The Effects of Land Management Policies on the Environment and People in Low- and Middle-Income Countries: A Systematic Review

Abstract

Background: Widespread land degradation is driven by unsustainable land use and human pressures, and is accelerating climate change and biodiversity loss. Access to land is also essential for livelihoods and well-being, particularly in low- and middle-income countries (L&MICs). Urgent and inclusive land management interventions are needed to mitigate climate impacts, preserve biodiversity, and support vulnerable communities. Governments and international organisations are increasingly promoting land management interventions, such as establishing protected areas, granting land rights and decentralising land management and monitoring. These efforts aim to enable the realisation of several of the Sustainable Development Goals (SDGs) and the Kunming-Montreal Global Biodiversity Framework (KMGBF). The environmental and human development impacts of these interventions, and especially their potential interactions, remain under-researched. This synthesis of the literature can help to inform policy and research decisions in these contexts.

Objectives: The purpose of this systematic review was to appraise and synthesise evidence of the effects of three types of land management interventions (community-based or decentralised land management and monitoring, land rights, and protected areas) on environmental and human well-being outcomes in L&MICs. We examined how these effects vary across geographical and social contexts. We also discussed the barriers, enablers, and trade-offs between environmental and human well-being outcomes that impact the effectiveness of the specified interventions. Lastly, we aimed to synthesise cost evidence of included projects.

Search methods: The search of included studies was based on the evidence gathered in the Climate Change and Biodiversity Evidence Gap Map (CCB EGM, Marion et al., 2024). The CCB EGM displayed the available rigorous evidence on the effects of climate change and biodiversity interventions on environmental and human well-being outcomes in L&MICs. The systematic search of studies was conducted in March 2024.

Selection criteria: From the CCB EGM, we focused on impact evaluations of projects which covered three types of land management interventions in L&MICs: (1) community-based or decentralised land management and monitoring; (2) land rights; and (3) protected areas. In this review, we considered studies published after 2000 that reported intervention effects on both environmental and human well-being outcome groups, aiming to identify possible trade-offs, synergies, and unintended consequences of these land management interventions.

Data collection, and analysis: We identified 64 papers representing 58 unique studies. These impact evaluations assessed 60 projects across 26 L&MICs, reporting effects on both environmental and human well-being outcomes. Two independent reviewers conducted the data extraction and risk of bias assessments of projects’ effects. With the available evidence, we conducted meta-analyses to synthesise effectiveness findings for comparable outcomes. For the set of included projects, we searched for additional qualitative information to identify barriers, enablers, unintended consequences and trade-offs that can affect project effectiveness, and for cost-related evidence that can inform the cost-effectiveness of these projects. Limitations of this review include: 1) the CCB EGM’s search only covered studies published up to March 2024; (2) the CCB EGM’s search only used terms in English; (3) the meta-analyses conducted in this review were most often based on a small number of estimates; and 4) half of the included quantitative evidence was judged to have a high risk of bias.

Main results: The available evidence looking at the environmental and human well-being effects of these three land management interventions is sparse. Quantitative and qualitative evidence suggests that these interventions represent potentially effective strategies to promote shifts towards sustainable use of land resources and improvements in human livelihoods. These interventions can, however, result in significant trade-offs between environmental health and human welfare. The three interventions reviewed present dissimilar levels of evidence, mostly focused on community-based or decentralised land management and monitoring. Nearly all these projects took place in Africa, Latin America and the Caribbean, and East Asia and the Pacific. Available evidence on final environmental outcomes, such as biodiversity, climate adaptation and mitigation measures, is limited. Decentralised land management and monitoring increased income, possibly driven by improvements in the governance of natural resources and greater adoption of improved practices and technology. There is evidence that these interventions initiated a shift away from agricultural and forestry harvesting, as they promote forest preservation and development. Land rights led to a small increase in agricultural and livestock productivity, greater diversification of income sources and investments in natural resources or conservation. In turn, protected areas slightly reduced deforestation and improved agricultural and livestock productivity, as well as income and assets. Aligning interventions with existing communities’ economic activities and values can create virtuous cycles for the sustainability of land and people’s livelihoods. Knowledge-sharing and collaboration opportunities often enabled intervention success, whereas a lack of clarity, governance and funding structures were identified as barriers to intervention success. Conservation interventions can potentially and inadvertently lead to deforestation, land degradation, and biodiversity loss if the local population lacks the incentive to adopt these new practices. Increased wildlife from conservation efforts can also come at the cost of decreased food security and damaged properties, potentially affecting people’s livelihoods. The extent and quality of cost evidence were generally insufficient to enable a comprehensive analysis of cost-effectiveness across included land management interventions. The review, however, identified useful insights from four well-documented cases.

