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Estimating the effectiveness of forest protection using regression discontinuity

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  • Neal, Timothy

Abstract

This article uses satellite data to estimate the effectiveness of government protection on forested land across the globe over 2000–2022. Since deforestation can have significant negative externalities, measuring the effectiveness of protected areas is important for the future of conservation. It uses a regression discontinuity design at the boundaries of protected forest to counter the fact that protection is not randomly assigned. It estimates that protected areas are 30% effective on average, with significant heterogeneity between countries. Many countries with significant forest have extremely ineffective protection, such as Indonesia, the DRC, and Bolivia, suggesting that improvements to the quality of protection are just as important as the quantity of protected areas to conserve biodiversity.

Suggested Citation

  • Neal, Timothy, 2024. "Estimating the effectiveness of forest protection using regression discontinuity," Journal of Environmental Economics and Management, Elsevier, vol. 127(C).
    • Handle: RePEc:eee:jeeman:v:127:y:2024:i:c:s0095069624000950
    DOI: 10.1016/j.jeem.2024.103021
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    References listed on IDEAS

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    Cited by:

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    3. Liu, Qi & Liu, Shilei & Liu, Zhaoyang & Deng, Xiangzheng & Xu, Jintao & Kontoleon, Andreas, 2025. "A regression discontinuity assessment of the differential impacts of China’s natural forest protection program across forestland property right regimes," World Development, Elsevier, vol. 196(C).
    4. Savari, Moslem & Khaleghi, Bagher, 2025. "Promoting safe and pro-environmental behaviors for sustainable forest management: Integrating technology acceptance model and the norm activation model," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).
    5. Lu, Yifan & Yamazaki, Satoshi, 2023. "Antarctic Sanctuary: Behavioural Impact of International Marine Protected Areas," 2023 Annual Meeting, July 23-25, Washington D.C. 335483, Agricultural and Applied Economics Association.
    6. 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.

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    Keywords

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    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • P48 - Political Economy and Comparative Economic Systems - - Other Economic Systems - - - Legal Institutions; Property Rights; Natural Resources; Energy; Environment; Regional Studies
    • Q23 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Forestry
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics

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