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.
Highlights
Policy makers want direct answers to simple questions, yet such demands are frequently at odds with the complexity of economic analysis and forecasting
Probabilistic projections of consumption losses are such that no expert is expected to be exactly “right.” Like most questions in social science, the economic impact of carbon budgets on future consumption patterns cannot be neatly summarized with unique probability distributions, even if the latter are updated over time with Bayesian methods
We focus on future consumption losses with respect to a world without any climate change damages
Summary
Policy makers want direct answers to simple questions, yet such demands are frequently at odds with the complexity of economic analysis and forecasting. This contrasts with [19,20,21,22,23] who incorporate stochasticity but do not take model uncertainty into account Another important difference in this context involves our work’s focus on one-shot future goals (e.g., sustainability guarantees for the year 2100) as opposed to dynamic constraints in optimal-control settings. Unlike both stochastic viability and RDM, our work does not rely on simulation as a tool for calculating success probabilities, as it exploits the problem’s structure to provide exact numbers for these probabilities.
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