Cost-effectiveness of payments for ecosystem services with dual goals of environment and poverty alleviation

Environ Manage. 2010 Mar;45(3):488-501. doi: 10.1007/s00267-009-9321-9. Epub 2009 Jun 18.

Abstract

The goal of this article is to understand strategies by which both the environmental and poverty alleviation objectives of PES programs can be achieved cost effectively. To meet this goal, we first create a conceptual framework to understand the implications of alternative targeting when policy makers have both environmental and poverty alleviation goals. We then use the Grain for Green program in China, the largest PES program in the developing world, as a case study. We also use a data set from a survey that we designed and implemented to evaluate the program. Using the data set we first evaluate what factors determined selection of program areas for the Grain for Green program. We then demonstrate the heterogeneity of parcels and households and examine the correlations across households and their parcels in terms of their potential environmental benefits, opportunity costs of participating, and the asset levels of households as an indicator of poverty. Finally, we compare five alternative targeting criteria and simulate their performance in terms of cost effectiveness in meeting both the environmental and poverty alleviation goals when given a fixed budget. Based on our simulations, we find that there is a substantial gain in the cost effectiveness of the program by targeting parcels based on the "gold standard," i.e., targeting parcels with low opportunity cost and high environmental benefit managed by poorer households.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • China
  • Conservation of Natural Resources / economics*
  • Conservation of Natural Resources / methods*
  • Cost-Benefit Analysis
  • Ecosystem*
  • Family Characteristics
  • Humans
  • Models, Econometric
  • Models, Theoretical
  • Poverty / prevention & control*
  • Rural Population