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 co
The increasing integration of LLMs into critical decision-making across various sectors necessitates an understanding of their inherent biases, especially concerning environmental sustainability.
Organizations and governments relying on LLM outputs for sustainability reporting or public communication need to be aware of the environmental attitudes these models embody, as it impacts policy, strategy, and public perception.
This research provides a framework for systematically evaluating the environmental cognition of LLMs, enabling more informed selection and deployment of these models for sustainability-related applications.
- · AI ethics researchers
- · Environmental NGOs
- · Regulators
- · Responsible AI developers
- · LLMs with embedded anti-environmental biases
- · Organizations deploying unchecked LLMs for sustainability
Increased scrutiny and demand for 'green' or environmentally aligned LLMs.
Development of new LLM training methodologies focused on embedding pro-environmental values and removing biases.
The emergence of environmental sustainability as a core benchmark for evaluating general AI performance, impacting model adoption and market share.
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