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Environmental Efficiency of Chinese Open-Field Grape Production: An Evaluation Using Data Envelopment Analysis and Spatial Autocorrelation

Author

Listed:
  • Dong Tian

    (College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China)

  • Fengtao Zhao

    (College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China)

  • Weisong Mu

    (College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China)

  • Radoslava Kanianska

    (Faculty of Natural Sciences, Matej Bel University in Banská Bystrica, Tajovského 40, Banská Bystrica 974 01, Slovakia)

  • Jianying Feng

    (College of Information and Electrical Engineering, China Agricultural University, Beijing100083, China)

Abstract

Grape production is associated with some negative environmental externalities. However, they are not considered in the traditional data envelopment analysis (DEA) efficiency assessment models and the research literature. Hence, the assessment results cannot correctly reflect the technical efficiency level of open-field grape production. We measured the environmental efficiency of China’s open-field grape production under the constraint of carbon emissions using the slacks-based measure (SBM) model, including the undesirable outputs. In addition, spatial relations of environmental efficiency in different open-field grape production areas in China were evaluated by adopting spatial econometric methods. The results indicate that the average environmental efficiency score of grape production in China is at a low level of 0.651. Overall, the average environmental efficiencies in southern, southwest, and northeast regions are lower than the average levels, which implies the imbalance in economic outputs, resource consumption, and environmental efficiency in open-field grape cultivation. Moreover, the spatial autocorrelation results show that the environmental efficiency of grape production has obvious continuity in neighboring regions and spatial correlation.

Suggested Citation

  • Dong Tian & Fengtao Zhao & Weisong Mu & Radoslava Kanianska & Jianying Feng, 2016. "Environmental Efficiency of Chinese Open-Field Grape Production: An Evaluation Using Data Envelopment Analysis and Spatial Autocorrelation," Sustainability, MDPI, vol. 8(12), pages 1-13, November.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:12:p:1246-:d:84112
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    3. Leonidas Sotirios Kyrgiakos & Georgios Kleftodimos & George Vlontzos & Panos M. Pardalos, 2023. "A systematic literature review of data envelopment analysis implementation in agriculture under the prism of sustainability," Operational Research, Springer, vol. 23(1), pages 1-38, March.
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    5. Muhammad Faisal Majid & Muhammad Meraj & Muhammad Shujaat Mubarik, 2022. "In the Pursuit of Environmental Sustainability: The Role of Environmental Accounting," Sustainability, MDPI, vol. 14(11), pages 1-20, May.
    6. Paul Mugambi & Miguel Blanco & Daniel Ogachi & Marcos Ferasso & Lydia Bares, 2021. "Analysis of the Regional Efficiency of European Funds in Spain from the Perspective of Renewable Energy Production: The Regional Dimension," IJERPH, MDPI, vol. 18(9), pages 1-16, April.
    7. Karambu Kiende Gatimbu & Maurice Juma Ogada & Nancy L. M. Budambula, 2020. "Environmental efficiency of small-scale tea processors in Kenya: an inverse data envelopment analysis (DEA) approach," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(4), pages 3333-3345, April.
    8. Gang Tian & Jian Shi & Licheng Sun & Xingle Long & Benhai Guo, 2017. "Dynamic changes in the energy–carbon performance of Chinese transportation sector: a meta-frontier non-radial directional distance function approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(2), pages 585-607, November.
    9. George Vlontzos & Spyros Niavis & Panos Pardalos, 2017. "Testing for Environmental Kuznets Curve in the EU Agricultural Sector through an Eco-(in)Efficiency Index," Energies, MDPI, vol. 10(12), pages 1-15, December.

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