U.S. Environmental Protection Agency, Office of Research and Development , 109 TW Alexander Drive , Research Triangle Park , North Carolina 27711 , United States.
Eastern Research Group, Inc. , 110 Hartwell Avenue , Lexington , Massachusetts 02421 , United States.
Environ Sci Technol. 2018 Jul 17;52(14):8027-8038. doi: 10.1021/acs.est.8b00575. Epub 2018 Jul 9.
The energy system is the primary source of air pollution. Thus, evolution of the energy system into the future will affect society's ability to maintain air quality. Anticipating this evolution is difficult because of inherent uncertainty in predicting future energy demand, fuel use, and technology adoption. We apply scenario planning to address this uncertainty, developing four very different visions of the future. Stakeholder engagement suggested that technological progress and social attitudes toward the environment are critical and uncertain factors for determining future emissions. Combining transformative and static assumptions about these factors yields a matrix of four scenarios that encompass a wide range of outcomes. We implement these scenarios in the U.S. Environmental Protection Agency MARKet ALlocation (MARKAL) model. Results suggest that both shifting attitudes and technology transformation may lead to emission reductions relative to the present, even without additional policies. Emission caps, such as the Cross-State Air-Pollution Rule, are most effective at protecting against future emission increases. An important outcome of this work is the scenario-implementation approach, which uses technology-specific discount rates to encourage scenario-specific technology and fuel choices. End-use energy demands are modified to approximate societal changes. This implementation allows the model to respond to perturbations in manners consistent with each scenario.
能源系统是空气污染的主要来源。因此,未来能源系统的发展将影响社会维持空气质量的能力。由于未来能源需求、燃料使用和技术采用的预测存在固有不确定性,因此很难预测这种演变。我们应用情景规划来解决这一不确定性,提出了未来的四个截然不同的愿景。利益相关者的参与表明,技术进步和社会对环境的态度是决定未来排放的关键和不确定因素。结合对这些因素的变革性和静态假设,产生了一个包含广泛结果的四个情景矩阵。我们将这些情景应用于美国环境保护署的 MARKet ALlocation(MARKAL)模型中。结果表明,即使没有额外的政策,态度和技术的转变都可能导致排放量相对于现在减少。排放上限,如跨州空气污染规则,在防止未来排放增加方面最为有效。这项工作的一个重要成果是情景实施方法,该方法使用特定于技术的折扣率来鼓励特定于情景的技术和燃料选择。最终用途能源需求进行了修改,以近似社会变化。这种实施方式使模型能够以符合每个情景的方式对干扰做出响应。