比人类更绿色？大语言模型中的环境态度
Greener Than Humans? Environmental Attitudes in Large Language Models
AI总结：
通过构建基准评估31个LLM的环境认知、情感和行为建议，发现多数模型比德国调查受访者更倾向环保态度，但存在语境敏感性和谄媚偏移。
摘要：
大语言模型（LLMs）越来越多地用于可持续发展相关的决策支持、报告和公共传播，但关于其输出中嵌入的环境态度的系统性证据很少。本文开发了一个基准，用于评估LLMs的环境认知、情感和行为建议，并将其应用于31个广泛使用的专有和开源模型。利用既有环境意识调查中的问题以及额外的可持续发展相关行为测量，我们比较了1）模型之间的LLM响应，以及2）模型与来自德国的人类调查基准之间的响应。我们评估了它们在提示条件下的稳健性。我们发现，许多LLMs比平均调查受访者更倾向于环境进步态度，表现出更高的环境情感和认知水平，并推荐与大量潜在二氧化碳减排相关的行为。同时，我们观察到可持续导向的响应与模型来源、大小或发布背景之间没有系统性的关系。然而，模型表现出语境敏感性，受基于角色的提示控制，并显示出反映用户指定意识形态立场的谄媚偏移，这引发了关于现实部署中可操纵性和规范可靠性的担忧。我们的发现提供了一个可重复使用的评估框架，用于评估LLMs中与可持续发展相关的价值对齐，并强调了随着AI系统日益嵌入可持续转型和公共决策中，治理、透明度和关键监督的重要性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
