Carbon emission reduction effects of industrial robot applications: Heterogeneity characteristics and influencing mechanisms

Abstract

Industrial robots are a key enabling technology of Industry 4.0 and the artificial intelligence revolution, which is of great significance in sustainable development. Existing research has focused on the economic effects of industrial robot application, but research on the environmental effects remains insufficient. Based on the environmental Kuznets curve (EKC) model and using sample data from 35 countries from 1993 to 2017, this paper empirically examines the carbon emission reduction effects of industrial robot application. Three key findings emerged. First, the application of industrial robots significantly reduces carbon intensity. The application of industrial robots leads to increased productivity, the optimisation of factor structures, and technological innovation in production, which improve energy efficiency and reduce carbon intensity. Second, there is a two-dimensional heterogeneity in the carbon intensity reduction effects of industrial robot application in terms of the application fields and possible countries for application. Compared to other fields, the application of industrial robots in manufacturing, agriculture, and electricity, gas, and water supply fields significantly promotes carbon intensity reduction. Furthermore, industrial robots in developed countries have better emission reduction effects than in developing countries. Third, the application of industrial robots has a dual-channel mediating mechanism for carbon intensity reduction: first, there is a mediating role of green total factor productivity and energy intensity; second, absorptive capacity plays a moderating role. On the one hand, high absorptive capacity brings about a better innovation environment and enhances the effects of carbon emission reduction; on the other hand, the application of industrial robots promotes carbon intensity reduction by positively influencing the improvement of green total factor productivity and energy intensity. Finally, policy recommendations are provided based on the results.