Systemic Configuration Pathways for Carbon Emission Reduction in Energy-Intensive Industries: A Dynamic fsQCA Analysis Within the TOE Framework

Amid global climate change, China’s energy-intensive industries face substantial challenges in achieving low-carbon transformation. While existing studies largely focus on individual determinants of emission reduction, insufficient attention has been paid to the dynamic interactions among multiple dimensions. To complement this perspective, this study integrates co-evolution theory with the technology–organization–environment (TOE) framework, applying dynamic fuzzy-set qualitative comparative analysis (fsQCA) to panel data from 30 Chinese provinces between 2005 and 2022. The results indicate that low carbon intensity arises from the synergistic interaction of factors rather than isolated elements, whereas high intensity is driven by systemic mismatches rather than the mere absence of low-carbon conditions. This research identifies five enabling configurations, led by the digital-green dual drive, alongside three inhibiting pathways, most notably the regulation-volatility trap. Evolutionary trajectories exhibit significant regional variation: the eastern region leverages digital-market mechanisms through innovation strategies, whereas the central region shifted toward government-led upgrading following the 2016 supply-side structural reform. The western region relies on top-down administrative governance to compensate for limited digital capabilities. Meanwhile, the northeast region remains trapped in a composite lock-in due to the structural misalignment between legacy industrial scale and the integration of digital and green innovations. These findings provide a systems-oriented basis for differentiated policymaking in emerging economies.