Decarbonization Through Digital Twins: Integrating Management Competencies and Industrial Energy Efficiency
DOI:
https://doi.org/10.23055/ijietap.2026.33.1.11445Abstract
Industrial sectors face mounting pressure to reduce greenhouse gas emissions while maintaining productivity, prompting growing interest in advanced digitalization strategies. Among these, digital twins (DTs) have emerged as a powerful approach for achieving significant energy efficiency gains and supporting decarbonization objectives. This review critically examines the foundations, applications, and implications of DTs in energy-intensive industries. We begin by outlining the technical principles that differentiate DTs from digital models and shadows, emphasizing their bidirectional connectivity, predictive capacity, and real-time synchronization with physical assets. We then survey a range of applications—from manufacturing processes and building energy management to grid integration and transportation systems—where DTs have delivered measurable improvements in energy optimization, maintenance scheduling, and operational flexibility. Evidence from both research and practice highlights reductions in energy consumption of 20–40% and improved integration of renewable energy sources. Beyond technical mechanisms, this review underscores the pivotal role of organizational and managerial competencies in shaping DT outcomes. Successful implementations require workforce training, leadership engagement, and effective data governance, while challenges such as high upfront costs, data quality issues, and cybersecurity risks remain barriers to widespread deployment. We also discuss controversies regarding whether DTs are overhyped or underutilized, and emphasize the need for transparent evaluation metrics and interdisciplinary collaboration. By integrating technical innovation with managerial alignment, DTs have the potential to become a cornerstone of industrial strategies for sustainability and competitiveness, accelerating the transition toward low-carbon operations.
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