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Research on the Heterogeneity of Green Biased Technology Progress in Chinese Industries: Decomposition Index Analysis Based on the Slacks-based measure integrating

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Listed:
  • Yuxin Meng

    (College of Economics and Management, Xinjiang University, Urumqi, China)

  • Lu Liu

    (College of Economics and Management, Xinjiang University, Urumqi, China)

  • Zhenlong Xu

    (Kogod Business School, American University, Washington, USA)

  • Wenwen Gong

    (College of Economics, Xinjiang Institute of Technology, Aksu, China)

  • Guanpeng Yan

    (School of Economics, Shandong University, Jinan, China)

Abstract

Green-biased technological progress takes into account the influence of energy input and pollution emissions, which is of great significance to China's green development. This paper decomposes technological progress into two categories: green input-biased technological progress (IBTC) and green output-biased technological progress (OBTC), using the Slacks-based measure integrating (SBM) model. The factor bias in technological progress is determined based on data from 34 industries in China from 2000 to 2015. The results show that green-biased technological progress exists significantly in the industry, and most of it promotes the growth of green total factor productivity. IBTC first tends to consume energy to pursue capital between capital input and energy input, while it tends to save energy after the Eleventh Five-Year Plan. Between labor input and energy input, it is biased towards saving labor and consuming resources. OBTC is biased towards promoting industrial growth and curbing pollution emissions. Medium and light-polluting industries are biased toward promoting industrial growth and curbing pollution emissions, while heavy-polluting industries are biased towards emitting more pollution.

Suggested Citation

  • Yuxin Meng & Lu Liu & Zhenlong Xu & Wenwen Gong & Guanpeng Yan, 2022. "Research on the Heterogeneity of Green Biased Technology Progress in Chinese Industries: Decomposition Index Analysis Based on the Slacks-based measure integrating," Journal of Economic Analysis, Anser Press, vol. 1(2), pages 17-34, December.
    • Handle: RePEc:bba:j00001:v:1:y:2022:i:2:p:17-34:d:17
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    References listed on IDEAS

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    1. Rolf Fare & Shawna Grosskopf & Wen-Fu Lee, 2001. "Productivity and technical change: the case of Taiwan," Applied Economics, Taylor & Francis Journals, vol. 33(15), pages 1911-1925.
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    2. Jing Rong & Wei Wang & Haijun Zhang, 2024. "Does artificial intelligence improve energy productivity in China's industrial sector? Empirical evidence based on the spatial moderation model," Energy & Environment, , vol. 35(8), pages 4026-4048, December.
    3. Xiaowei Song & Jun Yang, 2024. "Assessing the impact of digitization and servitization of manufacturing firms in the context of carbon emission reduction: Evidence from a microsurvey in China," Energy & Environment, , vol. 35(7), pages 3340-3385, November.

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