Rewiring industrial futures: The role of AI-enabled digital twins in energy productivity transitions

The pursuit of sustainable development requires technological innovations that can simultaneously enhance productivity and reduce environmental burdens. This study investigates the role of digital twin technologies, enabled by artificial intelligence, in transforming energy productivity within manufacturing systems. Drawing on a multi-country panel dataset, the analysis applies dynamic ordinary least squares, fully modified ordinary least squares, canonical cointegration regression, and moment-based quantile regression to capture both long-run dynamics and distributional heterogeneity. The results show that the adoption of digital twins consistently improves energy productivity, with the strongest gains evident in economies supported by advanced digital infrastructure, a skilled workforce, and high levels of research investment. Structural factors—including industrial scale, energy pricing regimes, and human capital—emerge as critical enablers of these technological benefits. Causality tests confirm a unidirectional link from digital twin adoption to energy productivity, underscoring the proactive influence of technological change rather than reactive efficiency adjustments. These findings contribute to current debates on digital transformation by demonstrating how cyber-physical systems reshape systemic energy efficiency and reduce disparities in industrial performance. They also highlight policy priorities: targeted investment in digital connectivity, education, and innovation ecosystems; regulatory frameworks that align energy pricing with sustainability incentives; and support for small and medium-sized enterprises to ensure inclusive adoption. By embedding digital twins within broader socio-technical systems, this study shows how technological innovation can reconfigure industrial futures, strengthen competitiveness, and advance progress toward long-term sustainability.