Industrial Robot Exposure and Urban Low-Carbon Transition Efficiency: Evidence from Chinese Cities

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Abstract
Industrial robots are increasingly embedded in urban production systems, yet their association with city-level low-carbon transition efficiency remains insufficiently understood. Using panel data for 278 Chinese cities from 2010 to 2023, this study measures urban low-carbon transition efficiency (LCTE), the relative efficiency of urban resource conversion under environmental constraints, with a super-efficiency slack-based measure (Super-SBM) model. We estimate its association with industrial robot exposure using a two-way fixed-effects model, followed by robustness checks, a lagged-exposure instrumental-variable analysis, innovation-pathway analysis, city-size heterogeneity tests and spatial econometric modeling. Industrial robot exposure is positively associated with LCTE; the direction of this association is preserved across sensitivity analyses, although its magnitude falls to about one quarter of the baseline estimate under constant returns to scale. The coefficient patterns for green technological innovation and science and technology investment intensity are consistent with potential innovation-related pathways. The positive association is more pronounced in small and medium-sized cities than in major cities. Spatial Durbin model estimates confirm spatial dependence in LCTE and a robust positive direct association with local LCTE; the indirect association with spatially connected cities is negative in point estimates but not statistically significant once standard errors are clustered by city. These conditional associations suggest that the relationship between production automation and LCTE varies across innovation conditions, city sizes and spatial contexts.
Funding Information
  • National Natural Science Foundation of China (12265025)
  • Shaanxi Provincial Department of Education Youth Innovation team (25JP210)

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