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The role of different paths of technological progress in improving China's energy efficiency

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  • Jun Shao
  • Lianghu Wang

Abstract

Improving energy efficiency is one of the most effective ways to address environmental constraints and achieve high-quality economic development. The existing literature suggests that technological progress has an important impact on energy efficiency, but ignores the role of different paths of technological progress. Herein, the impact of different technological progress paths on energy efficiency was analyzed using the dynamic panel data model and the threshold model based on the energy efficiency measurement using the Meta-frontier and Nonradial Directional Distance Function model. The findings of this study were as follows: (1) During the study sample period, a fluctuating upward trend was observed in China's energy efficiency, and there were significant differences in energy efficiency in different regions of China. (2) The energy efficiency in China could be significantly improved by domestic innovation and regional technology diffusion. Even though the imported technologies do not play a significant role in promoting energy efficiency and have no positive effect on the improvement of energy efficiency from the perspective of domestic technology absorption or foreign technology absorption. (3) The eastern and central regions of China improve their energy efficiency through domestic innovation. However, none of the technological progress paths in the western and low-energy-efficiency regions have played a positive role in promoting energy efficiency. (4) The effect of promoting domestic innovation and regional technology diffusion on energy efficiency is gradually increasing with human capital improvement, and the restriction of foreign technology import on energy efficiency has obviously decreased. In a nutshell, different paths of technological progress have differential effects on energy efficiency improvements. Based on the above results, this paper makes some targeted policy recommendations on the choice of technological progress paths to improve energy efficiency in different regions of China.

Suggested Citation

  • Jun Shao & Lianghu Wang, 2024. "The role of different paths of technological progress in improving China's energy efficiency," Energy & Environment, , vol. 35(4), pages 2008-2030, June.
  • Handle: RePEc:sae:engenv:v:35:y:2024:i:4:p:2008-2030
    DOI: 10.1177/0958305X221148284
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    References listed on IDEAS

