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Input-Output Efficiency of Water-Energy-Food and Its Driving Forces: Spatial-Temporal Heterogeneity of Yangtze River Economic Belt, China

Author

Listed:
  • Min Ge

    (Business School, Hohai University, Changzhou 213022, China)

  • Kaili Yu

    (Business School, Hohai University, Changzhou 213022, China)

  • Ange Ding

    (Business School, Hohai University, Changzhou 213022, China)

  • Gaofeng Liu

    (Business School, Hohai University, Changzhou 213022, China)

Abstract

The high-quality development of the Yangtze River Economic Belt (YREB) plays a crucial role in economic transformation in China. Climate change, rapid population growth, and increased urbanization have contributed towards increased pressures on the water, energy, food (WEF) nexus system of YREB. Thus, there is an imperative need to improve the efficiency of WEF in YREB. However, few studies have conducted spatial-temporal heterogeneity exploration of YREB about the input-output efficiency of WEF (IOE-WEF). Using panel data from 2008–2017, a super slack based model (SSBM), combined with the spatial autocorrelation and spatial econometric method, were proposed to calculate the IOE-WEF of YREB’s 11 provinces, the results indicated that: (1) From the perspective of time, the IOE-WEF in YREB was relatively low and displayed a fluctuating downward pattern while considering the undesirable outputs. (2) From the perspective of space, the spatial distribution of IOE-WEF in YREB was uneven. The efficiency values of the three sub-regions of YREB were “the lower reaches > the middle reaches > the upper reaches”. The IOE-WEF of YREB had a prominent positive spatial correlation and also had a spatial spillover effect. (3) The spatial aggregation effect of IOE-WEF of YREB is gradually weakening. The spatial aggregation types of IOE-WEF in YREB were “high-high” cluster areas in lower reaches and “low-low” cluster areas in upper reaches. (4) From the perspective of driving forces, environmental regulation and technological innovation promoted the improvement of IOE-WEF of YREB, while the industrial structure and mechanization level inhibited the improvement of IOE-WEF of YREB. Furthermore, the role of government support of IOE-WEF of YREB was not obvious. The improvement of IOE-WEF in adjacent regions also had a notable positive spatial spillover effect on the region.

Suggested Citation

  • Min Ge & Kaili Yu & Ange Ding & Gaofeng Liu, 2022. "Input-Output Efficiency of Water-Energy-Food and Its Driving Forces: Spatial-Temporal Heterogeneity of Yangtze River Economic Belt, China," IJERPH, MDPI, vol. 19(3), pages 1-15, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:3:p:1340-:d:734034
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    References listed on IDEAS

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    1. 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.
    2. Daohan Huang & Zihao Shen & Chengshuang Sun & Guijun Li, 2021. "Shifting from Production-Based to Consumption-Based Nexus Governance: Evidence from an Input–Output Analysis of the Local Water-Energy-Food Nexus," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(6), pages 1673-1688, April.
    3. Wang, Keying & Wu, Meng & Sun, Yongping & Shi, Xunpeng & Sun, Ao & Zhang, Ping, 2019. "Resource abundance, industrial structure, and regional carbon emissions efficiency in China," Resources Policy, Elsevier, vol. 60(C), pages 203-214.
    4. Hua, En & Wang, Xinyu & Engel, Bernie A. & Qian, Haiyang & Sun, Shikun & Wang, Yubao, 2021. "Water competition mechanism of food and energy industries in WEF Nexus: A case study in China," Agricultural Water Management, Elsevier, vol. 254(C).
    5. 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).
    6. Howarth, Candice & Monasterolo, Irene, 2016. "Understanding barriers to decision making in the UK energy-food-water nexus: The added value of interdisciplinary approaches," Environmental Science & Policy, Elsevier, vol. 61(C), pages 53-60.
    7. Wang, Shuhong & Zhao, Danqing & Chen, Hanxue, 2020. "Government corruption, resource misallocation, and ecological efficiency," Energy Economics, Elsevier, vol. 85(C).
    8. bahri, muhamad, 2020. "Analysis of the water, energy, and food nexus using system archetypes: A case study in the Jatiluhur reservoir, West Java, Indonesia," SocArXiv c9b7d, Center for Open Science.
    9. Tone, Kaoru, 2002. "A slacks-based measure of super-efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 143(1), pages 32-41, November.
    10. Xiao, Zhengyan & Yao, Meiqin & Tang, Xiaotong & Sun, Luxi, 2019. "Identifying critical supply chains: An input-output analysis for Food-Energy-Water Nexus in China," Ecological Modelling, Elsevier, vol. 392(C), pages 31-37.
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