[2606.02741] Greener Than Humans? Environmental Attitudes in Large Language Models
[Submitted on 1 Jun 2026]
Title:Greener Than Humans? Environmental Attitudes in Large Language Models
View a PDF of the paper titled Greener Than Humans? Environmental Attitudes in Large Language Models, by Stefanie Kunkel and Tilman Hartwig and Marcus Voss and Emma K. Sch\"utt and Angelika Gellrich
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Abstract:Large language models (LLMs) are increasingly used in sustainability-related decision support, reporting, and public communication, yet little systematic evidence exists on the environmental attitudes embedded in their outputs. This paper develops a benchmark for evaluating environmental cognition, affect, and behavioural recommendations in LLMs and applies it to 31 widely used proprietary and open-weight models. Drawing on questions from established environmental awareness surveys and additional sustainability-related behavioural measures, we compare LLM responses 1) among models and 2) between models and human survey benchmarks from Germany. We assess their robustness across prompting conditions. We find that many LLMs align more closely with environmentally progressive attitudes than the average survey respondent, exhibiting higher levels of environmental affect and cognition and recommending behaviours associated with substantial potential CO2 reductions. At the same time, we observe no systematic relationship between sustainability-oriented responses and model origin, size, or release context. However, models exhibit contextual sensitivity, controlled by persona-based prompting and show sycophantic shifts mirroring user-specified ideological positions, which raises concerns about steerability and normative reliability in real-world deployments. Our findings provide a reusable evaluation framework for assessing sustainability-related value alignment in LLMs and highlight the importance of governance, transparency, and critical oversight as AI systems become increasingly embedded in sustainability transformations and public decision-making.
Comments: Code can be found at this https URL Benchmark data and results can be found at this https URL
Subjects:
Computation and Language (cs.CL); Computers and Society (cs.CY)
Cite as: arXiv:2606.02741 [cs.CL]
(or arXiv:2606.02741v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.02741
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tilman Hartwig [view email] [v1] Mon, 1 Jun 2026 18:05:53 UTC (469 KB)
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