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.

该项目通过利用城市绿色基础设施来缓解热量、促进碳中和并支持美国城市的决策过程，寻求可行的气候解决方案。过去几十年的全球城市化以及随之而来的人类活动的蓬勃发展，一直是气候变化的主要且最不可逆转的驱动因素。如今，城市地区容纳了世界 56% 的人口，消耗了约 70% 的能源，产生了全球约四分之三的碳排放。实际上，人类社会的可持续未来很大程度上取决于城市的可持续性。该研究有助于揭示气候-碳反馈，提高利益相关者、政策制定者和公众对当地和区域气候变化的新模式的认识和准备。该项目通过定期会议和年度研讨会，积极吸引利益相关者，特别是来自当地城市的利益相关者参与项目评估和外展活动。此外，该项目还涉及少数族裔群体的预科生、本科生和研究生参与教育、研究和利益相关者参与，以促进公平、多样性和包容性的原则和举措。该项目整合了跨学科的定量和大气科学、气候建模、数据科学和城市可持续性领域的定性方法，以发现气候变化下可持续城市发展的基于自然的解决方案。总体目标是通过集成基于物理的城市系统动力学建模和基于机器学习的技术来开发一个变革性平台，以支持决策和城市规划。该项目的具体研究任务和成果包括：（i）提高城市系统动力学与建筑环境中人为压力因素之间复杂相互作用的建模能力，（ii）为城市气候建模创建基于机器学习的灵活替代品，以克服高计算量的问题成本和其他技术障碍，(iii) 弥合决策过程和气候建模之间的差距，并通过综合分析和多目标优化发现可持续的基于自然的解决方案。此外，认识到全球气候变化对全球城市的社会经济增长和可持续未来构成挑战，新的可扩展平台使政策制定者能够及时找到并评估针对具体城市发展计划的可行的缓解和适应战略该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

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

优先考虑城市热岛的环境决定因素：针对中国主要城市的机器学习研究

• DOI: 10.1016/j.jag.2023.103411
• 发表时间: 2023-08
• 期刊: International Journal of Applied Earth Observation and Geoinformation
• 影响因子: 7.5
• 作者: Hou, Haoran;Longyang, Qianqiu;Su, Hongbo;Zeng, Ruijie;Xu, Tianfang;Wang, Zhi
• 通讯作者: Wang, Zhi

The potential of urban irrigation for counteracting carbon-climate feedback

城市灌溉抵消碳-气候反馈的潜力

• 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: Peiyuan Li;Zhi;Chenghao Wang
• 通讯作者: Chenghao Wang

Megacities are causal pacemakers of extreme heatwaves

特大城市是极端热浪的起因

• DOI: 10.1038/s42949-024-00148-x
• 发表时间: 2024-02-27
• 期刊: npj Urban Sustainability
• 作者: Xueli Yang;Zhi;Chenghao Wang;Ying
• 通讯作者: Ying

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

美国城市位势高度对城市温度的​​因果调节

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