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The impact of industrial robots on low-carbon green performance: Evidence from the belt and road initiative countries

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  • Long, Guoren
  • Duan, Dingyun
  • Wang, Hua
  • Chen, Shaojian

Abstract

Global warming is intensifying, with Belt and Road Initiative (BRI) countries accounting for over two-thirds of global carbon dioxide emissions. As a pivotal technology in advanced manufacturing, industrial robots significantly impact the overall carbon footprint through their role in production processes. This study examines how the use of industrial robots influences the Low-Carbon Green Performance (LCGP) across BRI nations, utilizing a dataset spanning from 2004 to 2020. The findings reveal that the integration of industrial robots notably boosts the LCGP within these countries, a conclusion supported by extensive robustness evaluations. The novelty of this study lies in uncovering the underlying mechanism by which industrial robots improve LCGP through the promotion of technological innovation. Specifically, we find that industrial robots have a pronounced effect on technological progress (TC), an impact that is further amplified by increases in labor productivity and human capital levels. This discovery provides policy implications for BRI governments, suggesting that actively promoting the development of industrial robots can accelerate energy transformation and upgrading, thereby reducing carbon emissions. Our research fills a significant void concerning the environmental impact of industrial robots and offers new perspectives and strategies for BRI countries to achieve low-carbon development goals. These findings contribute significant theoretical and practical value to global environmental protection and sustainable development.

Suggested Citation

  • Long, Guoren & Duan, Dingyun & Wang, Hua & Chen, Shaojian, 2024. "The impact of industrial robots on low-carbon green performance: Evidence from the belt and road initiative countries," Technology in Society, Elsevier, vol. 79(C).
    • Handle: RePEc:eee:teinso:v:79:y:2024:i:c:s0160791x24002604
    DOI: 10.1016/j.techsoc.2024.102712
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    Cited by:

    1. Yingji Liu & Ju Guo & Fangbing Shen & Yuegang Song, 2025. "Can artificial intelligence technology improve green total factor efficiency in energy utilisation? Empirical evidence from 282 cities in China," Economic Change and Restructuring, Springer, vol. 58(2), pages 1-34, April.

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    More about this item

    Keywords

    Industrial robots; Low-carbon green performance; Belt and road initiative;
    All these keywords.

    JEL classification:

    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products

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