ClimatePolicyGen: A multi-agent framework for climate policy generation using multivariate and multimodal time series inputs
Issued Date
2026-05-01
Resource Type
ISSN
26673053
Scopus ID
2-s2.0-105035263097
Journal Title
Intelligent Systems with Applications
Volume
30
Rights Holder(s)
SCOPUS
Bibliographic Citation
Intelligent Systems with Applications Vol.30 (2026)
Suggested Citation
Poopradubsil T., Thaipisutikul T. ClimatePolicyGen: A multi-agent framework for climate policy generation using multivariate and multimodal time series inputs. Intelligent Systems with Applications Vol.30 (2026). doi:10.1016/j.iswa.2026.200662 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/116219
Title
ClimatePolicyGen: A multi-agent framework for climate policy generation using multivariate and multimodal time series inputs
Author(s)
Author's Affiliation
Corresponding Author(s)
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Abstract
Effective climate policy formulation requires integrating multivariate, multi-view time-series data to develop actionable insights. However, existing approaches struggle with data synthesis, contextual alignment, and coherence in automated policy generation. This study introduces ClimatePolicyGen, a multi-agent framework leveraging large language models (LLMs) to automate climate policy development. The framework employs domain-specific agents to analyze environmental, socio-economic, and infrastructure trends, synthesizing structured policy recommendations. Experimental results demonstrate that ClimatePolicyGen surpasses baseline models, achieving a 12.3% improvement in coherence and an 18.7% increase in relevance, as validated by GEval and BERTScore. A case study on national climate strategies highlights its adaptability across diverse policy contexts. By enabling data-driven, adaptive, and region-specific policymaking, ClimatePolicyGen enhances global climate resilience and provides a foundation for data-driven policy drafting, with results validated through automated metrics as a first step toward practical deployment.