Authors’ conclusions: Environmental and human well-being outcomes are closely linked, and evaluating them in isolation risks overlooking their interacting mechanisms. Interventions should be grounded in local socio-economic and ecological contexts, allow for continuous learning and adjustment, and prioritise inclusive, fair and equitable approaches. More rigorous, theory-informed evaluations that report on both environmental and human well-being are needed. Future research should also better integrate cost-effectiveness analyses from the outset and track long-term and equity-related outcomes. Using agile evaluation methods and remote-sensing data can help close evidence gaps and improve decision-making in complex and dynamic environments.

Plain Language Summary

Navigating trade-offs while creating opportunities for virtuous cycles between environmental protection and human well-being through land management interventions in low- and middle-income countries.

The review in brief The evidence reporting on both the environmental and human well-being effects of land management interventions is limited. The findings suggest that these interventions result in small positive effects on improved natural resource use and management, productivity and income. Effects on biodiversity, climate mitigation and other longer-term human well-being outcomes remain inconclusive.

What is this review about? The accelerating rise in global temperature and depletion of land ecosystems and fauna are highly interconnected, increasingly threatening livelihoods and human well-being in L&MICs. Land management policies aim to conserve, preserve and restore natural resources and ecosystems, while also offering long-term and sustainable economic opportunities for local communities. This review synthesised the available evidence reporting on both the environmental and human well-being effects of three land management policies: community-based or decentralised land management and monitoring, land rights, and protected areas.

What is the aim of this review? This Campbell Systematic Review examined the effects of three land management interventions, appraising and synthesising the available evidence. We also explored the barriers, enablers, and trade-offs that can affect these projects, and aimed to synthesise cost evidence of included projects.

What are the main findings of this review?

What studies are included? We included impact evaluations of land management interventions which reported on both environmental and human well-being effects in L&MICs. We relied on evidence gathered in the Climate Change and Biodiversity Evidence Gap Map (CCB EGM, Marion et al., 2024), identifying 60 land management interventions in 26 L&MICs. Across the three intervention types, the evidence base is limited, especially on the effects of protected areas. Almost half of the evidence was assessed as having a high risk of bias. We also identified 112 documents providing qualitative information, mostly of high or moderate quality, on factors affecting the successful implementation of these projects. Additionally, we found reliable cost evidence for four of the included projects.

How has this intervention worked? The effects varied across the three land management interventions. The evidence shows that decentralised land management and monitoring increased income, likely due to better natural resource governance and adoption of improved practices. This intervention type also led to a modest decline in land area used for agriculture and forestry, indicating a shift toward forest preservation. We also observed some improvements in intermediate environmental and human well-being outcomes from land rights interventions. We found that aligning with existing community livelihoods and providing knowledge-sharing and collaboration opportunities contributed to project success in some cases. Lack of clarity and governance, as well as inadequate funding structures, were identified as barriers to project success. We also found that project efforts to conserve the environment can inadvertently result in environmental degradation if local communities lack the incentive to adopt new practices.

What do the findings of this review mean? The extent of the current evidence base remains small. Further rigorous quantitative and qualitative analyses are essential to understand the effects of these interventions and to design effective land management policies. While there can be important trade-offs to consider when implementing land management interventions in L&MICs, they can also support virtuous cycles of environmental protection and human well-being. Reporting projects’ cost data can also provide crucial insight into the variability of expenditure needed across different contexts and enhance our understanding of their value for money.

How up-to-date is this review? The search for studies was completed in March 2024. This Campbell Systematic Review was published in 2026.

Background

The Problem, Condition, or Issue

The World Economic Forum estimated that more than half of global economic activity (US$ 44 trillion) relies heavily on natural resources (WEF, 2020). Local communities and individuals’ livelihoods in low- or middle-income countries (L&MICs), especially the most vulnerable populations, often directly depend on land for livestock, hunting and farming (Cannizzo et al., 2024; IPCC, 2023; OECD, 2023), highlighting trade-offs between the maintenance of natural systems and the achievement of human well-being aspirations. Around 20% of the world’s population relies on wildlife for income and food (IPBES, 2019), while access to traditional medicines and nutrition remains closely tied to land, forests, and ecosystem services (IPCC, 2023; OECD, 2023).

Land resources are currently depleting at an alarming rate globally. The loss of land’s fresh water, soil richness and ecosystems has devastating impacts on the climate, biodiversity and human well-being. Globally, almost 75% of land has been altered, and at least 20% has been degraded (UNCCD, 2022). Biodiversity outcomes have also significantly deteriorated in recent decades, with an estimated one million species at risk of extinction (Dasgupta, 2021; IPCC, 2023; Secretariat of the Convention on Biological Diversity, 2020). Animal pollination continues to decline, the population of vertebrate species has declined by 60% since 1970, and the world’s forest area has diminished by 32% (IPBES, 2019).

