Reducing emissions from deforestation and forest degradation (REDD) is based on a core idea: reward individuals, communities, projects and countries that reduce greenhouse gas (GHG) emissions from forests. However, many difficult questions must be addressed before mechanisms that fully exploit thepotential of REDD can be created: How can we measure reductions in emissions when data are poor or do not exist?; How can we raise the billions of dollars needed to put a REDD mechanism in place?; How can we make sure that any reductions in deforestation and degradation are real (additional), and that they do not lead to more trees being chopped down in other forest areas (leakage) or next year (permanence)?; How can we make sure that the poor benefit? This book discusses these questions, and discusses the design options for REDD in a global climate regime. Each chapter looks at a question that UNFCCC negotiators and others involved in the global REDD debate must address. It lays out the key problems, present the options on how to deal with them, and then assess the options based on the ‘3E’ criteria:

effectiveness: can the mechanism bring significant emission reductions?
efficiency: are these reductions achieved at the minimum cost?
equity: are benefits and costs distributed fairly among and within countries?

This book highlights the fact that countries differ widely in terms of their monitor, report and verify (MRV) infrastructure, institutional capacity to implement REDD policies and measures, drivers of deforestation and forest degradation, and so on. This heterogeneity needs to be reflected in the global REDD architecture. The mechanisms must be flexible enough to ensure broad country participation from the beginning. At the same time, they should also include incentives ‘to move on’, for example, to improve MRV and to graduate from a subnational (project) approach to a national approach. Flexibility is also needed for another reason: REDD is a large-scale experiment and room is needed for mid-course corrections as lessons are learned on what works and what does not.

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