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Coupling between Carbon Efficiency and Technology Absorptive Capacity—A Case Study of the Yangtze River Economic Belt

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  • Xiaoming Jiang

    (Business School, Hohai University, Nanjing 211100, China)

  • Chuiyong Zheng

    (Business School, Hohai University, Nanjing 211100, China)

  • Chao Liu

    (School of Public Administration, Hohai University, Nanjing 211100, China)

  • Wenjian Zhang

    (School of Internet of Things Technology, Wuxi Institute of Technology, Wuxi 214121, China)

Abstract

Regional carbon efficiency (CE) improvement is critical to China’s “taking concerted efforts to achieve ecological protection” strategy in the Yangtze River Economic Belt (YREB) and their program to build a leading demonstration belt for ecological civilization. This study applied the super efficiency slacks-based measure to calculate the regional differences and evolution characteristics of the YREB’s CE from the year of 2006 to 2017. It also constructed a coupling evaluation model to empirically analyze the interactions between CE and technology absorptive capacity (TAC). The results showed that (1) the CE for all YREB provinces followed a “U-shaped” trend. TAC generally increased and incrementally decreased in the sequence of the upper stream, middle stream, and downstream. The gap among the downstream, upper stream, and middle stream increased; (2) coupling between the CE and TAC for the YREB provinces can be characterized as a relatively stable medium to low coupling degree and medium-to-high coordination degree. To improve coupling and achieve balanced, sustainable development in the YREB, this study proposes several measures, including promoting balanced, high-quality economic development, building the YREB talent pool, appropriately guiding foreign capital flows, implementing the strategy of driving economic development through innovation, and launching the network for coordinated technological innovation in YREB.

Suggested Citation

  • Xiaoming Jiang & Chuiyong Zheng & Chao Liu & Wenjian Zhang, 2020. "Coupling between Carbon Efficiency and Technology Absorptive Capacity—A Case Study of the Yangtze River Economic Belt," Sustainability, MDPI, vol. 12(19), pages 1-16, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:19:p:8010-:d:420737
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    References listed on IDEAS

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    1. Tol, Richard S.J. & Pacala, Stephen W. & Socolow, Robert H., 2009. "Understanding Long-Term Energy Use and Carbon Dioxide Emissions in the USA," Journal of Policy Modeling, Elsevier, vol. 31(3), pages 425-445, May.
    2. Meng, Fanyi & Su, Bin & Thomson, Elspeth & Zhou, Dequn & Zhou, P., 2016. "Measuring China’s regional energy and carbon emission efficiency with DEA models: A survey," Applied Energy, Elsevier, vol. 183(C), pages 1-21.
    3. Per Andersen & Niels Christian Petersen, 1993. "A Procedure for Ranking Efficient Units in Data Envelopment Analysis," Management Science, INFORMS, vol. 39(10), pages 1261-1264, October.
    4. Ang, B. W., 1999. "Is the energy intensity a less useful indicator than the carbon factor in the study of climate change?," Energy Policy, Elsevier, vol. 27(15), pages 943-946, December.
    5. Zhang, Ning & Wei, Xiao, 2015. "Dynamic total factor carbon emissions performance changes in the Chinese transportation industry," Applied Energy, Elsevier, vol. 146(C), pages 409-420.
    6. Marcucci, Adriana & Fragkos, Panagiotis, 2015. "Drivers of regional decarbonization through 2100: A multi-model decomposition analysis," Energy Economics, Elsevier, vol. 51(C), pages 111-124.
    7. Yao, Xin & Zhou, Hongchen & Zhang, Aizhen & Li, Aijun, 2015. "Regional energy efficiency, carbon emission performance and technology gaps in China: A meta-frontier non-radial directional distance function analysis," Energy Policy, Elsevier, vol. 84(C), pages 142-154.
    8. K Tone, 2002. "A strange case of the cost and allocative efficiencies in DEA," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 53(11), pages 1225-1231, November.
    9. Sun, J. W., 2005. "The decrease of CO2 emission intensity is decarbonization at national and global levels," Energy Policy, Elsevier, vol. 33(8), pages 975-978, May.
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    1. Tianlin Zhai & Jing Wang & Ying Fang & Jingjing Liu & Longyang Huang & Kun Chen & Chenchen Zhao, 2021. "Identification and Prediction of Wetland Ecological Risk in Key Cities of the Yangtze River Economic Belt: From the Perspective of Land Development," Sustainability, MDPI, vol. 13(1), pages 1-17, January.

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