% and 39%, respectively, of what is being collected properly through the dedicated special waste collection schemes. This shows that a large amount of batteries are being discarded with the residual waste, whereas WEEE seems to be collected relatively successfully through the dedicated special waste collection schemes. Characterisation of the misplaced batteries showed that 20% (w/w) of the discarded batteries were discarded as part of WEEE (built-in). Primarily alkaline batteries, carbon zinc batteries and alkaline button cell batteries were found to be discarded with the residual household waste. Characterisation of WEEE showed that primarily small WEEE (WEEE directive categories 2, 5a, 6, 7 and 9) and light sources (WEEE directive category 5b) were misplaced. Electric tooth brushes, watches, clocks, headphones, flashlights, bicycle lights, and cables were items most frequently found. It is recommended that these

  17. "Table A41. Average Prices of Selected Purchased Energy Sources by Census Region,"

    U.S. Energy Information Administration (EIA) (indexed site)

    1" " (Estimates in Dollars per Physical Units)" " "," ","Residual","Distillate ","Natural"," "," ","RSE" " ","Electricity","Fuel Oil","Fuel Oil(b)","Gas(c)","LPG","Coal","Row" "Economic Characteristics(a)","(kWh)","(gallons)","(gallons)","(1000 cu

  18. Recovery and separation of high-value plastics from discarded household appliances

    SciTech Connect (OSTI)

    Karvelas, D.E.; Jody, B.J.; Poykala, J.A. Jr.; Daniels, E.J.; Arman, B. |

    1996-03-01

    Argonne National Laboratory is conducting research to develop a cost- effective and environmentally acceptable process for the separation of high-value plastics from discarded household appliances. The process under development has separated individual high purity (greater than 99.5%) acrylonitrile-butadiene-styrene (ABS) and high- impact polystyrene (HIPS) from commingled plastics generated by appliance-shredding and metal-recovery operations. The process consists of size-reduction steps for the commingled plastics, followed by a series of gravity-separation techniques to separate plastic materials of different densities. Individual plastics of similar densities, such as ABS and HIPS, are further separated by using a chemical solution. By controlling the surface tension, the density, and the temperature of the chemical solution we are able to selectively float/separate plastics that have different surface energies. This separation technique has proven to be highly effective in recovering high-purity plastics materials from discarded household appliances. A conceptual design of a continuous process to recover high-value plastics from discarded appliances is also discussed. In addition to plastics separation research, Argonne National Laboratory is conducting research to develop cost-effective techniques for improving the mechanical properties of plastics recovered from appliances.

  19. Survey of Recipients of WAP Services Assessment of Household Budget and Energy Behaviors Pre to Post Weatherization DOE

    SciTech Connect (OSTI)

    Tonn, Bruce Edward; Rose, Erin M.; Hawkins, Beth A.

    2015-10-01

    This report presents results from the national survey of weatherization recipients. This research was one component of the retrospective and Recovery Act evaluations of the U.S. Department of Energy s Weatherization Assistance Program. Survey respondents were randomly selected from a nationally representative sample of weatherization recipients. The respondents and a comparison group were surveyed just prior to receiving their energy audits and then again approximately 18 months post-weatherization. This report focuses on budget issues faced by WAP households pre- and post-weatherization, whether household energy behaviors changed from pre- to post, the effectiveness of approaches to client energy education, and use and knowledge about thermostats.

  20. 1997 Housing Characteristics Tables Home Office Equipment Tables

    U.S. Energy Information Administration (EIA) (indexed site)

    Percent of U.S. Households; 13 pages, 48 kb) Contents Pages HC7-1b. Home Office Equipment by Climate Zone, Percent of U.S. Households, 1997 1 HC7-2b. Home Office Equipment by Year of Construction, Percent of U.S. Households, 1997 1 HC7-3b. Home Office Equipment by Household Income, Percent of U.S. Households, 1997 1 HC7-4b. Home Office Equipment by Type of Housing Unit, Percent of U.S. Households, 1997 1 HC7-5b. Home Office Equipment by Type of Owner-Occupied Housing Unit, Percent of U.S.

  1. An Analysis of the Price Elasticity of Demand for Household Appliances

    SciTech Connect (OSTI)

    Fujita, Kimberly; Dale, Larry; Fujita, K. Sydny

    2008-01-25

    This report summarizes our study of the price elasticity of demand for home appliances, including refrigerators, clothes washers, and dishwashers. In the context of increasingly stringent appliance standards, we are interested in what kind of impact the increased manufacturing costs caused by higher efficiency requirements will have on appliance sales. We begin with a review of existing economics literature describing the impact of economic variables on the sale of durable goods.We then describe the market for home appliances and changes in this market over the past 20 years, performing regression analysis on the shipments of home appliances and relevant economic variables including changes to operating cost and household income. Based on our analysis, we conclude that the demand for home appliances is price inelastic.

  2. Table HC6.5 Space Heating Usage Indicators by Number of Household Members, 2005

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Space Heating Usage Indicators by Number of Household Members, 2005 Total U.S. Housing Units.................................. 111.1 30.0 34.8 18.4 15.9 12.0 Do Not Have Heating Equipment..................... 1.2 0.3 0.3 Q 0.2 0.2 Have Space Heating Equipment....................... 109.8 29.7 34.5 18.2 15.6 11.8 Use Space Heating Equipment........................ 109.1 29.5 34.4 18.1 15.5 11.6 Have But Do Not Use Equipment.................... 0.8 Q Q Q Q Q Space Heating Usage During 2005

  3. Household energy conservation attitudes and behaviors in the Northwest: Tracking changes between 1983 and 1985

    SciTech Connect (OSTI)

    Fang, J.M.; Hattrup, M.P.; Nordi, R.T.; Shankle, S.A.; Ivey, D.L.

    1987-05-01

    Pacific Northwest Laboratory (PNL) has analyzed the changes in consumer energy conservation attitudes and behaviors in the Pacific Northwest between 1983 and 1985. The information was collected through stratified random telephone surveys on 2000 and 1058 households, respectively, for 1983 and 1985 in the Bonneville Power Administration (BPA) service area in Idaho, Oregon, Washington and Western Montana. This report covers four topic areas and tests two hypotheses. The topics are as follows: consumer perceptions and attitudes of energy use and conservation in the home; consumer perceptions of energy institutions and other entities; past and intended conservation actions and investments; and segmentation of homeowners into market prospect groups. The hypotheses tested are as follows: (1) There has been no change in the size and psychographic make-up of the original three market segments found in the 1983 survey analysis; and (2) image profiles of institutions with respect to familiarity, overall impression, and believability as sources of energy conservation information remain unchanged since 1983.

  4. Particle and gas emissions from a simulated coal-burning household fire pit

    SciTech Connect (OSTI)

    Linwei Tian; Donald Lucas; Susan L. Fischer; S. C. Lee; S. Katharine Hammond; Catherine P. Koshland

    2008-04-01

    An open fire was assembled with firebricks to simulate the household fire pit used in rural China, and 15 different coals from this area were burned to measure the gaseous and particulate emissions. Particle size distribution was studied with a microorifice uniform-deposit impactor (MOUDI). Over 90% of the particulate mass was attributed to sub-micrometer particles. The carbon balance method was used to calculate the emission factors. Emission factors for four pollutants (particulate matter, CO{sub 2}, total hydrocarbons, and NOx) were 2-4 times higher for bituminous coals than for anthracites. In past inventories of carbonaceous emissions used for climate modeling, these two types of coal were not treated separately. The dramatic emission factor difference between the two types of coal warrants attention in the future development of emission inventories. 25 refs., 8 figs., 1 tab.

  5. A Green Approach to SNF Reprocessing: Are Common Household Reagents the Answer?

    SciTech Connect (OSTI)

    Peper, Shane M.; McNamara, Bruce K.; O'Hara, Matthew J.; Douglas, Matthew

    2008-04-03

    It has been discovered that UO2, the principal component of spent nuclear fuel (SNF), can efficiently be dissolved at room temperature using a combination of common household reagents, namely hydrogen peroxide, baking soda, and ammonia. This rather serendipitous discovery opens up the possibility, for the first time, of considering a non-acidic process for recycling U from SNF. Albeit at the early stages of development, our unconventional dissolution approach possesses many attractive features that could make it a reality in the future. With dissolution byproducts of water and oxygen, our approach poses a minimal threat to the environment. Moreover, the use of common household reagents to afford actinide oxide dissolution suggests a certain degree of economic favorability. With the use of a “closed” digestion vessel as a reaction chamber, our approach has substantial versatility with the option of using either aqueous or gaseous reactant feeds or a combination of both. Our approach distinguishes itself from all existing reprocessing technologies in two important ways. First and foremost, it is an alkaline rather than an acidic process, using mild non-corrosive chemicals under ambient conditions to effect actinide separations. Secondly, it does not dissolve the entire SNF matrix, but rather selectively solubilizes U and other light actinides for subsequent separation, resulting in potentially faster head-end dissolution and fewer downstream separation steps. From a safeguards perspective, the use of oxidizing alkaline solutions to effect actinide separations also potentially offers a degree of inherent proliferation resistance, by allowing the U to be selectively removed from the remaining dissolver solution while keeping Pu grouped with the other minor actinides and fission products. This paper will describe the design and general experimental setup of a “closed” digestion vessel for performing uranium oxide dissolutions under alkaline conditions using

  6. Residential energy use and conservation in Venezuela: Results and implications of a household survey in Caracas

    SciTech Connect (OSTI)

    Figueroa, M.J.; Ketoff, A.; Masera, O.

    1992-10-01

    This document presents the final report of a study of residential energy use in Caracas, the capital of Venezuela. It contains the findings of a household energy-use survey held in Caracas in 1988 and examines options for introducing energy conservation measures in the Venezuelan residential sector. Oil exports form the backbone of the Venezuelan economy. Improving energy efficiency in Venezuela will help free domestic oil resources that can be sold to the rest of the world. Energy conservation will also contribute to a faster recovery of the economy by reducing the need for major investments in new energy facilities, allowing the Venezuelan government to direct its financial investments towards other areas of development. Local environmental benefits will constitute an important additional by-product of implementing energy-efficiency policies in Venezuela. Caracas`s residential sector shows great potential for energy conservation. The sector is characterized by high saturation levels of major appliances, inefficiency of appliances available in the market, and by careless patterns of energy use. Household energy use per capita average 6.5 GJ/per year which is higher than most cities in developing countries; most of this energy is used for cooking. Electricity accounts for 41% of all energy use, while LPG and natural gas constitute the remainder. Specific options for inducing energy conservation and energy efficiency in Caracas`s residential sector include energy-pricing policies, fuel switching, particularly from electricity to gas, improving the energy performance of new appliances and customer information. To ensure the accomplishment of an energy-efficiency strategy, a concerted effort by energy users, manufacturers, utility companies, government agencies, and research institutions will be needed.

  7. Commercial Buildings Characteristics 1992

    U.S. Energy Information Administration (EIA) (indexed site)

    schedules and the number of workers across all shifts as well as the main shift. * Energy Management Characteristics - Energy management questions were expanded to ask whether or...

  8. Household mold and dust allergens: Exposure, sensitization and childhood asthma morbidity

    SciTech Connect (OSTI)

    Gent, Janneane F.; Kezik, Julie M.; Hill, Melissa E.; Tsai, Eling; Li, De-Wei; Leaderer, Brian P.

