Navigating the Dual Transition: AI Energy Consumption, Energy-efficient AI Practices, and Green Business Performance in Emerging Economies

The proliferation of artificial intelligence (AI) across global industries has intensified the debate surrounding digital transformation and environmental sustainability. This study develops and tests a moderated mediation framework examining how AI energy consumption influences green business performance, with energy-efficient AI practices as a mediating mechanism and organizational sustainability commitment as a dual moderator. Drawing on survey data from 385 managers and IT professionals across diverse industries—an emerging economy undergoing rapid digital and green transitions—and employing Partial Least Squares Structural Equation Modeling (PLS-SEM), we find that AI energy consumption exerts a significant negative direct effect on green business performance. This negative effect is substantially attenuated through energy-efficient AI practices, which partially mediates the relationship. Critically, sustainability commitment operates as a dual moderator: it amplifies the transformation of AI energy consumption challenges into energy-efficient AI practices, and simultaneously enhances the effectiveness of energy-efficient AI practices in generating environmental performance gains. These findings extend established sustainable technology management theory to emerging economy contexts and offer novel insights into the complementary rather than competitive relationship between digital advancement and environmental stewardship. Practical implications for managers and policymakers seeking to align AI deployment with national sustainability objectives are discussed.