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A Top-Down Approach to Estimating Spatially Heterogeneous Impacts of Development Aid on Vegetative Carbon Sequestration

Author

Listed:
  • Daniel Runfola

    (Institute for the Theory and Practice of International Relations, The College of William and Mary, Williamsburg, VA 23188, USA)

  • Ariel BenYishay

    (Institute for the Theory and Practice of International Relations, The College of William and Mary, Williamsburg, VA 23188, USA)

  • Jeffery Tanner

    (Independent Evaluation Group, The World Bank, Washington, DC 20433, USA)

  • Graeme Buchanan

    (Center for Conservation Science, Royal Society for the Protection of Birds, Lydney, South-west GL154JA, UK)

  • Jyoteshwar Nagol

    (Global Land Cover Facility, University of Maryland, College Park, MD 20742, USA)

  • Matthias Leu

    (Department of Biology, The College of William and Mary, Williamsburg, VA 23185, USA)

  • Seth Goodman

    (Institute for the Theory and Practice of International Relations, The College of William and Mary, Williamsburg, VA 23188, USA)

  • Rachel Trichler

    (Institute for the Theory and Practice of International Relations, The College of William and Mary, Williamsburg, VA 23188, USA)

  • Robert Marty

    (Institute for the Theory and Practice of International Relations, The College of William and Mary, Williamsburg, VA 23188, USA)

Abstract

Since 1945, over $4.9 trillion dollars of international aid has been allocated to developing countries. To date, there have been no estimates of the regional impact of this aid on the carbon cycle. We apply a geographically explicit matching method to estimate the relative impact of large-scale World Bank projects implemented between 2000 and 2010 on sequestered carbon, using a novel and publicly available data set of 61,243 World Bank project locations. Considering only carbon sequestered due to fluctuations in vegetative biomass caused by World Bank projects, we illustrate the relative impact of World Bank projects on carbon sequestration. We use this information to illustrate the geographic variation in the apparent effectiveness of environmental safeguards implemented by the World Bank. We argue that sub-national data can help to identify geographically heterogeneous impact effects, and highlight many remaining methodological challenges.

Suggested Citation

  • Daniel Runfola & Ariel BenYishay & Jeffery Tanner & Graeme Buchanan & Jyoteshwar Nagol & Matthias Leu & Seth Goodman & Rachel Trichler & Robert Marty, 2017. "A Top-Down Approach to Estimating Spatially Heterogeneous Impacts of Development Aid on Vegetative Carbon Sequestration," Sustainability, MDPI, vol. 9(3), pages 1-9, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:3:p:409-:d:92590
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    References listed on IDEAS

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    5. Susan Athey & Guido Imbens, 2015. "Recursive Partitioning for Heterogeneous Causal Effects," Papers 1504.01132, arXiv.org, revised Dec 2015.
    6. Ho, Daniel & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2011. "MatchIt: Nonparametric Preprocessing for Parametric Causal Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i08).
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    1. Mandon, Pierre & Woldemichael, Martha Tesfaye, 2023. "Has Chinese aid benefited recipient countries? Evidence from a meta-regression analysis," World Development, Elsevier, vol. 166(C).
    2. Daniel Runfola & Geeta Batra & Anupam Anand & Audrey Way & Seth Goodman, 2020. "Exploring the Socioeconomic Co-benefits of Global Environment Facility Projects in Uganda Using a Quasi-Experimental Geospatial Interpolation (QGI) Approach," Sustainability, MDPI, vol. 12(8), pages 1-13, April.

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