Evaluating mangrove community forestry outcomes: a mixed synthetic control and field-based approach

Abstract

Contemporary evaluations of outcomes in human-managed systems have been constrained by a lack of counterfactual analysis. Community forestry (CF), a widely adopted strategy to achieve both conservation and management in coupled human-environment systems, is no exception, and counterfactual analysis would greatly enhance CF research. We used a mixed method approach incorporating quantification of deforestation and forest regrowth rates, synthetic control analysis, and focus groups discussions to evaluate mangrove CF outcomes in the Ayeyarwady Delta, Myanmar, from to 1990–2021. CF resulted in an overall increase in net forest gain and reduction in net forest loss across sites. More than two-thirds of CF sites had superior outcomes relative to synthetic controls for at least one metric (deforestation or forest regrowth); however, CF tended to perform better for only one outcome while avoiding underperformance in the other. The annual rate of forest regrowth in CFs accelerated beginning three years prior to certification and peaked two years after certification, likely related to pre-certification engagement with the Forest Department or non-government organizations. Moreover, control sites near CFs experienced more rapid forest regrowth than controls further from CFs, suggesting spillover effects. The predominant challenge facing successful CF management was illegal extraction and overharvesting, and poor performing CFs experienced a complex array of challenges facing forest regrowth, likely related to the private nature of individual land claims within the CFs. Most supporting factors for CF were related to community management capacity, strongly indicating a need for extended close engagement with competent government and non-government actors to develop long-term management and governance capacities, which are sustainably funded. Our mixed-method approach can be replicated in other human-managed systems to evaluate the biophysical impacts of policies and gain insights into the underlying drivers of outcomes.