Effects of decentralized forest management (DFM) on deforestation and poverty in low- and middle-income countries

Additional Info

  • Authors: Cyrus Samii, Laura Paler, Larry Chavis, Parashar Kulkarni, Matthew Lisiecki
  • Published date: 2014-12-19
  • Coordinating group(s): International Development
  • Type of document: Title, Protocol, Review
  • Volume: 10
  • Issue nr: 10
  • Category Image: Category Image
  • Title: Effects of decentralized forest management (DFM) on deforestation and poverty in low- and middle-income countries
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Background

Natural forest preservation in the tropics, and thus in developing countries, must be an element of any effective effort to manage climate change. Forests serve as natural carbon sinks, which help to mitigate the effect of other carbon emissions. However, forest cover is being reduced and it is estimated that deforestation is responsible for 10-17 per cent of global carbon emissions. Since 2007, governments have coordinated conservation efforts under the Reducing Emissions through Deforestation and Forest Degradation (REDD+) initiative, which has led to the implementation of various programs designed to reduce the amount of forested land converted to other purposes. Decentralized forest management (DFM) is one approach to forest management which has been widely implemented to reduce deforestation in developing countries. DFM programs relocate decision-making authority on forest use in the direction of forest communities, rather than central government actors. While the primary goal of reducing deforestation is clear, the policy and academic literature debates the extent to which DFM programs in developing countries should incorporate goals of poverty reduction. Some argue that the targeting of poverty goals will undermine conservation effectiveness (e.g. because behavioral change among poorer households does not have as much potential to promote conservation as that of wealthier households or commercial entities). Others argue that targeting benefits toward the poor would contribute to conservation effectiveness by either promoting sustainable livelihoods or helping to legitimize conservation programming. To move the debate around DFM beyond theoretical discussions and into better-informed, evidence-based discussion we examine the evidence on the effects of DFM programs on deforestation and welfare outcomes in low- and middle-income countries (LMICs), aiming also to assess whether these goals are at odds with each other.

Objectives

The first objective of this review is to assess the evidence on the effects of DFM programs on the conservation and poverty outcomes in LMICs. A second objective is to assess the extent to which these programs’ effects on poverty in turn affect whether conservation benefits are realized. The third objective is to evaluate how institutional and social conditions (namely, inequality, institutional capacity, corruption, and democratic accountability) moderate the effects of DFM programs.

Selection criteria

The review includes studies of DFM programs that assess effects on (i) deforestation outcomes in forest areas in developing countries or (ii) poverty conditions of populations residing in communities that are proximate to natural growth forest areas in developing countries. We included studies using a range of measures for both deforestation (on-the-ground point samples, samples created from satellite imagery) and welfare (consumption, income, or income potential).

For a program to be considered a DFM program, de jure responsibility for managing natural forest resources must pass from centralized to local authorities. This responsibility must grant local authorities the right to grant concessions or establish use restrictions. We were flexible as to the precise level of administration to which responsibilities are passed as well as whether or not the decentralization also involves granting local authorities the right to sell or transfer titles to forested lands.

For the quantitative synthesis we included (a) randomized studies or (b) quasi-experimental studies that employ strategies for causal identification with clearly delineated treated and control areas and use some method for removing biases due to non-random assignment of the intervention. Qualitative data is used in the synthesis to provide descriptions and context for interventions that are included in the quantitative synthesis. Such data were extracted from the quantitative studies themselves as well as qualitative studies that cover the same programs or settings as the quantitative studies.

Search strategy

To identify the articles included in this review, we searched a variety of databases using key words related to DFM programs. The set of databases and lists of keywords were assembled based on consultation with a Campbell Collaboration information retrieval specialist. We also carried out hand searches of key journals in relevant fields, using publisher search engines and references cited in papers accepted for review as well as references in review papers or thematically relevant papers identified during the search.

Data collection and analysis

We collected data on the study characteristics, findings, and moderators of all included studies. Risk of bias was assessed based on the guidance of the IDCG Risk of Bias Tool (version March 2012). We extracted qualitative information from both the included quantitative studies as well as qualitative studies that covered the same types of programs and contexts as our quantitative studies. We used such qualitative data to establish that conditions recorded in quantitative data are being interpreted correctly and to provide descriptions and context for interventions that are included in the quantitative synthesis. For effects on forest cover, whenever possible, we standardize them to annual forest cover change rates. For effects on material welfare and poverty outcomes we use percentage change over estimated average counterfactual outcome (e.g., for income effects, per cent change in income relative to the average income of the control group). For each hypothesis, we synthesized estimates using meta-analysis when the following conditions were met: (i) more than two studies meeting the quantitative inclusion criteria; (ii) effect sizes for common outcome constructs; and (iii) effects measured against similar comparators.

Results

Our database search returned 1,272 articles on DFM programs. After eliminating articles that were not relevant to our hypotheses or conducted with appropriate methodological rigor, we were left with 12 studies of DFM programs. Of these, eight DFM studies were quantitative impact evaluations. The studies cover eight programs in seven countries (Bolivia, Ethiopia, India, Kenya, Malawi, Nepal, and Uganda). The evidence base is limited both in terms of the number of eligible studies and the methodological quality of included studies. None of the studies are based on randomized experiments, and so the potential for hidden selection or confounding biases is the most pertinent problem. Few of the studies create comparison groups that allow them to address spillover and leakage of effects from program areas to non-program areas. Finally, none of the studies investigated forest conservation and welfare effects jointly, which made it difficult to address our question of how these two goals relate.

Our database search returned 1272 articles on DFM programs. After eliminating articles that were not relevant to our hypotheses or conducted with appropriate methodological rigor, we were left with 12 studies of DFM programs. Of these, eight DFM studies were quantitative impact evaluations. The studies cover eight programs in seven countries (Bolivia, Ethiopia, India, Kenya, Malawi, Nepal, and Uganda). The evidence base is limited both in terms of the number of eligible studies and the methodological quality of included studies. None of the studies are based on randomized experiments, and so the potential for hidden selection or confounding biases is the most pertinent problem. Few of the studies create comparison groups that allow them to address spillover and leakage of effects from program areas to non-program areas. Finally, none of the studies investigated forest conservation and welfare effects jointly, which made it difficult to address our question of how these two goals relate.

Authors’ conclusions

Limitations in the evidence base preclude definitive hypothesis tests, but we do find that DFM reduce deforestation rates. In terms of program effects on human welfare and poverty outcomes, the evidence is very limited and we cannot conclude that the evidence indicates non-negative effects. Our review aimed to assess the extent to which conservation and poverty reduction goals conflict, and the scope for “win-win” strategies that generate both significant environmental and human welfare benefits. Based on the evidence available, we do not find that an evidence-based case can be made for conservation and poverty-reduction goals being complementary in DFM programming. Our final conclusion re-emphasizes the poor state of the evidence base for conservation programming. Much advanced scientific effort and extensive investment has gone into measuring forest conditions around the world. Relative to that, efforts to assess the effects of DFM programs on deforestation and poverty is limited and methodologically weak. Conservations researchers should look to recent work in development economics for guidance on executing field experiments that might provide more credible evidence (Banerjee and Duflo, 2011; Casey et al., 2012; Karlan and Appel, 2012).

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