On Large Language Models as Data Sources for Policy Deliberation on Climate Change and Sustainability

Rachel Bina, Kha Luong, Shrey Mehta, Daphne Pang, Mingjun Xie, Christine Chou and Steven O. Kimbrough

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Abstract: We pose the research question, "Can LLMs provide credible evaluation scores, suitable for constructing starter MCDM models that support commencing deliberation regarding climate and sustainability policies?" In this exploratory study we i. Identify a number of interesting policy alternatives that are actively considered by local governments in the United States (and indeed around the world). ii. Identify a number of quality-of-life indicators as apt evaluation criteria for these policies. iii. Use GPT-4 to obtain evaluation scores for the policies on multiple criteria. iv. Use the TOPSIS MCDM method to rank the policies based on the obtained evaluation scores. v. Evaluate the quality and validity of the resulting table ensemble of scores by comparing the TOPSIS-based policy rankings with those obtained by an informed assessment exercise. We find that GPT-4 is in rough agreement with the policy rankings of our informed assessment exercise. Hence, we conclude (always provisionally and assuming a modest level of vetting) that GPT-4 can be used as a credible input, even starting point, for subsequent deliberation processes on climate and sustainability policies.

Date: 2025-02
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