    2012-10-15

    Background: Few studies address concurrent exposures to common household allergens, specific allergen sensitization and childhood asthma morbidity. Objective: To identify levels of allergen exposures that trigger asthma exacerbations in sensitized individuals. Methods: We sampled homes for common indoor allergens (fungi, dust mites (Der p 1, Der f 1), cat (Fel d 1), dog (Can f 1) and cockroach (Bla g 1)) for levels associated with respiratory responses among school-aged children with asthma (N=1233) in a month-long study. Blood samples for allergy testing and samples of airborne fungi and settled dust were collected at enrollment. Symptoms and medication use were recorded on calendars. Combined effects of specific allergen sensitization and level of exposure on wheeze, persistent cough, rescue medication use and a 5-level asthma severity score were examined using ordered logistic regression. Results: Children sensitized and exposed to any Penicillium experienced increased risk of wheeze (odds ratio [OR] 2.12 95% confidence interval [CI] 1.12, 4.04), persistent cough (OR 2.01 95% CI 1.05, 3.85) and higher asthma severity score (OR 1.99 95% CI 1.06, 3.72) compared to those not sensitized or sensitized but unexposed. Children sensitized and exposed to pet allergen were at significantly increased risk of wheeze (by 39% and 53% for Fel d 1>0.12 {mu}g/g and Can f 1>1.2 {mu}g/g, respectively). Increased rescue medication use was significantly associated with sensitization and exposure to Der p 1>0.10 {mu}g/g (by 47%) and Fel d 1>0.12 {mu}g/g (by 32%). Conclusion: Asthmatic children sensitized and exposed to low levels of common household allergens Penicillium, Der p 1, Fel d 1 and Can f 1 are at significant risk for increased morbidity. - Highlights: Black-Right-Pointing-Pointer Few studies address concurrent allergen exposures, sensitization and asthma morbidity. Black-Right-Pointing-Pointer Children with asthma were tested for sensitivity to common indoor allergens

  9. LCA for household waste management when planning a new urban settlement

    SciTech Connect (OSTI)

    Slagstad, Helene; Brattebo, Helge

    2012-07-15

    Highlights: Black-Right-Pointing-Pointer Household waste management of a new carbon neutral settlement. Black-Right-Pointing-Pointer EASEWASTE as a LCA tool to compare different centralised and decentralised solutions. Black-Right-Pointing-Pointer Environmental benefit or close to zero impact in most of the categories. Black-Right-Pointing-Pointer Paper and metal recycling important for the outcome. Black-Right-Pointing-Pointer Discusses the challenges of waste prevention planning. - Abstract: When planning for a new urban settlement, industrial ecology tools like scenario building and life cycle assessment can be used to assess the environmental quality of different infrastructure solutions. In Trondheim, a new greenfield settlement with carbon-neutral ambitions is being planned and five different scenarios for the waste management system of the new settlement have been compared. The results show small differences among the scenarios, however, some benefits from increased source separation of paper and metal could be found. The settlement should connect to the existing waste management system of the city, and not resort to decentralised waste treatment or recovery methods. However, as this is an urban development project with ambitious goals for lifestyle changes, effort should be put into research and initiatives for proactive waste prevention and reuse issues.

  10. Weatherization assistance for low-income households: An evaluation of local program performance

    SciTech Connect (OSTI)

    Schweitzer, M.; Rayner, S.; Wolfe, A.K.; Mason, T.W.; Ragins, B.R.; Cartor, R.A.

    1987-08-01

    The US Department of Energy's Weatherization Assistance Program (WAP) funds local agencies to provide weatherization services to low-income households. This report describes the most salient features of this program, examines relationships between organization and program outcomes, and presents recommendations for the program's further development. Data were collected by written surveys administered to local weatherization agencies, a telephone survey of 38 states and eight DOE support offices, and site visits to selected local agencies. Locally controlled factors found to be significantly related to program performance include the amount of the weatherization director's time spent on program administration, the use of established client selection criteria, the frequency of evaluation of local goal attainment, and the type of weatherization crews used. Factors controlled at the state or federal levels that influence program performance include delays in state reimbursements of local agency expenditures and local flexibility in the choice of weatherization measures. Data-gathering difficulties experienced during this project indicate a need for possible improvements in goal-setting and record-keeping procedures.

  11. Influence of assumptions about household waste composition in waste management LCAs

    SciTech Connect (OSTI)

    Slagstad, Helene; Brattebo, Helge

    2013-01-15

    Highlights: Black-Right-Pointing-Pointer Uncertainty in waste composition of household waste. Black-Right-Pointing-Pointer Systematically changed waste composition in a constructed waste management system. Black-Right-Pointing-Pointer Waste composition important for the results of accounting LCA. Black-Right-Pointing-Pointer Robust results for comparative LCA. - Abstract: This article takes a detailed look at an uncertainty factor in waste management LCA that has not been widely discussed previously, namely the uncertainty in waste composition. Waste composition is influenced by many factors; it can vary from year to year, seasonally, and with location, for example. The data publicly available at a municipal level can be highly aggregated and sometimes incomplete, and performing composition analysis is technically challenging. Uncertainty is therefore always present in waste composition. This article performs uncertainty analysis on a systematically modified waste composition using a constructed waste management system. In addition the environmental impacts of several waste management strategies are compared when applied to five different cities. We thus discuss the effect of uncertainty in both accounting LCA and comparative LCA. We found the waste composition to be important for the total environmental impact of the system, especially for the global warming, nutrient enrichment and human toxicity via water impact categories.

  12. Municipal solid waste generation in municipalities: Quantifying impacts of household structure, commercial waste and domestic fuel

    SciTech Connect (OSTI)

    Lebersorger, S.; Beigl, P.

    2011-09-15

    Waste management planning requires reliable data concerning waste generation, influencing factors on waste generation and forecasts of waste quantities based on facts. This paper aims at identifying and quantifying differences between different municipalities' municipal solid waste (MSW) collection quantities based on data from waste management and on socio-economic indicators. A large set of 116 indicators from 542 municipalities in the Province of Styria was investigated. The resulting regression model included municipal tax revenue per capita, household size and the percentage of buildings with solid fuel heating systems. The model explains 74.3% of the MSW variation and the model assumptions are met. Other factors such as tourism, home composting or age distribution of the population did not significantly improve the model. According to the model, 21% of MSW collected in Styria was commercial waste and 18% of the generated MSW was burned in domestic heating systems. While the percentage of commercial waste is consistent with literature data, practically no literature data are available for the quantity of MSW burned, which seems to be overestimated by the model. The resulting regression model was used as basis for a waste prognosis model (Beigl and Lebersorger, in preparation).

  13. The evolving price of household LED lamps: Recent trends and historical comparisons for the US market

    SciTech Connect (OSTI)

    Gerke, Brian F.; Ngo, Allison T.; Alstone, Andrea L.; Fisseha, Kibret S.

    2014-10-14

    In recent years, household LED light bulbs (LED A lamps) have undergone a dramatic price decline. Since late 2011, we have been collecting data, on a weekly basis, for retail offerings of LED A lamps on the Internet. The resulting data set allows us to track the recent price decline in detail. LED A lamp prices declined roughly exponentially with time in 2011-2014, with decline rates of 28percent to 44percent per year depending on lumen output, and with higher-lumen lamps exhibiting more rapid price declines. By combining the Internet price data with publicly available lamp shipments indices for the US market, it is also possible to correlate LED A lamp prices against cumulative production, yielding an experience curve for LED A lamps. In 2012-2013, LED A lamp prices declined by 20-25percent for each doubling in cumulative shipments. Similar analysis of historical data for other lighting technologies reveals that LED prices have fallen significantly more rapidly with cumulative production than did their technological predecessors, which exhibited a historical decline of 14-15percent per doubling of production.

  14. ARM - Measurement - Soil characteristics

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    characteristics ARM Data Discovery Browse Data Comments? We would love to hear from you! Send us a note below or call us at 1-888-ARM-DATA. Send Measurement : Soil characteristics Includes available water capacity, bulk density, permeability, porosity, rock fragment classification, rock fragment volume, percent clay, percent sand, and texture classification Categories Surface Properties Instruments The above measurement is considered scientifically relevant for the following instruments. Refer

  15. Status of not-in-kind refrigeration technologies for household space conditioning, water heating and food refrigeration

    SciTech Connect (OSTI)

    Bansal, Pradeep; Vineyard, Edward Allan; Abdelaziz, Omar

    2012-07-19

    This paper presents a review of the next generation not-in-kind technologies to replace conventional vapor compression refrigeration technology for household applications. Such technologies are sought to provide energy savings or other environmental benefits for space conditioning, water heating and refrigeration for domestic use. These alternative technologies include: thermoacoustic refrigeration, thermoelectric refrigeration, thermotunneling, magnetic refrigeration, Stirling cycle refrigeration, pulse tube refrigeration, Malone cycle refrigeration, absorption refrigeration, adsorption refrigeration, and compressor driven metal hydride heat pumps. Furthermore, heat pump water heating and integrated heat pump systems are also discussed due to their significant energy saving potential for water heating and space conditioning in households. The paper provides a snapshot of the future R&D needs for each of the technologies along with the associated barriers. Both thermoelectric and magnetic technologies look relatively attractive due to recent developments in the materials and prototypes being manufactured.

  16. Source segregation and food waste prevention activities in high-density households in a deprived urban area

    SciTech Connect (OSTI)

    Rispo, A.; Williams, I.D. Shaw, P.J.

    2015-10-15

    Highlights: • Study of waste management in economically and socially deprived high-density housing. • Food waste segregation, prevention and recycling activities investigated. • Study involved a waste audit and household survey of 1034 households. • Populations in such areas are “hard-to-reach”. • Exceptional efforts and additional resources are required to improve performance. - Abstract: A waste audit and a household questionnaire survey were conducted in high-density housing estates in one of the most economically and socially deprived areas of England (Haringey, London). Such areas are under-represented in published research. The study examined source segregation, potential participation in a food waste segregation scheme, and food waste prevention activities in five estates (1034 households). The results showed that: contamination of recyclables containers was low; ca. 28% of the mixed residual waste’s weight was recyclable; food waste comprised a small proportion of the waste from these residents, probably because of their relatively disadvantaged economic circumstances; and the recycling profile reflected an intermittent pattern of behaviour. Although the majority of respondents reported that they would participate in a food waste separation scheme, the response rate was low and many responses of “don’t know” were recorded. Municipalities committed to foster improved diversion from landfill need to recognise that there is no “quick and easy fix”, regardless of local or national aspirations. Lasting and sustained behaviour change requires time and the quality of service provision and associated infrastructure play a fundamental role in facilitating residents to participate effectively in waste management activities that maximise capture of source-segregated materials. Populations in deprived areas that reside in high-rise, high-density dwellings are “hard-to-reach” in terms of participation in recycling schemes and exceptional

  17. Cost comparison between private and public collection of residual household waste: Multiple case studies in the Flemish region of Belgium

    SciTech Connect (OSTI)

    Jacobsen, R.; Buysse, J.; Gellynck, X.

    2013-01-15

    Highlights: Black-Right-Pointing-Pointer The goal is to compare collection costs for residual household waste. Black-Right-Pointing-Pointer We have clustered all municipalities in order to find mutual comparable pairs. Black-Right-Pointing-Pointer Each pair consists of one private and one public operating waste collection program. Black-Right-Pointing-Pointer All cases show that private service has lower costs than public service. Black-Right-Pointing-Pointer Municipalities were contacted to identify the deeper causes for the waste management program. - Abstract: The rising pressure in terms of cost efficiency on public services pushes governments to transfer part of those services to the private sector. A trend towards more privatizing can be noticed in the collection of municipal household waste. This paper reports the findings of a research project aiming to compare the cost between the service of private and public collection of residual household waste. Multiple case studies of municipalities about the Flemish region of Belgium were conducted. Data concerning the year 2009 were gathered through in-depth interviews in 2010. In total 12 municipalities were investigated, divided into three mutual comparable pairs with a weekly and three mutual comparable pairs with a fortnightly residual waste collection. The results give a rough indication that in all cases the cost of private service is lower than public service in the collection of household waste. Albeit that there is an interest in establishing whether there are differences in the costs and service levels between public and private waste collection services, there are clear difficulties in establishing comparisons that can be made without having to rely on a large number of assumptions and corrections. However, given the cost difference, it remains the responsibility of the municipalities to decide upon the service they offer their citizens, regardless the cost efficiency: public or private.

  18. US prep plant census 2008

    SciTech Connect (OSTI)

    Fiscor, S.

    2008-10-15

    Each year Coal Age conducts a fairly comprehensive survey of the industry to produce the US coal preparation plant survey. This year's survey shows how many mergers and acquisitions have given coal operators more coal washing capacity. The plants are tabulated by state, giving basic details including company owner, plant name, raw feed, product ash %, quality, type of plant builder and year built. 1 tab., 1 photo.

