Abstract
Reducing emissions from forests—generating carbon credits—in return for REDD+ (Reducing Emissions from Deforestation and forest Degradation) payments represents a primary objective of forestry and development projects worldwide. Setting reference levels (RLs), establishing a target for emission reductions from avoided deforestation and degradation, and implementing an efficient monitoring system underlie effective REDD+ projects, as they are key factors that affect the generation of carbon credits. We analyzed the interdependencies among these factors and their respective weights in generating carbon credits. Our findings show that the amounts of avoided emissions under a REDD+ scheme mainly vary according to the monitoring technique adopted; nevertheless, RLs have a nearly equal influence. The target for reduction of emissions showed a relatively minor impact on the generation of carbon credits, particularly when coupled with low RLs. Uncertainties in forest monitoring can severely undermine the derived allocation of benefits, such as the REDD+ results-based payments to developing countries. Combining statistically-sound sampling designs with Lidar data provides a means to reduce uncertainties and likewise increases the amount of accountable carbon credits that can be claimed. This combined approach requires large financial resources; we found that results-based payments can potentially pay-off the necessary investment in technologies that would enable accurate and precise estimates of activity data and emission factors. Conceiving of measurement, reporting and verification (MRV) systems as investments is an opportunity for tropical countries in particular to implement well-defined, long-term forest monitoring strategies.
Highlights
Since the first REDD-style project initiated in1997, the focus of REDD+ has broadened from the avoidance of deforestation as the “single largest opportunity for cost-effective and immediate reductions of carbon emissions” [1] to a holistic concept for sustainable development
Infirst the part–and second part the study, we evaluated andofcompared scenarios on the amount of carbon credits generated from reducing forest carbon emissions
Findings highlight the fundamental role of Lidar sensors in forest carbon monitoring, in REDD+; combining statistical features of forest sampling with Lidar data enables a significant generation of carbon credits
Summary
1997, the focus of REDD+ has broadened from the avoidance of deforestation as the “single largest opportunity for cost-effective and immediate reductions of carbon emissions” [1] to a holistic concept for sustainable development. The primary focus of REDD+ remains the reduction of carbon emissions associated with deforestation and forest degradation. For any national or sub-national REDD+ initiative, the associated emission reductions have to be assessed. This includes the assessment of both changes of forest area (activity data) and changes of forest carbon stocks (emission factors). Activity data and emission factors have to be estimated by countries participating in REDD+ through the implementation of reliable measurement, reporting and verification (MRV)
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