Nonlinear impacts of industrial intelligence on synergistic reduction of pollution and carbon emissions: The role of technological factors

The integration of digital and intelligent technologies into industrial processes positions industrial intelligence as a potential driver of improved environmental performance. However, its role in the synergistic reduction of pollution and carbon emissions (PCSR) under varying technological conditions remains underexplored. This study compiles data on industrial intelligence, greenhouse gas emissions, and pollutant emissions across China, and quantifies both the level of industrial intelligence and the degree of PCSR. By incorporating technological factors into the transition function, we employ panel smooth transition regression (PSTR) and instrumental variable PSTR models to examine the nonlinear effects of industrial intelligence on PCSR. Results show that industrial intelligence significantly improves PCSR, but its marginal effect declines after technological thresholds are exceeded. Moreover, the effect of industrial intelligence on PCSR differs significantly across technological threshold levels. Specifically, key thresholds are identified for R&D investment (14.940), digital economy development (0.284), and innovation output (2.761). Mechanism analysis reveals that green technological innovation, total factor productivity improvements, and industrial structure upgrading are key channels through which industrial intelligence promotes PCSR. Significant regional heterogeneity is also observed. The positive impact of industrial intelligence on PCSR is substantially greater in coastal regions than in inland areas. In regions with stricter environmental regulations, policy pressure further amplifies this effect. In regions with a higher share of clean energy consumption, industrial intelligence yields the most pronounced emission reduction benefits. Our findings provide effective pathways and policy recommendations for achieving synergistic emissions reduction.