    as
    1. Li, Mengjie & Du, Weijian, 2021. "Can Internet development improve the energy efficiency of firms: Empirical evidence from China," Energy, Elsevier, vol. 237(C).
    2. Destek, Mehmet & Sinha, Avik, 2020. "Renewable, non-renewable energy consumption, economic growth, trade openness and ecological footprint: Evidence from organisation for economic Co-operation and development countries," MPRA Paper 104246, University Library of Munich, Germany, revised 2020.
    3. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers 09/02, Institute for Fiscal Studies.
    4. Mary O'Mahony & Marcel P. Timmer, 2009. "Output, Input and Productivity Measures at the Industry Level: The EU KLEMS Database," Economic Journal, Royal Economic Society, vol. 119(538), pages 374-403, June.
    5. Wang, Ailun & Hu, Shuo & Lin, Boqiang, 2021. "Emission abatement cost in China with consideration of technological heterogeneity," Applied Energy, Elsevier, vol. 290(C).
    6. Witajewski-Baltvilks, Jan & Verdolini, Elena & Tavoni, Massimo, 2017. "Induced technological change and energy efficiency improvements," Energy Economics, Elsevier, vol. 68(S1), pages 17-32.
    7. Stephen Bond, 2002. "Dynamic panel data models: a guide to microdata methods and practice," CeMMAP working papers CWP09/02, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    8. Verdolini, Elena & Galeotti, Marzio, 2011. "At home and abroad: An empirical analysis of innovation and diffusion in energy technologies," Journal of Environmental Economics and Management, Elsevier, vol. 61(2), pages 119-134, March.
    9. Chen, Zhe & Song, Pei & Wang, Baolu, 2021. "Carbon emissions trading scheme, energy efficiency and rebound effect – Evidence from China's provincial data," Energy Policy, Elsevier, vol. 157(C).
    10. Meng, Ming & Qu, Danlei, 2022. "Understanding the green energy efficiencies of provinces in China: A Super-SBM and GML analysis," Energy, Elsevier, vol. 239(PA).
    11. Dierk Herzer, 2011. "The Long-run Relationship between Outward Foreign Direct Investment and Total Factor Productivity: Evidence for Developing Countries," Journal of Development Studies, Taylor & Francis Journals, vol. 47(5), pages 767-785.
    12. Hansen, Bruce E., 1999. "Threshold effects in non-dynamic panels: Estimation, testing, and inference," Journal of Econometrics, Elsevier, vol. 93(2), pages 345-368, December.
    13. Saunders, Harry D., 2008. "Fuel conserving (and using) production functions," Energy Economics, Elsevier, vol. 30(5), pages 2184-2235, September.
    14. repec:dau:papers:123456789/10972 is not listed on IDEAS
    15. Dogan, Eyup & Altinoz, Buket & Madaleno, Mara & Taskin, Dilvin, 2020. "The impact of renewable energy consumption to economic growth: A replication and extension of Inglesi-Lotz (2016)," Energy Economics, Elsevier, vol. 90(C).
    16. Tang, Liwei & He, Gang, 2021. "How to improve total factor energy efficiency? An empirical analysis of the Yangtze River economic belt of China," Energy, Elsevier, vol. 235(C).
    17. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    18. Harty D. Saunders, 1992. "The Khazzoom-Brookes Postulate and Neoclassical Growth," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 131-148.
    19. Stern, David I., 2012. "Modeling international trends in energy efficiency," Energy Economics, Elsevier, vol. 34(6), pages 2200-2208.
    20. Ghasemi-Mobtaker, Hassan & Mostashari-Rad, Fatemeh & Saber, Zahra & Chau, Kwok-wing & Nabavi-Pelesaraei, Ashkan, 2020. "Application of photovoltaic system to modify energy use, environmental damages and cumulative exergy demand of two irrigation systems-A case study: Barley production of Iran," Renewable Energy, Elsevier, vol. 160(C), pages 1316-1334.
    21. Bentzen, Jan, 2004. "Estimating the rebound effect in US manufacturing energy consumption," Energy Economics, Elsevier, vol. 26(1), pages 123-134, January.
    22. Stephen R. Bond, 2002. "Dynamic panel data models: a guide to micro data methods and practice," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 1(2), pages 141-162, August.
    23. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    24. Kamal Saggi, 2002. "Trade, Foreign Direct Investment, and International Technology Transfer: A Survey," The World Bank Research Observer, World Bank, vol. 17(2), pages 191-235, September.
    25. He, Yong & Fu, Feifei & Liao, Nuo, 2021. "Exploring the path of carbon emissions reduction in China’s industrial sector through energy efficiency enhancement induced by R&D investment," Energy, Elsevier, vol. 225(C).
    26. Wurlod, Jules-Daniel & Noailly, Joëlle, 2018. "The impact of green innovation on energy intensity: An empirical analysis for 14 industrial sectors in OECD countries," Energy Economics, Elsevier, vol. 71(C), pages 47-61.
    27. George Battese & D. Rao & Christopher O'Donnell, 2004. "A Metafrontier Production Function for Estimation of Technical Efficiencies and Technology Gaps for Firms Operating Under Different Technologies," Journal of Productivity Analysis, Springer, vol. 21(1), pages 91-103, January.
    28. Herrerias, M.J. & Cuadros, A. & Luo, D., 2016. "Foreign versus indigenous innovation and energy intensity: Further research across Chinese regions," Applied Energy, Elsevier, vol. 162(C), pages 1374-1384.
    29. Zhou, Anhua & Li, Jun, 2021. "Investigate the impact of market reforms on the improvement of manufacturing energy efficiency under China’s provincial-level data," Energy, Elsevier, vol. 228(C).
    30. Haider, Salman & Mishra, Prajna Paramita, 2021. "Does innovative capability enhance the energy efficiency of Indian Iron and Steel firms? A Bayesian stochastic frontier analysis," Energy Economics, Elsevier, vol. 95(C).
    31. Yi, Ming & Wang, Yiqian & Sheng, Mingyue & Sharp, Basil & Zhang, Yao, 2020. "Effects of heterogeneous technological progress on haze pollution: Evidence from China," Ecological Economics, Elsevier, vol. 169(C).
    32. Dabo Guan & Zhu Liu & Yong Geng & Sören Lindner & Klaus Hubacek, 2012. "The gigatonne gap in China’s carbon dioxide inventories," Nature Climate Change, Nature, vol. 2(9), pages 672-675, September.
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