Abstract
The rapid integration of artificial intelligence (AI) in renewable energy systems presents a paradox: while AI optimizes energy efficiency and forecasting accuracy, its computational demands impose substantial environmental costs. From this perspective, the approaches proposed by researchers to address this issue are of interest, as they aim to ensure that the benefits outweigh the costs. Progress in their implementation will determine whether AI ultimately accelerates or hinders renewable energy transitions and transforms from a potentially double-edged technology into a genuinely sustainable catalyst for decarbonization. This scoping review addresses a critical knowledge gap at the intersection of digital innovation and environmental sustainability. It synthesizes evidence from 76 peer-reviewed studies (2014–2025) to examine AI's energy footprint, operational benefits, and trade-off dynamics. These findings challenge simplistic narratives about AI as either uniformly beneficial or harmful for sustainability in relation to the studied sector. AI’s energy consumption is not evenly distributed across the various stages of the value chain, and the benefits of increased equipment efficiency are offset by the growing complexity of the AI models. The study proposes a framework for balancing AI energy consumption and sustainability, providing evidence-based guidance for policymakers and practitioners navigating AI deployment decisions in renewable energy transitions.
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