A hybrid optimization-agent-based model of REDD+ payments to households on an old deforestation frontier in the Brazilian Amazon

REDD+ was initially conceived of as a multi-level carbon-based payment for environmental services (PES). It is still often assumed to be a cost-effective climate change mitigation strategy, but this assumption is mostly based on theoretical studies and static opportunity cost calculations. We used spatial and socioeconomic datasets from an Amazonian deforestation frontier in Brazil to construct a simulation model of REDD + payments to households that can be used to assess REDD + interventions. Our SimREDD + model consists of dynamic optimization and land-use/cover change allocation submodels built into an agent-based model platform. The model assumes that households maximize profit under perfect market conditions and calculates the optimal household land-use/cover configuration at equilibrium under a given REDD + PES scenario. These scenarios include PES based on (1) forest area and (2) carbon stocks. Insights gained from simulations under different conditions can assist in the design of more effective, efficient, and equitable REDD + programs.