Decision Support Algorithm for Evaluating Carbon Dioxide Emissions from Electricity Generation in the United States
Abstract
Summary This article presents an algorithm to aid practitioners in determining the most appropriate method to estimate carbon dioxide emissions from an electricity load. Applications include sustainability assessments of products, processes, energy efficiency improvements, changes in generation infrastructure, and changes in electricity demand. Currently, there is no consensus on appropriate methods for calculating greenhouse gas emissions resulting from specific electricity loads. Previous research revealed significant differences in emissions when different methods were used, a situation that could result in divergent sustainability or policy recommendations. In this article, we illustrate the distribution of emissions estimates based on method characteristics such as region size, temporal resolution, average or marginal approaches, and time scales. Informed by these findings, a decision support algorithm is presented that uses a load's key features and an analyst's research question to provide recommendations on appropriate method types. We defined four different cases to demonstrate the utility of the algorithm and to illustrate the variability of methods used in previous studies. Prior research often employed simplifying assumptions, which, in some cases, can result in electricity being allocated to the incorrect generating resources and improper calculation of emissions. This algorithm could reduce inappropriate allocation, variability in assumptions, and increase appropriatenessmore »
- Authors:
-
[2]
- School for Environment and Sustainability University of Michigan Ann Arbor MI USA, Department of Mechanical Engineering, College of Engineering University of Michigan Ann Arbor MI USA
- School for Environment and Sustainability University of Michigan Ann Arbor MI USA
- School for Environment and Sustainability University of Michigan Ann Arbor MI USA, Department of Civil and Environmental Engineering University of Michigan Ann Arbor MI USA
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1408270
- Grant/Contract Number:
- PI0000012
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Journal of Industrial Ecology
- Additional Journal Information:
- Journal Name: Journal of Industrial Ecology Journal Volume: 22 Journal Issue: 6; Journal ID: ISSN 1088-1980
- Publisher:
- Wiley-Blackwell
- Country of Publication:
- United States
- Language:
- English
Citation Formats
Ryan, Nicole A., Johnson, Jeremiah X., Keoleian, Gregory A., and Lewis, Geoffrey M. Decision Support Algorithm for Evaluating Carbon Dioxide Emissions from Electricity Generation in the United States. United States: N. p., 2017.
Web. doi:10.1111/jiec.12708.
Ryan, Nicole A., Johnson, Jeremiah X., Keoleian, Gregory A., & Lewis, Geoffrey M. Decision Support Algorithm for Evaluating Carbon Dioxide Emissions from Electricity Generation in the United States. United States. https://doi.org/10.1111/jiec.12708
Ryan, Nicole A., Johnson, Jeremiah X., Keoleian, Gregory A., and Lewis, Geoffrey M. Fri .
"Decision Support Algorithm for Evaluating Carbon Dioxide Emissions from Electricity Generation in the United States". United States. https://doi.org/10.1111/jiec.12708.
@article{osti_1408270,
title = {Decision Support Algorithm for Evaluating Carbon Dioxide Emissions from Electricity Generation in the United States},
author = {Ryan, Nicole A. and Johnson, Jeremiah X. and Keoleian, Gregory A. and Lewis, Geoffrey M.},
abstractNote = {Summary This article presents an algorithm to aid practitioners in determining the most appropriate method to estimate carbon dioxide emissions from an electricity load. Applications include sustainability assessments of products, processes, energy efficiency improvements, changes in generation infrastructure, and changes in electricity demand. Currently, there is no consensus on appropriate methods for calculating greenhouse gas emissions resulting from specific electricity loads. Previous research revealed significant differences in emissions when different methods were used, a situation that could result in divergent sustainability or policy recommendations. In this article, we illustrate the distribution of emissions estimates based on method characteristics such as region size, temporal resolution, average or marginal approaches, and time scales. Informed by these findings, a decision support algorithm is presented that uses a load's key features and an analyst's research question to provide recommendations on appropriate method types. We defined four different cases to demonstrate the utility of the algorithm and to illustrate the variability of methods used in previous studies. Prior research often employed simplifying assumptions, which, in some cases, can result in electricity being allocated to the incorrect generating resources and improper calculation of emissions. This algorithm could reduce inappropriate allocation, variability in assumptions, and increase appropriateness of electricity emissions estimates.},
doi = {10.1111/jiec.12708},
journal = {Journal of Industrial Ecology},
number = 6,
volume = 22,
place = {United States},
year = {Fri Nov 10 00:00:00 EST 2017},
month = {Fri Nov 10 00:00:00 EST 2017}
}
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https://doi.org/10.1111/jiec.12708
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