  19. A life cycle approach to the management of household food waste - A Swedish full-scale case study

    SciTech Connect (OSTI)

    Bernstad, A.; Cour Jansen, J. la

    2011-08-15

    Research Highlights: > The comparison of three different methods for management of household food waste show that anaerobic digestion provides greater environmental benefits in relation to global warming potential, acidification and ozone depilation compared to incineration and composting of food waste. Use of produced biogas as car fuel provides larger environmental benefits compared to a use of biogas for heat and power production. > The use of produced digestate from the anaerobic digestion as substitution for chemical fertilizer on farmland provides avoidance of environmental burdens in the same ratio as the substitution of fossil fuels with produced biogas. > Sensitivity analyses show that results are highly sensitive to assumptions regarding the environmental burdens connected to heat and energy supposedly substituted by the waste treatment. - Abstract: Environmental impacts from incineration, decentralised composting and centralised anaerobic digestion of solid organic household waste are compared using the EASEWASTE LCA-tool. The comparison is based on a full scale case study in southern Sweden and used input-data related to aspects such as source-separation behaviour, transport distances, etc. are site-specific. Results show that biological treatment methods - both anaerobic and aerobic, result in net avoidance of GHG-emissions, but give a larger contribution both to nutrient enrichment and acidification when compared to incineration. Results are to a high degree dependent on energy substitution and emissions during biological processes. It was seen that if it is assumed that produced biogas substitute electricity based on Danish coal power, this is preferable before use of biogas as car fuel. Use of biogas for Danish electricity substitution was also determined to be more beneficial compared to incineration of organic household waste. This is a result mainly of the use of plastic bags in the incineration alternative (compared to paper bags in the

  20. Commercial Buildings Characteristics, 1992

    SciTech Connect (OSTI)

    Not Available

    1994-04-29

    Commercial Buildings Characteristics 1992 presents statistics about the number, type, and size of commercial buildings in the United States as well as their energy-related characteristics. These data are collected in the Commercial Buildings Energy Consumption Survey (CBECS), a national survey of buildings in the commercial sector. The 1992 CBECS is the fifth in a series conducted since 1979 by the Energy Information Administration. Approximately 6,600 commercial buildings were surveyed, representing the characteristics and energy consumption of 4.8 million commercial buildings and 67.9 billion square feet of commercial floorspace nationwide. Overall, the amount of commercial floorspace in the United States increased an average of 2.4 percent annually between 1989 and 1992, while the number of commercial buildings increased an average of 2.0 percent annually.

  1. " Million Housing Units, Final"

    U.S. Energy Information Administration (EIA) (indexed site)

    4 Structural and Geographic Characteristics of U.S. Homes, by Number of Household Members, 2009" " Million Housing Units, Final" ,,"Number of Household Members" ,"Total U.S.1 (millions)" "Structural and Geographic Characteristics",,,,,,"5 or More Members" ,,"1 Member","2 Members","3 Members","4 Members" "Total Homes",113.6,31.3,35.8,18.1,15.7,12.7 "Census Region and Division"

  2. Insights from Smart Meters: Identifying Specific Actions, Behaviors, and Characteristics That Drive Savings in Behavior-Based Programs

    Energy.gov [DOE]

    In this report, we use smart meter data to analyze specific actions, behaviors, and characteristics that drive energy savings in a BB program. Specifically, we examine a Home Energy Report (HER) program. These programs typically obtain 1% to 3% annual savings, and recent studies have shown hourly savings of between 0.5% and 3%. But what is driving these savings? What types of households tend to be "high-savers," and what behaviors are they adopting? There are several possibilities: one-time behaviors (e.g., changing thermostat settings), reoccurring habitual behaviors (e.g., turning off lights), and equipment purchase behaviors (e.g., energy efficient appliances); these may vary across households, regions, and over time.

  3. Insights from Smart Meters. Identifying Specific Actions, Behaviors and Characteristics that drive savings in Behavior-Based Programs

    SciTech Connect (OSTI)

    Todd, Annika; Perry, Michael; Smith, Brian; Sullivan, Michael; Cappers, Peter; Goldman, Charles A.

    2014-12-01

    In this report, we use smart meter data to analyze specific actions, behaviors, and characteristics that drive energy savings in a behavior-based (BB) program. Specifically, we examine a Home Energy Report (HER) program. These programs typically obtain 1% to 3% annual savings, and recent studies have shown hourly savings of between 0.5% and 3%. But what is driving these savings? What types of households tend to be “high-savers”, and what behaviors are they adopting? There are several possibilities: one-time behaviors (e.g., changing thermostat settings); reoccurring habitual behaviors (e.g., turning off lights); and equipment purchase behaviors (e.g., energy efficient appliances), and these may vary across households, regions, and over time.

  4. Residential energy consumption across different population groups: Comparative analysis for Latino and non-Latino households in U.S.A.

    SciTech Connect (OSTI)

    Poyer, D.A.; Teotia, A.P.S.; Henderson, L.

    1998-05-01

    Residential energy cost, an important part of the household budget, varies significantly across different population groups. In the United States, researchers have conducted many studies of household fuel consumption by fuel type -- electricity, natural gas, fuel oil, and liquefied petroleum gas (LPG) -- and by geographic areas. The results of past research have also demonstrated significant variation in residential energy use across various population groups, including white, black, and Latino. However, research shows that residential energy demand by fuel type for Latinos, the fastest-growing population group in the United States, has not been explained by economic and noneconomic factors in any available statistical model. This paper presents a discussion of energy demand and expenditure patterns for Latino and non-Latino households in the United States. The statistical model developed to explain fuel consumption and expenditures for Latino households is based on Stone and Geary`s linear expenditure system model. For comparison, the authors also developed models for energy consumption in non-Latino, black, and nonblack households. These models estimate consumption of and expenditures for electricity, natural gas, fuel oil, and LPG by various households at the national level. The study revealed significant variations in the patterns of fuel consumption for Latinos and non-Latinos. The model methodology and results of this research should be useful to energy policymakers in government and industry, researchers, and academicians who are concerned with economic and energy issues related to various population groups.

  5. A CENSUS OF MID-INFRARED-SELECTED ACTIVE GALACTIC NUCLEI IN MASSIVE GALAXY CLUSTERS AT 0 {approx}< z {approx}< 1.3

    SciTech Connect (OSTI)

    Tomczak, Adam R.; Tran, Kim-Vy H.; Saintonge, Amelie

    2011-09-01

    We conduct a deep mid-infrared (mid-IR) census of nine massive galaxy clusters at (0 < z < 1.3) with a total of {approx}1500 spectroscopically confirmed member galaxies using Spitzer/IRAC photometry and established mid-IR color selection techniques. Of the 949 cluster galaxies that are detected in at least three of the four IRAC channels at the {>=}3{sigma} level, we identify 12 that host mid-IR-selected active galactic nuclei (IR-AGNs). To compare the IR-AGNs across our redshift range, we define two complete samples of cluster galaxies: (1) optically selected members with rest-frame V{sub AB} magnitude < - 21.5 and (2) mid-IR-selected members brighter than (M*{sub 3.6} + 0.5), i.e., essentially a stellar mass cut. In both samples, we measure f{sub IR-AGN} {approx} 1% with a strong upper limit of {approx}3% at z < 1. This uniformly low IR-AGN fraction at z < 1 is surprising given that the fraction of 24 {mu}m sources in the same galaxy clusters is observed to increase by about a factor of four from z {approx} 0 to z {approx} 1; this indicates that most of the detected 24 {mu}m flux is due to star formation. Only in our single galaxy cluster at z = 1.24 is the IR-AGN fraction measurably higher at {approx}15% (all members; {approx}70% for late-types only). In agreement with recent studies, we find that the cluster IR-AGNs are predominantly hosted by late-type galaxies with blue optical colors, i.e., members with recent/ongoing star formation. The four brightest IR-AGNs are also X-ray sources; these IR+X-ray AGNs all lie outside the cluster core (R{sub proj} {approx}> 0.5 Mpc) and are hosted by highly morphologically disturbed members. Although our sample is limited, our results suggest that f{sub IR-AGN} in massive galaxy clusters is not strongly correlated with star formation at z < 1 and that IR-AGNs have a more prominent role at z {approx}> 1.

  6. CENSUS AND STATISTICAL CHARACTERIZATION OF SOIL AND WATER QUALITY AT ABANDONED AND OTHER CENTRALIZED AND COMMERCIAL DRILLING-FLUID DISPOSAL SITES IN LOUISIANA, NEW MEXICO, OKLAHOMA, AND TEXAS

    SciTech Connect (OSTI)

    Alan R. Dutton; H. Seay Nance

    2003-06-01

    Commercial and centralized drilling-fluid disposal (CCDD) sites receive a portion of spent drilling fluids for disposal from oil and gas exploration and production (E&P) operations. Many older and some abandoned sites may have operated under less stringent regulations than are currently enforced. This study provides a census, compilation, and summary of information on active, inactive, and abandoned CCDD sites in Louisiana, New Mexico, Oklahoma, and Texas, intended as a basis for supporting State-funded assessment and remediation of abandoned sites. Closure of abandoned CCDD sites is within the jurisdiction of State regulatory agencies. Sources of data used in this study on abandoned CCDD sites mainly are permit files at State regulatory agencies. Active and inactive sites were included because data on abandoned sites are sparse. Onsite reserve pits at individual wells for disposal of spent drilling fluid are not part of this study. Of 287 CCDD sites in the four States for which we compiled data, 34 had been abandoned whereas 54 were active and 199 were inactive as of January 2002. Most were disposal-pit facilities; five percent were land treatment facilities. A typical disposal-pit facility has fewer than 3 disposal pits or cells, which have a median size of approximately 2 acres each. Data from well-documented sites may be used to predict some conditions at abandoned sites; older abandoned sites might have outlier concentrations for some metal and organic constituents. Groundwater at a significant number of sites had an average chloride concentration that exceeded nonactionable secondary drinking water standard of 250 mg/L, or a total dissolved solids content of >10,000 mg/L, the limiting definition for underground sources of drinking water source, or both. Background data were lacking, however, so we did not determine whether these concentrations in groundwater reflected site operations. Site remediation has not been found necessary to date for most abandoned

  7. The galactic census of high- and medium-mass protostars. II. Luminosities and evolutionary states of a complete sample of dense gas clumps

    SciTech Connect (OSTI)

    Ma, Bo; Tan, Jonathan C.; Barnes, Peter J.

    2013-12-10

    The Census of High- and Medium-mass Protostars (CHaMP) is the first large-scale (280° < l < 300°, –4° < b < 2°), unbiased, subparsec resolution survey of Galactic molecular clumps and their embedded stars. Barnes et al. presented the source catalog of ∼300 clumps based on HCO{sup +}(1-0) emission, used to estimate masses M. Here we use archival midinfrared-to-millimeter continuum data to construct spectral energy distributions. Fitting two-temperature gray-body models, we derive bolometric luminosities, L. We find that the clumps have 10 ≲ L/L {sub ☉} ≲ 10{sup 6.5} and 0.1 ≲ L/M/[L {sub ☉}/M {sub ☉}] ≲ 10{sup 3}, consistent with a clump population spanning a range of instantaneous star-formation efficiencies from 0 to ∼50%. We thus expect L/M to be a useful, strongly varying indicator of clump evolution during the star cluster formation process. We find correlations of the ratio of warm-to-cold component fluxes and of cold component temperature with L/M. We also find a near-linear relation between L/M and Spitzer-IRAC specific intensity (surface brightness); thus, this relation may also be useful as a star-formation efficiency indicator. The lower bound of the clump L/M distribution suggests that the star-formation efficiency per free-fall time is ε{sub ff} < 0.2. We do not find strong correlations of L/M with mass surface density, velocity dispersion, or virial parameter. We find a linear relation between L and L{sub HCO{sup +}(1--0)}, although with large scatter for any given individual clump. Fitting together with extragalactic systems, the linear relation still holds, extending over 10 orders of magnitude in luminosity. The complete nature of the CHaMP survey over a several kiloparsec-scale region allows us to derive a measurement at an intermediate scale, bridging those of individual clumps and whole galaxies.

