Building Green Generative AI: An Ecosystem-Wide Approach to Environmental Sustainability

Generative AI's (GenAI) rapid growth raises environmental concerns due to high energy consumption. Despite accelerating technological advancements, understanding how different stakeholders in the GenAI ecosystem can contribute to environmental sustainability remains limited. We address this gap with a taxonomy of actions for environmentally sustainable GenAI ecosystems. Our taxonomy, developed through a design science approach combining literature review and case analysis, categorizes environmental sustainability interventions across resources, models, and usage. We identify key stakeholders (hardware manufacturers, cloud providers, model developers, application providers) and map their roles in implementing these actions. The taxonomy reveals trade-offs between performance, cost, and environmental sustainability, highlighting the need for context-specific strategies. Through an illustrative vignette, we demonstrate how GenAI application providers can systematically implement sustainability measures. We provide a framework for researchers and practitioners to develop environmentally responsible GenAI solutions, fostering coordinated action to ensure GenAI benefits without compromising environmental well-being.