AUTHOR=McCallister Margaret , Krasovskiy Andrey , Platov Anton , Pietracci Breno , Golub Alexander , Lubowski Ruben , Leslie Gabriela TITLE=Forest protection and permanence of reduced emissions JOURNAL=Frontiers in Forests and Global Change VOLUME=5 YEAR=2022 URL=https://www.frontiersin.org/journals/forests-and-global-change/articles/10.3389/ffgc.2022.928518 DOI=10.3389/ffgc.2022.928518 ISSN=2624-893X ABSTRACT=

Tropical forests are essential for climate change mitigation. With growing interest over the use of credits from reducing emissions from deforestation and forest degradation (REDD+) and other natural climate solutions within both voluntary and compliance carbon markets, key concerns about the long-term durability of the reductions, or their permanence, arise for countries, corporations, regulators, and policy makers. This paper seeks to analyze the longevity of emissions reductions from different policies to slow down and stop deforestation. To establish conditions of permanence, we conduct numerical analyses using a model based on a cellular automata algorithm that learns from historical deforestation patterns and other spatial features in the Brazilian state of Mato Grosso. First, we simulate increased law enforcement to curb deforestation at a jurisdictional scale from 2025 to 2034, followed by potential policy rollbacks from 2035 to 2050. Second, we consider alternative scenarios to avoid potentially legal deforestation coupled with reforestation. We find spatial and path dependence – a successful policy intervention may permanently change the deforestation trajectory even after potential policy reversals. Hence, permanence depends both on the probability of policy reversals and the risk of emissions overshooting. Our results are important for advancing the understanding around the unsettled debate on the permanence of avoided emissions. Further, this paper argues that as policies to prevent deforestation or reduce emissions otherwise are reversible, permanence should be understood and discussed in a probabilistic and time-dependent framework.