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A.M.W. van Laarhoven
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Embedding Environmental Sustainability in GenAI Usage
A design science approach to explore interventions for sustainable GenAI interaction
Master thesis
(2025)
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A.M.W. van Laarhoven, J. Ubacht, M.E. Warnier, Erik Vermeulen, Jasper Snijder
This thesis investigates how organisations can embed environmental sustainability within the operational use of Generative Artificial Intelligence (GenAI) tools through targeted, user-centred interventions. While research has largely focused on reducing emissions in AI model training, the inference phase, where GenAI is integrated into daily workflows, remains a substantial and under-addressed source of environmental impact.
Using a Design Science Research approach, the study combines a literature review, interviews with GenAI users and AI experts, and behavioural theories including the COM-B model, Theory of Planned Behaviour, Nudging, and Affordance Theory. Enabling factors for pro-environmental GenAI use were translated into functional and non-functional requirements, guiding the development of three persona-specific interventions: (1) Sustainable by Default for externally motivated users, embedding energy-efficient model settings and a monitoring dashboard; (2) Sustainability Guidance for aware but uncertain users, offering a sustainable prompt builder and impact estimator widget; and (3) Collective Sustainability for unaware users, providing monthly emissions feedback and rotating green tips.
The resulting integration framework and decision-support tool offer practical guidance for embedding sustainability into organisational AI practices, demonstrating that environmental impact reduction in GenAI requires socio-technical, behavioural, and cultural change alongside technical optimisation.
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Using a Design Science Research approach, the study combines a literature review, interviews with GenAI users and AI experts, and behavioural theories including the COM-B model, Theory of Planned Behaviour, Nudging, and Affordance Theory. Enabling factors for pro-environmental GenAI use were translated into functional and non-functional requirements, guiding the development of three persona-specific interventions: (1) Sustainable by Default for externally motivated users, embedding energy-efficient model settings and a monitoring dashboard; (2) Sustainability Guidance for aware but uncertain users, offering a sustainable prompt builder and impact estimator widget; and (3) Collective Sustainability for unaware users, providing monthly emissions feedback and rotating green tips.
The resulting integration framework and decision-support tool offer practical guidance for embedding sustainability into organisational AI practices, demonstrating that environmental impact reduction in GenAI requires socio-technical, behavioural, and cultural change alongside technical optimisation.
...
This thesis investigates how organisations can embed environmental sustainability within the operational use of Generative Artificial Intelligence (GenAI) tools through targeted, user-centred interventions. While research has largely focused on reducing emissions in AI model training, the inference phase, where GenAI is integrated into daily workflows, remains a substantial and under-addressed source of environmental impact.
Using a Design Science Research approach, the study combines a literature review, interviews with GenAI users and AI experts, and behavioural theories including the COM-B model, Theory of Planned Behaviour, Nudging, and Affordance Theory. Enabling factors for pro-environmental GenAI use were translated into functional and non-functional requirements, guiding the development of three persona-specific interventions: (1) Sustainable by Default for externally motivated users, embedding energy-efficient model settings and a monitoring dashboard; (2) Sustainability Guidance for aware but uncertain users, offering a sustainable prompt builder and impact estimator widget; and (3) Collective Sustainability for unaware users, providing monthly emissions feedback and rotating green tips.
The resulting integration framework and decision-support tool offer practical guidance for embedding sustainability into organisational AI practices, demonstrating that environmental impact reduction in GenAI requires socio-technical, behavioural, and cultural change alongside technical optimisation.
Using a Design Science Research approach, the study combines a literature review, interviews with GenAI users and AI experts, and behavioural theories including the COM-B model, Theory of Planned Behaviour, Nudging, and Affordance Theory. Enabling factors for pro-environmental GenAI use were translated into functional and non-functional requirements, guiding the development of three persona-specific interventions: (1) Sustainable by Default for externally motivated users, embedding energy-efficient model settings and a monitoring dashboard; (2) Sustainability Guidance for aware but uncertain users, offering a sustainable prompt builder and impact estimator widget; and (3) Collective Sustainability for unaware users, providing monthly emissions feedback and rotating green tips.
The resulting integration framework and decision-support tool offer practical guidance for embedding sustainability into organisational AI practices, demonstrating that environmental impact reduction in GenAI requires socio-technical, behavioural, and cultural change alongside technical optimisation.