Understanding the Social Cost of Carbon: A Technical Assessment. Rose, S. K., Turner, D., Blanford, G., Bistline, J., de la Chesnaye, F. C., & Wilson, T. October, 2014. abstract bibtex The social cost of carbon (SCC) is a monetary estimate of the climate change damages to society from an additional unit of carbon dioxide (CO2) emitted to the earth’s atmosphere. In 2010, the United States Government (USG) developed SCC estimates to value the benefits of CO2 emissions reductions in federal rulemakings. In 2013, the USG revised their estimates following the same procedure, but with newer versions of the underlying models, and the SCCs notably increased. SCC estimates of one kind or another have been applied in almost fifty federal regulations since 2008, including rules associated with appliances, transportation, industry, and power generation. However, not until earlier this year have the USG estimates been subject to explicit public comment. Despite their use, the USG SCC estimates are difficult to interpret and evaluate. What does the central value of $37 per metric ton of CO2 mean? What sorts of damages to society does it represent? What are the key drivers of estimated damages? How good is the current estimation approach, and can it be improved? The USG SCC estimates are the result of significant aggregation across many dimensions: time, socioeconomic scenarios, uncertain parameters, world regions, damage categories, and models. This aggregation obscures the underlying details and drivers of results, as well as the variation across models. Making sense of the estimates requires delving into these details. Overall, there is a need for greater technical clarity on what underlies the estimates. This study presents an in-depth examination of the three models underlying the current USG SCC estimates (DICE, FUND, and PAGE) as well as the overall USG approach. Specifically, we assess the major SCC modeling components—projected socioeconomics and emissions, climate modeling, and climate damages modeling— in isolation. For each component, we review the modeling, program the component from all three models, develop and run diagnostic scenarios, and make comparisons to learn about and assess the raw modeling and results (undiscounted and disaggregated to underlying elements). With the component assessments in hand as a technical basis, we then evaluate the USG approach of combining 150,000 model results to determine a USG SCC. In the climate and damage component assessments we explore a variety of perspectives, including deterministic and probabilistic model responses, and total and incremental model responses. The latter is particularly relevant because the SCC is an estimate of damages from an incremental increase in CO2 emissions. Our assessment reveals significant variation across models in their structure, behavior, and results and identifies fundamental issues and opportunities for improvements. The objective of this work is to improve understanding of SCC modeling and estimates in order to inform and facilitate public discussion, future SCC modeling and use, and future climate research broadly.
@misc{rose_understanding_2014,
title = {Understanding the {Social} {Cost} of {Carbon}: {A} {Technical} {Assessment}},
abstract = {The social cost of carbon (SCC) is a monetary estimate of the climate change damages to society from an additional unit of carbon dioxide (CO2) emitted to the earth’s atmosphere. In 2010, the United States Government (USG) developed SCC estimates to value the benefits of CO2 emissions reductions in federal rulemakings. In 2013, the USG revised their estimates following the same procedure, but with newer versions of the underlying models, and the SCCs notably increased. SCC estimates of one kind or another have been applied in almost fifty federal regulations since 2008, including rules associated with appliances, transportation, industry, and power generation. However, not until earlier this year have the USG estimates been subject to explicit public comment.
Despite their use, the USG SCC estimates are difficult to interpret and evaluate. What does the central value of \$37 per metric ton of CO2 mean? What sorts of damages to society does it represent? What are the key drivers of estimated damages? How good is the current estimation approach, and can it be improved?
The USG SCC estimates are the result of significant aggregation across many dimensions: time, socioeconomic scenarios, uncertain parameters, world regions, damage categories, and models. This aggregation obscures the underlying details and drivers of results, as well as the variation across models. Making sense of the estimates requires delving into these details. Overall, there is a need for greater technical clarity on what underlies the estimates.
This study presents an in-depth examination of the three models underlying the current USG SCC estimates (DICE, FUND, and PAGE) as well as the overall USG approach. Specifically, we assess the major SCC modeling components—projected socioeconomics and emissions, climate modeling, and climate damages modeling— in isolation. For each component, we review the modeling, program the component from all three models, develop and run diagnostic scenarios, and make comparisons to learn about and assess the raw modeling and results (undiscounted and disaggregated to underlying elements). With the component assessments in hand as a technical basis, we then evaluate the USG approach of combining 150,000 model results to determine a USG SCC. In the climate and damage component assessments we explore a variety of perspectives, including deterministic and probabilistic model responses, and total and incremental model responses. The latter is particularly relevant because the SCC is an estimate of damages from an incremental increase in CO2 emissions.
Our assessment reveals significant variation across models in their structure, behavior, and results and identifies fundamental issues and opportunities for improvements. The objective of this work is to improve understanding of SCC modeling and estimates in order to inform and facilitate public discussion, future SCC modeling and use, and future climate research broadly.},
publisher = {Electric Power Research Institute},
author = {Rose, Steven K. and Turner, Delavene and Blanford, Geoffrey and Bistline, John and de la Chesnaye, Francisco C. and Wilson, Tom},
month = oct,
year = {2014},
keywords = {CK, Untagged},
}
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SCC estimates of one kind or another have been applied in almost fifty federal regulations since 2008, including rules associated with appliances, transportation, industry, and power generation. However, not until earlier this year have the USG estimates been subject to explicit public comment. Despite their use, the USG SCC estimates are difficult to interpret and evaluate. What does the central value of $37 per metric ton of CO2 mean? What sorts of damages to society does it represent? What are the key drivers of estimated damages? How good is the current estimation approach, and can it be improved? The USG SCC estimates are the result of significant aggregation across many dimensions: time, socioeconomic scenarios, uncertain parameters, world regions, damage categories, and models. This aggregation obscures the underlying details and drivers of results, as well as the variation across models. Making sense of the estimates requires delving into these details. Overall, there is a need for greater technical clarity on what underlies the estimates. This study presents an in-depth examination of the three models underlying the current USG SCC estimates (DICE, FUND, and PAGE) as well as the overall USG approach. Specifically, we assess the major SCC modeling components—projected socioeconomics and emissions, climate modeling, and climate damages modeling— in isolation. For each component, we review the modeling, program the component from all three models, develop and run diagnostic scenarios, and make comparisons to learn about and assess the raw modeling and results (undiscounted and disaggregated to underlying elements). With the component assessments in hand as a technical basis, we then evaluate the USG approach of combining 150,000 model results to determine a USG SCC. 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