Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries

En-Ze Wang, Chien-Chiang Lee () and Yaya Li

Energy Economics, 2022, vol. 105, issue C

Abstract: Considering the continuing slowdown of the improvement in energy intensity around the world, it is essential to seek a more effective measure to address the dilemma of energy and sustainable development. To this end, this research attempts to provide fresh insight into the determinants of energy intensity from the perspective of industrial robots and an industry-based view. By applying the dynamic panel GMM estimate methodology to a new data panel that includes 38 countries and 17 manufacturing sectors, this study provides the first comprehensive assessment of the use of industrial robots on manufacturing energy intensity. We found that industrial robots could significantly improve manufacturing energy intensity, and our hypotheses passed a series of robustness tests. Moreover, this improvement effect works through the technology improvement effect and technological complement effect between industrial robots and labor. Finally, we found a heterogeneous nexus exists between industrial robots and manufacturing energy intensity. Specifically, industrial robots can exert influence on non-renewable energy intensity rather than renewable energy intensity. Compared to capital-intensive sectors, we found that the use of industrial robots mainly affected labor-intensive sectors. We also found that Industry 4.0 could promote the improvement effects of industrial robots on manufacturing energy intensity.

Keywords: Energy intensity; Industrial robots; Manufacturing sectors; Technology improvement effect and complement effect; Industry 4.0 (search for similar items in EconPapers)
JEL-codes: C33 L60 O13 Q43 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (71)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:105:y:2022:i:c:s0140988321005934

DOI: 10.1016/j.eneco.2021.105748

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