  8. Word Pro - Untitled1

    U.S. Energy Information Administration (EIA) (indexed site)

    Household Energy Consumption Household Energy Consumpton by Census Region, Selected Years, 1978-2009¹ Household Energy Consumption by Source, 2009 Energy Consumption per Household, Selected Years, 1978-2009¹ Energy Consumption per Household, by Census Region, 2009 50 U.S. Energy Information Administration / Annual Energy Review 2011 1 For years not shown, there are no data available. 2 Liquefied petroleum gases. Notes: * Data include natural gas, electricity, distillate fuel oil, kerosene,

  9. LED Color Characteristics

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Color Characteristics Color quality is an important consideration when evaluating lighting products. This fact sheet reviews the fundamentals regarding light and color, summarizing the most important color issues related to white-light LED systems, including color consistency, stability, tuning, and rendering, as well as chromaticity. LED Emission Attributes Individual LED dies, often referred to as chips, emit light in a narrow range of wavelengths, giving the appearance of a monochromatic

  10. R93HC.PDF

    U.S. Energy Information Administration (EIA) (indexed site)

    7a. Conservation by Census Region and Climate Zone, Million U.S. Households, 1993 Conservation-Related Items RSE Column Factor: Total Census Region Climate Zone RSE Row Factors ...

  11. THE GALACTIC CENSUS OF HIGH- AND MEDIUM-MASS PROTOSTARS. I. CATALOGS AND FIRST RESULTS FROM MOPRA HCO{sup +} MAPS

    SciTech Connect (OSTI)

    Barnes, Peter J.; Hernandez, Audra K.; O'Dougherty, Stefan N.; Tan, Jonathan C.; Yonekura, Yoshinori; Fukui, Yasuo; Miyamoto, Yosuke; Furukawa, Naoko; Miller, Andrew T.; Muehlegger, Martin; Agars, Lawrence C.; Papadopoulos, George; Jones, Scott L.

    2011-09-01

    The Census of High- and Medium-mass Protostars (CHaMP) is the first large-scale, unbiased, uniform mapping survey at sub-parsec-scale resolution of 90 GHz line emission from massive molecular clumps in the Milky Way. We present the first Mopra (ATNF) maps of the CHaMP survey region (300{sup 0} > l > 280{sup 0}) in the HCO{sup +} J = 1 {yields} 0 line, which is usually thought to trace gas at densities up to 10{sup 11} m{sup -3}. In this paper, we introduce the survey and its strategy, describe the observational and data reduction procedures, and give a complete catalog of moment maps of the HCO{sup +} J = 1{yields}0 emission from the ensemble of 303 massive molecular clumps. From these maps we also derive the physical parameters of the clumps, using standard molecular spectral-line analysis techniques. This analysis yields the following range of properties: integrated line intensity 1-30 K km s{sup -1}, peak line brightness 1-7 K, linewidth 1-10 km s{sup -1}, integrated line luminosity 0.5-200 K km s{sup -1} pc{sup 2}, FWHM size 0.2-2.5 pc, mean projected axial ratio 2, optical depth 0.08-2, total surface density 30-3000 M{sub sun} pc{sup -2}, number density (0.2-30) x 10{sup 9} m{sup -3}, mass 15-8000 M{sub sun}, virial parameter 1-55, and total gas pressure 0.3-700 pPa. We find that the CHaMP clumps do not obey a Larson-type size-linewidth relation. Among the clumps, there exists a large population of subthermally excited, weakly emitting (but easily detectable) dense molecular clumps, confirming the prediction of Narayanan et al. These weakly emitting clumps comprise 95% of all massive clumps by number, and 87% of the molecular mass, in this portion of the Galaxy; their properties are distinct from the brighter massive star-forming regions that are more typically studied. If the clumps evolve by slow contraction, the 95% of fainter clumps may represent a long-lived stage of pressure-confined, gravitationally stable massive clump evolution, while the CHaMP clump

  12. Crude Oil Characteristics Research

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    SAE Plan June 29, 2015 Page 1 Crude Oil Characteristics Research Sampling, Analysis and Experiment (SAE) Plan The U.S. is experiencing a renaissance in oil and gas production. The Energy Information Administration projects that U.S. oil production will reach 9.3 million barrels per day in 2015 - the highest annual average level of oil production since 1972. This domestic energy boom is due primarily to new unconventional production of light sweet crude oil from tight-oil formations like the

  13. Emissions from small-scale energy production using co-combustion of biofuel and the dry fraction of household waste

    SciTech Connect (OSTI)

    Hedman, Bjoern . E-mail: bjorn.hedman@chem.umu.se; Burvall, Jan; Nilsson, Calle; Marklund, Stellan

    2005-07-01

    In sparsely populated rural areas, recycling of household waste might not always be the most environmentally advantageous solution due to the total amount of transport involved. In this study, an alternative approach to recycling has been tested using efficient small-scale biofuel boilers for co-combustion of biofuel and high-energy waste. The dry combustible fraction of source-sorted household waste was mixed with the energy crop reed canary-grass (Phalaris Arundinacea L.), and combusted in both a 5-kW pilot scale reactor and a biofuel boiler with 140-180 kW output capacity, in the form of pellets and briquettes, respectively. The chlorine content of the waste fraction was 0.2%, most of which originated from plastics. The HCl emissions exceeded levels stipulated in new EU-directives, but levels of equal magnitude were also generated from combustion of the pure biofuel. Addition of waste to the biofuel did not give any apparent increase in emissions of organic compounds. Dioxin levels were close to stipulated limits. With further refinement of combustion equipment, small-scale co-combustion systems have the potential to comply with emission regulations.

  14. Modern technical solutions of gas-fired heating devices of household and communal use and analysis of their testing

    SciTech Connect (OSTI)

    Bodzon, L.; Radwan, W.

    1995-12-31

    A review of technical solutions for gas-fired heating devices for household and communal use in Poland is presented. Based upon the analysis it is stated that the power output of Polish and foreign boilers ranges between 9 and 35 kW. The carbon monoxide content in flue gases reaches (on average) 0.005 vol.%, i.e., it is much lower than the maximum permissible level. Temperature of flue gases (excluding condensation boilers and those with air-tight combustion chamber) ranges between 150 and 200{degrees}C and their heating efficiency reaches 87-93%. The best parameters are given for condensation boilers, however they are still not widespread in Poland for the high cost of the equipment and assembling works. Among the heaters, the most safe are convection devices with closed combustion chamber; their efficiency is also the highest. Thus, it is concluded that a wide spectrum of high efficiency heating devices with good combustion parameters are available. The range of output is sufficient to meet household and communal requirement. They are however - predominantly - units manufactured abroad. It is difficult to formulate the program aimed at the improvement of the technique of heating devices made in Poland, and its implementation is uncertain because the production process is broken up into small handicraft workshops.

  15. Housing characteristics, 1987: Residential Energy Consumption Survey

    SciTech Connect (OSTI)

    Not Available

    1989-05-26

    This report is the first of a series of reports based on data from the 1987 RECS. The 1987 RECS is the seventh in the series of national surveys of households and their energy suppliers. These surveys provide baseline information on how households in the United States use energy. A cross section of housing types such as single-family detached homes, townhouses, large and small apartment buildings, condominiums, and mobile homes were included in the survey. Data from the RECS and a companion survey, the Residential Transportation Energy Consumption Survey (RTECS), are available to the public in published reports such as this one and on public use tapes. 10 figs., 69 tabs.

  16. Wafer characteristics via reflectometry

    DOE Patents [OSTI]

    Sopori, Bhushan L.

    2010-10-19

    Various exemplary methods (800, 900, 1000, 1100) are directed to determining wafer thickness and/or wafer surface characteristics. An exemplary method (900) includes measuring reflectance of a wafer and comparing the measured reflectance to a calculated reflectance or a reflectance stored in a database. Another exemplary method (800) includes positioning a wafer on a reflecting support to extend a reflectance range. An exemplary device (200) has an input (210), analysis modules (222-228) and optionally a database (230). Various exemplary reflectometer chambers (1300, 1400) include radiation sources positioned at a first altitudinal angle (1308, 1408) and at a second altitudinal angle (1312, 1412). An exemplary method includes selecting radiation sources positioned at various altitudinal angles. An exemplary element (1650, 1850) includes a first aperture (1654, 1854) and a second aperture (1658, 1858) that can transmit reflected radiation to a fiber and an imager, respectfully.

  17. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    7 Air-Conditioning Usage Indicators by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Air-Conditioning Usage Indicators"

  18. " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1"

    U.S. Energy Information Administration (EIA) (indexed site)

    3 Lighting Usage Indicators by Household Income, 2005" " Million U.S. Housing Units" ,,"2005 Household Income",,,,,"Below Poverty Line","Eligible for Federal Assistance1" ,"Housing Units (millions)" ,,"Less than $20,000","$20,000 to $39,999","$40,000 to $59,999","$60,000 to $79,999","$80,000 or More" "Lighting Usage Indicators" "Total U.S. Housing

  19. Separate collection of household food waste for anaerobic degradation - Comparison of different techniques from a systems perspective

    SciTech Connect (OSTI)

    Bernstad, A.; Cour Jansen, J. la

    2012-05-15

    Highlight: Black-Right-Pointing-Pointer Four modern and innovative systems for household food waste collection are compared. Black-Right-Pointing-Pointer Direct emissions and resource use were based on full-scale data. Black-Right-Pointing-Pointer Conservation of nutrients/energy content over the system was considered. Black-Right-Pointing-Pointer Systems with high energy/nutrient recovery are most environmentally beneficial. - Abstract: Four systems for household food waste collection are compared in relation the environmental impact categories eutrophication potential, acidification potential, global warming potential as well as energy use. Also, a hotspot analysis is performed in order to suggest improvements in each of the compared collection systems. Separate collection of household food waste in paper bags (with and without drying prior to collection) with use of kitchen grinders and with use of vacuum system in kitchen sinks were compared. In all cases, food waste was used for anaerobic digestion with energy and nutrient recovery in all cases. Compared systems all resulted in net avoidance of assessed environmental impact categories; eutrophication potential (-0.1 to -2.4 kg NO{sub 3}{sup -}eq/ton food waste), acidification potential (-0.4 to -1.0 kg SO{sub 2}{sup -}eq/ton food waste), global warming potential (-790 to -960 kg CO{sub 2}{sup -}eq/ton food waste) and primary energy use (-1.7 to -3.6 GJ/ton food waste). Collection with vacuum system results in the largest net avoidance of primary energy use, while disposal of food waste in paper bags for decentralized drying before collection result in a larger net avoidance of global warming, eutrophication and acidification. However, both these systems not have been taken into use in large scale systems yet and further investigations are needed in order to confirm the outcomes from the comparison. Ranking of scenarios differ largely if considering only emissions in the foreground system, indicating the

  20. Process for the utilization of household rubbish or garbage and other organic waste products for the production of methane gas

    SciTech Connect (OSTI)

    Hunziker, M.; Schildknecht, A.

    1985-04-16

    Non-organic substances are separated from household garbage and the organic substances are fed in proportioned manner into a mixing tank and converted into slurry by adding liquid. The slurry is crushed for homogenization purposes in a crushing means and passed into a closed holding container. It is then fed over a heat exchanger and heated to 55/sup 0/ to 60/sup 0/ C. The slurry passes into a plurality of reaction vessels in which the methane gas and carbon dioxide are produced. In a separating plant, the mixture of gaseous products is broken down into its components and some of the methane gas is recycled by bubbling it through both the holding tank and the reaction tank, the remainder being stored in gasholders. The organic substances are degraded much more rapidly through increasing the degradation temperature and as a result constructional expenditure can be reduced.

  1. Table 2.5 Household Energy Consumption and Expenditures by End Use, Selected Years, 1978-2005

    U.S. Energy Information Administration (EIA) (indexed site)

    5 Household 1 Energy Consumption and Expenditures by End Use, Selected Years, 1978-2005 Year Space Heating Air Conditioning Water Heating Appliances, 2 Electronics, and Lighting Natural Gas Elec- tricity 3 Fuel Oil 4 LPG 5 Total Electricity 3 Natural Gas Elec- tricity 3 Fuel Oil 4 LPG 5 Total Natural Gas Elec- tricity 3 LPG 5 Total Consumption (quadrillion Btu)<//td> 1978 4.26 0.40 2.05 0.23 6.94 0.31 1.04 0.29 0.14 0.06 1.53 0.28 1.46 0.03 1.77 1980 3.41 .27 1.30 .23 5.21 .36 1.15 .30 .22

  2. Sensor Characteristics Reference Guide

    SciTech Connect (OSTI)

    Cree, Johnathan V.; Dansu, A.; Fuhr, P.; Lanzisera, Steven M.; McIntyre, T.; Muehleisen, Ralph T.; Starke, M.; Banerjee, Pranab; Kuruganti, T.; Castello, C.

