Critical Aspects of Sustainability (CAS)-Climate: Actionable Heat and Carbon Mitigation by Urban Greening--Integrating Physical Modeling and Machine Learning for Decision Support

可持续发展的关键方面（CAS）-气候：通过城市绿化实现可行的热量和碳减排——集成物理模型和机器学习以提供决策支持

• 批准号: 2300548
• 负责人: Zhihua Wang
• 金额: $ 74.61万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2300548&HistoricalAwards=false
• 关键词: Critical; Aspects; Sustainability; CAS; Climate

This project seeks actionable climate solutions by using urban green infrastructure to mitigate heat, promote carbon neutrality, and support decision making processes in U.S. cities. Global urbanization in past decades, with concomitant burgeoning anthropogenic activities, has been the primary and the most irreversible driver to climate changes. Today, urban areas are accommodating 56% of the world population, consuming about 70% of energy, and producing about three quarters of global carbon emissions. Practically speaking, the sustainable future of human societies depends largely on urban sustainability. The research helps to unravel climate-carbon feedback and improve the awareness and preparedness of stakeholders, policy makers, and the general public to emergent patterns of local and regional climate changes. This project actively engages stakeholders, especially those from local cities, for project evaluation and outreach activities through regular meetings and annual workshops. In addition, this project involves participation of pre-college, undergraduate, and graduate students from ethnically underrepresented groups in education, research, and stakeholder engagement, to prompt the principle and initiatives of equity, diversity, and inclusion.This project integrates transdisciplinary quantitative and qualitative methods in the fields of atmospheric science, climate modeling, data science, and urban sustainability to discover nature-based solutions for sustainable urban development under changing climates. The overarching goal is to develop a transformative platform by integrating the physically based modeling of urban system dynamics and machine learning-based techniques in support of decision-making and urban planning. Specific research tasks and outcomes of this project include: (i) improving modeling capability of complex interplays between urban system dynamics and anthropogenic stressors in the built environment, (ii) creation of machine learning-based nimble surrogates for urban climate modeling to overcome high computational cost and other technical barriers, and (iii) bridging the gap between decision-making processes and climate modeling and enable discovery of sustainable nature-based solutions via synthesis analysis and multi-objective optimization. Furthermore, by recognizing that global climate changes are challenging to the socioeconomic growth and sustainable futures of cities worldwide, the new scalable platform enables policy makers to find and evaluate actionable mitigation and adaptation strategies that are tailored to city-specific development plans in a timely manner.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.

该项目通过使用城市绿色基础设施来减轻热量，促进碳中立性并支持美国城市的决策过程来寻求可行的气候解决方案。在过去的几十年中，全球城市化以及随之而来的人为活动随之而来，一直是气候变化的主要和最不可逆转的驱动因素。如今，城市地区已容纳56％的世界人口，消耗了约70％的能源，并产生了大约四分之三的全球碳排放量。实际上，人类社会的可持续未来在很大程度上取决于城市可持续性。这项研究有助于阐明气候碳反馈，并提高利益相关者，政策制定者以及公众对当地和地区气候变化的新兴模式的意识和准备。该项目通过常规会议和年度研讨会积极吸引利益相关者，尤其是来自当地城市的利益相关者进行项目评估和外展活动。 In addition, this project involves participation of pre-college, undergraduate, and graduate students from ethnically underrepresented groups in education, research, and stakeholder engagement, to prompt the principle and initiatives of equity, diversity, and inclusion.This project integrates transdisciplinary quantitative and qualitative methods in the fields of atmospheric science, climate modeling, data science, and urban sustainability to discover nature-based solutions for在气候变化下的可持续城市发展。总体目标是通过整合基于物理的城市系统动力学和基于机器学习的技术来支持决策和城市规划的基于物理的建模。该项目的具体研究任务和结果包括：（i）提高城市系统动力学和人为压力之间的复杂相互作用的建模能力，在建筑环境中，（ii）创建基于机器学习的敏捷性代替城市气候建模，以克服高度计算成本和其他技术障碍，以及（iii）的实现，以及（iii）的差异，以及（iii），（iii）的差异，以及（iii），（iii）实现了模型，合成分析和多目标优化。此外，通过认识到全球全球城市的社会经济增长和可持续未来的全球气候变化，新的可扩展平台使决策者能够找到并评估可行的缓解措施和适应性策略，这些策略是针对城市特定发展计划的及时而及时依赖于NSF的Infortral Inforts Inforthory Mertion the Inforthorial te Elem deem deem for the Infortial of deem for nectial of deem for teem teem for te em deem for的范围。审查标准。

项目成果

Megacities are causal pacemakers of extreme heatwaves

• DOI: 10.1038/s42949-024-00148-x
• 发表时间: 2024-02
• 期刊: npj Urban Sustainability
• 作者: Xueli Yang;Zhi-Hua Wang;Chenghao Wang;Ying-Cheng Lai

The potential of urban irrigation for counteracting carbon-climate feedback

• DOI: 10.1038/s41467-024-46826-3
• 发表时间: 2024-03-18
• 期刊: NATURE COMMUNICATIONS
• 影响因子: 16.6
• 作者: Li，Peiyuan;Wang，Zhi-Hua;Wang，Chenghao
• 通讯作者: Wang，Chenghao

Prioritizing environmental determinants of urban heat islands: A machine learning study for major cities in China

• DOI: 10.1016/j.jag.2023.103411
• 发表时间: 2023-08
• 期刊: Int. J. Appl. Earth Obs. Geoinformation
• 作者: Haoran Hou;Q. Longyang;H. Su;R. Zeng;Tianfang Xu;Zhihong Wang

Causal mediation of urban temperature by geopotential height in U.S. cities

• DOI: 10.1016/j.scs.2023.105010
• 发表时间: 2024-01
• 期刊: Sustainable Cities and Society
• 影响因子: 11.7
• 作者: Yihang Wang;Xueli Yang;Zhi-Hua Wang