This workshop convenes leading researchers from diverse disciplines to explore the development of better and more credible models of the dynamic interactions of climate change and economic activity. We will leverage advances from a variety of ﬁelds to explore novel and revealing forms of uncertainty quantiﬁcation pertinent to the design of prudent policies related to climate change. Advanced computational methods are an essential tool for solving and analyzing dynamic models under broad-based formulations of uncertainty at the required levels of spatial and temporal granularity. This workshop will bring together world-renowned experts in the areas of climate science, computational methods, geosciences, statistics, and economics to explore synergies that will lead to modeling improvements in the future.
Mitigating Disaster Risks in the Age of Climate Change
Speaker: Harrison Hong (Columbia University)
Emissions abatement alone cannot address the consequences of global warming for frequency of weather disasters. We model regional-level adaptation to mitigate disaster risks to capital stock. Optimal adaptation—a mix of private efforts and public spending funded by a tax on capital—depends on learning regarding the adverse consequences of global warming from disaster arrivals. We apply our model to country-level control of flooding from major tropical cyclones. Learning is needed to rationalize new findings on the response of asset prices to disaster arrivals. The value of adaptation with learning is much higher than under the counterfactual without learning. Finally, we quantify how learning and adaptation alters projections for the social cost of carbon over time.
Tutorial on Conformal Prediction and Distribution-Free Uncertainty Quantification
Speaker: Anastasios N. Angelopoulos (University of California, Berkeley (UC Berkeley))
As we begin deploying machine learning models in consequential settings like medical diagnostics or self-driving vehicles, we need ways of knowing when the model may make a consequential error (for example, that the car doesn’t hit a human). I’ll be discussing how to generate rigorous, finite-sample confidence intervals for any prediction task, any model, and any dataset, for little computational cost. This will be a chalk talk. I will primarily discuss conformal prediction and related methods, which work for a large class of prediction problems including those with high-dimensional, structured outputs (e.g. instance segmentation, multiclass or hierarchical classification, protein folding, and so on).
TBA – Research Related to Computational and Data-driven Methods for Understanding Climate Dynamics
Speaker: Duncan Watson-Parris (University of California, San Diego)
TBA – Research Related to the Use of Solving and Analyzing Nonlinear models of economics and climate change under uncertainty
Speaker: Felix Kubler (University of Zurich), Simon Scheidegger (University of Lausanne)
TBA – Research Related to Climate Change and the Amazon Rain Forest
Speaker: Jose Scheinkman (Columbia University)
Reception + Poster Session (IMSI’s Lower Lounge)
Saturday, April 1, 2023
Anticipating Climate Change Risk Across the United States
This paper evaluates the impact of extreme weather events for the social cost of climate change. To that end we develop a dynamic spatial model of the US economy divided in over 3,000 counties that features risk-averse individuals, forward-looking migration and investment, and idiosyncratic and aggregate climate risk. We achieve tractability by relying on recent methodological advances that leverage analytic perturbations of the Master Equation representation of the economy. We estimate the model on US counties by combining climatic county-level data for weather events such as heat waves, floods and hurricanes over the course of the 20th century with population, migration, investment and income data. We run event studies that trace out the impact of extreme weather events on productivity, amenities, mortality, and capital depreciation. Our findings are twofold. First, climate impacts on capital depreciation and mobility are the main source of climate damages. They substantially magnify the costs of climate change in the business-as-usual warming scenario. Second, adaptation through migration and investment are crucial mitigators of climate damages, without which welfare losses would be much more spatially concentrated.
TBA – Research Related to Using Machine learning to Solve PDE-Based Models of Economics and Climate Change
Speaker: Michael Barnett (Arizona State University), Ruimeng Hu (UC Santa Barbara), Joseph Huang (University of Pennsylvania)