Abstract
Improving total factor carbon productivity (TFCP) is the core pathway to China’s low-carbon economic transformation and achieving the “dual carbon” goals. Based on panel data of 30 Chinese provincial-level regions from 2010 to 2023, this paper measures regional TFCP via an undesirable-output super-efficiency SBM model and empirically analyzes the impacts and spatial spillover characteristics of industrial intelligence and the digital economy on TFCP using a Spatial Durbin Model (SDM). Results show China’s TFCP rose overall but exhibited a widening regional gap of “higher in the east, lower in the west”, with significant positive spatial autocorrelation in regional TFCP. The digital economy exerts a significantly positive direct effect and strong positive spatial spillover effect on TFCP, forming a “local driving + spatial radiation” promotion pattern. Industrial intelligence has an insignificantly negative direct effect on local TFCP, yet its positive spatial spillover effect is significant at the 1% level, leading to a significantly positive total effect that reflects its obvious spatial externality, with low-carbon dividends more prominent in regional coordination. Both factors show notable regional heterogeneity: industrial intelligence has a significantly negative direct effect in the east, significantly positive in the central region and insignificant in the west, with positive indirect effects in the east and west; the digital economy presents “local-spillover dual drive” in the east, “local-dominated drive” in the central region and “spillover-dominated drive” in the west. Among control variables, coal-based energy consumption structure and secondary industry-dominated industrial structure significantly inhibit regional TFCP with strong negative spatial spillovers; green finance has an insignificant positive effect, while FDI shows an insignificantly positive direct effect and significantly negative indirect effect due to the “pollution haven” effect. The work clarifies the spatial effects and regional heterogeneity of industrial intelligence and the digital economy on TFCP, providing empirical evidence and policy references for formulating differentiated regional coordination policies, leveraging the two as a “dual engine” to boost China’s regional TFCP and advance high-quality green and low-carbon economic development.
Data availability
The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. All scripts and custom analysis code are deposited in an established DOI‑minting version control repository at [https://zenodo.org/](https:/zenodo.org), the DOI is: doi.org/10.5281/zenodo.18973539.
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Funding
The 2025 Social Science Research Project of Shandong University of Political Science and Law: “Research on the Mechanism and Path to Enhance the Resilience and Security of Industrial and Supply Chains” (2025); The 2025 Special Cooperative Project of Humanities and Social Sciences of Shandong Social Sciences Association: “Research on the Path and Mechanism for Enhancing the Resilience of the Intelligent Home Appliance Industrial Chain in Shandong under the Dual Circulation in the SCO Demonstration Zone" (2025); The 2025 Humanities and Social Sciences Project of Shandong Provincial Social Sciences Association: Research on the Path of Promoting High-Quality Development of Shandong’s Semiconductor Industry through the Integration of “Four Chains” under the Reconstruction of Global Supply Chains (2025); Key Project of Shandong Social Science Planning Research in 2025: “Research on Empowering the Green and Low-Carbon Transformation of Energy in Shandong Province by Digital New Quality Productivity (25BJJJ05)”.
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Dandan Xiao: Conceptualization, Writing—review & editing, Writing—original draft; Methodology, Writing—review & editing. Jinwang Liu: Formal analysis; Project administration, Supervision, Data curation.
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Xiao, D., Liu, J. Study on the impact of industrial intelligence and the digital economy on China’s regional total factor carbon productivity under carbon neutrality. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45039-6
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DOI: https://doi.org/10.1038/s41598-026-45039-6
