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Title: A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California

California aims to reduce greenhouse gas (GHG) emissions to 40% below 1990 levels by 2030. We compare six energy models that have played various roles in informing the state policymakers in setting climate policy goals and targets. These models adopt a range of modeling structures, including stock-turnover back-casting models, a least-cost optimization model, macroeconomic/macro-econometric models, and an electricity dispatch model. Results from these models provide useful insights in terms of the transformations in the energy system required, including efficiency improvements in cars, trucks, and buildings, electrification of end-uses, low- or zero-carbon electricity and fuels, aggressive adoptions of zero-emission vehicles (ZEVs), demand reduction, and large reductions of non-energy GHG emissions. Some of these studies also suggest that the direct economic costs can be fairly modest or even generate net savings, while the indirect macroeconomic benefits are large, as shifts in employment and capital investments could have higher economic returns than conventional energy expenditures. These models, however, often assume perfect markets, perfect competition, and zero transaction costs. They also do not provide specific policy guidance on how these transformative changes can be achieved. Greater emphasis on modeling uncertainty, consumer behaviors, heterogeneity of impacts, and spatial modeling would further enhance policymakers' ability tomore » design more effective and targeted policies. Here, this paper presents an example of how policymakers, energy system modelers and stakeholders interact and work together to develop and evaluate long-term state climate policy targets. Lastly, even though this paper focuses on California, the process of dialogue and interactions, modeling results, and lessons learned can be generally adopted across different regions and scales.« less
Authors:
ORCiD logo [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [3] ;  [9] ;  [6] ;  [6] ;  [6]
  1. Univ. of California, Davis, CA (United States). Inst. of Transportation Studies; Chalmers Univ. of Technology, Gothenburg (Sweden). Dept. of Energy and Environment
  2. Univ. of California, Davis, CA (United States). Inst. of Transportation Studies
  3. California Air Resources Board, Sacramento, CA (United States)
  4. Univ. of California, Berkeley, CA (United States). Dept. of Agricultural and Resource Economics
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Energy Analysis and Environmental Impacts Division and Joint Center for Artificial Photosynthesis
  6. Energy and Environmental Economics (E3), San Francisco, CA (United States)
  7. Univ. of Southern California, Los Angeles, CA (United States). Sol Price School of Public Policy
  8. National Renewable Energy Lab. (NREL), Golden, CO (United States)
  9. ClimateWorks Foundation, San Francisco, CA (United States)
Publication Date:
Report Number(s):
NREL/JA-6A20-67459
Journal ID: ISSN 2211-467X
Grant/Contract Number:
AC36-08GO28308
Type:
Accepted Manuscript
Journal Name:
Energy Strategy Reviews
Additional Journal Information:
Journal Volume: 13-14; Journal Issue: C; Journal ID: ISSN 2211-467X
Publisher:
Elsevier
Research Org:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Country of Publication:
United States
Language:
English
Subject:
29 ENERGY PLANNING, POLICY, AND ECONOMY; modeling comparison; GHG abatement; non-energy GHG; emissions reduction scenarios; climate policies
OSTI Identifier:
1333279

Yeh, Sonia, Yang, Christopher, Gibbs, Michael, Roland-Holst, David, Greenblatt, Jeffery, Mahone, Amber, Wei, Dan, Brinkman, Gregory, Cunningham, Joshua, Eggert, Anthony, Haley, Ben, Hart, Elaine, and Williams, Jim. A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California. United States: N. p., Web. doi:10.1016/j.esr.2016.10.001.
Yeh, Sonia, Yang, Christopher, Gibbs, Michael, Roland-Holst, David, Greenblatt, Jeffery, Mahone, Amber, Wei, Dan, Brinkman, Gregory, Cunningham, Joshua, Eggert, Anthony, Haley, Ben, Hart, Elaine, & Williams, Jim. A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California. United States. doi:10.1016/j.esr.2016.10.001.
Yeh, Sonia, Yang, Christopher, Gibbs, Michael, Roland-Holst, David, Greenblatt, Jeffery, Mahone, Amber, Wei, Dan, Brinkman, Gregory, Cunningham, Joshua, Eggert, Anthony, Haley, Ben, Hart, Elaine, and Williams, Jim. 2016. "A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California". United States. doi:10.1016/j.esr.2016.10.001. https://www.osti.gov/servlets/purl/1333279.
@article{osti_1333279,
title = {A modeling comparison of deep greenhouse gas emissions reduction scenarios by 2030 in California},
author = {Yeh, Sonia and Yang, Christopher and Gibbs, Michael and Roland-Holst, David and Greenblatt, Jeffery and Mahone, Amber and Wei, Dan and Brinkman, Gregory and Cunningham, Joshua and Eggert, Anthony and Haley, Ben and Hart, Elaine and Williams, Jim},
abstractNote = {California aims to reduce greenhouse gas (GHG) emissions to 40% below 1990 levels by 2030. We compare six energy models that have played various roles in informing the state policymakers in setting climate policy goals and targets. These models adopt a range of modeling structures, including stock-turnover back-casting models, a least-cost optimization model, macroeconomic/macro-econometric models, and an electricity dispatch model. Results from these models provide useful insights in terms of the transformations in the energy system required, including efficiency improvements in cars, trucks, and buildings, electrification of end-uses, low- or zero-carbon electricity and fuels, aggressive adoptions of zero-emission vehicles (ZEVs), demand reduction, and large reductions of non-energy GHG emissions. Some of these studies also suggest that the direct economic costs can be fairly modest or even generate net savings, while the indirect macroeconomic benefits are large, as shifts in employment and capital investments could have higher economic returns than conventional energy expenditures. These models, however, often assume perfect markets, perfect competition, and zero transaction costs. They also do not provide specific policy guidance on how these transformative changes can be achieved. Greater emphasis on modeling uncertainty, consumer behaviors, heterogeneity of impacts, and spatial modeling would further enhance policymakers' ability to design more effective and targeted policies. Here, this paper presents an example of how policymakers, energy system modelers and stakeholders interact and work together to develop and evaluate long-term state climate policy targets. Lastly, even though this paper focuses on California, the process of dialogue and interactions, modeling results, and lessons learned can be generally adopted across different regions and scales.},
doi = {10.1016/j.esr.2016.10.001},
journal = {Energy Strategy Reviews},
number = C,
volume = 13-14,
place = {United States},
year = {2016},
month = {10}
}