R. Beach, Jared Creason, S. Ohrel, Shaun Ragnauth, S. Ogle, Changsheng Li, P. Ingraham, W. Salas
{"title":"到2030年减少农业非二氧化碳温室气体排放的全球缓解潜力和成本","authors":"R. Beach, Jared Creason, S. Ohrel, Shaun Ragnauth, S. Ogle, Changsheng Li, P. Ingraham, W. Salas","doi":"10.1080/1943815X.2015.1110183","DOIUrl":null,"url":null,"abstract":"Abstract Agricultural emissions account for 53% of 2010 global non-CO2 emissions and are projected to increase substantially over the next 20 years, especially in Asia, Latin America and Africa. While agriculture is a substantial source of emissions, it is also generally considered to be a potential source of cost-effective non-CO2 GHG abatement. Previous “bottom-up” analyses provided marginal abatement cost (MAC) curves for use in modeling these options within economy-wide and global mitigation analyses. In this paper, we utilize updated economic and biophysical data and models developed by the US Environmental Protection Agency (EPA) to investigate regional mitigation potential for major sources of agricultural GHG emissions. In addition, we explore mitigation potential available at costs at or below the estimated benefits of mitigation, as represented by the social cost of carbon. Key enhancements over previous regional assessments include incorporation of additional mitigation options, updated baseline emissions projections, greater spatial disaggregation, and development of MAC curves through 2030. For croplands and rice cultivation, biophysical, process-based models (DAYCENT and DNDC) are used to simulate yields and net GHG emissions under baseline and mitigation scenarios while the livestock sector is modeled by applying key mitigation options to baselines compiled by EPA. MAC curves are generated accounting for net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. MAC curves are developed at the regional level and reveal large potential for non-CO2 GHG mitigation at low carbon prices, especially in Asia.","PeriodicalId":16194,"journal":{"name":"Journal of Integrative Environmental Sciences","volume":"26 1","pages":"105 - 87"},"PeriodicalIF":2.6000,"publicationDate":"2015-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"70","resultStr":"{\"title\":\"Global mitigation potential and costs of reducing agricultural non-CO2 greenhouse gas emissions through 2030\",\"authors\":\"R. Beach, Jared Creason, S. Ohrel, Shaun Ragnauth, S. Ogle, Changsheng Li, P. Ingraham, W. Salas\",\"doi\":\"10.1080/1943815X.2015.1110183\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract Agricultural emissions account for 53% of 2010 global non-CO2 emissions and are projected to increase substantially over the next 20 years, especially in Asia, Latin America and Africa. While agriculture is a substantial source of emissions, it is also generally considered to be a potential source of cost-effective non-CO2 GHG abatement. Previous “bottom-up” analyses provided marginal abatement cost (MAC) curves for use in modeling these options within economy-wide and global mitigation analyses. In this paper, we utilize updated economic and biophysical data and models developed by the US Environmental Protection Agency (EPA) to investigate regional mitigation potential for major sources of agricultural GHG emissions. In addition, we explore mitigation potential available at costs at or below the estimated benefits of mitigation, as represented by the social cost of carbon. Key enhancements over previous regional assessments include incorporation of additional mitigation options, updated baseline emissions projections, greater spatial disaggregation, and development of MAC curves through 2030. For croplands and rice cultivation, biophysical, process-based models (DAYCENT and DNDC) are used to simulate yields and net GHG emissions under baseline and mitigation scenarios while the livestock sector is modeled by applying key mitigation options to baselines compiled by EPA. MAC curves are generated accounting for net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. 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Global mitigation potential and costs of reducing agricultural non-CO2 greenhouse gas emissions through 2030
Abstract Agricultural emissions account for 53% of 2010 global non-CO2 emissions and are projected to increase substantially over the next 20 years, especially in Asia, Latin America and Africa. While agriculture is a substantial source of emissions, it is also generally considered to be a potential source of cost-effective non-CO2 GHG abatement. Previous “bottom-up” analyses provided marginal abatement cost (MAC) curves for use in modeling these options within economy-wide and global mitigation analyses. In this paper, we utilize updated economic and biophysical data and models developed by the US Environmental Protection Agency (EPA) to investigate regional mitigation potential for major sources of agricultural GHG emissions. In addition, we explore mitigation potential available at costs at or below the estimated benefits of mitigation, as represented by the social cost of carbon. Key enhancements over previous regional assessments include incorporation of additional mitigation options, updated baseline emissions projections, greater spatial disaggregation, and development of MAC curves through 2030. For croplands and rice cultivation, biophysical, process-based models (DAYCENT and DNDC) are used to simulate yields and net GHG emissions under baseline and mitigation scenarios while the livestock sector is modeled by applying key mitigation options to baselines compiled by EPA. MAC curves are generated accounting for net GHG reductions, yield effects, livestock productivity effects, commodity prices, labor requirements, and capital costs where appropriate. MAC curves are developed at the regional level and reveal large potential for non-CO2 GHG mitigation at low carbon prices, especially in Asia.
期刊介绍:
Journal of Integrative Environmental Sciences (JIES) provides a stimulating, informative and critical forum for intellectual debate on significant environmental issues. It brings together perspectives from a wide range of disciplines and methodologies in both the social and natural sciences in an effort to develop integrative knowledge about the processes responsible for environmental change. The Journal is especially concerned with the relationships between science, society and policy and one of its key aims is to advance understanding of the theory and practice of sustainable development.