In L&MICs, climate change furthers the depletion of land resources. Global temperatures have risen since the start of the Industrial Revolution and are likely to exceed Paris Agreement targets by 2100 (IPCC, 2023). L&MICs are facing more severe and frequent climatic shocks and natural disasters, such as floods, droughts, heat waves, and wildfires, than other countries (WMO, 2021). These shocks erode households’ income-generating capacities and can push them into poverty-traps (Carter & Barrett, 2006; Eichstelle et al., 2022), increasing exposure to future risks (e.g., Barbier, 2025). Uncontrolled rising temperatures reduce the ability of species to adapt (Dasgupta, 2021; IPBES, 2019), and may lead to the spread of non-native invasive species (Weiskopf et al., 2020). With rising temperatures, animals and plants tend to migrate to higher elevations or latitudes, disrupting ecosystems (UN, 2024). These changes intensify human-induced land use change and resource degradation.

Land use and management are central to addressing climate and biodiversity crises. Deforestation, for instance, emits carbon and weakens ecosystems’ ability to sequester it (Dasgupta, 2021; IPBES, 2019). Moreover, land tenure insecurity can result in conflict, or land grabs by powerful actors, including the state. This creates incentives for short-term land exploitation, often leading to degradation (Besley, 1995; Carrero et al., 2022; UNCCD, 2022).

Without direct intervention, land management and climate change will interconnectedly worsen, threatening livelihoods and well-being (Armand & Kim Taveras, 2022). An urgent change is required in the way land use issues and potential solutions are framed and addressed through policy action, and understood through research (Kuemmerle, 2024). Effective, fair and inclusive land management interventions are urgently needed and can slow down this perverse cycle by better preserving, conserving and restoring water, soil and biodiversity. These can contribute to climate change mitigation and adaptation and, therefore, support livelihoods and well-being.

The Intervention

In this review, we synthesised and critically appraised the evidence on the effects of three types of land management interventions in L&MICs: (1) community-based or decentralised land management and monitoring, (2) land rights and (3) protected areas. We relied on the Climate Change and Biodiversity Evidence Gap Map (CCB EGM; Marion et al., 2024), drawing from interventions within the Land and Forests group. None of the ten other intervention types in the Land and Forests group focus on land management.

Community-Based or Decentralised Land Management and Monitoring

This intervention involves transferring the management and decision-making authority over natural resources from national to regional and/or local actors. Community participation may take the form of fully or partially decentralised land management, monitoring of external managerial authorities, or reporting on natural resource use. This intervention type aims to use local knowledge and presence to preserve and sustainably use natural resources. Examples include community-based forest management, community representation in forest managerial organisations (e.g., community councils), and crowdsourced reporting and monitoring systems (e.g., wildfires risk management, timber tracing).

Land Rights

We define land rights as a formal registration of land, either through the conversion of communal or non-demarcated rural land to freehold title, or the statutory recognition of customary or communal rural land rights. Land rights are registered and recognised by a central authority enabling right holders to legally defend and enforce their access and control of this land. In turn, right holders can determine land use in terms of activities (e.g., pasture, forestry, agriculture, left fallow, leasing, and sale of land), investment level, and hiring of labour.

Protected Areas

The International Union for Conservation of Nature (IUCN) describes a protected area as “a clearly defined geographical space, recognised, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values” (Dudley, 2008, p. 8). Protected areas have defined boundaries, formally recognised under statutory civil law or traditional rules aiming to influence natural systems and human behaviour to protect, maintain, and restore ecosystems and biodiversity. Several categories of protected areas exist, ranging from conservation activities (such as wildlife conservation) to a more comprehensive area management for the sustainable use of resources. Protected areas are often essential to most national biodiversity conservation strategies in L&MICs (Jonas et al., 2024; Lopoukhine & de Souza Dias, 2012).

How the Intervention Might Work

This review was based on rich theoretical and empirical literature describing how these three land management interventions may affect both the environment and people in positive or negative ways. Importantly, interventions’ implementation could also generate situations where positive changes in one dimension (i.e., environmental) have demonstrably burdened the other (i.e., human well-being, or vice versa), leading to trade-offs. For instance, interventions may help preserve and restore ecosystems and biodiversity, but in the process, they may limit local communities’ access to land resources essential for their livelihoods. These trade-offs can have differing consequences on members of the same communities as a function of existing or exacerbated inequalities and vulnerabilities related to gender, age, and other aspects.

We aimed to establish and synthesise the conditions under which these outcomes throughout intervention implementation (i.e., along the theory-of-change) have been achieved, as well as contextual and other factors that served as enablers or impediments of the desired change (i.e., assumptions). In this section, we outline the conceptual framework used in this review, discussing the key expected outcomes, mechanisms of change, assumptions, and possible unintended consequences of these three interventions (e.g., negative spillovers or leakage both through time and space). Figure 1 provides a graphical representation of this framework across these three land management interventions and, in turn, guides this review in terms of inclusion criteria and analysis.

Figure 1.

Conceptual Framework For The Review of Land Management Interventions.

