REDD+ governance

Lederer, Markus

doi:10.1002/wcc.155

Cited by 34 publications

(31 citation statements)

References 27 publications

(30 reference statements)

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“…Fundamental to REDD+'s success is the nature of the institutions that facilitate the trade between sellers and buyers of forest carbon (Bushley and Khatri 2011, Corbera and Schroeder 2011, Lederer 2012. Trade-offs and synergies between current forest uses and its usage for carbon sequestration will depend on how existing policies and institutions adjust to accommodate carbon markets (Smith and Scherr 2003).…”

Section: Introductionmentioning

confidence: 99%

Costs, cobenefits, and community responses to REDD+: a case study from Nepal

Sharma¹,

Shyamsundar²,

Nepal³

et al. 2017

E&S

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ABSTRACT. We examine the role of subnational institutions in carbon sequestration and assess whether community forest user groups can meet both existing forest needs and international carbon demand. By conducting a qualitative evaluation of a pilot program in Nepal that made carbon payments to forest user groups, we examine if community forestry institutions can be effective, efficient, and equitable in implementing Reducing Emissions from Deforestation and Forest Degradation (REDD)+. Our evaluation relies on focus group discussions, meetings, and community and program documents of forestry user groups that participated in the REDD+ pilot and matched groups that did not. Compared to control groups, REDD+ user groups appear to be more effective in carbon sequestration, perhaps because of increased prevention of forest fires and grazing, nursery establishment, and other forest management. REDD+ user groups report a larger number of forest conservation, forest utilization, and community development activities relative to control groups. Participating communities bear transaction costs of US$4.5/hectare and implementation costs of US$2.5/hectare on average (or NPR 50,000 (US$600) per year). The mean REDD+ rent per ton of additional carbon sequestered was US$1.3. Targeting of benefits improves partly because some marginalized groups, particularly women, participate more in the planning and management. In terms of equity, microcredit and capacity development activities were skewed to the poorest households, whereas alternate fuel and carbon monitoring were more advantageous to middle or high income households. Overall, our analyses suggest that REDD+ activities can be successfully executed, if communities receive technical and capacity building support for institutional strengthening, in addition to carbon payments.

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Section: Introductionmentioning

confidence: 99%

Costs, cobenefits, and community responses to REDD+: a case study from Nepal

Sharma¹,

Shyamsundar²,

Nepal³

et al. 2017

E&S

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“…REDD+ is intended to align the views of a variety of actors who are active across different social and political jurisdictions on how to frame and address the problem of deforestation and forest degradation in developing countries [1][2][3][4][5][6]. During the last decade, REDD+ has become increasingly relevant in the environmental and land-use policy agendas of many developing countries.…”

Section: Introductionmentioning

confidence: 99%

A Dominant Voice amidst Not Enough People: Analysing the Legitimacy of Mexico’s REDD+ Readiness Process

Špirić

Corbera

Reyes‐García

et al. 2016

Forests

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Abstract:In the development of national governance systems for Reducing Emissions from Deforestation and forest Degradation (REDD+), countries struggle with ensuring that decision-making processes include a variety of actors (i.e., input legitimacy) and represent their diverse views in REDD+ policy documents (i.e., output legitimacy). We examine these two dimensions of legitimacy using Mexico's REDD+ readiness process during a four-year period (2011-2014) as a case study. To identify REDD+ actors and how they participate in decision-making we used a stakeholder analysis; to assess actors' views and the extent to which these views are included in the country's official REDD+ documents we conducted a discourse analysis. We found low level of input legitimacy in so far as that the federal government environment agencies concentrate most decision-making power and key land-use sectors and local people's representatives are absent in decision-making forums. We also observed that the REDD+ discourse held by government agencies and both multilateral and international conservation organisations is dominant in policy documents, while the other two identified discourses, predominantly supported by national and civil society organisations and the academia, are partly, or not at all, reflected in such documents. We argue that Mexico's REDD+ readiness process should become more inclusive, decentralised, and better coordinated to allow for the deliberation and institutionalisation of different actors' ideas in REDD+ design. Our analysis and recommendations are relevant to other countries in the global South embarking on REDD+ design and implementation.

