Twenty-Second International Conference on Environmental, Cultural, Economic & Social Sustainability

Abstract

This paper explores the paradox of Generative AI as both a catalyst for sustainability innovations and a contributor to environmental degradation. It interrogates the escalating energy demands, carbon emissions, and infrastructure disparities associated with large-scale AI models, raising concerns about the hidden risks they pose to long-term ecological well-being. Employing a multidisciplinary approach, this study integrates a systematic literature review, comparative case studies, and critical policy analysis. It synthesizes data from peer-reviewed publications, corporate sustainability reports, and global policy frameworks. The study focuses on three key dimensions: energy consumption and environmental impact; technological innovation and optimization strategies; and ethical, regulatory, and policy considerations. The findings uncover critical gaps in AI sustainability practices, including the unequal global accessibility of AI infrastructure and the environmental cost of both training and inference processes. It emphasizes that AI’s expansion must not outpace its environmental accountability. The study proposes a roadmap for sustainable AI including energy-efficient model architectures, renewable energy integration, federated learning, and enforceable sustainability regulations. These recommendations aim to ensure that Generative AI aligns with global sustainability objectives without compromising innovation. By addressing the unseen environmental and socioeconomic costs embedded in the AI lifecycle, the paper contributes to ongoing global efforts to harmonize technological advancement with long-term ecological integrity.