Harnessing the power of industrial robots for green development: Evidence from China's manufacturing industry

Haiyan Deng, Zhonghua Huang, Jian Wu, Fatma Güneri, Z.Y. Shen and Changxin Yu

Technological Forecasting and Social Change, 2025, vol. 215, issue C

Abstract: Amid growing concerns over energy consumption and environmental degradation, the integration of industrial robots into manufacturing systems has emerged as a potential catalyst for green growth. However, there is a scarcity of studies examining how industrial robot deployment affects green productivity. Utilizing provincial input-output data from China's manufacturing sector (2013–2019), this study investigates the impact of industrial robot adoption on green productivity growth and its spatial spillover effects. Our findings reveal three key contributions to the literature. First, we enrich the intersection of industrial robotics and green production by demonstrating that robot adoption significantly enhances green productivity, with a particularly strong impact in eastern regions and high-productivity provinces. Second, we highlight the relevance of industrial robots as a cost-saving technology for achieving China's dual goals of “carbon peaking” by 2030 and “carbon neutrality” by 2060, providing empirical evidence to support targeted policy interventions. Third, we expand the understanding of green productivity by uncovering spatial spillover effects, showing that robotics industry expansion not only boosts local green productivity but also catalyzes growth in neighboring regions. These findings underscore the importance of government initiatives to promote industrial robot adoption, particularly through regionally differentiated policies that address disparities in infrastructure and absorptive capacity.

Keywords: Industrial robot; Green productivity; Manufacturing industry; Technology innovation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:215:y:2025:i:c:s0040162525001301

DOI: 10.1016/j.techfore.2025.124099

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