Large language models in climate and sustainability policy: limits and opportunities

• URL: http://arxiv.org/abs/2502.02191v1
• Date: Tue, 04 Feb 2025 10:13:14 GMT
• Title: Large language models in climate and sustainability policy: limits and opportunities
• Authors: Francesca Larosa, Sergio Hoyas, H. Alberto Conejero, Javier Garcia-Martinez, Francesco Fuso Nerini, Ricardo Vinuesa,
• Abstract summary: We apply different NLP techniques, tools and approaches to climate and sustainability documents to derive policy-relevant and actionable measures.<n>We find that the use of LLMs is successful at processing, classifying and summarizing heterogeneous text-based data.<n>Our work presents a critical but empirically grounded application of LLMs to complex policy problems and suggests avenues to further expand Artificial Intelligence-powered computational social sciences.
• Score: 1.4843690728082002
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: As multiple crises threaten the sustainability of our societies and pose at risk the planetary boundaries, complex challenges require timely, updated, and usable information. Natural-language processing (NLP) tools enhance and expand data collection and processing and knowledge utilization capabilities to support the definition of an inclusive, sustainable future. In this work, we apply different NLP techniques, tools and approaches to climate and sustainability documents to derive policy-relevant and actionable measures. We focus on general and domain-specific large language models (LLMs) using a combination of static and prompt-based methods. We find that the use of LLMs is successful at processing, classifying and summarizing heterogeneous text-based data. However, we also encounter challenges related to human intervention across different workflow stages and knowledge utilization for policy processes. Our work presents a critical but empirically grounded application of LLMs to complex policy problems and suggests avenues to further expand Artificial Intelligence-powered computational social sciences.

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