Climate Risk Management
CEVE 421/521 | Spring 2026
How should cities invest in flood protection when historical data no longer predicts the future? How do we weigh costs against uncertain benefits, or balance efficiency against equity?
These questions sit at the intersection of climate science, engineering, and decision-making under uncertainty. In practice, such decisions are often guided by intuition, politics, or precedent. This course focuses on the cases where rigorous quantitative analysis can do better.
The course follows a three-part arc:
- Risk Analysis: Characterize climate hazards and quantify uncertainty using probabilistic methods.
- Decision Analysis: Evaluate options under uncertainty using cost-benefit analysis, sensitivity analysis, and value of information.
- Optimization Under Uncertainty: Find robust strategies using exploratory modeling, sequential decision-making, and multi-objective optimization.
Readings cover both foundational methods and real-world applications; programming labs let you implement and extend key concepts.
For policies and grading, see the syllabus.
Instructor
Dr. James Doss-Gollin is an assistant professor of Civil and Environmental Engineering at Rice University. His research integrates Earth science, data science, and decision science to address challenges in climate risk management, water resources, and energy system resilience. He believes the best way to learn quantitative methods is to apply them to problems you care about.
Learning Approach
Readings & Discussion
Each week, you will engage with a mix of current events, cutting-edge research, and foundational papers. Class sessions emphasize discussion over lecture: come prepared to question assumptions, challenge conclusions, and defend your reasoning.
Computational Labs
Weekly labs apply theory to practice. You will develop risk management strategies for the “Island City on a Wedge.,” a hypothetical coastal city inspired by real places facing real threats. Along the way, you will learn Julia for scientific computing, Quarto for reproducible analysis, and GitHub for collaboration.
The layout for this site was inspired by and draws from Vivek Srikrishnan’s Environmental Systems Analysis course at Cornell, STA 210 at Duke University, and Andrew Heiss’s course materials at Georgia State. It builds heavily from my data science for climate hazard assessment course, CEVE 543.