Effective environmental policymaking requires understanding the physical environment and its interactions with human decisions and institutions. Although both the natural world and human social systems are complex, more basic representations of environmental processes and decision-making frameworks can often be sufficiently accurate and powerful to illuminate policy options. These types of simple models that nevertheless capture relevant connections and dynamics are used to inform regulatory thresholds, benchmark policy choices, guide implementation, evaluate impacts, and allocate resources. This course is organized around a series of in-depth case studies and assignments that will provide students with hands-on experience modeling core physical concepts, assessing different types of uncertainty, and understanding the implications and limitations of their analyses for decision making. Students should have some experience with linear algebra and differential equations (equivalent to MATH 51), comfort with basic programming (in R/Python or equivalent), and excitement about exploring a range of environmental policy contexts.
3 units · Letter or Credit/No Credit
Effective environmental policymaking requires understanding the physical environment and its interactions with human decisions and institutions. Although both the natural world and human social systems are complex, more basic representations of environmental processes and decision-making frameworks can often be sufficiently accurate and powerful to illuminate policy options. These types of simple models that nevertheless capture relevant connections and dynamics are used to inform regulatory thresholds, benchmark policy choices, guide implementation, evaluate impacts, and allocate resources. This course is organized around a series of in-depth case studies and assignments that will provide students with hands-on experience modeling core physical concepts, assessing different types of uncertainty, and understanding the implications and limitations of their analyses for decision making. Students should have some experience with linear algebra and differential equations (equivalent to MATH 51), comfort with basic programming (in R/Python or equivalent), and excitement about exploring a range of environmental policy contexts.
Offered in Spring 2027 at Stanford University.