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Hidden cost of conservation: A demonstration using losses from human-wildlife conflicts under a payments for ecosystem services program

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  • Yang, Hongbo
  • Lupi, Frank
  • Zhang, Jindong
  • Liu, Jianguo

Abstract

As global efforts to protect ecosystems expand, there is increasing concern about conservation costs borne by rural communities. To date, these costs have often been narrowly estimated in terms of foregone livelihood opportunities directly caused by conservation, while unintended human burdens that accrue with ecological gains from conservation are often ignored. As a first attempt to quantify this previously hidden cost, we estimated the impact of converting cropland to forest under one of the world’s largest conservation policies, China’s Grain-to-Green Program (GTGP), on crop raiding in a demonstration site using the matching approach. We found that GTGP afforestation was responsible for 64 % of the crop damage by wildlife on remaining cropland, a cost worth 27 % of GTGP’s total payment to farmers. Our study highlights that the conservation cost to communities through influencing human-wildlife conflicts can be substantial, which should be quantified and considered in global conservation efforts to avoid unintended burdens on rural communities.

Suggested Citation

  • Yang, Hongbo & Lupi, Frank & Zhang, Jindong & Liu, Jianguo, 2020. "Hidden cost of conservation: A demonstration using losses from human-wildlife conflicts under a payments for ecosystem services program," Ecological Economics, Elsevier, vol. 169(C).
  • Handle: RePEc:eee:ecolec:v:169:y:2020:i:c:s092180091831187x
    DOI: 10.1016/j.ecolecon.2019.106462
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    References listed on IDEAS

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    1. United Nations, 2016. "The Sustainable Development Goals 2016," Working Papers id:11456, eSocialSciences.
    2. Alexis Diamond & Jasjeet S. Sekhon, 2013. "Genetic Matching for Estimating Causal Effects: A General Multivariate Matching Method for Achieving Balance in Observational Studies," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 932-945, July.
    3. Sekhon, Jasjeet S., 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i07).
    4. Schomers, Sarah & Matzdorf, Bettina, 2013. "Payments for ecosystem services: A review and comparison of developing and industrialized countries," Ecosystem Services, Elsevier, vol. 6(C), pages 16-30.
    5. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
    6. Alberto Abadie & Guido W. Imbens, 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, Econometric Society, vol. 74(1), pages 235-267, January.
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    Cited by:

    1. Kaiwen Su & Jie Ren & Jie Yang & Yilei Hou & Yali Wen, 2020. "Human-Elephant Conflicts and Villagers’ Attitudes and Knowledge in the Xishuangbanna Nature Reserve, China," IJERPH, MDPI, vol. 17(23), pages 1-16, November.
    2. Wang, Yahui & Yang, Aoxi & Li, Yuanqing & Yang, Qingyuan, 2023. "Effect of e-commerce popularization on farmland abandonment in rural China: Evidence from a large-scale household survey," Land Use Policy, Elsevier, vol. 135(C).

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