Cost-effectiveness and income effects of alternative forest conservation policy mixes for the Peruvian Amazon.

To reduce deforestation and mitigate climate change, the Peruvian government proposed and partially implemented incentive- and disincentive-based forest conservation policies, especially in the Amazon, where most of the country's deforestation occurs. However, to date, little is known about the magnitude of the trade-offs between cost-effectiveness and welfare effects of such a policy mix. To explore this question, this paper develops a spatially explicit simulation model of landholders' deforestation decisions based on fines, payments for ecosystem services (PES), and the probability of enforcement that incorporates enforcement costs and deforestation dynamics. Simulations show that a policy approach solely based on disincentives (i.e. fines) could avoid annual deforestation at 2128 soles ¹ 1 1 sol ∼ USD 0.28 in 2022. /ha (0.81 USD/tCO 2) and a total income loss of 425 million soles for sanctioned landholders due to fines and lost agricultural rents (10-year net present values). On the other hand, a policy approach based only on PES could reduce deforestation at 7692 soles/ha or ∼2.6 USD/tCO 2 with a total income gain of 146 million soles for landholders receiving PES. Our findings show where and by how much mixing different levels of fines and PES could mitigate such trade-offs and attaining similar levels of avoided deforestation. • Imposing fines proves most cost-effective for deforestation reduction. • Adding incentives alleviates trade-offs between cost-effectiveness and income loss. • Collecting fine revenues further enhances the cost-effectiveness of command-and-control. • We use the Poisson distribution for generating deforestation scenarios. • We consider the field operations costs to calculate the enforcement probability. [ABSTRACT FROM AUTHOR]