Is There an Ideal REDD+ Program? An Analysis of Policy Trade-Offs at the Local Level

We use economy-wide simulation methods to analyze the outcome of a simple REDD+ program in a mixed subsistence/commercial-agriculture economy. Alternative scenarios help trace REDD+’s causal chain, revealing how trade-offs between the program’s public and private costs and benefits determine its effectiveness, efficiency and equity (the 3Es). Scenarios reveal a complex relationship between the 3Es not evident in more aggregate analyses. Setting aside land as a carbon sink always influences the productivity of agriculture and its supply of non-market goods and services; but the overall returns to land and labor–which ultimately determine the opportunity cost of enrollment, the price of carbon and the distribution of gains and losses–depend on local conditions. In the study area, market-oriented landowners could enroll 30% of local land into a cost-effective program, but local subsistence demands would raise their opportunity costs as REDD+ unfurls, increasing the marginal cost of carbon. A combination of rent and wage changes would create net costs for most private stakeholders, including program participants. Increasing carbon prices undermines the program’s efficiency without solving its inequities; expanding the program reduces inefficiencies but increases private costs with only minor improvements in equity. A program that prevents job losses could be the best option, but its efficiency compared to direct compensation could depend on program scale. Overall, neither the cost nor the 3Es of alternative REDD+ programs can be assessed without accounting for local demand for subsistence goods and services. In the context of Mexico’s tropical highlands, a moderate-sized REDD+ program could at best have no net impact on rural households. REDD+ mechanisms should avoid general formulas by giving local authorities the necessary flexibility to address the trade-offs involved. National programs themselves should remain flexible enough to adjust for spatially and temporally changing contexts.