Notes. Moderators include mechanisms through which elements of the interventions act (i.e., drivers). Vertical arrows are indicative of some but not all possible interactions between mechanisms and within intermediate and final outcomes

A Conceptual Model for Desired Long-Term Change

This review aimed to explore how these interventions rely on mechanisms that could lead to impacts associated with improved climate change mitigation and adaptation conditions, favourable biodiversity status, and enhanced human well-being conditions. Intermediate outcomes that support longer-term changes include strengthening the skills and capacities of decision-makers and resource managers, including local communities. Likewise, plausible resulting intermediate outcomes refer to improved resource management and better economic prospects for local actors, such as encouraging the dissemination of knowledge and the adoption of environmentally friendly and innovative practices and technologies. These three interventions can also promote sustainable use and management of natural resources and offer new economic opportunities.

In turn, final biodiversity outcomes may improve. These include the preservation and development of habitat integrity and species diversity (Brodie et al., 2023; Nowakowski et al., 2023) and maintenance and enhancement of ecosystem services (Lawry et al., 2012; Samii et al., 2015), sometimes leading to direct and indirect health benefits (MacKinnon et al., 2019). Additional evidence has also suggested that the sustainable use of resources and improved biodiversity outcomes can contribute to greater adaptation to and mitigation of the adverse effects of climate change and biodiversity loss (Cannizzo et al., 2024; Duncanson et al., 2023).

Previous research has also linked land management interventions with poverty alleviation and improvement in final human welfare outcomes in the context of the interconnected crises of climate change and biodiversity loss (Samii et al., 2015; Turner et al., 2012). In these cases, their implementation has helped reduce social tensions and improve overall well-being (Hajjar et al., 2021; Miller et al., 2021, 2022). Therefore, changes in one outcome can influence others. However, the magnitude of these changes is likely to vary with the characteristics of those affected (i.e., positively or negatively) and through the implementation of these interventions. Beyond issues related to participants’ social and economic status, including power and other asymmetries inherent to each group based on gender, age, ethnicity and other factors, access to social connections, institutions, and resources (including resources from provision of social protection), as well as the time elapsed since the implementation of the interventions, will shape their outcomes and ultimate impacts.

Mechanisms

These three land management interventions can promote sustainable use and management of natural resources through several mechanisms. First, these interventions can improve the enforcement of sustainable land practices. Protected areas impose specific punishment penalties against illegal deforestation, hunting or over-exploitation (Koch et al., 2019), which in turn can facilitate the preservation and restoration of the local environment (Dudley, 2008; Pullin et al., 2013; Schmidt-Soltau 2003; West et al., 2006). Similarly, in the context of land rights and decentralised land management, enforcement is more likely than in open-access scenarios. Resource overuse is less likely when land is individually owned (Lawry et al., 2023; Rakotonarivo et al., 2023), or when incentives, even implicit, support the continuation of traditional collective resource management practices through the establishment of social institutions, rules and norms. In decentralised systems, resources are restricted to local users, bringing the consequences of land management and use closer to those directly affected.

Second, land rights and decentralised land management can contribute to sustainable use and management of natural resources by granting individuals and local communities control over resources. Local actors are often most knowledgeable about the local conditions and can best monitor changing situations (Ferraro & Agrawal, 2021; Lawry et al., 2012, 2016; McLain et al., 2021; Samii et al., 2015). Compared to central authorities, they are more likely to adapt land management practices and technologies to local needs.

Control of local land and resources can also strengthen local actors’ incentives to use natural resources sustainably. These three interventions can reduce land insecurity, thereby eliminating land conflict costs and encouraging long-term land investments (Besley, 1995). When there is control of land and resources, the sustainability of resource use can maximise long-term gains (Barbier & Burgess, 2001; Bohn & Deacon, 2000; Gibson et al., 2000; Lawry et al., 2012). Decentralised land management may alter interactions among local groups and result in the design of social norms and institutions that could implement informal sanctions or social pressures, aligning individual actions with group interests (Agrawal et al., 2023; Agrawal & Ostrom, 2001; Baragwanath et al., 2023). In other cases, elite capture and power-grabbing may result (Persha & Andersson, 2014).

These three interventions can also provide economic opportunities and potential welfare gains for local actors, as their livelihoods often rely on land and natural resources (Cannizzo et al., 2024; IPCC, 2023; OECD, 2023). These opportunities can, for example, translate into ecotourism in and around protected areas and other land utilisation and development from land rights and decentralised land management. Protected areas are often accompanied by infrastructure and road developments that facilitate economic activities within local communities (Ferraro & Hanauer, 2011; Zhang et al., 2023). Land rights can be used as collateral and improve access to credit (Besley, 1995; Fenske, 2011), enabling landowners to invest in sustainable land production and use. Land ownership can also provide greater control to individual households in decisions linked to selling, leasing out, sharecropping land, or hiring labour (Lawry et al., 2016). As pointed out earlier, some of these opportunities are not equally accessible to all sectors of communities. Some local actors, given inherent vulnerabilities (e.g., age, disability) and internal political economies of power, may suffer costs.

