:Reducingemissionsfromforests—generatingcarboncredits—inreturnforREDD+(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 theREDD+results-basedpaymentstodevelopingcountries. Combiningstatistically-soundsampling 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.
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3390/f8080271|
|Titolo:||Understanding Measurement Reporting and Verification Systems for REDD+ as an Investment for Generating Carbon Benefits|
|Appare nelle tipologie:||1.1 Articolo in rivista|