A Satisficing Framework for Environmental Policy Under Model Uncertainty

Abstract

We propose a novel framework for the economic assessment of environmental policy. Our main point of departure from existing work is the adoption of a satisficing, as opposed to optimizing, modeling approach. Along these lines, we place primary emphasis on the extent to which different policies meet a set of goals at a specific future date instead of their performance vis-a-vis some intertemporal objective function. Consistent to the nature of environmental policymaking, our model takes explicit account of model uncertainty. To this end, the decision criterion we propose is an analog of the well-known success-probability criterion adapted to settings characterized by model uncertainty. We apply our criterion to the climate-change context and the probability distributions constructed by Drouet et al. (2015) linking carbon budgets to future consumption. Insights from computational geometry facilitate computations considerably and allow for the efficient application of the model in high-dimensional settings.

Forecasting the Future Sustainability of Technology Choices: Qualitative Predictive Validity of Models as a Complement to Quantitative Uncertainty

“…IAMs, type 9, are most open to include more relevant mechanisms. Such models will still show deep layers of empirical uncertainty, as Athanasoglou et al (2021) indicate for scenario studies in the climate domain.…”

“…Hof et al (2010) aim to reduce the chance on the worst outcomes for the climate domain. Similarly, Athanasoglou et al (2021) apply this approach to climate policy choices, minimizing the maximum loss. The shift from optimizing to satisficing (Simon, 1956) next to minimax is to avoid the worst outcomes.…”