Angel Hsu
Machine learning, generative AI, including Large Language Models (LLMs) hold immensepotential in addressing critical data and information gaps within city climate policy. Thesetechnologies enable cities to estimate greenhouse gas emission sources, identify environmentalhotspots, evaluate policy performance trends, and comprehend the diverse impacts of climatechange. This presentation will showcase several case studies that illustrate how AI-drivenapproaches can innovate data collection, analysis, and policy formulation in the context of urbanclimate management, including evaluating net-zero climate policy and strategy with LLMs andtopic modeling, distributional climate and environmental impacts within urban areas, theintegration of satellite remote sensing for participatory heat-stress mapping, among otherexamples. It will also discuss some of the challenges and pitfalls of applying AI to urban climatepolicy and management, such as the "black box" AI problem and biases of underlying trainingdata.