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Factors Contributing to Efficient Forest Production in the Region of the Three-North Shelter Forest Program, China

Author

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  • Chao Wang

    (State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Xi Chu

    (State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Jinyan Zhan

    (State Key Laboratory of Water Environment Simulation, School of Environment, Beijing Normal University, Beijing 100875, China)

  • Pei Wang

    (College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China)

  • Fan Zhang

    (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    Center for Chinese Agricultural Policy, Chinese Academy of Sciences, Beijing 100101, China)

  • Zhongling Xin

    (School of Land Science and Technology, China University of Geosciences, Beijing 100083, China)

Abstract

Forests are the most important renewable resources and provide critical ecosystem services worldwide, especially the provisioning service, making a great contribution to human well-being. The Three-North Shelter Forest Program (TNSFP) is a large-scale ecological project aimed at improving ecological environments and consolidating economic construction in China through the development of artificial forests. In our study, stochastic frontier analysis was adopted to estimate forest production efficiency (FPE) by using dynamic panel data. Based on the FPE of 13 provinces located within the TNSFP region during the period 2000–2016, the effects of the natural and socioeconomic influencing factors on FPE were further explored by using the Tobit regression model. The estimated results confirmed the validity of the constructed model and revealed an increasing trend of the mean annual FPE value, which ranged from 0.3147 in 2000 to 0.5681 in 2016. The FPE was declining from the eastern region to the western region in 2000. However, this spatial distribution characteristic changed enormously in 2016; regions with low FPE were in the center of the TNSFP region, surrounded by the regions with high FPE. Moreover, the following factors positively influenced FPE: average temperature (1.4476), total annual rainfall (0.0800), per capita GDP (0.0882), the education levels of forestry employees (0.2120), the number of forest technology stations in townships (0.0149), and disease and pest control areas (0.0190). However, the impacts of the policy relating to the Natural Forest Protection Program on FPE were insignificant. These influencing factors had differential effects on FPE within the TNSFP’s three sub-regions during the period 2000–2016. These findings can contribute to more efficient forest management and strengthen resource and environment management.

Suggested Citation

  • Chao Wang & Xi Chu & Jinyan Zhan & Pei Wang & Fan Zhang & Zhongling Xin, 2019. "Factors Contributing to Efficient Forest Production in the Region of the Three-North Shelter Forest Program, China," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:12:y:2019:i:1:p:302-:d:303430
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    References listed on IDEAS

    as
    1. Amemiya, Takeshi, 1973. "Regression Analysis when the Dependent Variable is Truncated Normal," Econometrica, Econometric Society, vol. 41(6), pages 997-1016, November.
    2. Kuosmanen, Timo & Saastamoinen, Antti & Sipiläinen, Timo, 2013. "What is the best practice for benchmark regulation of electricity distribution? Comparison of DEA, SFA and StoNED methods," Energy Policy, Elsevier, vol. 61(C), pages 740-750.
    3. Deng, Xiangzheng & Gibson, John, 2019. "Improving eco-efficiency for the sustainable agricultural production: A case study in Shandong, China," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 394-400.
    4. Lin, Boqiang & Wang, Xiaolei, 2015. "Carbon emissions from energy intensive industry in China: Evidence from the iron & steel industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 746-754.
    5. Gershom Endelani Mwalupaso & Shangao Wang & Sanzidur Rahman & Essiagnon John-Philippe Alavo & Xu Tian, 2019. "Agricultural Informatization and Technical Efficiency in Maize Production in Zambia," Sustainability, MDPI, vol. 11(8), pages 1-17, April.
    6. Makridou, Georgia & Andriosopoulos, Kostas & Doumpos, Michael & Zopounidis, Constantin, 2016. "Measuring the efficiency of energy-intensive industries across European countries," Energy Policy, Elsevier, vol. 88(C), pages 573-583.
    7. Jinyan Zhan & Fan Zhang & Siqi Jia & Xi Chu & Yifan Li, 2018. "Spatial Pattern of Regional Urbanization Efficiency: An Empirical Study of Shanghai," Computational Economics, Springer;Society for Computational Economics, vol. 52(4), pages 1277-1291, December.
    8. Fall, François & Akim, Al-mouksit & Wassongma, Harouna, 2018. "DEA and SFA research on the efficiency of microfinance institutions: A meta-analysis," World Development, Elsevier, vol. 107(C), pages 176-188.
    9. Marcos Adami & Bernardo Friedrich Theodor Rudorff & Ramon Morais Freitas & Daniel Alves Aguiar & Luciana Miura Sugawara & Marcio Pupin Mello, 2012. "Remote Sensing Time Series to Evaluate Direct Land Use Change of Recent Expanded Sugarcane Crop in Brazil," Sustainability, MDPI, vol. 4(4), pages 1-12, April.
    10. Li, Nan & Jiang, Yuqing & Mu, Hailin & Yu, Zhixin, 2018. "Efficiency evaluation and improvement potential for the Chinese agricultural sector at the provincial level based on data envelopment analysis (DEA)," Energy, Elsevier, vol. 164(C), pages 1145-1160.
    11. Gidion, Dickson K. & Hong, Jin & Adams, Magdalene Z.A. & Khoveyni, Mohammad, 2019. "Network DEA models for assessing urban water utility efficiency," Utilities Policy, Elsevier, vol. 57(C), pages 48-58.
    12. 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.
    13. Du, Kerui & Lin, Boqiang, 2017. "International comparison of total-factor energy productivity growth: A parametric Malmquist index approach," Energy, Elsevier, vol. 118(C), pages 481-488.
    14. Eggers, Jeannette & Holmgren, Sara & Nordström, Eva-Maria & Lämås, Tomas & Lind, Torgny & Öhman, Karin, 2019. "Balancing different forest values: Evaluation of forest management scenarios in a multi-criteria decision analysis framework," Forest Policy and Economics, Elsevier, vol. 103(C), pages 55-69.
    15. Wanke, Peter & Abul Kalam Azad, Md & Emrouznejad, Ali & Antunes, Jorge, 2019. "A dynamic network DEA model for accounting and financial indicators: A case of efficiency in MENA banking," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 52-68.
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