title: Global Patterns in the Implementation of Payments for Environmental Services.
abstract: Assessing global tendencies and impacts of conditional payments for environmental services (PES) programs is challenging because of their heterogeneity, and scarcity of comparative studies. This meta-study systematizes 55 PES schemes worldwide in a quantitative database. Using categorical principal component analysis to highlight clustering patterns, we reconfirm frequently hypothesized differences between public and private PES schemes, but also identify diverging patterns between commercial and non-commercial private PES vis-à-vis their service focus, area size, and market orientation. When do these PES schemes likely achieve significant environmental additionality? Using binary logistical regression, we find additionality to be positively influenced by three theoretically recommended PES 'best design' features: spatial targeting, payment differentiation, and strong conditionality, alongside some contextual controls (activity paid for and implementation time elapsed). Our results thus stress the preeminence of customized design over operational characteristics when assessing what determines the outcomes of PES implementation.
authors.affiliation: CIRAD (Centre de Coopération Internationale en Recherche Agronomique pour le Développement), Montpellier, France.
authors.forename: Driss
authors.initials: D
authors.lastname: Ezzine-de-Blas
authors.affiliation: CIFOR (Center for International Forestry Research), Lima, Peru.
authors.forename: Sven
authors.initials: S
authors.lastname: Wunder
authors.affiliation: UAM (Autonomous University of Madrid), Madrid, Spain.
authors.forename: Manuel
authors.initials: M
authors.lastname: Ruiz-Pérez
authors.affiliation: Conservation Strategy Fund, Bogota, Colombia.
authors.forename: Rocio Del Pilar
authors.initials: Rdel P
authors.lastname: Moreno-Sanchez
mesh_terms: D003247:Conservation of Natural Resources / Q000191:economics*; D004784:Environmental Monitoring / Q000191:economics*; D006801:Humans; D016015:Logistic Models; D025341:Principal Component Analysis