Assumptions and Unintended Consequences

The success of these three interventions relies on assumptions linked to resources, skills and information access, and adequate governance (Baynes et al., 2015). ¹ For example, Lessmann et al. (2024) highlighted a substantial funding deficit hindering the effective management of protected areas. Corrupt, under-resourced or ineffective control of protected areas and land can also prevent improvements in environmental and human outcomes (Blackman et al., 2017; Kahsay & Bulte, 2021; Liu et al., 2001). Failures within credit, input, or output markets, or information asymmetries across local actors, can compromise the potential benefits of these interventions.

It is also important to acknowledge that these interventions may have unintended consequences for land and people. Protected areas, and to a lesser extent, decentralised land management, may reduce local economic welfare by restricting land use choices and constraining human actions, as it limits local actors’ ability to use land for agriculture, logging, and hunting (Pullin et al., 2013; Samii et al., 2015; Schmidt-Soltau 2003; West et al., 2006). Although protected areas may increase income and employment from ecotourism (Balmford et al., 2009; den Braber et al., 2018; Pullin et al., 2013), ecotourism may also displace other existing jobs or increase local prices (Robalino, 2007). There have also been cases of local communities being forced out so that protected areas can be established (Brockington et al., 2006; Lunstrum & Ybarra, 2018; Robalino, 2007).

These interventions may exacerbate income inequalities if benefits are captured by large commercial enterprises or wealthier households (Kahsay & Bulte, 2021; Samii et al., 2015). Implementation may also be co-opted by actors seeking private, short-term gains, whether internal (e.g., elite capture: Jagger et al., 2014; Persha & Andersson, 2014) or external to the communities (i.e., carbon cowboys: Rifai et al., 2015; West et al., 2023). If these interventions only benefit the targeted areas, inequality between protected and non-protected areas, or between areas with and without land rights, may increase (den Braber et al., 2024; Kandel et al., 2022; Lawry et al., 2016). In contexts of contested lands, lack of regional planning and inconsistent governmental actions can lead to social-environmental failures (Kennedy et al., 2023). Lastly, overlapping interventions in the same location and timeframe can also affect each other’s objectives.

Likewise, these land interventions may not always encourage long-term incentives to adopt environmentally friendly practices and to use resources sustainably. Local actors’ incentives are likely to change over time based on external factors, such as the implementation of new development programmes or changes in regional and international markets. Trade-offs between the preservation of the local environment and economic aspirations may emerge, for example, when decentralised land management or land rights encourages transferring negative externalities to neighbouring areas (i.e., negative spillovers or leakage). Community-level land management groups may also be incentivised to increase pollution if they know they can transfer it downstream to other communities and there is no national or regional enforcement (Lipscomb & Mobarak, 2017). Land right holders’ incentives may not always follow practices that are aligned with long-term land and biodiversity conservation. For example, a steep increase in a crop price may encourage landowners to convert forests into agricultural land to make it more efficient and profitable (Godoy et al., 2001; Wood & Walker, 2000). Another example of trade-offs across these interventions is the use of credit policies to increase access to funds for rural people, which may lead to an inadvertent increase in deforestation (Assunçao et al., 2020).

Why it is Important to do This Review

Many governments and international organisations are promoting these land management interventions to mitigate and adapt to the effects of climate change and biodiversity loss. Target 3 of the Kunming-Montreal Global Biodiversity Framework (GBF) is to effectively manage and equitably govern, protect and conserve at least 30% of the Earth’s land and water by 2030 (Kunming-Montreal Global Biodiversity Framework, 2024). Protected areas are becoming increasingly prevalent in national conservation policies. Approximately 18% of the world’s terrestrial and inland water areas are now covered by more than 300,000 reported protected areas (UNEP-WCMC and IUCN, 2024). Similarly, governments frequently transfer land and forest management and decision-making to local communities. In L&MICs, 30% of forests are managed by communities (i.e., indigenous people and local communities) according to the Rights and Resources Initiative (2021).

National and international organisations are also incentivised to support these land management interventions as they are linked to the Sustainable Development Goals (SDGs), especially SDGs 13 and 15 (IUCN, 2022). SDG 13 aims to “take urgent action to combat climate change and its impacts” (UNDESA, 2020, p. 15), and SDG 15 seeks to “protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss” (UNDESA, 2020, p. 17).

With this increased interest, the number of studies evaluating the effects of these interventions has grown rapidly. Following a systematic search and screening of studies, the Climate Change and Biodiversity Evidence Gap Map (CCB EGM; Marion et al., 2024) identified the available impact evaluations (IEs) on the effects of climate change and biodiversity interventions (including the three intervention types reviewed) on environmental and human well-being outcomes in L&MICs. In this EGM, 75% of the evaluations assessing the effects of these three intervention types were published since 2014.

