不同大模型对可持续发展的看法差异显著，选型直接影响决策方向。
Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI
- 用标准化问卷测试5个主流大模型，每模型100次采样。
- GPT怀疑AI与可持续性兼容，LLaMA则对多个可持续发展目标打满分。
- 模型在责任归属认知上差异大，影响技术治理策略选择。
随着组织越来越多依赖人工智能系统进行可持续发展相关决策支持，理解大型语言模型（LLMs）中内嵌的偏见与视角变得至关重要。本研究系统比较了五款前沿大模型——Claude、DeepSeek、GPT、LLaMA和Mistral——对可持续发展的认知及其与人工智能的关系。我们采用经验证的心理测量学可持续发展问卷，每模型重复测试100次，以捕捉响应模式与变异性。结果显示模型间存在显著差异：例如，GPT对人工智能与可持续性的兼容性持怀疑态度，而LLaMA则表现出极端技术乐观主义，在多个可持续发展目标（SDGs）上获得满分。此外，各模型在将人工智能与可持续性融合的责任归属上也存在分歧，这对技术治理路径具有重要启示。研究证明，模型选择可能显著影响组织的可持续发展战略，强调在部署大模型开展可持续性相关决策时需警惕其特定偏见。
原文摘要 · Abstract (English)
As organizations increasingly rely on AI systems for decision support in sustainability contexts, it becomes critical to understand the inherent biases and perspectives embedded in Large Language Models (LLMs). This study systematically investigates how five state-of-the-art LLMs -- Claude, DeepSeek, GPT, LLaMA, and Mistral - conceptualize sustainability and its relationship with AI. We administered validated, psychometric sustainability-related questionnaires - each 100 times per model -- to capture response patterns and variability. Our findings revealed significant inter-model differences: For example, GPT exhibited skepticism about the compatibility of AI and sustainability, whereas LLaMA demonstrated extreme techno-optimism with perfect scores for several Sustainable Development Goals (SDGs). Models also diverged in attributing institutional responsibility for AI and sustainability integration, a results that holds implications for technology governance approaches. Our results demonstrate that model selection could substantially influence organizational sustainability strategies, highlighting the need for awareness of model-specific biases when deploying LLMs for sustainability-related decision-making.
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