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With the widespread application of industrial robots, exploring the patterns of consumption-based embodied carbon intensity is crucial for understanding the fairness of carbon emissions responsibility and supporting global sustainability efforts. Employing the dynamic panel Generalized Method of Moments estimation methodology to a dataset including 52 countries, this paper evaluates the influence of the applications of industrial robot on the consumption-based embodied carbon intensity and its corresponding mechanisms. The main findings reveal that: (i) the application of industrial robots significantly reduces consumption-based embodied carbon intensity. The result remains robust after undergoing a series of robustness checks. (ii) In countries with greater advantages in environmental technology and a higher abundance of natural resources, the application of industrial robots results in more noticeable environmental benefits. (iii) While the impact of industrial robots on consumption is complex, their implementation boosts human capital and reduces income inequality, thereby lowering carbon intensity. The findings highlight the importance of human capital and reduced inequality in promoting eco-friendly consumption behaviors. Furthermore, the adoption of industrial robots has driven green innovation, enhancing environmental quality from the supply side. The findings provide policy implications for fostering global low-carbon transformation using industrial intelligence approaches.
Wenwen Jin (Mon,) studied this question.