Author(s): Emmanuel Imuede Oyasor
This study investigates the spatial effects of industrial robot adoption on green total factor productivity (GTFP) across 30 provinces in mainland China from 2013 to 2019, using a panel spatial Durbin model (PSDM). Drawing on data from national statistical yearbooks and customs databases, the analysis incorporates key control variables including trade openness, R&D intensity, energy structure, human capital, and industrial structure. The results reveal that robot density significantly improves GTFP both within provinces and through spatial spillovers to neighboring regions, underscoring the regional interdependence of technological progress. R&D intensity and clean energy use are found to have positive effects, while trade openness is negatively associated with GTFP, indicating potential environmental trade-offs in export-led growth. The study contributes to the literature on automation, spatial economics, and sustainable development by highlighting how industrial robot deployment can support regional green transformation. Policy implications include the promotion of coordinated robotics adoption, innovation collaboration, and energy restructuring to enhance sustainable productivity across spatially connected economies.