    2013-04-01

    The Buildings Technologies Office (BTO), within the U.S. Department of Energy (DOE), Office of Energy Efficiency and Renewable Energy (EERE), is initiating a new program in Sensor and Controls. The vision of this program is: • Buildings operating automatically and continuously at peak energy efficiency over their lifetimes and interoperating effectively with the electric power grid. • Buildings that are self-configuring, self-commissioning, self-learning, self-diagnosing, self-healing, and self-transacting to enable continuous peak performance. • Lower overall building operating costs and higher asset valuation. The overarching goal is to capture 30% energy savings by enhanced management of energy consuming assets and systems through development of cost-effective sensors and controls. One step in achieving this vision is the publication of this Sensor Characteristics Reference Guide. The purpose of the guide is to inform building owners and operators of the current status, capabilities, and limitations of sensor technologies. It is hoped that this guide will aid in the design and procurement process and result in successful implementation of building sensor and control systems. DOE will also use this guide to identify research priorities, develop future specifications for potential market adoption, and provide market clarity through unbiased information

  3. Emissions of polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans from the open burning of household waste in barrels

    SciTech Connect (OSTI)

    Lemieux, P.M.; Lutes, C.C.; Abbott, J.A.; Aldous, K.M.

    2000-02-01

    Backyard burning of household waste in barrels is a common waste disposal practice for which pollutant emissions have not been well characterized. This study measured the emissions of several pollutants, including polychlorinated dibenzo-p-dioxins and polychlorinated dibenzofurans (PCDDs/PCDFs), from burning mixtures designed to simulate waste generated by a recycling and a nonrecycling family in a 208-L (55-gal) burn barrel at the EPA's Open Burning Test Facility. This paper focuses on the PCDD/PCDF emissions and discusses the factors influencing PCDD/PCDF formation for different test burns. Four test burns were made in which the amount of waste placed in the barrel varied from 6.4 to 13.6 kg and the amount actually burned varied from 46.6% to 68.1%. Emissions of total PCDDs/PCDFs ranged between 0.0046 and 0.48 mg/kg of waste burned. Emissions are also presented in terms of 2,3,7,8-TCDD toxic equivalents. Emissions of PCDDs/PCDFs appear to correlate with both copper and hydrochloric acid emissions. The results of this study indicate that backyard burning emits more PCDDs/PCDFs on a mass of refuse burned basis than various types of municipal waste combustors (MWCs). Comparison of burn barrel emissions to emissions from a hypothetical modern MWC equipped with high-efficiency flue gas cleaning technology indicates that about 2--40 households burning their trash daily in barrels can produce average PCDD/PCDF emissions comparable to a 182,000 kg/day (200 ton/day) MWC facility. This study provides important data on a potentially significant source of emissions of PCDDs/PCDFs.

  4. Agricultural production in the United States by county: a compilation of information from the 1974 census of agriculture for use in terrestrial food-chain transport and assessment models

    SciTech Connect (OSTI)

    Shor, R.W.; Baes, C.F. III; Sharp, R.D.

    1982-01-01

    Terrestrial food-chain models that simulate the transport of environmentally released radionuclides incorporate parameters describing agricultural production and practice. Often a single set of default parameters, such as that listed in USNRC Regulatory Guide 1.109, is used in lieu of site-specific information. However, the geographical diversity of agricultural practice in the United States suggests the limitations of a single set of default parameters for assessment models. This report documents default parameters with a county-wide resolution based on analysis of the 1974 US Census of Agriculture for use in terrestrial food chain models. Data reported by county, together with state-based information from the US Department of Agriculture, Economic and Statistics Service, provided the basis for estimates of model input parameters. This report also describes these data bases, their limitations, and lists default parameters by county. Vegetable production is described for four categories: leafy vegetables; vegetables and fruits exposed to airborne material; vegetables, fruits, and nuts protected from airborne materials; and grains. Livestock feeds were analyzed in categories of hay, silage, pasture, and grains. Pasture consumption was estimated from cattle and sheep inventories, their feed requirements, and reported quantities of harvested forage. The results were compared with assumed yields of the pasture areas reported. In addition, non-vegetable food production estimates including milk, beef, pork, lamb, poultry, eggs, goat milk, and honey are described. The agricultural parameters and land use information - in all 47 items - are tabulated in four appendices for each of the 3067 counties of the US reported to the Census of Agriculture, excluding those in Hawaii and Alaska.

  5. The impact of rising energy prices on household energy consumption and expenditure patterns: The Persian Gulf crisis as a case example

    SciTech Connect (OSTI)

    Henderson, L.J. ); Poyer, D.A.; Teotia, A.P.S. . Energy Systems Div.)

    1992-09-01

    The Iraqi invasion of Kuwait and the subsequent war between Iraq and an international alliance led by the United States triggered immediate increases in world oil prices. Increases in world petroleum prices and in US petroleum imports resulted in higher petroleum prices for US customers. In this report, the effects of the Persian Gulf War and its aftermath are used to demonstrate the potential impacts of petroleum price changes on majority, black, and Hispanic households, as well as on poor and nonpoor households. The analysis is done by using the Minority Energy Assessment Model developed by Argonne National Laboratory for the US Department of Energy (DOE). The differential impacts of these price increases and fluctuations on poor and minority households raise significant issues for a variety of government agencies, including DOE. Although the Persian Gulf crisis is now over and world oil prices have returned to their prewar levels, the differential impacts of rising energy prices on poor and minority households as a result of any future crisis in the world oil market remains a significant long-term issue.

  6. EIA - Household Transportation report: Household Vehicles Energy...

    U.S. Energy Information Administration (EIA) (indexed site)

    National Research Council, Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards (Washington, DC: National Academy of Sciences, 2002), p. 85. 4 8.3 million...

  7. CBECS Buildings Characteristics --Revised Tables

    U.S. Energy Information Administration (EIA) (indexed site)

    Totals and Means of Floorspace, Number of Workers, and Hours of Operation, 1995 Building Characteristics RSE Column Factor: All Buildings (thousand) Total Floorspace (million...

  8. Property:Other Characteristics | Open Energy Information

    Open Energy Information (Open El) [EERE & EIA]

    Characteristics Jump to: navigation, search Property Name Other Characteristics Property Type String Pages using the property "Other Characteristics" Showing 8 pages using this...

  9. Residential Energy Consumption Survey (RECS) - Data - U.S. Energy...

    Gasoline and Diesel Fuel Update

    2001 RECS Survey Data 2009 | 2005 | 2001 | 1997 | 1993 | Previous Housing characteristics ... PDF UrbanRural Location PDF Northeast Census Region PDF Midwest Census Region PDF ...

  10. User interface in ORACLE for the Worldwide Household Goods Information System for Transportation Modernization (WHIST-MOD)

    SciTech Connect (OSTI)

    James, T. ); Loftis, J. )

    1990-07-01

    The Directorate of Personal Property of the Military Traffic Management Command (MTMC) requested that Oak Ridge National laboratory (ORNL) design a prototype decision support system, the Worldwide Household Goods Information System for Transportation Modernization (WHIST-MOD). This decision support system will automate current tasks and provide analysis tools for evaluating the Personal Property Program, predicting impacts to the program, and planning modifications to the program to meet the evolving needs of military service members and the transportation industry. The system designed by ORNL consists of three application modules: system dictionary applications, data acquisition and administration applications, and user applications. The development of the user applications module is divided into two phases. Round 1 is the data selection front-end interface, and Round 2 is the output or back-end interface. This report describes the prototyped front-end interface for the user application module. It discusses user requirements and the prototype design. The information contained in this report is the product of in-depth interviews with MTMC staff, prototype meetings with the users, and the research and design work conducted at ORNL. 18 figs., 2 tabs.

  11. Travel Patterns and Characteristics of Elderly Subpopulation in New York State

    SciTech Connect (OSTI)

    Hwang, Ho-Ling; Wilson, Daniel W.; Reuscher, Tim; Yang, Jianjiang; Taylor, Rob D.; Chin, Shih-Miao

    2015-03-01

    With the increasing demographic shift towards a larger population of elderly (individuals 65 years and older), it is essential for policy makers and planners to have an understanding of transportation issues that affect the elderly. These issues include livability of the community, factors impacting travel behavior and mobility, transportation safety, etc. In this study, Oak Ridge National Laboratory was tasked by the New York State (NYS) Department of Transportation to conduct a detailed examination of travel behaviors, and identify patterns and trends of the elderly within NYS. The National Household Travel Survey (NHTS) was used as the primary data source to analyze subjects and address questions such as: Are there differences in traveler demographics between the elderly population and those of younger age groups who live in various NYS regions; e.g., New York City, other urban areas of NYS, or other parts of the country? How do they compare with the population at large? Are there any regional differences (e.g., urban versus rural)? Gender differences? Do any unique travel characteristics or patterns exist within the elderly group? In addition to analysis of NHTS data, roadway travel safety concerns associated with elderly travelers were also investigated in this study. Specifically, data on accidents involving the elderly (including drivers, passengers, and others) as captured in the Fatal Analysis Reporting System (FARS) database was analyzed to examine elderly driver and elderly pedestrian travel safety issues in NYS. The analyses of these data sets provide a greater understanding of the elderly within NYS and their associated transportation issues. Through this study, various key findings on elderly population size, household characteristics, and travel patterns were produced and are report herein this report.

  12. Characteristics RSE Column Factor: Total

    U.S. Energy Information Administration (EIA) (indexed site)

    and 1994 Vehicle Characteristics RSE Column Factor: Total 1993 Family Income Below Poverty Line Eli- gible for Fed- eral Assist- ance 1 RSE Row Factor: Less than 5,000 5,000...

  13. Boundary Layer Cloud Turbulence Characteristics

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Boundary Layer Cloud Turbulence Characteristics Virendra Ghate Bruce Albrecht Parameter Observational Readiness (/10) Modeling Need (/10) Cloud Boundaries 9 9 Cloud Fraction Variance Skewness Up/Downdraft coverage Dominant Freq. signal Dissipation rate ??? Observation-Modeling Interface

  14. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-12a

    U.S. Energy Information Administration (EIA) (indexed site)

    2a. Usage Indicators by West Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | West Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | Total | | | | Usage Indicators | U.S. | Total | Mountain | Pacific | |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 | 1.0 | 1.6 | 1.2 |Factors

  15. file://C:\Documents%20and%20Settings\VM3\My%20Documents\hc6-9a_

    U.S. Energy Information Administration (EIA) (indexed site)

    9a. Usage Indicators by Northeast Census Region, Million U.S. Households, 2001 ____________________________________________________________________________________________ | | | | | Northeast Census Region | | |___________________________________| | | | | | | | Census Division | | | |_______________________| | | | | | | Total | | Middle | | Usage Indicators | U.S. | Total | Atlantic |New England| |___________|___________|___________|___________| RSE | | | | | Row RSE Column Factor: | 0.5 | 1.0 |

  16. Next Generation Household Refrigerator

    Energy.gov [DOE]

    Lead Performer: Oak Ridge National Laboratory - Oak Ridge, TN Partner: Whirlpool - Benton Harbor, MI

  17. Lands with Wilderness Characteristics | Open Energy Information

    Open Energy Information (Open El) [EERE & EIA]

    with Wilderness Characteristics Jump to: navigation, search Retrieved from "http:en.openei.orgwindex.php?titleLandswithWildernessCharacteristics&oldid647799...

  18. Principal Characteristics of a Modern Grid

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    ... January 2007 Impact of electric vehicles Office of ... AAM helps utilities reduce costs and operate more ... Characteristic - Milestone Map Smart Grid Characteristic CE ...

