UC Berkeley

Global policy challenges, such as addressing climate change and improving global health, require coordinated international efforts. Enacting such policies is often hampered by difficulties in quantifying the public benefit brought about by action. Without this information, policy decisions may focus primarily on the costs of action and thus disproportionately harm the groups for which inaction enacts the most damage. While classical econometric approaches are designed to identify policy impacts, they fall short when globally representative data is missing or when present or future contexts have no clear historical precedent. In these situations, new approaches are needed to incorporate evolving global data sources, such as satellite imagery, and process-based understanding from relevant fields, such as climate science or epidemiology. This dissertation leverages recent computational advances to develop and apply scalable tools for incorporating these data and models into policy impact estimation. Specifically, the first chapter describes a novel method for quickly extracting socio-environmental measurements using satellite imagery and machine learning. The second and third chapters describe applications of interdisciplinary methods to (a) project climate-driven changes to hurricane damages and (b) estimate the effect of COVID-19 policies on the pandemic's spread. In general, the findings suggest that the previously unmeasured benefits of global action in these contexts are substantial, and they demonstrate how such interdisciplinary approaches can better inform policy-making for complex global challenges.