AI beyond efficiency, navigating the rebound effect in AI-driven sustainable development - Mhlanga, David (Frontiers in Energy Research, 2025)
Integrating Artificial Intelligence (AI) across industries has significantly enhanced operational effectiveness, positioning AI as a critical driver of sustainable development. However, this relationship is complex due to the rebound effect, where efficiency improvements paradoxically increase overall resource consumption. This study employs a systematic literature review of 150 articles published in the last decade, with 41 analyzed in detail, focusing on AI applications in transportation, energy, and manufacturing. The findings reveal that while AI-driven advancements reduce energy use per unit, they often lead to higher overall consumption, potentially negating environmental benefits and hindering progress toward sustainability objectives. This research explores the dualistic impact of AI on sustainable development and provides a comprehensive analysis of its influence on energy consumption patterns and broader implications for sustainability goals. To address these challenges, the study proposes a comprehensive strategy combining technological innovation, legislative measures, and behavioural interventions to mitigate the rebound effect and maximize AI’s potential for long-term sustainability. This work contributes to the ongoing dialogue on sustainable development by highlighting the importance of a balanced approach that addresses AI’s benefits and challenges in achieving sustainability objectives.