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“…This knowledge has been used to develop causal deforestation models (Combes Motel et al 2009) and predict changes in land use (Veldkamp and Lambin 2001). More recently, attention has also shifted toward setting baselines and developing the monitoring approaches needed to underpin response strategies such as Reduced Emissions from Deforestation and Degradation programs (REDD+) (Lederer 2012).…”

Section: Albert Einsteinmentioning

confidence: 99%

“…Two situations that provide an example of when the consequences of methodology selection may prove important are in supporting operationalization of calculations (and financial payments) relating to the REDD+ program (Lederer 2012) and the Convention on Biological Diversity (CBD) (CBD 2011). The REDD+ scheme provides a mechanism for developing countries to gain financial benefits for both avoiding deforestation and adopting a sustainable forest management approach , Robinson et al 2013 and is a good example of how different parts of a problem can have different requirements from a machine learning model.…”

Section: Albert Einsteinmentioning

confidence: 99%

“…In many instances, the objective is to pass on information to policy makers on a range of topics including protected area effectiveness (Mas 2005, Gaveau et al 2009), estimated impact of infrastructure developments (Deng et al 2011) and more recently in setting a baseline for REDD+ schemes (Scharlemann 2010, Lederer 2012. In other instances, the purpose will be to develop an understanding of the factors contributing to deforestation, which is essential for protecting existing forests (Geist and Lambin 2001).…”

Section: Deforestation Prediction and Analysismentioning

confidence: 99%

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Making the most of machine learning and freely available datasets: a deforestation case study

Mayfield¹

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There are many reasons why we study deforestation including predicting at risk areas, predicting deforestation rate and informing the development of conservation policies and programs. Each study will have its own set of objectives to meet (such as setting a deforestation baseline or advising on forest protection policies) and constraints to work within (such as time and data constraints and access to experts). This thesis develops a framework for helping to decide which of several statistical and machine learning methodologies; generalised linear models (GLMs), generalised linear mixed models (GLMMs), artificial neural networks (ANNs), Bayesian networks (BNs) and Gaussian processes (GPs) might be suitable for a given deforestation study.One common constraint on deforestation studies is data availability, as it is often not possible to acquire all the datasets that would ideally be included. High resolution demographic or socioeconomic information can be costly, and obtaining the value of dynamic variables such as road location for the correct point in time may be difficult. By using either freely available or low cost datasets to generate the variables for this thesis, it was possible to evaluate the usefulness of these data in predicting deforestation and identifying its predisposing factors. Their proven utility demonstrates that they could provide effective substitutes in those cases where the ideal datasets are not available.The main datasets used were the Conservation International land use change data for southern Mexico and north-eastern Madagascar, which are raster datasets at 30 m resolution showing forest loss for either two (Mexico) or three (Madagascar) time steps. Predictor variables were also generated from the World Database on Protected Areas, the NASA Landsat digital elevation model and several Natural Earth datasets on city and river location. Random sample points were generated across the forested areas of the study zones and models were then trained to predict whether there would be any deforestation within a 500 m x 500 m zone around each point.Models were implemented using either R or Matlab and their performance was evaluated using sensitivity, specificity, true skill statistic and the area under the receiver operating curve. The results of the best performing model designs for each methodology were mapped to examine whether the predicted high risk areas were close to where actual deforestation occurred. Separate maps were produced for the predicted results at a 50 % probability cut off, as well as across all probabilities to produce a map of predicted high risk areas. Additional maps were created showing the predictions after correcting for the rate of expected deforestation.ii When applied to complex problems such as deforestation analysis, machine learning (ML) techniques have several theoretical and practical advantages over classical statistics, primarily the ability to take into account non-linear relationships. The ML models were therefore expected to outperform the simpler...

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REDD+ governance

Cited by 34 publications

References 27 publications

Costs, cobenefits, and community responses to REDD+: a case study from Nepal

Costs, cobenefits, and community responses to REDD+: a case study from Nepal

A Dominant Voice amidst Not Enough People: Analysing the Legitimacy of Mexico’s REDD+ Readiness Process

Making the most of machine learning and freely available datasets: a deforestation case study

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