Generative AI for climate governance and acceptability-constrained policy design.

Area of Science:

• Environmental policy
• Computational social science
• Artificial intelligence

Background:

• Climate policies frequently encounter implementation barriers when they conflict with societal values, cultural norms, and perceptions of fairness.
• Integrating public acceptability into policy design is crucial for successful climate action.

Purpose of the Study:

• To introduce Acceptability-Constrained Climate Policy Design (ACCPD) as a novel framework for developing climate policies.
• To leverage large language models (LLMs) as sophisticated tools for simulating public responses to proposed policies.

Main Methods:

• ACCPD integrates LLMs, functioning as "cultural world models," within generative agent-based models and physical system simulators.
• This approach allows for the simulation of public reactions and social dynamics prior to policy implementation.

Main Results:

• The proposed ACCPD framework enables policymakers to simultaneously optimize for both the effectiveness of climate policies and their social legitimacy.
• Simulations can reveal potential conflicts between policy objectives and public values, allowing for proactive adjustments.

Conclusions:

• ACCPD offers a promising methodology for enhancing the social acceptability and practical success of climate policies.
• Addressing methodological limitations, such as LLM representation and opacity, is essential for the robust application of this approach.