Evidence map›Paper›PMID 41883750›Full record

Reviewnpj climate action2026

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

Ajaykumar Manivannan, Viktoria Spaiser, Tristan J B Cann, James Evans, Jordan P Everall, Max Falkenberg, David Garcia, Weisi Guo, Rico Herzog, Ilona M Otto and 7 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in npj climate action, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Ajaykumar ManivannanSchool of Politics and International Studies, University of Leeds, Leeds, UK.
Viktoria SpaiserSchool of Politics and International Studies, University of Leeds, Leeds, UK.
Tristan J B CannCentre for Climate Communication and Data Science, University of Exeter, Exeter, UK.
James EvansDepartment of Sociology, University of Chicago, Illinois, US.
Jordan P EverallWegener Center for Climate & Global Change, University of Graz, Graz, Austria.
Max FalkenbergDepartment of Network and Data Science, Central European University, Vienna, Austria.
David GarciaDepartment of Politics and Public Administration, University of Konstanz, Konstanz, Germany.
Weisi GuoCentre for Assured & Connected Autonomy, Cranfield University, Cranfield, UK.
Rico HerzogCity Science Lab, HafenCity University Hamburg, Hamburg, Germany.
Ilona M OttoWegener Center for Climate & Global Change, University of Graz, Graz, Austria.
Yannick OswaldInstitute of Geography and Sustainability, University of Lausanne, Lausanne, Switzerland.
Nicolò PaganDepartment of Informatics, University of Zurich, Zurich, Switzerland.
Max PellertBarcelona Supercomputing Center, Catalonia, Spain.
Charlie PilgrimSchool of Mathematics, University of Leeds, Leeds, UK.
Carlos Rodriguez-PardoPolitecnico di Milano, Milan, Italy.
Indira Senchair for Data-Science in the Economic and Social Sciences, University of Mannheim, Mannheim, Germany.
Alexander Sasha VezhnevetsGoogle DeepMind, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Science, technology and societySocial sciences

Identifiers

PMID41883750
PMCPMC13008763

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.