What is the role for carbon cycle science in the proposed EPA power plant rule? View Full Text


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Article Info

DATE

2015-01-29

AUTHORS

Kevin Robert Gurney

ABSTRACT

On June 2, 2014, the United States Environmental Protection Agency proposed goals and guidelines aimed at lowering carbon dioxide (CO2) emissions from existing power plants in the United States. Should it be successfully implemented, US power plant CO2 emissions would be reduced approximately 30 percent below 2005 levels by the year 2030. Rather than a single national reduction goal, the proposed rule specifies reduction targets unique to each US state but leaves the means by which states meet those targets, flexible to individual state conditions. Regardless of the policy mixture adopted in each US state, quantification of CO2 emissions at the level of individual power plants will be a critical need. Recent research examining power plant CO2 emissions has noted potentially large uncertainties at the individual facility level, uncertainty that remains poorly understood. At the same time, carbon scientists working on aspects of monitoring, reporting and verification of anthropogenic CO2 emissions have developed a mixture of measurement and modeling capabilities as part of the development of a “carbon monitoring system”, that could assist in assessing how well independent emissions quantification is performing currently and identify a path towards improved monitoring. Equally important is an assessment of uncertainty at the various space and time scales the EPA proposed rule implies. Application of these recent scientific capabilities to the needs of the EPA’s proposed rule could offer a cogent, near-term example of how scientific research can directly enable better decision-making. This paper provides a review of the proposed rule and what role scientific research could play in the evolution of the rulemaking and its application in the future. More... »

PAGES

1

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URI

http://scigraph.springernature.com/pub.10.1186/s40322-015-0028-1

DOI

http://dx.doi.org/10.1186/s40322-015-0028-1

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https://app.dimensions.ai/details/publication/pub.1027277774


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