However, the EGM also identified a need to synthesise this evidence to understand whether these interventions effectively address climate change, biodiversity loss and human well-being. Thirty-five systematic reviews (SRs) examined the effects of community-based or decentralised land management or monitoring, land rights, and protected areas (including three SR protocols). Although the number of SRs seems high, 27 of these SRs were assessed as having low confidence due to the lack of alignment with international standards for conducting and reporting SRs (Higgins et al., 2024; The Methods Group of the Campbell Collaboration, 2019a, 2019b). As a result, they have a higher risk of being subject to biases when searching, selecting and analysing the evidence. Among the five SRs that were appraised as having high or medium confidence, the latest one was published more than five years ago (Geldmann et al., 2013; Lawry et al., 2014; Pullin et al., 2013; Samii et al., 2015; Waddington et al., 2019). In the context of the rising number of evaluations of these three interventions, these reviews do not reflect the most recent implementation advancements or knowledge of what works.

Moreover, four of these SRs focused on the review interventions individually. Geldmann et al. (2013) focused on the effects of protected areas on habitat cover and species populations, whereas Pullin et al. (2013) were interested in the effects of protected areas on human well-being. The mixed-methods review of land rights interventions (Lawry et al., 2016) considered implications for environmental themes; however, it included primary studies of land rights interventions without a direct focus on environmental topics. In their review of decentralised forest management, Samii et al. (2015) reported on different types of forests across seven countries. Waddington et al. (2019) focused on two of the three land interventions selected for this review. The authors explored citizen engagement interventions, including community-based or decentralised land management and monitoring programmes, community-maintained irrigation programmes, and protected areas. Across these SRs, substantial gaps were highlighted in the evidence base for both environmental outcomes (e.g., the effects of protected areas on species complexity, as noted by Geldmann et al., 2013) and longer-term human well-being outcomes (e.g., the impact of decentralised forest management programmes on income, as discussed in Samii et al., 2015).

To understand trade-offs, potential synergies and the extent to which these interventions can provide triple wins for climate, biodiversity and livelihoods in land systems, we aimed to systematically review the effects of these three land management interventions on both environmental and human well-being outcomes in L&MICs (as identified in the CCB EGM). We synthesised the latest evidence, so it is accessible to policymakers and researchers. This review intends to help inform future programming decision-making by highlighting what works across contexts based on the latest and most rigorous evidence. It also advises researchers on areas where new evaluations are needed to fill evidence gaps. Given the growing prominence of nature-based solutions to address the climate change and biodiversity crises and the need to contribute to improving prospects for well-being (Sandbrook et al., 2023), the knowledge generated by this systematic review is expected to increase the scope for improved decision-making and investments in conservation and development (Kinol et al., 2023).

Objectives

The purpose of this review was to identify, assess and synthesise the evidence of the effects of three land management interventions (community-based or decentralised land management and monitoring; land rights; and protected areas) on environmental and human well-being outcomes in L&MICs. We aimed to answer the following questions:

• (1) What are the effects of these three interventions on environmental and human well-being outcomes? Do effects vary by population, location, or other factors?

• (2) What are the barriers, enablers, and trade-offs between environmental and human outcomes that impact the effectiveness of the specified interventions?

• (3) What is the cost-effectiveness of these interventions?

Methods

We prepared a review protocol (Marion et al., 2025) to follow best practices for conducting and reporting SRs of effectiveness in international development (The Methods Group of the Campbell Collaboration, 2019a, 2019b; Waddington et al., 2012). The review relied on evidence gathered in the CCB EGM (Marion et al., 2024). ² The CCB EGM displayed the available rigorous evidence on the effects of climate change and biodiversity interventions on environmental and human well-being outcomes in L&MICs. It included 1,605 IEs and SRs of interventions across four natural systems and productive activities: i) land and forests; ii) agriculture and livestock; iii) aquaculture and fisheries; and iv) coasts and oceans. A systematic search of studies was conducted in March 2024, followed by the screening and selection of studies from April to June 2024. For research question 1, we did not search or screen for further rigorous evidence. The evidence for research questions 2 and 3 relied on the programmes that were rigorously evaluated.

Criteria for Considering Studies for This Review

Types of Studies

The review covered rigorous IEs from the CCB EGM. Specifically, we included studies using experimental and quasi-experimental designs (QEDs; the full list of included designs is presented in Appendix 1). These study designs can establish causal relationships between interventions and outcomes if carefully and credibly executed by constructing a counterfactual to address what would have happened in the absence of the intervention (Aloe et al., 2017; Gertler et al., 2011; Reeves et al., 2017). The counterfactual may be inferred from a group that did not receive the intervention (as in a randomised controlled trial, RCT), received an alternative intervention, or was constructed retrospectively by researchers (as in propensity score matching or interrupted time series designs).

Types of Participants

We considered studies of the effects of interventions focused on participants residing in L&MICs as categorised by the World Bank income classification. Appendix 2 provides the list of these countries. The classification reflected the country’s income status when the intervention started. Studies analysing more than one country were also eligible for inclusion if they provided at least one effectiveness estimate specific to L&MICs. ³ We did not exclude studies based on their setting. Hence, we included evaluations of interventions implemented across rural and urban areas, whether at local, regional, national, or international scales, and those targeting communities, households, or individuals.

