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Do Differences in Modes of Production Affect the Ability of Ecological Restoration Projects to Improve Local Livelihoods?

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  • Bei Xiao

    (School of Economics, Renmin University of China, Beijing 100872, China)

  • Dongying Zhang

    (China Chengxin International Credit Rating Co., Ltd., Beijing 100031, China)

  • Renjun Li

    (School of Economics, Renmin University of China, Beijing 100872, China)

Abstract

Large ecological restoration projects have been widely implemented across the world since the 20th century, yielding complex ecological, economic, and social results. Today, balancing ecological restoration with local people’s livelihoods is a key issue. Based on the existing literature, this study proposes a “shock adaptation” mechanism to describe the response of rural residents’ livelihoods to the impact of ecological restoration projects. We hypothesize that adaptability varies across the modes of production. To verify our hypothesis, we used the machine-learning-based local projection (LP) method to analyze China’s Three-North Shelter Forest Program (TNSFP), with data for 596 counties from 2001 to 2020. After the TNSFP started, rural residents’ income dropped, rose, and then exceeded the starting point over 8 years. Moreover, significant heterogeneity exists between agricultural and pastoral areas. Agricultural areas recover faster and improve livelihoods, while pastoral areas take longer to bounce back. The results confirmed the “shock adaptation” mechanism and suggested the importance of the mode of production. Policymakers should add more social–ecological indicators to their evaluation systems, allow local communities more self-management, and offer extra help to those struggling to recover from shocks.

Suggested Citation

  • Bei Xiao & Dongying Zhang & Renjun Li, 2024. "Do Differences in Modes of Production Affect the Ability of Ecological Restoration Projects to Improve Local Livelihoods?," Land, MDPI, vol. 13(10), pages 1-19, September.
  • Handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1563-:d:1486096
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    References listed on IDEAS

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    1. Edwards, P.E.T. & Sutton-Grier, A.E. & Coyle, G.E., 2013. "Investing in nature: Restoring coastal habitat blue infrastructure and green job creation," Marine Policy, Elsevier, vol. 38(C), pages 65-71.
    2. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    3. Costanza, Robert & de Groot, Rudolf & Braat, Leon & Kubiszewski, Ida & Fioramonti, Lorenzo & Sutton, Paul & Farber, Steve & Grasso, Monica, 2017. "Twenty years of ecosystem services: How far have we come and how far do we still need to go?," Ecosystem Services, Elsevier, vol. 28(PA), pages 1-16.
    4. Susan Athey & Mohsen Bayati & Nikolay Doudchenko & Guido Imbens & Khashayar Khosravi, 2021. "Matrix Completion Methods for Causal Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1716-1730, October.
    5. Xunming Wang & Quansheng Ge & Xin Geng & Zhaosheng Wang & Lei Gao & Brett A. Bryan & Shengqian Chen & Yanan Su & Diwen Cai & Jiansheng Ye & Jimin Sun & Huayu Lu & Huizheng Che & Hong Cheng & Hongyan L, 2023. "Unintended consequences of combating desertification in China," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
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