# Greener Than Humans? Environmental Attitudes in Large Language Models

arXiv: https://arxiv.org/abs/2606.02741 · HTML: https://mlsanity.com/paper/2606.02741/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 19 of its 90 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Builds on (most cited first, 19 of 19)

- [LIMA: Less Is More for Alignment](https://mlsanity.com/paper/2305.11206/) (2023)
- [ClimateBert: A Pretrained Language Model for Climate-Related Text](https://mlsanity.com/paper/2110.12010/) (2021)
- [On the Algorithmic Bias of Aligning Large Language Models with RLHF: Preference Collapse and Matching Regularization](https://mlsanity.com/paper/2405.16455/) (2024)
- [The Self-Perception and Political Biases of ChatGPT](https://mlsanity.com/paper/2304.07333/) (2023)
- [Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs](https://mlsanity.com/paper/2310.07251/) (2023)
- [Language Models Encode Numbers Using Digit Representations in Base 10](https://mlsanity.com/paper/2410.11781/) (2024)
- [Towards LLMs Robustness to Changes in Prompt Format Styles](https://mlsanity.com/paper/2504.06969/) (2025)
- [Brittlebench: Quantifying LLM robustness via prompt sensitivity](https://mlsanity.com/paper/2603.13285/) (2026)
- [Evaluating Biased Attitude Associations of Language Models in an Intersectional Context](https://mlsanity.com/paper/2307.03360/) (2023)
- [Decoding Multilingual Moral Preferences: Unveiling LLM's Biases Through the Moral Machine Experiment](https://mlsanity.com/paper/2407.15184/) (2024)
- [Arabic Mini-ClimateGPT : A Climate Change and Sustainability Tailored Arabic LLM](https://mlsanity.com/paper/2312.09366/) (2023)
- [Reinforcement Learning from Human Feedback: Whose Culture, Whose Values, Whose Perspectives?](https://mlsanity.com/paper/2407.17482/) (2024)
- [Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansion](https://mlsanity.com/paper/2502.12598/) (2025)
- [Aligning to What? Limits to RLHF Based Alignment](https://mlsanity.com/paper/2503.09025/) (2025)
- [A Cautionary Tale About "Neutrally" Informative AI Tools Ahead of the 2025 Federal Elections in Germany](https://mlsanity.com/paper/2502.15568/) (2025)
- [ESG Reporting Lifecycle Management with Large Language Models and AI Agents](https://mlsanity.com/paper/2603.10646/) (2026)
- [Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI](https://mlsanity.com/paper/2505.14435/) (2025)
- [Are My Optimized Prompts Compromised? Exploring Vulnerabilities of LLM-based Optimizers](https://mlsanity.com/paper/2510.14381/) (2025)
- [Large language models in climate and sustainability policy: limits and opportunities](https://mlsanity.com/paper/2502.02191/) (2025)

## Abstract (arXiv)

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.