  19. Health Care Buildings : Basic Characteristics Tables

    U.S. Energy Information Administration (EIA) (indexed site)

    Basic Characteristics Tables Buildings and Size Data by Basic Characteristics for Health Care Buildings Number of Buildings (thousand) Percent of Buildings Floorspace (million...

  20. NQA-1 Commercial Grade Dedication Critical Characteristics |...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    NQA-1 Commercial Grade Dedication Critical Characteristics NQA-1 Commercial Grade Dedication Critical Characteristics May 5, 2015 Presenter: Randy P. Lanham, PE, CSP, Fire ...

  1. charts.pdf

    Gasoline and Diesel Fuel Update

    Household Tables (Million U.S. Households; 24 pages, 122 kb) Contents Pages HC2-1a. Household Characteristics by Climate Zone, Million U.S. Households, 2001 2 HC2-2a. Household Characteristics by Year of Construction, Million U.S. Households, 2001 2 HC2-3a. Household Characteristics by Household Income, Million U.S. Households, 2001 2 HC2-4a. Household Characteristics by Type of Housing Unit, Million U.S. Households, 2001 2 HC2-5a. Household Characteristics by Type of Owner-Occupied Housing

  2. LED Color Characteristics | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Color Characteristics LED Color Characteristics DOE EERE Building Technologies Program Solid-State Lighting fact sheet led-color-characteristics-factsheet.pdf (804.38 KB) More Documents & Publications LED Color Characteristics Evaluating Color Rendition Using IES TM-30-15 Report 23: Photometric Testing of White Tunable LED Luminaires

  3. " Generation by Census Region, Industry Group,...

    U.S. Energy Information Administration (EIA) (indexed site)

    ... Microbial",60,0,12,48,"*",11.6 ," Bioprocessing",376,"Q","W","W",0,11.3 ," Gasification of Biomass Feedstocks","W","Q","*","W",0,18.3 ," Fast Pyrolysis of Biomass ...

  4. " Electricity Generation by Census Region, Industry...

    U.S. Energy Information Administration (EIA) (indexed site)

    "," "," ","Coke"," ","Row" "Code(a)","Industry Groups and Industry","Total","Electricity(b)","Fuel Oil","Fuel Oil(c)","Natural Gas(d)","LPG","Coal","and ...

  5. Figure F1. United States Census Divisions

    Gasoline and Diesel Fuel Update

    53 Figure 17. Natural gas delivered to consumers in the United States, 2015 Volumes in Million Cubic Feet Trillion Cubic Feet trillion cubic feet All Other States Wisconsin Indiana Texas Pennsylvania New Jersey Ohio Michigan Illinois California New York 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Residential All Other States Minnesota Massachusetts Pennsylvania New Jersey Ohio Michigan Texas Illinois California New York 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Commercial trillion cubic feet Res idential 4,609,670

  6. Figure 1. Census Regions and Divisions

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    US Federal Regions>

  7. Women and development: a highland New Guinea example

    SciTech Connect (OSTI)

    Johnson, P.L.

    1988-06-01

    This paper presents an analysis of household variables and their relationship to success in cash cropping among the Gainj of Madang Province, Papua New Guinea. Censuses and household surveys from 1978, the year in which cash cropping began, and 1983 provide data that show different patterns of change in household structure for more and less commercially successful households. The results illustrate the importance of women's labor in economic development and the dynamic nature of the relationship between household structure and economic development.

  8. The impact of a shade coffee certification program on forest conservation using remote sensing and household data

    SciTech Connect (OSTI)

    Takahashi, Ryo; Todo, Yasuyuki

    2014-01-15

    In recent years, shade coffee certification programs have attracted increasing attention from forest conservation and development organizations. The certification programs could be expected to promote forest conservation by providing a premium price to shade coffee producers. However, little is known about the significance of the conservation efforts generated by certification programs. In particular, the relationship between the impact of the certification and producer characteristics has yet to be examined. The purpose of this study, which was conducted in Ethiopia, was to examine the impact of a shade coffee certification program on forest conservation and its relationship with the socioeconomic characteristics of the producers. Remote sensing data of 2005 and 2010 was used to gauge the changes in forest area. Employing a probit model, we found that a forest coffee area being certified increased the probability of forest conservation by 19.3 percentage points relative to forest coffee areas lacking certification. We also found that although economically poor producers tended to engage in forest clearing, the forest coffee certification program had a significant impact on these producers. This result suggests that the certification program significantly affects the behaviors of economically poor producers and motivates these producers to conserve the forest. -- Highlights: • We employed the probit mode to evaluate the impact of the shade coffee certification on forest conservation in Ethiopia. • We estimated how the impact of the certification varied among producers with different characteristics. • The certification increased the probability of conserving forest by 19.3 percentage points. • Certification program motivated the economically poor producers to conserve the forest.

  9. LED Color Characteristics | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Color Characteristics LED Color Characteristics Reviews the fundamentals regarding light and color, summarizing the most important color issues related to white-light LED systems. (6 pages, April 2016) led-color-characteristics-factsheet.pdf (1.77 MB) More Documents & Publications Evaluating Color Rendition Using IES TM-30-15 LED Color Characteristics IES TM-30 Offers Comprehensive System for Evaluating Color Rendition of Light Sources

  10. Doppler characteristics of sea clutter.

    SciTech Connect (OSTI)

    Raynal, Ann Marie; Doerry, Armin Walter

    2010-06-01

    Doppler radars can distinguish targets from clutter if the target's velocity along the radar line of sight is beyond that of the clutter. Some targets of interest may have a Doppler shift similar to that of clutter. The nature of sea clutter is different in the clutter and exo-clutter regions. This behavior requires special consideration regarding where a radar can expect to find sea-clutter returns in Doppler space and what detection algorithms are most appropriate to help mitigate false alarms and increase probability of detection of a target. This paper studies the existing state-of-the-art in the understanding of Doppler characteristics of sea clutter and scattering from the ocean to better understand the design and performance choices of a radar in differentiating targets from clutter under prevailing sea conditions.

  11. Tier identification (TID) for tiered memory characteristics

    SciTech Connect (OSTI)

    Chang, Jichuan; Lim, Kevin T; Ranganathan, Parthasarathy

    2014-03-25

    A tier identification (TID) is to indicate a characteristic of a memory region associated with a virtual address in a tiered memory system. A thread may be serviced according to a first path based on the TID indicating a first characteristic. The thread may be serviced according to a second path based on the TID indicating a second characteristic.

  12. RACORO Forecasting

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    7a. Space Heating by Census Region and Climate Zone, Million U.S. Households, 1993 Space Heating Characteristics RSE Column Factor: Total Census Region Climate Zone RSE Row Factors Northeast Midwest South West Fewer than 2,000 CDD and -- More than 2,000 CDD and Few- er than 4,000 HDD More than 7,000 HDD 5,500 to 7,000 HDD 4,000 to 5,499 HDD Few- er than 4,000 HDD 0.5 0.9 1.1 0.8 0.8 1.6 1.3 1.2 1.2 1.1 Total ................................................. 96.6 19.5 23.3 33.5 20.4 8.7 26.5

  13. BIOENERGIZEME INFOGRAPHIC CHALLENGE: Biomass: Types/Characteristics |

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Department of Energy Biomass: Types/Characteristics BIOENERGIZEME INFOGRAPHIC CHALLENGE: Biomass: Types/Characteristics BIOENERGIZEME INFOGRAPHIC CHALLENGE: Biomass: Types/Characteristics This infographic was created by students from Albany Academies and Academy of the Holy Names in Albany, NY, as part of the U.S. Department of Energy-BioenergizeME Infographic Challenge. The BioenergizeME Infographic Challenge encourages young people to improve their foundational understanding of bioenergy,

  14. Characteristics of Strong Programs | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Characteristics of Strong Programs Characteristics of Strong Programs Existing financing programs offer a number of important lessons on effective program design. Some characteristics of strong financing programs drawn from past program experience are described below. Engage Contractor Networks The programs with the highest volume of loans have strong contractor networks and regular program communication with those contractors. Significant time and effort are often expended to make sure the

  15. CASL - The Michigan Parallel Characteristics Transport Code

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    The Michigan Parallel Characteristics Transport Code Verification of MPACT: The Michigan Parallel Characteristics Transport Code Benjamin Collins, Brendan Kochunas, Daniel Jabbay, Thomas Downar, William Martin Department of Nuclear Engineering and Radiological Sciences University of Michigan Andrew Godfrey Oak Ridge National Laboroatory MPACT (Michigan PArallel Characteristics Transport Code) is a new reactor analysis tool being developed at the University of Michigan as an advanced pin-resolved

  16. Fracture characteristics and their relationships to producing...

    Open Energy Information (Open El) [EERE & EIA]

    characteristics and their relationships to producing zones in deep wells, Raft River geothermal area Jump to: navigation, search OpenEI Reference LibraryAdd to library Book:...

  17. DERIVATION OF STOCHASTIC ACCELERATION MODEL CHARACTERISTICS FOR...

    Office of Scientific and Technical Information (OSTI)

    FOR SOLAR FLARES FROM RHESSI HARD X-RAY OBSERVATIONS Citation Details In-Document Search Title: DERIVATION OF STOCHASTIC ACCELERATION MODEL CHARACTERISTICS FOR SOLAR FLARES ...

  18. 1999 Commercial Buildings Characteristics--Year Constructed

    U.S. Energy Information Administration (EIA) (indexed site)

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  19. 1999 Commercial Buildings Characteristics--Building Size

    U.S. Energy Information Administration (EIA) (indexed site)

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  20. 1999 Commercial Buildings Characteristics--Disaggregated Principal...

    U.S. Energy Information Administration (EIA) (indexed site)

    (202) 586-8800. Energy Information Administration Commercial Buildings Energy Consumption Survey Top Return to: "1999 CBECS-Commercial Buildings Characteristics" Specific questions...

  1. Mechanical properties and energy absorption characteristics of...

    Office of Scientific and Technical Information (OSTI)

    Mechanical properties and energy absorption characteristics of a polyurethane foam Citation Details In-Document Search Title: Mechanical properties and energy absorption ...

  2. http://2010.census.gov/2010census/data/apportionment-dens-text...

    National Nuclear Security Administration (NNSA)

    ... Density Rank 10 10 10 10 10 10 11 11 11 11 12 Oklahoma Population 1,657,155 2,028,283 2,396,040 2,336,434 2,233,351 2,328,284 2,559,229 3,025,290 3,145,585 3,450,654 3,751,351 ...

  3. Direct and indirect effect of changes in family structure and lifestyle upon energy consumption, 1950-1080

    SciTech Connect (OSTI)

    Stever, C.J.

    1985-01-01

    This research project examines both the direct and indirect influence of changes in family structure and lifestyle dimensions upon residential energy consumption patterns from 1950 to 1980. These relationships are investigated on a macro level using three national energy surveys administered from 1974 to 1980 and the Census Bureau and other government sources of documenting changes in social characteristics and energy consumption levels over thirty years. Stage I looks at changes in residential consumption from 1950 to 1980 and conservation behavior from 1965 to 1980. The objective of Stage II is to identify those family structure and lifestyle characteristics that constrain conservation measures in which a household engages. Stage III examines the commonly held assumption that investment in conservation equipment will result in reduced consumption. Stage IV explores the potential influence that changes in structural and lifestyle characteristics of householders may have upon average consumption levels from 1950 to 1980. The primary implications of this study are: (1) in order to obtain a complete picture of the current energy situation, it is necessary to examine consumption and conservation behavior both before and after the 1973 oil embargo, and (2) changes in social structural and lifestyle of households over time appear to have contributed as much, if not more, to reduce consumption in the late 1970s as did conscious conservation efforts by householders.

