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

Arizona State University, Scottsdale AZ

Investigators

Abstract

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.