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Impact of Agricultural Industrial Agglomeration on Agricultural Environmental Efficiency in China: A Spatial Econometric Analysis

Author

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  • Rendao Ye

    (School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Yue Qi

    (School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

  • Wenyan Zhu

    (School of Economics, Hangzhou Dianzi University, Hangzhou 310018, China)

Abstract

In recent years, China has made remarkable progress in increasing yield at the expense of resource depletion, excessive consumption, and overexploitation. Improving agricultural environmental efficiency (AEE) is crucial to achieve agricultural modernization and facilitate a green transformation. Agricultural industrial agglomeration (AIA), as a main policy in industrial space organization, is an effective way to promote resource allocation optimization. This paper selects panel data of 31 provinces in China from 2000 to 2020 and employs the fixed-effects stochastic frontier analysis with hyperbolic distance function to measure AEE. Based on this, an empirical analysis is conducted to investigate the impact of AIA on AEE. The study finds that the average value of AEE is 0.909, which needs to be further improved. Meanwhile, AEE demonstrates obvious agglomeration characteristics and positive correlations with space. AIA exerts an inverted U-shaped effect on AEE in local and neighboring regions. Therefore, this paper believes that to improve AEE, it is essential to carry out dynamic and differentiated strategies of industrial agglomeration, ensuring the level of AIA remains within a reasonable range and effectively eliminates the congestion effect.

Suggested Citation

  • Rendao Ye & Yue Qi & Wenyan Zhu, 2023. "Impact of Agricultural Industrial Agglomeration on Agricultural Environmental Efficiency in China: A Spatial Econometric Analysis," Sustainability, MDPI, vol. 15(14), pages 1-18, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:14:p:10799-:d:1190531
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    as
    1. Amigues, Jean-Pierre & Moreaux, Michel, 2019. "Competing land uses and fossil fuel, and optimal energy conversion rates during the transition toward a green economy under a pollution stock constraint," Journal of Environmental Economics and Management, Elsevier, vol. 97(C), pages 92-115.
    2. Martin Andersson & Hans Lööf, 2011. "Agglomeration and productivity: evidence from firm-level data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 46(3), pages 601-620, June.
    3. Yuan, Huaxi & Feng, Yidai & Lee, Chien-Chiang & Cen, Yan, 2020. "How does manufacturing agglomeration affect green economic efficiency?," Energy Economics, Elsevier, vol. 92(C).
    4. Wu, Jianxin & Xu, Hui & Tang, Kai, 2021. "Industrial agglomeration, CO2 emissions and regional development programs: A decomposition analysis based on 286 Chinese cities," Energy, Elsevier, vol. 225(C).
    5. Shen, Neng & Peng, Hui, 2021. "Can industrial agglomeration achieve the emission-reduction effect?," Socio-Economic Planning Sciences, Elsevier, vol. 75(C).
    6. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    7. J. Paul Elhorst, 2014. "Spatial Panel Data Models," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 37-93, Springer.
    8. Belotti, Federico & Ilardi, Giuseppe, 2018. "Consistent inference in fixed-effects stochastic frontier models," Journal of Econometrics, Elsevier, vol. 202(2), pages 161-177.
    9. Henderson, J. Vernon, 1986. "Efficiency of resource usage and city size," Journal of Urban Economics, Elsevier, vol. 19(1), pages 47-70, January.
    10. Schmidt, Peter & Sickles, Robin C, 1984. "Production Frontiers and Panel Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 2(4), pages 367-374, October.
    11. Cuesta, Rafael A. & Lovell, C.A. Knox & Zofío, José L., 2009. "Environmental efficiency measurement with translog distance functions: A parametric approach," Ecological Economics, Elsevier, vol. 68(8-9), pages 2232-2242, June.
    12. Derek Headey & Mohammad Alauddin & D.S. Prasada Rao, 2010. "Explaining agricultural productivity growth: an international perspective," Agricultural Economics, International Association of Agricultural Economists, vol. 41(1), pages 1-14, January.
    13. Chen, Yi-Yi & Schmidt, Peter & Wang, Hung-Jen, 2014. "Consistent estimation of the fixed effects stochastic frontier model," Journal of Econometrics, Elsevier, vol. 181(2), pages 65-76.
    14. Baomin Dong & Jiong Gong & Xin Zhao, 2012. "FDI and environmental regulation: pollution haven or a race to the top?," Journal of Regulatory Economics, Springer, vol. 41(2), pages 216-237, April.
    15. Wang, Yafei & Bai, Ying & Quan, Tianshu & Ran, Rong & Hua, Lei, 2023. "Influence and effect of industrial agglomeration on urban green total factor productivity—On the regulatory role of innovation agglomeration and institutional distance," Economic Analysis and Policy, Elsevier, vol. 78(C), pages 1158-1173.
    16. Cullinane, Kevin & Wang, Teng-Fei & Song, Dong-Wook & Ji, Ping, 2006. "The technical efficiency of container ports: Comparing data envelopment analysis and stochastic frontier analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(4), pages 354-374, May.
    17. Honma, Satoshi & Hu, Jin-Li, 2014. "A panel data parametric frontier technique for measuring total-factor energy efficiency: An application to Japanese regions," Energy, Elsevier, vol. 78(C), pages 732-739.
    18. Greene, William, 2005. "Reconsidering heterogeneity in panel data estimators of the stochastic frontier model," Journal of Econometrics, Elsevier, vol. 126(2), pages 269-303, June.
    19. Cohen, Jeffrey P. & Paul, Catherine J. Morrison, 2005. "Agglomeration economies and industry location decisions: the impacts of spatial and industrial spillovers," Regional Science and Urban Economics, Elsevier, vol. 35(3), pages 215-237, May.
    20. Li, Xuehui & Xu, Yangyang & Yao, Xin, 2021. "Effects of industrial agglomeration on haze pollution: A Chinese city-level study," Energy Policy, Elsevier, vol. 148(PA).
    21. Shang, Hua & Jiang, Li & Pan, Xianyou & Pan, Xiongfeng, 2022. "Green technology innovation spillover effect and urban eco-efficiency convergence: Evidence from Chinese cities," Energy Economics, Elsevier, vol. 114(C).
    22. Kumbhakar, Subal C., 1990. "Production frontiers, panel data, and time-varying technical inefficiency," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 201-211.
    23. Michael Kevane, 1996. "Agrarian Structure and Agricultural Practice: Typology and Application to Western Sudan," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 78(1), pages 236-245.
    24. Gong, Binlei, 2018. "Agricultural reforms and production in China: Changes in provincial production function and productivity in 1978–2015," Journal of Development Economics, Elsevier, vol. 132(C), pages 18-31.
    25. Stevenson, Rodney E., 1980. "Likelihood functions for generalized stochastic frontier estimation," Journal of Econometrics, Elsevier, vol. 13(1), pages 57-66, May.
    26. Greene, William H., 1990. "A Gamma-distributed stochastic frontier model," Journal of Econometrics, Elsevier, vol. 46(1-2), pages 141-163.
    27. Reinhard, Stijn & Knox Lovell, C. A. & Thijssen, Geert J., 2000. "Environmental efficiency with multiple environmentally detrimental variables; estimated with SFA and DEA," European Journal of Operational Research, Elsevier, vol. 121(2), pages 287-303, March.
    28. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
    29. Han, Feng & Xie, Rui & Fang, Jiayu, 2018. "Urban agglomeration economies and industrial energy efficiency," Energy, Elsevier, vol. 162(C), pages 45-59.
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