Quasi-Experimental Evaluation of the Association Between Payments for Ecosystem Services and Satellite-Based Vegetation Dynamics in Cundinamarca, Colombia

Abstract

Payments for Ecosystem Services (PES) are increasingly used to promote conservation, but evidence of their biophysical outcomes remains mixed because participation is rarely assigned at random. This study assessed the association between participation in the Yo Protejo ¡Agua para Todos! PES program and satellite-based vegetation dynamics in Cundinamarca, Colombia, during 2015–2024. Annual NDVI, EVI, and SAVI values were derived from Landsat 8 and 9 imagery processed in Google Earth Engine. The empirical strategy combined fixed-effects difference-in-differences models, two spatial comparison groups, propensity-score adjustment, inverse probability of treatment weighting, propertyclustered inference, event-study diagnostics, differential-trend models, and property-year robustness analyses. Pre-treatment and covariate-balance diagnostics indicated imperfect counterfactual comparability, particularly for the external 500–1000 m buffer-ring controls. No vegetation index produced uniformly positive and robust associations across specifications. NDVI showed a small positive association during program implementation under the 500 m neighboring-control definition, but this result was not preserved after differential-trend adjustment or property-year aggregation. EVI produced positive pointlevel estimates under the external control, although this specification retained substantial residual imbalance and the estimates became negative after property-year aggregation. SAVI provided no robust positive evidence and showed negative post-implementation differentials in some external-control models. Overall, the estimated associations were sensitive to the vegetation metric, comparison-group definition, temporal phase, inferential level, and spatial aggregation. The results should therefore be interpreted as specificationdependent adjusted associations rather than definitive causal effects.