A Satisficing Framework for Environmental Policy Under Model Uncertainty

Area of Science:

• Environmental economics
• Policy analysis
• Decision theory

Background:

• Traditional economic models for environmental policy often use an optimizing approach.
• Existing frameworks may not adequately address model uncertainty inherent in policymaking.
• Assessing policy performance against intertemporal objective functions can be complex.

Purpose of the Study:

• To propose a novel framework for the economic assessment of environmental policy.
• To introduce a satisficing, rather than optimizing, modeling approach.
• To develop a decision criterion that accounts for model uncertainty.

Main Methods:

• Adoption of a satisficing modeling approach, focusing on goal achievement.
• Incorporation of explicit consideration of model uncertainty.
• Adaptation of the success-probability criterion for settings with model uncertainty.
• Application to climate change scenarios using established probability distributions.

Main Results:

• The proposed framework emphasizes meeting specific future goals over optimizing intertemporal functions.
• The satisficing approach offers a practical alternative for environmental policy assessment.
• The model successfully incorporates and addresses model uncertainty.
• Computational geometry aids in efficient application within high-dimensional settings.

Conclusions:

• The novel satisficing framework provides a robust method for economic assessment of environmental policy.
• The approach is particularly valuable for complex issues like climate change, where uncertainty is prevalent.
• This work offers a more realistic and applicable tool for policymakers facing environmental challenges.