Lenox Carol S, Loughlin Daniel H
National Risk Management Research Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA.
Clean Technol Environ Policy. 2017 Sep 21;19(9):2277-2290. doi: 10.1007/s10098-017-1417-y.
Recent projections of future United States carbon dioxide (CO) emissions are considerably lower than projections made just a decade ago. A myriad of factors have contributed to lower forecasts, including reductions in end-use energy service demands, improvements in energy efficiency, and technological innovations. Policies that have encouraged these changes include renewable portfolio standards, corporate vehicle efficiency standards, smart growth initiatives, revisions to building codes, and air and climate regulations. Understanding the effects of these and other factors can be advantageous as society evaluates opportunities for achieving additional CO reductions. Energy system models provide a means to develop such insights. In this analysis, the MARKet ALlocation (MARKAL) model was applied to estimate the relative effects of various energy system changes that have happened since the year 2005 on CO projections for the year 2025. The results indicate that transformations in the transportation and buildings sectors have played major roles in lowering projections. Particularly influential changes include improved vehicle efficiencies, reductions in projected travel demand, reductions in miscellaneous commercial electricity loads, and higher efficiency lighting. Electric sector changes have also contributed significantly to the lowered forecasts, driven by demand reductions, renewable portfolio standards, and air quality regulations.
近期对美国未来二氧化碳（CO）排放量的预测远低于仅在十年前做出的预测。众多因素导致了预测值降低，包括终端能源服务需求的减少、能源效率的提高以及技术创新。鼓励这些变化的政策包括可再生能源组合标准、企业车辆效率标准、智能增长倡议、建筑法规修订以及空气和气候法规。随着社会评估实现进一步减少CO排放的机会，了解这些及其他因素的影响可能会带来益处。能源系统模型提供了一种获得此类见解的方法。在本分析中，应用市场分配（MARKAL）模型来估计自2005年以来发生的各种能源系统变化对2025年CO预测的相对影响。结果表明，交通运输和建筑部门的转型在降低预测值方面发挥了主要作用。特别有影响力的变化包括车辆效率提高、预计出行需求减少、杂项商业电力负荷减少以及照明效率提高。电力部门的变化也对预测值降低做出了重大贡献，这是由需求减少、可再生能源组合标准和空气质量法规推动的。
