Impact and mechanisms of digital-intelligent development on the low-carbon transition of energy consumption

Digital-intelligent development, as a core engine for reshaping the new pattern of economic growth, provides new pathways for the low-carbon transition of energy consumption. Based on panel data of 283 Chinese cities from 2011 to 2023, this study employs a range of econometric models—including two-way fixed effects, mediation, panel quantile regression, panel threshold, and spatial econometric models—to empirically investigate the impact and mechanisms of digital-intelligent development on the low-carbon transformation of energy consumption. The findings indicate that: (1) Digital-intelligent development facilitates the low-carbon transition of energy consumption by accelerating the dual processes of "replacing coal with oil and gas" and "substituting fossil fuels with clean energy". This conclusion remains robust after a series of robustness tests. (2) The energy transition effects of digital-intelligent development exhibit notable heterogeneity, with a pronounced "first-mover advantage" in cities characterized by higher levels of clean energy consumption, non-key environmental protection cities, non-old industrial bases, and coastal cities. (3) Digital-intelligent development functions through three mechanisms: efficiency enhancement, factor support, and clean production. In terms of efficiency enhancement, it significantly improves both energy utilization and allocation efficiency. In terms of factor support, it strengthens the supply of green capital and green innovation factors. In terms of clean production, it facilitates industrial upgrading and collaborative agglomeration. (4) The energy transition effect of digital-intelligent development exhibits nonlinear characteristics. It demonstrates asymmetric impacts at different stages of energy transition, revealing an "inclusive effect". Moreover, under the triple constraints of performance assessment, fiscal balance, and public opinion pressure, threshold effects are observed. Excessive performance pressure may crowd out the transformation effect, whereas rising fiscal pressure can stimulate local governments to foster new growth drivers through digital-intelligent means. Social opinion pressure exhibits an inverted U-shaped relationship. Additionally, digital-intelligent development generates a "peer effect" on the low-carbon transformation of energy consumption in neighboring regions, yet this spatial spillover effect decays beyond approximately 170 kilometers.