Generative AI for climate governance and acceptability-constrained policy design

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.

Authors

Institutions

Publication Details

Journal
npj Climate Action
Published
2026-03-23
DOI
https://doi.org/10.1038/s44168-026-00362-6
Primary Topic
Sustainability and Climate Change Governance
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Generative AI for climate governance and acceptability-constrained policy design

Ilona M. Otto, Alexander Sasha Vezhnevets, Indira Sen, Tristan J. B. Cann et al.
npj Climate Action
Sustainability and Climate Change Governance
article

Generative AI for climate governance and acceptability-constrained policy design

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

Abstract

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.

npj Climate ActionVol. 5(1)
Technische Hochschule Mannheim (DE), University of Leeds (GB), University of Graz (AT), University of Mannheim (DE), University of Konstanz (DE), Central European University (AT), University of Zurich (CH), University of Exeter (GB), Barcelona Supercomputing Center (ES), HafenCity University Hamburg (DE), University of Illinois Chicago (US), University of Chicago (US), Google DeepMind (United Kingdom) (GB), RFF-CMCC European Institute on Economics and the Environment (IT), CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT), Google (United Kingdom) (GB), Cranfield University (GB), Politecnico di Milano (IT), Universitat Politècnica de Catalunya (ES), University of Lausanne (CH)
Climate action
Openalex Percentile: Top 9%
Sustainability and Climate Change Governance
DOIOpen Access PDFOpenAlex
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.