A methodology to estimate impacts of domestic policies on deforestation: Compensated Successful Efforts for "avoided deforestation" (REDD)
Climate change mitigation would benefit from Reduced Emissions from Deforestation and Degradation (REDD) in developing countries. The REDD mechanism is in charge of distilling the right incentives for fostering forest conservation with appropriate compensation of foregone revenues, which in turn is related to avoided deforestation (how many hectares of forests are saved). Although any prediction of deforestation rates (i.e. business-as-usual scenarios) is challenging, and any negotiated target is subject to political influence, these two ways have been prioritirized so far. In other words, proposals have focused on a baseline (or cap)-and-trade approach, which relevance is questionable because resulting financial compensations are subject to unfairness if estimations of avoided deforestation are not reliable. Rather than considering overall deforestation (predicted and observed), we argue that a REDD mechanism would gain from linking compensations to real efforts that developing countries implement for slowing deforestation rates. This would provide more efficient incentives to design and enforce suitable policies and measures. The methodology we present to measure these efforts (labeled Compensated Successful Efforts) is based on the rationale that overall deforestation is due partly to structural factors, and partly to domestic policies and measures. This typology differs from others presented in the literature such as proximate / underlying causes, or economic / institutional factors. Using an econometric model, our approach estimates efforts that are (i) independent of structural factors (economic development, population, initial forest area, agricultural export prices), (ii) estimated ex post at the end of the crediting period, and (iii) relative to other countries. In order to illustrate the methodology we apply the model to a panel of 48 countries (Asia, Latin America, Africa) and four periods between 1970 and 2005. We conclude on the feasibility to estimate
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