Choosing a Model, Shaping a Future: Comparing LLM Perspectives on Sustainability and its Relationship with AI (2025)

Abstract: 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.

## introduction in an era of accelerating digital transformation, large language models (llms) have emerged as powerful tools for supporting organizational decision-making processes @xcite . businesses are increasingly integrating ai systems into their operations -from policy development to strategic planning or environmental, social, and governance (esg) reporting @xcite . inherent biases in these models thus bear significant risk of impacting corporate practices related to sustainability and social responsibility. it is therefore crucial to understand how model-expressed stances reflect sustainability concepts. llms, trained on vast corpora of text data, inevitably reflect the societal values, cultural norms, and biases embedded in their training data @xcite . when applied to sustainability related tasks (e.g., corporate policies or communication strategies) their perspectives may significantly impact organizational approaches to environmental stewardship and social responsibility. the intersection of ai and sustainability -often called the "twin transition" @xcite -presents both opportunities and challenges for responsible organizational practices. while llms offer unprecedented capabilities for processing complex sustainability information @xcite , their outputs are necessarily influenced by the perspectives represented in their training data. this raises concerns about potentially biased or limited understandings of crucial sustainability concepts that might be propagated through ai-assisted decision-making. we address this critical gap by systematically investigating how five leading llms respond to validated psychometric instruments assessing sustainability perceptions. we compare model-expressed stances to identify patterns, similarities, and differences in their conceptualizations of sustainability. specifically, this study aims to answer the following research questions: through our interdisciplinary investigationcombining methods and insights from computer science, sustainability studies, psychology, and statistics -we seek to deepen the understanding of how llms conceptualize key sustainability issues. our findings aim to contribute to an informed development and deployment of ai systems in organizational contexts, where sustainability considerations are paramount. they also highlight the importance of careful model selection and potential bias mitigation when using llms to support sustainability-related decision-making. ## methodology based on our literature review and the identified research gaps, we employ a quantitative approach to assess llms' expressed stances of sustainability. our methodology builds upon recent research by @xcite al. (2024); weber et al. (2024 ); dormuth et al. (2025) on evaluating llms inherent biases regarding political orientation and selfperception as well as aksoy (2025)'s work on evaluation of moral biases in multilingual llms. we evaluated five state-of-the-art llms comprising both closed-, and open-source architectures: • closed-source models: gpt-4o (openai, 2024) and claude 3.7 sonnet (anthropic, 2024) • open-source models: llama 3.3 70b-instruct (meta ai, 2024), mistral large-instruct (mistral ai, 2024), and deepseek v3 (deepseek-ai, 2025) for gpt-4o and claude 3.7 sonnet, we accessed the models via their respective official apis (ope-nai python api and anthropic api) to systematically prompt the models and collect responses. for the open-source models, we deployed llama 3.3 and mistral large locally using pytorch and hugging face frameworks, while deepseek v3 was accessed through its api. all models were used in their most recent available versions at the time of the study to ensure that our findings reflect their current capabilities. ## results our analysis of llm responses to the ai-sdg17 assessment revealed variations in how the outputs of the five models project ai's impact across sustainability domains (figure 1 ). notably, all models consistently rated ai's impact on "reducing inequalities" (sdg 10) as the least significant sustainability area, with mean scores around the neutral point of the scale (@xmath0.83, @xmath1.58 over all runs and models). substantial inter-model differences emerged in overall impact assessments: llama demonstrated the highest impact ratings across all sdgs (see appendix), particularly for "affordable and clean energy" (sdg7; @xmath2.00), "industry, innovation and infrastructure (sdg 9; @xmath3.00) and "sustainable cities and economies" (sdg11; @xmath4.00) with zero variations in its ratings for these three catergories (@xmath5.00). in contrast, mistral exhibited the most conservative perspective on ai's sustainability potential providing the overall lowest impact ratings across all 17 domains, whereas claude and deepseek maintained moderate positions (see appendix). gpt demonstrated domain-specific optimism, particularly re-garding "affordable and clean energy" (sdg7; @xmath6.88, @xmath7.33) and "industry, innovation and infrastructure" (sdg9; @xmath8.77, @xmath9.42). the most pronounced model divergence appeared in assessments of ai's impact on "quality education" (sdg 4), where llama's optimistic rating (@xmath10.57, @xmath11.50) contrasted with mistral's reserved evaluation (@xmath12.31, @xmath13.46). our parallel human baseline study (@xmath14.57 across all sdgs. human participants demonstrated highest optimism for "innovative industries" (@xmath15.06, @xmath16.44) and "economic growth" (@xmath17.81, @xmath18.62), while expressing most reservation about ai's impact on "social inequality" (@xmath19.92, @xmath20.01) and "peace and justice" (@xmath21.16, @xmath22.07). the human ratings clustered relatively closely around the scale midpoint (@xmath23.46, @xmath24.77), with a narrow range from 3.92 to 5.06 across all seventeen domains. ## conclusion our findings extend the theoretical understanding of ai bias beyond traditional demographic or political dimensions to encompass sustainability worldviews. the identification of distinct "sustainability personalities" among llms -ranging from techno-pessimistic to techno-optimistic orientations -suggests that training data composition, model architecture, fine-tuning or imposed constraints shape not merely factual knowledge but fundamental perspectives on complex sociotechnical challenges. this discovery contributes to the emerging literature on ai system evaluation and highlights the multidimensional nature of bias in llms. the systematic differences we observed validate concerns about multi-stakeholder approaches to ai development that address embedded biases. llms embody distinct worldviews that influence their sustainability outputs, with profound implications for how organizations deploy these systems in contexts where sustainability considerations are paramount. as organizations increasingly integrate llms into sustainability-related decision-making processes, our research underscores the critical importance of understanding each model's inherent perspectives. the polarized nature of llm responses -contrasting with humans' more nuanced viewssuggests that relying on any single model could lead to skewed strategic directions. future research should explore the origins of these differences in perspectives, develop methods to mitigate their impact, and investigate how ensemble approaches might leverage diverse model viewpoints to support more balanced and effective sustainability initiatives. ultimately, achieving the twin transition will require not just technological advancement but also careful consideration of the values and assumptions embedded within our ai tools.