Experimental evaluation of the impact of a payment for environmental services program on deforestation

Emma Wiik,Rémi d'Annunzio,Nigel Asquith,Edwin Pynegar,David Crespo,Julia P. G. Jones

doi:10.1111/csp2.8

Abstract

Despite calls for greater use of randomized control trials (RCTs) to evaluate the impact of conservation interventions; such experimental evaluations remain extremely rare. Payments for environmental services (PES) are widely used to slow tropical deforestation but there is widespread recognition of the need for better evidence of effectiveness. A Bolivian nongovernmental organization took the unusual step of randomizing the communities where its conservation incentive program (Watershared) was offered. We explore the impact of the program on deforestation over 5 years by applying generalized additive models to Global Forest Change (GFC) data. The “intention‐to‐treat” model (where units are analyzed as randomized regardless of whether the intervention was delivered as planned) shows no effect; deforestation did not differ between the control and treatment communities. However, uptake of the intervention varied across communities so we also explored whether higher uptake might reduce deforestation. We found evidence of a small effect at high uptake but the result should be treated with caution. RCTs will not always be appropriate for evaluating conservation interventions due to ethical and practical considerations. Despite these challenges, randomization can improve causal inference and deserves more attention from those interested in improving the evidence base for conservation.

Highlights

Following calls for improvements in the quality of evidence underpinning conservation interventions
We investigate the effectiveness and efficacy of Watershared at reducing deforestation, over 5 years, by applying generalized additive models (GAMs) to global forest change (GFC) data (Hansen et al, 2013)
An important assumption in our deforestation models is that deviation from uptake propensity is independent of confounding factors. For this to be the case, some of the unexplained variation in the uptake model would need to be related to variation affecting uptake but not deforestation. We suggest that such variation may be due to differences in how the offer of the program was experienced across the communities, for example by the timing of Natura's visits to certain communities, the relationship between Natura technicians and communities, or the willingness of the community leader to spread the word about Natura's visit

Summary

| INTRODUCTION

Following calls for improvements in the quality of evidence underpinning conservation interventions The rarity of RCT in evaluating the impact of large-scale conservation interventions can be attributed to the numerous practical and ethical considerations involved (Baylis et al, 2016; Pynegar, Jones, Gibbons, & Asquith, 2018). One of these is scale itself: it clearly would not be feasible to randomly allocate Protected Areas in a landscape. When analyzing RCTs, including the outcomes for individuals as randomized in “intention-to-treat” (ITT) estimates is widely considered most appropriate for evaluating real world effectiveness (Gupta, 2011). Throughout, we control for factors that can relate to both uptake and deforestation, including propensity to enroll (endogeneity), and consider the potential influence of unobserved confounding factors

| METHODS

| RESULTS

| DISCUSSION

Findings

CONFLICT OF INTEREST