Samii, C, Lisiecki, M, Kulkarni, P, Paler, L and Chavis, L, 2015, Decentralised forest management for reducing deforestation and poverty in low- and middle- income countries: a systematic review, 3ie Systematic Review 16. London: International Initiative for Impact Evaluation (3ie).Link to Source
Headline Findings: a summary statement
The review finds some evidence for positive, though modest, effects of Decentralised Forest Management (DFM) on deforestation. The authors are unable to rule out a negative effect of DFM on poverty.
The authors included eight impact evaluations of eight different programmes in seven countries (Bolivia, Ethiopia, India, Kenya, Malawi, Nepal, and Uganda). Four qualitative studies provided additional details on the impact evaluations in India, Kenya, Nepal and Uganda. Five of these studies assessed effects on deforestation and three assess effects on human welfare outcomes. No studies assessed the effect of DFM on both forest cover and human welfare outcomes. All of the studies use quasi-experimental methods and the authors conclude the evidence base is limited in both quantity and quality.
Implications for policy and practice
Effects on deforestation outcomes
Five studies examined the effects of DFM programs on annual forest cover change rate. Meta-analysis was not feasible due to the differences between the outcome measures used between the studies. The observed effects range from 0.026 per cent (95% CI: [-0.09, 0.14]) for a study examining DFM and community forest use in India to 0.80 per cent (95% CI: [0.41, 1.19]) for a study examining DFM-based administration of protected forests in Bolivia.
Effects on human welfare outcomes
Three studies assessed the effects of DFM on welfare or poverty outcomes. The studies provide different comparisons, but all found that DFM did lead to an improvement in either a households’ forest or household income on average. The effects reported range from an estimated 35 per cent increase in per capita consumption expenditure in Ethiopia (95% CI: [16.5, 53.5]) to a two per cent gain in Uganda (95% CI: [-2.63, 6.63]). It is not clear how DFM effects poor households. A study in Malawi suggests improved incomes for poor households participating in DFM institutions. However, the included study from Uganda finds poor households in areas neighbouring a DFM program may have been harmed. Devolution to local public institutions in this case led to a six per cent reduction in per capita income among the lowest income quartile, though this was not statistically significant (95% CI: [-22.52, 10.52]). Devolution to local parastatals was found to reduce income by 10 per cent (95% CI: [-20.04, 0.04]). The authors are therefore unable to conclude that DFM interventions have non-negative impacts on local poverty levels.
The Role of Institutional and Social Conditions
The qualitative studies highlighted issues of institutional capacity, with some DFM programs unable to carry out their mandates. There was some evidence to suggest democratically accountable DFM institutions may result in larger conservation effects, although this may be based on the possibly erroneous assumption that all forest edge community members favour conservation, an assumption that was challenged in qualitative accounts from Mexico.
Implications for further research
The authors highlight the lack of high quality studies assessing the effects of DFM on environmental and human welfare outcomes. They encourage researchers to take advantage of the opportunity for randomisation provided by the phasing in of local DFM establishment programmes. They also recommend researchers replicate the quasi-experimental approaches employed in some of the studies included in the review and make use of tools such as Google Earth Engine’s high resolution forest cover mapping for formative research that can inform more finely targeted experimental studies. The authors also call for future research to examine welfare effects beyond DFM institution participants, including populations living in DFM adjacent areas. They suggest studies should measure effects on both environmental and human welfare outcomes to allow for a comprehensive assessment of the effects of DFM programmes. Moreover, they suggest quantitative studies should collect data on context, implementation and costs. Future research should aim to assess the effects of DFM across a diversity of contexts, including in particular contexts with high deforestation rates. Finally, studies should examine relevant moderators to inform the design and implementation of future conservation programmes.
Forests serve as natural carbon sinks and help to mitigate the effect of other carbon emissions. At the same time, forest cover is being reduced and it is estimated that deforestation is responsible for 10 to 17 per cent of global carbon emissions. Different approaches to natural forest preservation are therefore being considered for their potential to help manage climate change. Decentralised Forest Management (DFM) is one approach to forest management which has been widely implemented to reduce deforestation in L&MICs. DFM programmes relocate decision-making authority on forest use in the direction of forest communities, rather than central government actors. It is one of the core components of government and privately led forest management efforts around the world. Nevertheless, the effects of DFM mechanisms on deforestation and poverty are not clear. This study reviews the evidence on the effects of DFM on deforestation as well as host communities’ welfare.
The authors aimed to assess the evidence on the effects of DFM interventions on deforestation and poverty outcomes in L&MICs. In addition, they also aimed to assess whether there was a relationship between effects on poverty and whether or not conservation benefits are realised. Finally, the authors aimed to identify how institutional and social conditions can moderate the effects of DFM programmes.
The authors included experimental or quasi-experimental evaluations of DFM programs in L&MICs assessing outcomes related to deforestation or poverty among forest communities. They also included qualitative studies examining the same programs covered by included quantitative studies to provide background and context. The authors included both published and grey literature with no restriction on publication date. They searched a range of databases including JSTOR, IDEAS, Science Direct and Econlit, used search engines such as Google Scholar, as well as relevant websites including DFID and CIFOR. They also undertook citation-tracking of included studies and other relevant literature, carried out hand-searches of key journals and consulted experts.
The authors assessed included quantitative studies for risk of bias using the IDCG Risk of Bias Tool. They originally intended to undertake meta-analysis, but the lack of studies using common outcome constructs or similar comparators meant they were unable to do so. Instead they report the effect sizes from the included impact evaluations in tables, forest plots and narrative discussion. It was also not possible to undertake the planned meta-regression for testing moderators and mediators due to the low number of included studies. Instead, qualitative information from quantitative impact evaluations and related qualitative studies was used to comment on the moderator and mediator hypotheses.
The review has clear inclusion criteria, with a comprehensive search strategy, and appropriate methods for screening, data extraction, analysis and synthesis. A minor limitation is that the study authors provided a wealth of information on each study’s potential bias, but do not incorporate this information when synthesising the studies.