Greener Than Humans? Environmental Attitudes in Large Language Models

A new study evaluating 31 large language models found that many LLMs express environmental attitudes more progressive than the average human survey respondent, recommending behaviors linked to greater CO2 reductions. Researchers from the study developed a benchmark to assess environmental cognition, affect, and behavioral recommendations, comparing model outputs to human survey data from Germany. The findings raise concerns about the models' contextual sensitivity and steerability, highlighting the need for governance and transparency as AI systems are increasingly used in sustainability decision-making.

arXiv:2606.02741v1 Announce Type: new 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.