Types of Interventions

We defined an intervention as a deliberate decision or series of actions carried out by an entity—such as a government agency, non-governmental organisation (NGO), private company, or consortium—with the aim of positively impacting events or outcomes in a replicable way. The three interventions included in this review relate to land management, meaning that they belonged to the CCB EGM’s Land and Forests intervention group and occurred within major types of natural land systems in L&MICs, namely (tropical) forests, woodlands, grasslands, savannas, inland wetlands, and peatlands. The three intervention types included are: community-based or decentralised land management and monitoring, land rights and protected areas. We present definitions for these interventions in Appendix 3.

Types of Outcome Measures

Out of the 436 IEs in the CCB EGM that evaluated at least one of these three interventions, we focused on studies that reported intervention effects on both groups of outcomes, environmental and human well-being, aiming to identify possible trade-offs, synergies, and unintended consequences of these land management interventions. The review did not cover other relevant impact evaluations that reported outcomes for only one of these groups. While we mapped all outcomes covered in the CCB EGM (Appendix 3 provides the full list and definitions for these outcomes), we did not exclude other outcomes reported in these evaluations. If identified, we assessed the extent to which these new outcome measures could be incorporated into the analyses. Environmental outcomes consist of intermediate outcomes (e.g., natural resource use and management), biodiversity outcomes and climate mitigation outcomes. Human well-being outcomes include intermediate outcomes (e.g., knowledge acquisition; practice and technology adoption) and human welfare outcomes (e.g., income, productivity, food security, health, social relations, governance and empowerment).

Other Inclusion Criteria Based on the CCB EGM

• • Language: Studies published in any language, although the search terms used in the CCB EGM were in English only.

• • Study status: Published or unpublished studies (grey literature), whether completed or ongoing (i.e., prospective study records, protocols, and trial registrations). In the case of ongoing evaluations, we only included them in the analyses if we identified a completed report at the start of the review.

• • Publication date: Studies published in 2000 or later.

• • Publication type: Academic and grey literature, including peer-reviewed journals, working paper series, organisational reports, and unpublished author manuscripts.

Search Methods for Identification of Studies

As this review is based on studies identified in the CCB EGM, we did not search for additional quantitative evidence within academic and grey literature sources, nor performed citation tracking to address research question 1. However, to address research questions 2 and 3, we searched for qualitative and cost-related evidence, as described below.

Searching Other Resources

To address research questions 2 and 3, we draw from the “effectiveness+” framework (Snilstveit, 2012). We searched for additional qualitative information on included programmes to identify the barriers and enablers that can affect their effectiveness. Likewise, we searched for additional cost-related evidence of included projects to inform these interventions’ cost-effectiveness. According to Brown and Tanner (2019), only about 15% of IEs within the broader international development field reported intervention cost data, highlighting the need to search for other resources.

For additional qualitative evidence, we looked for qualitative studies, quantitative descriptive studies, process evaluations and other documents related to included projects. For cost data, we searched for primary studies reporting unit or total costs for intervention implementers, participants, or non-participants. Relevant types of studies included full economic evaluations (e.g., cost-benefit, cost-effectiveness, and cost-utility analyses), partial economic evaluations (e.g., cost analyses, cost-comparison studies, and cost-outcome descriptions), or any other documentation reporting cost data of the interventions found in the search for other resources (Shemilt & Mugford, 2009). Appendix 4 details the strategy used to identify additional documentation and presents an example of this search strategy for one project.

Data Collection and Analysis

Please refer to the CCB EGM report (Marion et al., 2024), which describes the process for searching and selecting impact evaluations included in this review.

Data Extraction and Management

Once we had identified all includable studies from the CCB EGM, we extracted the following types of data from each study included in the review, using a standardised data extraction form. The full data extraction form is provided in Appendix 5.

• • Descriptive data, such as study authors, publication date, study status, and other characteristics such as country, intervention and outcome types, and intervention design of the study’s interest.

• • Methodological information on study design, analysis method, and type of comparison, if relevant.

• • Quantitative data for outcome measures, including descriptive information about the outcomes, sample sizes of the intervention and comparison groups, outcome means and standard deviations (SDs), and test statistics (e.g., t-tests, F-tests, p-values, and 95% confidence intervals).

Some of these data had already been coded for the CCB EGM. For the extraction of additional fields, descriptive data, and methodological information were single coded by a trained reviewer and checked for agreement by another team member. Quantitative data were coded by two trained reviewers independently, and disagreements were resolved through discussion with a third reviewer.

Assessment of Risk of Bias in Included Studies

We assessed the risk of bias for all IEs included in this review using 3ie’s tools. Appendix 6 presents the tools used, which include the bias domains and extensions from Cochrane’s ROBINS-I tool and Risk of Bias 2.0 (Higgins et al., 2016; Sterne et al., 2016). This assessment examined both the internal validity and statistical conclusion validity of experimental and quasi-experimental IE designs (Waddington et al., 2012). Two reviewers independently conducted the risk of bias assessment and resolved disagreements with a third reviewer as needed.

