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The Impact of Industrial Robots on Green Total Factor Energy Efficiency: Empirical Evidence from Chinese Cities

Author

Listed:
  • Chuanyue Zhao

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

  • Zhishuang Zhu

    (Faculty of Economics, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Yujuan Wang

    (Faculty of Business Administration, Shanxi University of Finance and Economics, Taiyuan 030006, China)

  • Junhong Du

    (College of Mathematics and System Science, Xinjiang University, Urumqi 830046, China)

Abstract

Improving energy utilization efficiency is a crucial means to achieve energy conservation, emission reduction, and green development. At present, to establish a high-quality development framework and satisfy the growing need for a better life among all its people, China must steadfastly pursue the path of green development. Although China’s substantial economic scale and achievements in ecological civilization construction provide favorable conditions for green transformation, there remains a significant gap compared to developed countries in the application of green and clean technologies. Confronted with technological bottlenecks, leveraging emerging technologies such as industrial robots from the new round of scientific and technological revolutions to improve the green total factor energy efficiency (GTFEE) is of critical importance to China’s green development. This study explores the potential impact of industrial robots on enhancing China’s GTFEE. It begins by reviewing the current research landscape in this field, highlighting its shortcomings, and theorizing potential impact pathways of industrial robots. Subsequently, the paper analyzes data from 2010 to 2019 on the usage of industrial robots and GTFEE across 276 cities at the prefectural level or above in China. Through empirical regression models that incorporate control variables and interaction terms, the study investigates the specific impacts of industrial robots on energy efficiency and their mechanisms of action. The results indicate that industrial robots significantly enhance the GTFEE of Chinese cities, especially in the Northeastern region. Industrial robots notably improve the GTFEE in resource-based cities, old industrial bases, and low-carbon pilot cities. Additionally, robots indirectly boost GTFEE by increasing labor productivity. Enhanced levels of green innovation and environmental regulations also positively moderate the effectiveness of industrial robots in improving energy efficiency. The findings of this research can assist local government agencies in coordinating and implementing policies that are conducive to green development, making better use of industrial robots to serve the people, and are of significant importance for promoting the transformation of China’s economy and society towards high-quality development.

Suggested Citation

  • Chuanyue Zhao & Zhishuang Zhu & Yujuan Wang & Junhong Du, 2024. "The Impact of Industrial Robots on Green Total Factor Energy Efficiency: Empirical Evidence from Chinese Cities," Energies, MDPI, vol. 17(20), pages 1-24, October.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:20:p:5034-:d:1495770
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    References listed on IDEAS

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    1. Du, Junhong & He, Jiajia & Yang, Jing & Chen, Xiaohong, 2024. "How industrial robots affect labor income share in task model: Evidence from Chinese A-share listed companies," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    2. Wang, Jianlong & Wang, Weilong & Liu, Yong & Wu, Haitao, 2023. "Can industrial robots reduce carbon emissions? Based on the perspective of energy rebound effect and labor factor flow in China," Technology in Society, Elsevier, vol. 72(C).
    3. Jenne, C. A. & Cattell, R. K., 1983. "Structural change and energy efficiency in industry," Energy Economics, Elsevier, vol. 5(2), pages 114-123, April.
    4. Daron Acemoglu, 2002. "Directed Technical Change," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 69(4), pages 781-809.
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    1. Liying Cui & Min Peng & Hengshuo Zhang & Liwei Cui, 2025. "Analysis of the Dynamic System Driving High-Quality Transformation of Resource-Based Regions Through Smart Eco-Innovation: Evidence from Daqing City, China," Sustainability, MDPI, vol. 17(7), pages 1-22, April.
    2. Zhong, Kaijun & Lei, Yalin & Zhao, Jun & Jiang, Yong, 2025. "How to enhance China's total-factor energy efficiency via digital-real economy integration: New evidence from dynamic QCA analysis," Energy Economics, Elsevier, vol. 148(C).
    3. Boyu Yuan & Runde Gu & Peng Wang & Yuwei Hu, 2025. "How Does New Quality Productive Forces Affect Green Total Factor Energy Efficiency in China? Consider the Threshold Effect of Artificial Intelligence," Sustainability, MDPI, vol. 17(15), pages 1-21, August.
    4. Bing Fu & Suhaiza Zailani, 2025. "Factors Influencing Circular Carbon Economy Readiness Among Heavy Industries in China," Sustainability, MDPI, vol. 17(3), pages 1-30, January.
    5. Vaclovas Miskinis & Arvydas Galinis & Inga Konstantinaviciute & Viktorija Bobinaite & Jarek Niewierowicz & Eimantas Neniskis & Egidijus Norvaisa & Dalius Tarvydas, 2025. "Key Determinants of Energy Intensity and Greenhouse Gas Emission Savings in Commercial and Public Services in the Baltic States," Energies, MDPI, vol. 18(3), pages 1-26, February.

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