Implications of heterogeneous impacts of protected areas on deforestation and poverty

Philos Trans R Soc Lond B Biol Sci. 2015 Nov 5;370(1681):20140272. doi: 10.1098/rstb.2014.0272.

Abstract

Protected areas are a popular policy instrument in the global fight against loss of biodiversity and ecosystem services. However, the effectiveness of protected areas in preventing deforestation, and their impacts on poverty, are not well understood. Recent studies have found that Bolivia's protected-area system, on average, reduced deforestation and poverty. We implement several non-parametric and semi-parametric econometric estimators to characterize the heterogeneity in Bolivia's protected-area impacts on joint deforestation and poverty outcomes across a number of socioeconomic and biophysical moderators. Like previous studies from Costa Rica and Thailand, we find that Bolivia's protected areas are not associated with poverty traps. Our results also indicate that protection did not have a differential impact on indigenous populations. However, results from new multidimensional non-parametric estimators provide evidence that the biophysical characteristics associated with the greatest avoided deforestation are the characteristics associated with the potential for poverty exacerbation from protection. We demonstrate that these results would not be identified using the methods implemented in previous studies. Thus, this study provides valuable practical information on the impacts of Bolivia's protected areas for conservation practitioners and demonstrates methods that are likely to be valuable to researchers interested in better understanding the heterogeneity in conservation impacts.

Keywords: Bolivia; econometrics; moderators; program evaluation; protected areas; quasi-experiment.

MeSH terms

  • Animals
  • Biodiversity
  • Bolivia
  • Cities
  • Conservation of Natural Resources / economics*
  • Conservation of Natural Resources / methods
  • Conservation of Natural Resources / statistics & numerical data
  • Ecosystem
  • Humans
  • Linear Models
  • Models, Economic
  • Population Groups
  • Poverty* / statistics & numerical data
  • Social Change
  • Socioeconomic Factors