Research ArticleECONOMICS

Evaluating the impacts of protected areas on human well-being across the developing world

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Science Advances  03 Apr 2019:
Vol. 5, no. 4, eaav3006
DOI: 10.1126/sciadv.aav3006
  • Fig. 1 Geographic distribution of developing country household surveys.

    (A) Global distribution of surveys. (B) Inset of Nepal. Dots represent sampling clusters (blue, further than 10 km from a PA; red, within 10 km) in relation to International Union for Conservation of Nature (IUCN) categories I to VI PAs (green polygons) in countries with surveys.

  • Fig. 2 Conceptualizing PA impacts.

    Possible mechanisms of PA impacts on the health and wealth of nearby people. Individual pathways can be combined to conceptualize an impact mechanism; e.g., pathway ADG suggests how PAs can lead to better health outcomes via income gains from PA-related tourism employment that are then spent on improving children’s health.

  • Fig. 3 Postmatching regression model results.

    Regression coefficients and 95% credible intervals from Bayesian hierarchical models for the impacts of proximity to PA, as well as additional matching covariates and interactions (e.g., “Within 10 km × PA tourism”), on height-for-age growth scores (A) and likelihood of poverty (B). For (A), positive regression coefficients indicate variables that are associated with increased height-for-age scores in children under 5 years old. For (B), negative regression coefficients indicate variables that are associated with a reduction in the likelihood of household poverty. See fig. S2 for regression results for likelihood of stunting and household wealth scores. Colored symbols represent different categories of predictor variables: green, PAs; blue, environmental conditions; brown, socioeconomic information. Table S2 provides a detailed description of the matching covariates.

  • Fig. 4 Simulated well-being impacts of PA proximity.

    Predicted impacts (%) of proximity to PAs of various types, as well as impacts of changes in socioeconomic condition, relative to a baseline scenario, for height-for-age scores and likelihood of stunting of young children and household wealth scores and likelihood of poverty. Baseline = rural household located greater than 10 km from a strict (IUCN categories I to IV) PA having no tourism, with children that are breastfed. x axis is broken because of high percentage impacts of urban residency on household wealth and likelihood of poverty.

Supplementary Materials

  • Supplementary material for this article is available at http://advances.sciencemag.org/cgi/content/full/5/4/eaav3006/DC1

    Fig. S1. Assessment of matching effectiveness.

    Fig. S2. Postmatching regression model results.

    Fig. S3. Sensitivity of impact regression model results to PA-proximity threshold.

    Fig. S4. Sensitivity of scenario simulations to PA-proximity threshold.

    Table S1. Countries with DHS and associated number of observations used to assess impacts of PAs on human well-being.

    Table S2. Summary of matching covariates and PA characteristics used in quasi-experimental evaluation of the impacts of PA proximity on human well-being.

    Table S3. Critical P values from sensitivity tests (Rosenbaum bounds) to hidden bias, showing Γ values at which significant differences between observations within versus beyond 10 km from a PA disappear.

    Table S4. Absolute values of the mean standardized differences for unmatched versus matched comparison groups of children and households within and beyond 10 km of a PA.

    Table S5. Evaluation of candidate models for estimating impact of proximity to PA on growth scores, stunting, household wealth, and poverty.

  • Supplementary Materials

    This PDF file includes:

    • Fig. S1. Assessment of matching effectiveness.
    • Fig. S2. Postmatching regression model results.
    • Fig. S3. Sensitivity of impact regression model results to PA-proximity threshold.
    • Fig. S4. Sensitivity of scenario simulations to PA-proximity threshold.
    • Table S1. Countries with DHS and associated number of observations used to assess impacts of PAs on human well-being.
    • Table S2. Summary of matching covariates and PA characteristics used in quasi-experimental evaluation of the impacts of PA proximity on human well-being.
    • Table S3. Critical P values from sensitivity tests (Rosenbaum bounds) to hidden bias, showing Γ values at which significant differences between observations within versus beyond 10 km from a PA disappear.
    • Table S4. Absolute values of the mean standardized differences for unmatched versus matched comparison groups of children and households within and beyond 10 km of a PA.
    • Table S5. Evaluation of candidate models for estimating impact of proximity to PA on growth scores, stunting, household wealth, and poverty.

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