IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i10p1563-d1486096.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/10/1563/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/10/1563/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    2. Dmitry Arkhangelsky & Guido Imbens, 2023. "Causal Models for Longitudinal and Panel Data: A Survey," Papers 2311.15458, arXiv.org, revised Jun 2024.
    3. Arne Henningsen & Guy Low & David Wuepper & Tobias Dalhaus & Hugo Storm & Dagim Belay & Stefan Hirsch, 2024. "Estimating Causal Effects with Observational Data: Guidelines for Agricultural and Applied Economists," IFRO Working Paper 2024/03, University of Copenhagen, Department of Food and Resource Economics.
    4. Dmitry Arkhangelsky & Guido W. Imbens & Lihua Lei & Xiaoman Luo, 2021. "Design-Robust Two-Way-Fixed-Effects Regression For Panel Data," Papers 2107.13737, arXiv.org, revised Mar 2024.
    5. Retsef Levi & Elisabeth Paulson & Georgia Perakis & Emily Zhang, 2024. "Heterogeneous Treatment Effects in Panel Data," Papers 2406.05633, arXiv.org.
    6. Tianlin Zhai & Yuanbo Ma & Ying Fang & Mingyuan Chang & Longyang Huang & Ziyi Ma & Ling Li & Chenchen Zhao, 2024. "Research on the Optimization of Urban Ecological Infrastructure Based on Ecosystem Service Supply, Demand, and Flow," Land, MDPI, vol. 13(2), pages 1-25, February.
    7. Nicolaj N. Mühlbach, 2020. "Tree-based Synthetic Control Methods: Consequences of moving the US Embassy," CREATES Research Papers 2020-04, Department of Economics and Business Economics, Aarhus University.
    8. Ben Deaner & Chen-Wei Hsiang & Andrei Zeleneev, 2025. "Inferring Treatment Effects in Large Panels by Uncovering Latent Similarities," Papers 2503.20769, arXiv.org, revised Mar 2025.
    9. Alberto Abadie & Anish Agarwal & Raaz Dwivedi & Abhin Shah, 2024. "Doubly Robust Inference in Causal Latent Factor Models," Papers 2402.11652, arXiv.org, revised Oct 2024.
    10. Chen, Haojie & Costanza, Robert, 2024. "Valuation and management of desert ecosystems and their services," Ecosystem Services, Elsevier, vol. 66(C).
    11. Yixiao Sun & Haitian Xie & Yuhang Zhang, 2025. "Difference-in-Differences Meets Synthetic Control: Doubly Robust Identification and Estimation," Papers 2503.11375, arXiv.org, revised Sep 2025.
    12. Viviano, Davide & Bradic, Jelena, 2023. "Synthetic Learner: Model-free inference on treatments over time," Journal of Econometrics, Elsevier, vol. 234(2), pages 691-713.
    13. Ke Sun & Linglong Kong & Hongtu Zhu & Chengchun Shi, 2024. "ARMA-Design: Optimal Treatment Allocation Strategies for A/B Testing in Partially Observable Time Series Experiments," Papers 2408.05342, arXiv.org, revised Jan 2025.
    14. Asanov, Anastasiya-Mariya & Asanov, Igor & Buenstorf, Guido, 2024. "A low-cost digital first aid tool to reduce psychological distress in refugees: A multi-country randomized controlled trial of self-help online in the first months after the invasion of Ukraine," Social Science & Medicine, Elsevier, vol. 362(C).
    15. Aryal, Kishor & Maraseni, Tek & Apan, Armando, 2023. "Examining policy−institution−program (PIP) responses against the drivers of ecosystem dynamics. A chronological review (1960–2020) from Nepal," Land Use Policy, Elsevier, vol. 132(C).
    16. Sant’Anna, Pedro H.C. & Zhao, Jun, 2020. "Doubly robust difference-in-differences estimators," Journal of Econometrics, Elsevier, vol. 219(1), pages 101-122.
    17. Busch, Christin & Specht, Kathrin & Inostroza, Luis & Falke, Matthias & Zepp, Harald, 2024. "Disentangling cultural ecosystem services co-production in urban green spaces through social media reviews," Ecosystem Services, Elsevier, vol. 70(C).
    18. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    19. Kirill Borusyak & Peter Hull & Xavier Jaravel, 2025. "Design-based identification with formula instruments: a review," The Econometrics Journal, Royal Economic Society, vol. 28(1), pages 83-108.
    20. Yoganathan, Vignesh & Osburg, Victoria-Sophie, 2024. "The mind in the machine: Estimating mind perception's effect on user satisfaction with voice-based conversational agents," Journal of Business Research, Elsevier, vol. 175(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jlands:v:13:y:2024:i:10:p:1563-:d:1486096. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.