As we all know, the human and economic impact on the climate has been a topic that has recently received substantial attention.
While there is considerable evidence documenting impacts of climate change, a full understanding of the magnitude and the timing of this impact remains uncertain. Moreover, we still seek to comprehend better the economic consequences of climate change. The construction of quantitative models that connect economics and climate impacts currently confront uncertainty in simplistic ways. They serve as valuable illustrations, but there is much to be done in terms of developing models that are fully fledged quantitative tools for policy assessment.
Our aim is to take inventory on some of the challenges for building better models, models that can provide a more rigorous defense for say measurements of the social cost of carbon while recognizing that there are limits to our understanding of climate economic linkages. We will not provide precise answers but instead suggest a productive quantitative modeling agenda going forward.
This paper uses insights from decision theory under uncertainty to explore research challenges in climate economics. We embrace a broad perspective of uncertainty with three components: risk (probabilities assigned by a given model), ambiguity (level of confidence in alternative models), and misspecification (potential shortfalls in existing models). We survey recent climate science research that exposes the uncertainty in climate dynamics that is pertinent in economic analyses and relevant for using models to provide policy guidance. The uncertainty components and their implications for decision theory help us frame this evidence and expose the modeling and evidential challenges.