CAREER: CAS- Climate -- Air-quality-related environmental justice impacts of decarbonization scenarios

美国和其他国家正处于从以化石燃料为基础的经济向非气候强制能源发电驱动的重大技术转变的开始阶段。这种技术转移(“脱碳”)的速度和成功在一定程度上取决于它是否被认为是公平实施的，其好处广泛地惠及整个社会。能源生产与公平相交的一个主要方式是通过环境空气污染。环境空气污染是美国和全球最大的死亡原因之一，其健康负担在地区和国际范围内分布不均。然而，到目前为止，对不同脱碳战略对空气质量和环境正义的影响进行的研究很少。此外，目前用于制定脱碳战略的综合评估模式对空气质量评估的支持微乎其微，不适合用来量化环境不平等。该项目将为政策情景的气候、空气质量和环境公平影响的综合评估开发一个新的建模框架，并将该框架应用于气候政策的分析和评估。研究目标极大地提高了对脱碳政策情景的共同好处和坏处的理解，特别是在与环境公平有关的方面，无论是在美国国内还是在国际上。它还旨在提高对对公众进行气候科学和政策教育的有效方法的理解。将创建的开源计算工具旨在促进政策制定者、可持续发展科学家和其他人计算空气质量共同效益和相关权益。这一目标是为了为脱碳政策奠定更坚实的技术基础。此外，由于公平观念对于成功的脱碳至关重要，确定减轻环境不公正的战略，以及开发教育工具广泛宣传这些政策的好处，将有助于全面抗击气候变化。项目研究将分三个阶段进行。推力一号将把全球变化分析模型(GCAM)与空气污染干预模型(InMAP)结合起来。GCAM代表了能源、水、土地、气候和经济系统之间的联系。InMAP是由Pi Tessum开发的一个高分辨率、低复杂性的空气污染传输、转化和移除模型，广泛用于评估政策干预对空气污染健康和公平的影响。GCAM和InMAP都有以全球和美国为重点的版本，研究将结合相应的版本，以便在次国家、美国和国际范围内进行分析。推力2将扩展在推力1中开发的耦合模型，以实现高分辨率综合评估。要做到这一点，研究将把高分辨率的人口规模缩小纳入结果模型，以评估政策情景将如何不同地影响不同人口群体的空气污染暴露。技术实施决策也将在模型内以高空间分辨率实现，从而能够探索基于城市内部位置的政策实施。推力3将创建一个机器学习的单遍GCAM求解器，允许模型在几秒钟内运行，而不是在可能场景的子集上运行几个小时，以及用于构建交互式教育体验的灵活软件基础设施(GCAM-EDU)。这个职业项目由NSF ENG/CBET环境可持续和环境工程计划联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The U.S. and other nations are at the beginning of a major technological shift from a fossil fuel-based economy to one driven by non-climate-forcing energy generation. The speed and success of this technology shift (“decarbonization”) depend in part upon whether it is perceived to be implemented equitably with benefits that accrue broadly across society. A major way that energy generation intersects with equity is through ambient air pollution. Ambient air pollution is one of the largest causes of death in the US and globally, with health burdens that are distributed inequitably on both regional and international scales. However, minimal research has thus far been conducted into the air quality and environmental justice implications of different decarbonization strategies. Furthermore, current integrated assessment models, which are used to develop decarbonization strategies, have minimal support for air quality assessment and are not well-suited to quantify environmental inequity. This project will develop a new modeling framework for integrated assessment of climate, air quality, and environmental equity impacts of policy scenarios, and apply the framework in the analysis and evaluation of climate policy. The research targets greatly improved understanding of the co-benefits and dis-benefits of decarbonization policy scenarios, especially as related to environmental equity, both within the US and internationally. It also targets improved understanding of effective methods for educating the general public on climate science and policy. The open-source computational tools to be created aim to facilitate the calculation of air quality co-benefits and related equity by policymakers, sustainability scientists, and others. This goal is to result in a stronger technical grounding for decarbonization policy. Furthermore, as the perception of equity is critical to successful decarbonization, the identification of strategies that mitigate environmental injustice, and the development of educational tools to broadly communicate the benefits of those policies, will contribute to the fight against climate change overall. Project research will be organized in three Thrusts. Thrust 1 will couple the Global Change Analysis Model (GCAM) with the Intervention Model for Air Pollution (InMAP). GCAM represents linkages between energy, water, land, climate and economic systems. InMAP is a high-resolution, reduced-complexity model of the transport, transformation, and removal of air pollution developed by PI Tessum and widely used to estimate air pollution health and equity impacts of policy interventions. Both GCAM and InMAP have versions that are global- and US-focused and the research will couple the corresponding versions to allow analysis at sub-national US and international scales. Thrust 2 will expand the coupled models developed in Thrust 1 to allow high-resolution integrated assessment. To do this, the research will integrate high-resolution demographic population down-scaling into the resulting model to allow the assessment of how policy scenarios will differentially affect air pollution exposure across different demographic groups. Technology implementation decisions will also be enabled at high spatial resolution within the model, enabling the exploration of intra-urban location-based policy implementation. Thrust 3 will create a machine-learned single-pass GCAM solver that allows the model to run in seconds instead of hours for a subset of possible scenarios, as well as flexible software infrastructure (GCAM-EDU) for building interactive educational experiences.This CAREER project is jointly supported by the NSF ENG/CBET Environmental Sustainability and Environmental Engineering program.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.