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<p>Climate policies often fail when they clash with cultural values, social identities, and fairness perceptions. We propose Acceptability-Constrained Climate Policy Design (ACCPD), using large language models as “cultural world models” to simulate public responses before implementation. By embedding LLMs in generative agent-based models and physical system simulators, ACCPD aims to enable policymakers to co-optimize for climate-policy efficacy and social legitimacy. We discuss methodological limitations regarding representation and LLM opacity.</p>

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Generative AI for climate governance and acceptability-constrained policy design

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月24日

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  • Ajaykumar Manivannan,
  • Viktoria Spaiser,
  • Tristan J. B. Cann,
  • James Evans,
  • Jordan P. Everall,
  • Max Falkenberg,
  • David Garcia,
  • Weisi Guo,
  • Rico Herzog,
  • Ilona M. Otto,
  • Yannick Oswald,
  • Nicolò Pagan,
  • Max Pellert,
  • Charlie Pilgrim,
  • Carlos Rodriguez-Pardo,
  • Indira Sen,
  • Alexander Sasha Vezhnevets

摘要

Climate policies often fail when they clash with cultural values, social identities, and fairness perceptions. We propose Acceptability-Constrained Climate Policy Design (ACCPD), using large language models as “cultural world models” to simulate public responses before implementation. By embedding LLMs in generative agent-based models and physical system simulators, ACCPD aims to enable policymakers to co-optimize for climate-policy efficacy and social legitimacy. We discuss methodological limitations regarding representation and LLM opacity.