  4. A technical description of enhancements to the front-end user interface for the Worldwide Household Goods Information System for Transportation Modernization (WHIST-MOD)

    SciTech Connect (OSTI)

    Loftis, J.P.; Spears, P.M. ); James, T.L. )

    1990-08-01

    The Directorate of Personal Property of the Military Traffic Management Command (MTMC) asked Oak Ridge National Laboratory (ORNL) to design a decision support system, the Worldwide Household Goods Information System for Transportation Modernization. This decision support system will automate tasks and provide analysis tools for evaluating the Personal Property Program, predicting impacts to the program, and planning modifications to the program to meet the evolving needs of military service members and the transportation industry. The system designed by ORNL consists of three application modules: system dictionary applications, data acquisition and administration applications, and user applications. The user applications module is divided into two phases: the data selection front-end interface and the postprocessing back-end interface. This paper describes the prototyped front-end interface using ORACLE SQL*Forms, part of the ORACLE Relational Database Management System (RDBMS) toolset. The focus of this paper is a discussion of the need for enhancements to the initial design of the interface and the coding techniques used to prototype the enhancements. These enhancements make the front-end interface more flexible and easier to use by giving users options for identifying data to be used by the back-end interface. This report is based on in-depth interviews of MTMC staff, prototype meetings with the users, and the research and design work conducted at ORNL.

  5. Buildings Energy Data Book: 2.2 Residential Sector Characteristics

    Buildings Energy Data Book

    6 Residential Heated Floorspace, as of 2005 (Percent of Total Households) Floorspace (SF) Fewer than 500 6% 500 to 999 26% 1,000 to 1,499 24% 1,500 to 1,999 16% 2,000 to 2,499 9% 2,500 to 2,999 7% 3,000 or more 11% Total 100% Source(s): EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table HC1-3.

  6. Probability-theoretic characteristics of solar batteries

    SciTech Connect (OSTI)

    Lidorenko, N.S.; Asharin, L.N.; Borisova, N.A.; Evdokimov, V.M.; Ryabikov, S.V.

    1980-01-01

    Results are reported for an investigation into the characteristics of solar batteries on the basis of probability theory with the photocells treated as current generators; methods for reducing solar-battery circuit losses are considered.

  7. NREL: Wind Research - Site Wind Resource Characteristics

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    Site Wind Resource Characteristics A graphic showing the location of National Wind Technology Center and its wind power class 2. Click on the image to view a larger version. ...

  8. Principal Characteristics of a Modern Grid

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    ... Energy Reliability MODERN GRID S T R A T E G Y The Grid - Today vs. Tomorrow Characteristic Today Tomorrow MotivatesIncludes Consumer No price visibility, time-of- day pricing ...

  9. Crude Oil Characteristics Research | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Crude Oil Characteristics Research Crude Oil Characteristics Research July 9, 2015 - 1:00pm Addthis Paula Gant Paula Gant Principal Deputy Assistant Secretary The DOE Office of Fossil Energy wanted to identify the actions needed to obtain a science-based understanding of outstanding questions associated with the production, treatment, and transportation of various types of crude oil, including Bakken crude oil. In support of that effort, DOE - in collaboration with the Department of

  10. spaceheat_household2001.pdf

    Gasoline and Diesel Fuel Update

    ......... 0.6 Q Q Q 39.9 Fuel Oil ......Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  11. spaceheat_household2001.pdf

    Gasoline and Diesel Fuel Update

    ... 0.6 0.2 0.2 Q 0.1 34.8 Fuel Oil ......Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  12. spaceheat_household2001.pdf

    Annual Energy Outlook

    ......... 0.6 Q Q Q 39.6 Fuel Oil ......Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  13. spaceheat_household2001.pdf

    Annual Energy Outlook

    ......... 0.6 Q Q Q Q 40.2 Fuel Oil ......Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  14. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  15. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    ... For this report, the heating or cooling degree-days are a measure of how cold or how hot a location is over a period of one year, relative to a base temperature of 65 degrees ...

  16. appl_household2001.pdf

    U.S. Energy Information Administration (EIA) (indexed site)

    RSE Row Factors New York California Texas Florida 0.4 1.2 1.1 1.4 1.3 Total ... RSE Row Factors New York California Texas Florida 0.4 1.2 1.1 1.4 1.3 Age of Refrigerator ...

  17. homeoffice_household2001.pdf

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    RSE Column Factor: Total U.S. Four Most Populated States RSE Row Factors New York California Texas Florida 0.4 1.1 1.0 1.5 1.5 Total ......

  18. spaceheat_household2001.pdf

    Gasoline and Diesel Fuel Update

    RSE Row Factors New York California Texas Florida 0.5 1.1 1.0 1.2 1.6 Total ... RSE Row Factors New York California Texas Florida 0.5 1.1 1.0 1.2 1.6 Age of Main Heating ...

  19. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    a comparison between the 1991 and previous years RTECS designs; (2) the sample design; (3) the data-collection procedures; (4) the Vehicle Identification Number (VIN); (5)...

  20. Household Vehicles Energy Consumption 1994

    U.S. Energy Information Administration (EIA) (indexed site)

    DC, October 1995), Table DL-1B. 5. "Chained dollars" is a measure used to express real prices. Real prices are those that have been adjusted to remove the effect of changes...

  1. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    production vehicles in order to assess compliance with Corporate Average Fuel Economy (CAFE) standards. The EPA Composite MPG is based on the assumption of a "typical" vehicle-use...

  2. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    more fuel-efficient vehicles, and the implementation of Corporate Average Fuel Economy (CAFE) 6 standards. Figure 13. Average Fuel Efficiency of All Vehicles, by Model Year 6...

  3. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    of vehicles in the residential sector. Data are from the 1991 Residential Transportation Energy Consumption Survey. The "Glossary" contains the definitions of terms used in the...

  4. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    for 1994, will continue the 3-year cycle. The RTECS, a subsample of the Residential Energy Consumption Survey (RECS), is an integral part of a series of surveys designed by...

  5. housingunit_household2001.pdf

    Annual Energy Outlook

    ... 0.8 0.1 Q Q 0.4 Q 28.8 Solar ...... Notes: * To obtain the RSE percentage for any table cell, multiply the corresponding ...

  6. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    Matching: A model-based procedure used to impute for item nonresponse. This method uses logistic models to compute predicted means that are used to statistically match each...

  7. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    were imputed as disposed vehicles. To impute vehicle stock changes in the 1991 RTECS, logistic regression equations were used to compute a predicted probability (or propensity)...

  8. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) (indexed site)

    selected tabulations were produced using two different software programs, Table Producing Language (TPL) and Statistical Analysis System (SAS). Energy Information Administration...

  9. appl_household2001.pdf

    Gasoline and Diesel Fuel Update

    ... 1.1 1.1 1.1 1.6 18.3 Through-the-Door IceWater Service Yes ......Water Heater Equipment Water Heaters ...... 107.0 9.2 28.6 24.0 ...

  10. Superconducting wire with improved strain characteristics

    DOE Patents [OSTI]

    Luhman, T.; Klamut, C.J.; Suenaga, M.; Welch, D.

    1979-12-19

    A superconducting wire comprising a superconducting filament and a beryllium strengthened bronze matrix in which the addition of beryllium to the matrix permits a low volume matrix to exhibit reduced elastic deformation after heat treating which increases the compression of the superconducting filament on cooling and thereby improve the strain characteristics of the wire.

  11. Superconducting wire with improved strain characteristics

    DOE Patents [OSTI]

    Luhman, Thomas; Klamut, Carl J.; Suenaga, Masaki; Welch, David

    1982-01-01

    A superconducting wire comprising a superconducting filament and a beryllium strengthened bronze matrix in which the addition of beryllium to the matrix permits a low volume matrix to exhibit reduced elastic deformation after heat treating which increases the compression of the superconducting filament on cooling and thereby improves the strain characteristics of the wire.

  12. Superconducting wire with improved strain characteristics

    DOE Patents [OSTI]

    Luhman, Thomas; Klamut, Carl J.; Suenaga, Masaki; Welch, David

    1982-01-01

    A superconducting wire comprising a superconducting filament and a beryllium strengthened bronze matrix in which the addition of beryllium to the matrix permits a low volume matrix to exhibit reduced elastic deformation after heat treating which increases the compression of the superconducting filament on cooling and thereby improve the strain characteristics of the wire.

  13. Gas Electron Multiplier (GEM) Chamber Characteristics Test (Technical...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Gas Electron Multiplier (GEM) Chamber Characteristics Test Citation Details In-Document Search Title: Gas Electron Multiplier (GEM) Chamber Characteristics Test ...

  14. Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems...

    Office of Scientific and Technical Information (OSTI)

    Dataset: Plant Root Characteristics and Dynamics in Arctic Tundra Ecosystems, 1960-2012 Citation Details In-Document Search Title: Plant Root Characteristics and Dynamics in Arctic...

  15. A comparison between characteristics of atmospheric-pressure...

    Office of Scientific and Technical Information (OSTI)

    A comparison between characteristics of atmospheric-pressure plasma jets sustained by ... Title: A comparison between characteristics of atmospheric-pressure plasma jets sustained ...

  16. Spectral characteristics of time resolved magnonic spin Seebeck...

    Office of Scientific and Technical Information (OSTI)

    Spectral characteristics of time resolved magnonic spin Seebeck effect Citation Details In-Document Search Title: Spectral characteristics of time resolved magnonic spin Seebeck ...

  17. Origins of optical absorption characteristics of Cu2+ complexes...

    Office of Scientific and Technical Information (OSTI)

    Origins of optical absorption characteristics of Cu2+ complexes in solutions Citation Details In-Document Search Title: Origins of optical absorption characteristics of Cu2+ ...

  18. Oxidation characteristics of gasoline direct-injection (GDI)...

    U.S. Department of Energy (DOE) all webpages (Extended Search)

    characteristics of gasoline direct-injection (GDI) engine soot: Catalytic effects of ash and modified kinetic correlation Title Oxidation characteristics of gasoline...

  19. Characteristics of seismic waves from Soviet peaceful nuclear...

    Office of Scientific and Technical Information (OSTI)

    Technical Report: Characteristics of seismic waves from Soviet peaceful nuclear explosions in salt Citation Details In-Document Search Title: Characteristics of seismic waves from...

  20. Forward and reverse characteristics of irradiated MOSFETs

    SciTech Connect (OSTI)

    Paccagnella, A.; Ceschia, M.; Verzellesi, G.; Dalla Betta, G.F.; Soncini, G.; Bellutti, P.; Fuochi, P.G.

    1996-06-01

    pMOSFETs biased with V{sub gs} < V{sub gd} during Co{sup 60} {gamma} irradiation have shown substantial differences between the forward and reverse subthreshold characteristics, induced by a non-uniform charge distribution in the gate oxide. Correspondingly, modest differences have been observed in the over-threshold I-V characteristics. After irradiation, the forward subthreshold curves can shift at higher or lower gate voltages than the reverse ones. The former behavior has been observed in long-channel devices, in agreement with the classical MOS theory and numerical simulations. The latter result has been obtained in short-channel devices, and it has been correlated to a parasitic punch-through conduction mechanism.

  1. Measuring spatial variability in soil characteristics

    DOE Patents [OSTI]

    Hoskinson, Reed L.; Svoboda, John M.; Sawyer, J. Wayne; Hess, John R.; Hess, J. Richard

    2002-01-01

    The present invention provides systems and methods for measuring a load force associated with pulling a farm implement through soil that is used to generate a spatially variable map that represents the spatial variability of the physical characteristics of the soil. An instrumented hitch pin configured to measure a load force is provided that measures the load force generated by a farm implement when the farm implement is connected with a tractor and pulled through or across soil. Each time a load force is measured, a global positioning system identifies the location of the measurement. This data is stored and analyzed to generate a spatially variable map of the soil. This map is representative of the physical characteristics of the soil, which are inferred from the magnitude of the load force.

  2. Characteristics of potential repository wastes. Volume 1

    SciTech Connect (OSTI)

    Not Available

    1992-07-01

    This document, and its associated appendices and microcomputer (PC) data bases, constitutes the reference OCRWM data base of physical and radiological characteristics data of radioactive wastes. This Characteristics Data Base (CDB) system includes data on spent nuclear fuel and high-level waste (HLW), which clearly require geologic disposal, and other wastes which may require long-term isolation, such as sealed radioisotope sources. The data base system was developed for OCRWM by the CDB Project at Oak Ridge National Laboratory. Various principal or official sources of these data provided primary information to the CDB Project which then used the ORIGEN2 computer code to calculate radiological properties. The data have been qualified by an OCRWM-sponsored peer review as suitable for quality-affecting work meeting the requirements of OCRWM`s Quality Assurance Program. The wastes characterized in this report include: light-water reactor (LWR) spent fuel and immobilized HLW.

