A Systematic Review of Green and Sustainable AI: Taxonomy, Metrics, Challenges, and Open Research Directions

Abstract

Due to the recent rapid development of artificial intelligence (AI) and its expanding impact on the planet, green and sustainable AI research has increasingly gained attention. This systematic literature review searches main databases, including Scopus, Web of Science, and Google Scholar, using an organized methodological approach. Following a thorough screening process, 49 final studies published between 2016 and 2026 are selected from an initial identification of 325 original records. We identify and analyze four key categories of sustainable AI practices: (1) model-level algorithmic efficiency, (2) hardware- and system-level optimization, (3) lifecycle- and data-centric approaches, and (4) operational and policy-level sustainability. We also highlight and explain four dimensions at the intersection of AI and environmentally responsible behavior: AI for sustainable applications’ development in industries, ethical considerations and accountability in using AI, and opportunities enabled by generative AI. We then combine existing taxonomies, evaluation metrics, and challenges to identify areas for improvement and suggest future research directions. Based on our analysis, we emphasize the need for interdisciplinary cooperation to facilitate responsible AI innovation and match it with global sustainable development goals (SDGs). We also highlight the importance of developing adequate frameworks along with precisely defined and standardized metrics to assess the environmental impact of AI. This review aims to encourage more responsible and environmentally friendly AI practices by providing a structured framework for researchers, educators, and professionals engaged in sustainable AI.