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Investigating the transformation efficiency of scientific and technological achievements in China’s equipment manufacturing industry under the low-carbon economy
[Environment policy and technological change]

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  • Weijuan Li
  • Pengcheng Zhang

Abstract

The development of a low-carbon economy is an inevitable choice for the world to coordinate industrial economic growth and environmental issues actively. At the same time, science and technology are the cornerstones for the development of a low-carbon economy. The equipment manufacturing industry (EMI) in China is known as the base of the low-carbon sector. Still, the research of coordinating industry carbon emission and economic growth from the perspective of science and technology is insufficient. For this reason, this work comprehensively analyzed the economic development and carbon emission of China’s EMI. The DEA (data envelopment analysis) Malmquist method was used to measure the transformation efficiency of scientific and technological achievements of the EMI from 2009 to 2017. The results can show that: (1) the economic benefits of China’s EMI were increasing year by year, but the growth rate is declining. With the optimization of industrial structure, the energy consumption and carbon emission of the industry have improved, but there is still a large gap between different sectors; (2) the achievement transformation of EMI decreases by year due to the influence of technological progress efficiency; and (3) in terms of sector data efficiency in 2017, there is redundancy in the investment of general EMI (B2) and special EMI (B3). This work can provide a reference for the development of countries dominated by industry and to jointly realize the sustainable development of the world economy.

Suggested Citation

  • Weijuan Li & Pengcheng Zhang, 2021. "Investigating the transformation efficiency of scientific and technological achievements in China’s equipment manufacturing industry under the low-carbon economy [Environment policy and technologic," International Journal of Low-Carbon Technologies, Oxford University Press, vol. 16(1), pages 135-145.
  • Handle: RePEc:oup:ijlctc:v:16:y:2021:i:1:p:135-145.
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    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Kim, Wonjoon & Kim, Minki, 2015. "Reference quality-based competitive market structure for innovation driven markets," International Journal of Research in Marketing, Elsevier, vol. 32(3), pages 284-296.
    3. Caves, Douglas W & Christensen, Laurits R & Diewert, W Erwin, 1982. "The Economic Theory of Index Numbers and the Measurement of Input, Output, and Productivity," Econometrica, Econometric Society, vol. 50(6), pages 1393-1414, November.
    4. Chenxi Li & Xing Gao & Bao-Jie He & Jingyao Wu & Kening Wu, 2019. "Coupling Coordination Relationships between Urban-industrial Land Use Efficiency and Accessibility of Highway Networks: Evidence from Beijing-Tianjin-Hebei Urban Agglomeration, China," Sustainability, MDPI, vol. 11(5), pages 1-23, March.
    5. He, Bao-Jie & Zhu, Jin & Zhao, Dong-Xue & Gou, Zhong-Hua & Qi, Jin-Da & Wang, Junsong, 2019. "Co-benefits approach: Opportunities for implementing sponge city and urban heat island mitigation," Land Use Policy, Elsevier, vol. 86(C), pages 147-157.
    6. Lampe, Hannes W. & Hilgers, Dennis, 2015. "Trajectories of efficiency measurement: A bibliometric analysis of DEA and SFA," European Journal of Operational Research, Elsevier, vol. 240(1), pages 1-21.
    7. Bi, Kexin & Huang, Ping & Wang, Xiangxiang, 2016. "Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 275-284.
    8. Doukas, John & Switzer, Lorne, 1992. "The stock market's valuation of R&D spending and market concentration," Journal of Economics and Business, Elsevier, vol. 44(2), pages 95-114, May.
    9. Feng, Gen-Fu & Zheng, Mingbo & Wen, Jun & Chang, Chun-Ping & Chen, Yin E., 2019. "The assessment of globalization on innovation in Chinese manufacturing firms," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 190-202.
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    Cited by:

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    2. Yuan Cao & Jingxian Liu & Ying Yang & Xiaolin Liu & Zhixuan Liu & Ning Lv & Hongkun Ma & Zhenyao Wang & Hongtu Li, 2023. "Construct a Regional Innovation Ecosystem: A Case Study of the Beijing-Tianjin-Hebei Region in China," Sustainability, MDPI, vol. 15(9), pages 1-21, April.

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