  3. Nuclear reactor characteristics and operational history

    Gasoline and Diesel Fuel Update

    Nuclear > U.S. reactor operation status tables Nuclear Reactor Operational Status Tables Release date: November 22, 2011 Next release date: TBD See also: Table 1. Capacity and Generation, Table 2. Ownership Data Table 3. Nuclear Reactor Characteristics and Operational History PDF XLS Plant Name Generator ID Type Reactor Supplier and Model Construction Start Grid Connection Original Expiration Date License Renewal Application License Renewal Issued Extended Expiration Arkansas Nuclear One 1

  4. Building and occupant characteristics as determinants of residential energy consumption

    SciTech Connect (OSTI)

    Nieves, L.A.; Nieves, A.L.

    1981-10-01

    The major goals of the research are to gain insight into the probable effects of building energy performance standards on energy consumption; to obtain observations of actual residential energy consumption that could affirm or disaffirm comsumption estimates of the DOE 2.0A simulation model; and to investigate home owner's conservation investments and home purchase decisions. The first chapter covers the investigation of determinants of household energy consumption. The presentation begins with the underlying economic theory and its implications, and continues with a description of the data collection procedures, the formulation of variables, and then of data analysis and findings. In the second chapter the assumptions and limitations of the energy use projections generated by the DOE 2.0A model are discussed. Actual electricity data for the houses are then compared with results of the simulation. The third chapter contains information regarding households' willingness to make energy conserving investments and their ranking of various conservation features. In the final chapter conclusions and recommendations are presented with an emphasis on the policy implications of this study. (MCW)

  5. Buildings Energy Data Book: 2.2 Residential Sector Characteristics

    Buildings Energy Data Book

    1 Total Number of Households and Buildings, Floorspace, and Household Size, by Year 1980 80 N.A. 227 2.9 1981 83 N.A. 229 2.8 1982 84 N.A. 232 2.8 1983 85 N.A. 234 2.8 1984 86 N.A. 236 2.7 1985 88 N.A. 238 2.7 1986 89 N.A. 240 2.7 1987 91 N.A. 242 2.7 1988 92 N.A. 244 2.7 1989 93 N.A. 247 2.6 1990 94 N.A. 250 2.6 1991 95 N.A. 253 2.7 1992 96 N.A. 257 2.7 1993 98 N.A. 260 2.7 1994 99 N.A. 263 2.7 1995 100 N.A. 266 2.7 1996 101 N.A. 269 2.7 1997 102 N.A. 273 2.7 1998 104 N.A. 276 2.7 1999 105 N.A.

  6. Nuclear reactor characteristics and operational history

    Gasoline and Diesel Fuel Update

    2. Ownership Data, Table 3. Characteristics and Operational History Table 1. Nuclear Reactor, State, Type, Net Capacity, Generation, and Capacity Factor PDF XLS Plant/Reactor Name Generator ID State Type 2009 Summer Capacity Net MW(e)1 2010 Annual Generation Net MWh2 Capacity Factor Percent3 Arkansas Nuclear One 1 AR PWR 842 6,607,090 90 Arkansas Nuclear One 2 AR PWR 993 8,415,588 97 Beaver Valley 1 PA PWR 892 7,119,413 91 Beaver Valley 2 PA PWR 885 7,874,151 102 Braidwood Generation Station 1

  7. Coal-water slurry atomization characteristics

    SciTech Connect (OSTI)

    Caton, J.A.; Kihm, K.D.

    1994-04-01

    The overall objective of this work was to fully characterize the CWS fuel sprays of a medium-speed diesel engine injection system. Specifically, the spray plume penetration as a function of time was determined for a positive-displacement fuel injection system. The penetration was determined as a function of orifice diameter, coal loading, gas density in the engine, and fuel line pressure. Preliminary droplet information also was obtained. The results of this study will assist CWS engine development by providing much needed insight about the fuel spray. In addition, the results will aid the development and use of CWS engine cycle simulations which require information on the fuel spray characteristics.

  8. An Experimental Study of PM Emission Characteristics of Commercial...

    Office of Energy Efficiency and Renewable Energy (EERE) (indexed site)

    Study of PM Emission Characteristics of Commercial Diesel Engine with Urea-SCR System An Experimental Study of PM Emission Characteristics of Commercial Diesel Engine with Urea-SCR ...

  9. Characteristics of transverse waves in chromospheric mottles

    SciTech Connect (OSTI)

    Kuridze, D.; Mathioudakis, M.; Jess, D. B.; Keenan, F. P. [Astrophysics Research Center, School of Mathematics and Physics, Queen's University, Belfast BT7 1NN (United Kingdom); Verth, G.; Erdlyi, R. [Solar Physics and Space Plasma Research Center (SP2RC), University of Sheffield, Hicks Building, Hounsfield Road, Sheffield S3 7RH (United Kingdom); Morton, R. J. [Mathematics and Information Science, Northumbria University, Camden Street, Newcastle Upon Tyne NE1 8ST (United Kingdom); Christian, D. J., E-mail: dkuridze01@qub.ac.uk [Department of Physics and Astronomy, California State University, Northridge, CA 91330 (United States)

    2013-12-10

    Using data obtained by the high temporal and spatial resolution Rapid Oscillations in the Solar Atmosphere instrument on the Dunn Solar Telescope, we investigate at an unprecedented level of detail transverse oscillations in chromospheric fine structures near the solar disk center. The oscillations are interpreted in terms of propagating and standing magnetohydrodynamic kink waves. Wave characteristics including the maximum transverse velocity amplitude and the phase speed are measured as a function of distance along the structure's length. Solar magnetoseismology is applied to these measured parameters to obtain diagnostic information on key plasma parameters (e.g., magnetic field, density, temperature, flow speed) of these localized waveguides. The magnetic field strength of the mottle along the ?2 Mm length is found to decrease by a factor of 12, while the local plasma density scale height is ?280 80 km.

  10. Gas sensor with attenuated drift characteristic

    DOE Patents [OSTI]

    Chen, Ing-Shin [Danbury, CT; Chen, Philip S. H. [Bethel, CT; Neuner, Jeffrey W. [Bethel, CT; Welch, James [Fairfield, CT; Hendrix, Bryan [Danbury, CT; Dimeo, Jr., Frank [Danbury, CT

    2008-05-13

    A sensor with an attenuated drift characteristic, including a layer structure in which a sensing layer has a layer of diffusional barrier material on at least one of its faces. The sensor may for example be constituted as a hydrogen gas sensor including a palladium/yttrium layer structure formed on a micro-hotplate base, with a chromium barrier layer between the yttrium layer and the micro-hotplate, and with a tantalum barrier layer between the yttrium layer and an overlying palladium protective layer. The gas sensor is useful for detection of a target gas in environments susceptible to generation or incursion of such gas, and achieves substantial (e.g., >90%) reduction of signal drift from the gas sensor in extended operation, relative to a corresponding gas sensor lacking the diffusional barrier structure of the invention

  11. Buildings and Energy in the 1980s

    U.S. Energy Information Administration (EIA) (indexed site)

    No. PB83-199554, 220. Residential Energy Consumption Survey: Household Transportation Panel Monthly Gas Purchases and Vehicle and Household Characteristics, 679-981; Order...

  12. DOE/EIA-0516(85) Energy Information Administration Manufacturing...

    U.S. Energy Information Administration (EIA) (indexed site)

    Order No. PB83- 199554HAA Residential Energy Consumption Survey: HouseholdTransportation Panel Monthly Gas Purchases and Vehicle and Household Characteristics, 6179-9181 * Order...

  13. Buildings and Energy in the 1980s

    U.S. Energy Information Administration (EIA) (indexed site)

    conducted in two stages: (1) A Household (RECS)Building (CBECS) Survey and an Energy Suppliers Survey. The HouseholdBuilding Characteristics Survey consists of personal...

  14. Buildings Energy Data Book: 2.3 Residential Sector Expenditures

    Buildings Energy Data Book

    3 2005 Average Household Expenditures, by Census Region ($2010) Item Energy (1) Shelter (2) Food Telephone, water and other public services Household supplies, furnishings and equipment (3) Transportation (4) Healthcare Education Personal taxes (5) Other expenditures Average Annual Income Note(s): Source(s): 1) Average household energy expenditures are calculated from the Residential Energy Consumption Survey (RECS), while average expenditures for other categories are calculated from the

  15. Buildings Energy Data Book: 2.3 Residential Sector Expenditures

    Buildings Energy Data Book

    4 2005 Average Household Expenditures as Percent of Annual Income, by Census Region ($2010) Item Energy (1) Shelter (2) Food Telephone, water and other public services Household supplies, furnishings and equipment (3) Transportation (4) Healthcare Education Personal taxes (5) Average Annual Expenditures Average Annual Income Note(s): Source(s): 1) Average household energy expenditures are calculated from the Residential Energy Consumption Survey (RECS), while average expenditures for other

  16. Buildings Energy Data Book: 2.2 Residential Sector Characteristics

    Buildings Energy Data Book

    2 Share of Households, by Housing Type and Type of Ownership, as of 2005 (Percent) Housing Type Owned Rented Total Single-Family: 61.5% 10.3% 71.7% Detached 57.7% 7.2% 64.9% Attached 3.8% 3.1% 6.8% Multi-Family: 3.7% 18.3% 22.0% 2 to 4 units 1.6% 5.3% 6.9% 5 or more units 2.1% 13.0% 15.0% Mobile Homes 5.1% 1.1% 6.2% Total 70.3% 29.6% 100% Source(s): EIA, 2005 Residential Energy Consumption Survey, Oct. 2008, Table HC3-1 and HC4

  17. Foaming characteristics of refigerant/lubricant mixtures

    SciTech Connect (OSTI)

    Goswami, D.Y.; Shah, D.O.; Jotshi, C.K.; Bhagwat, S.; Leung, M.; Gregory, A.

    1997-04-01

    The air-conditioning and refrigeration industry has moved to HFC refrigerants which have zero ozone depletion and low global warming potential due to regulations on CFC and HCFC refrigerants and concerns for the environment. The change in refrigerants has prompted the switch from mineral oil and alkylbenzene lubricants to polyolester-based lubricants. This change has also brought about a desire for lubricant, refrigerant and compressor manufacturers to understand the foaming properties of alternative refrigerant/ lubricant mixtures, as well as the mechanisms which affect these properties. The objectives of this investigation are to experimentally determine the foaming absorption and desorption rates of HFC and blended refrigerants in polyolester lubricant and to define the characteristics of the foam formed when the refrigerant leaves the refrigerant/ lubricant mixture after being exposed to a pressure drop. The refrigerants being examined include baseline refrigerants: CFC-12 (R-12) and HCFC-22 (R-22); alternative refrigerants: HFC-32 (R-32), R-125, R-134a, and R-143a; and blended refrigerants: R-404A, R-407C, and R-410A. The baseline refrigerants are tested with ISO 32 (Witco 3GS) and ISO 68 (4GS) mineral oils while the alternative and blended refrigerants are tested with two ISO 68 polyolesters (Witco SL68 and ICI RL68H).

  18. http://factfinder.census.gov/servlet/MetadataBrowserServlet?typ

    National Nuclear Security Administration (NNSA)

    ... population 15 years and over P159A POVERTY STATUS IN 1999 BY AGE (WHITE ALONE) 17 Universe: White alone population for whom poverty status is determined P159B POVERTY STATUS ...

  19. Yukon-Koyukuk Census Area, Alaska: Energy Resources | Open Energy...

    Open Energy Information (Open El) [EERE & EIA]

    Alaska Nikolai, Alaska Nulato, Alaska Rampart, Alaska Ruby, Alaska Shageluk, Alaska Stevens Village, Alaska Takotna, Alaska Tanana, Alaska Venetie, Alaska Wiseman, Alaska...

  20. Bethel Census Area, Alaska: Energy Resources | Open Energy Information

    Open Energy Information (Open El) [EERE & EIA]

    Alaska Nunapitchuk, Alaska Oscarville, Alaska Platinum, Alaska Quinhagak, Alaska Red Devil, Alaska Sleetmute, Alaska Stony River, Alaska Toksook Bay, Alaska Tuluksak,...