Climatic Benefits From the 2006–2017 Avoided Deforestation in Amazonian Brazil
Open Access
- 18 September 2019
- journal article
- research article
- Published by Frontiers Media SA in Frontiers in Forests and Global Change
- Vol. 2
Abstract
Over three-fourths of Brazil's greenhouse gas (GHG) emissions originate from land-use/cover change (LUCC). Alarming rates of forest loss in the Amazon region gained international attention in the past, but deforestation declined substantially since 2004 (although the rate has trended upwards since 2012). This result conforms to the GHG emission reduction targets set by the National Climate Change Plan, originally divided into three phases: 2006–2009; 2010–2013; and 2014–2017. According to the plan, emission reductions are measured against a deforestation baseline, initially corresponding to the average forest loss observed from 1996 to 2005 (reference period). We used an artificial neural network (ANN) model to spatialize the plan's original three-phase baseline and compare (i) the simulated carbon emissions from the baseline scenario to (ii) the emissions from observed deforestation during 2006–2017. Baseline spatialization was performed at the state level and informed by the 2000–2004 deforestation patterns in each state, resulting in nine state-specific calibrated ANNs. Simulated (baseline) and observed deforestation were compared to five biomass-density maps in order to estimate Brazil's GHG emission reductions. Our results indicate that forest loss during the study period was 62,321 km2 lower than the baseline and associated with 1.5 ± 0.4 Pg of avoided CO2 emissions to the atmosphere. Virtually all emission reductions (93%) occurred in the states of Mato Grosso (74%) and Rondônia (19%). In contrast, Roraima, Amazonas, and Amapá states increased GHG emissions by 44.8, 36.7, and 14.7 Tg CO2, respectively. Lastly, we discuss the issue of attribution of deforestation reductions for results-based payments (REDD+) and the emission reduction certificates issued by the Amazon Fund.Keywords
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