For each included study, we assessed the risk of bias at the estimate level. We focused on the following domains, assessing whether the estimate was free from each bias, with a response set of “Yes,” “Probably Yes,” “Probably No,” “No,” and “No Information”:

• • Factors relating to baseline confounding and biases arising from differential selection into and out of the study (e.g., assignment mechanism).

• • Factors relating to bias due to missing outcome data (e.g., assessment of attrition).

• • Factors relating to biases due to deviations from intended interventions (e.g., performance bias and survey effects) and motivation bias (Hawthorne effects).

• • Factors relating to biases in outcomes measurement (e.g., social desirability or courtesy bias, recall bias).

• • Factors relating to biases in the reporting of analysis.

In addition, we used these results by dimension to produce an overall rating for each estimate as either “High risk of bias,” “Some concerns,” or “Low risk of bias,” drawing on the decision rules in Risk of Bias 2.0 (Sterne et al., 2019):

• • “High risk of bias”: if any of the bias domains were assessed as “No” or “Probably No” (i.e., the estimate is likely not free of bias).

• • “Some concerns”: if one or several domains were assessed as “No Information” and none were “No” or “Probably No” (i.e., the estimate may be free of bias, but more information is needed).

• • “Low risk of bias”: if all the bias domains were assessed as “Yes” or “Probably Yes” (i.e., the estimate is likely free of bias).

We described the reliability of the studies included in our analysis and explored whether differences in estimated effects were linked to varying risk of bias across studies. To test how the risk of bias affected the results, we performed a sensitivity analysis as described in the Sensitivity analysis section.

Measures of Treatment Effect

An effect size (or treatment effect) represents the direction and magnitude of the difference in outcomes between groups of observations, such as the difference between outcomes in intervention and comparison groups (Borenstein et al., 2009; Valentine et al., 2015).

Effect sizes reported in empirical studies, however, are often dependent on the outcome scale or unit used in the study, making them difficult to compare directly across studies. To enable cross-study comparisons of effect magnitudes, we extracted data from each study and calculated standardised effect sizes. The appropriate formulae for these calculations were selected based on the data provided in the included studies and the outcome type. Appendix 7 provides the details on the effect size formulae. In addition, we reversed some of the estimates so that all outcomes can be interpreted in the same direction, where all numerically positive effects represent a “good” outcome for either the environment or human well-being. These cases are clearly identified in the Quantitative synthesis section.

When interpreting average effects from meta-analyses (Standardised Mean Difference, SMD), we followed the interpretation thresholds from the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system (Guyatt et al., 2008):

• • Statistically significant SMD < |0.1|: very small effect

• • Statistically significant SMD > |0.1| but ≤ |0.2|: small effect

• • Statistically significant SMD > |0.2| but < |0.37|: moderate effect

• • Statistically significant SMD ≥ |0.37|: large effect

Unit of Analysis Issues

Unit of analysis errors can occur when the unit of allocation for a treatment differs from the unit of analysis for the effect size estimate, and this discrepancy is not accounted for in the analysis (e.g., by clustering standard errors, SEs, at the level of allocation). We assessed the included studies for the prevalence of these issues and, if identified, we adjusted the reported SEs using the following formula (Hedges, 2009; Higgins et al., 2024):

$$SE{\left(d\right)}^{\prime }=SE\left(d\right)*\sqrt{\hspace{0.5em}1+\left(m-1\right)c}$$

Where d is the effect size, m is the average number of observations per cluster, and c is the intra-cluster correlation coefficient (ICC). If the included studies used robust Huber-White SEs to account for clustering, we calculated the SE of d by dividing by the t-statistic for the coefficient of interest. We searched the literature for an appropriate ICC value. When no such value was available, we assumed an ICC of 0.05, as described by Waddington et al. (2014).

Criteria for Determination of Independent Findings

It was crucial that our analysis accurately captured and accounted for the co-dependencies between study estimates. Standard meta-analytic methods assume that effect size estimates are independent. Failing to recognise when estimates were derived from the same intervention could lead to a distorted or exaggerated understanding of the available evidence.

Dependent effect sizes can arise in several situations. For instance, co-dependencies between estimates can occur when multiple publications originate from a single study or when several studies use the same dataset. Additionally, some studies may have multiple treatment arms that are compared to a single control group, studies may report outcome measurements taken at different time points or use multiple measures to assess an outcome construct. All these scenarios result in statistically dependent effect size estimates (Borenstein et al., 2009).

We assessed the extent of relationships across included studies in the review. To avoid double-counting evidence, we linked related papers of the same intervention before conducting data analysis. We used information provided in the included studies, such as sample sizes, intervention characteristics, and key intervention implementing and funding partners, to support these assessments.

When multiple publications reported on the same effect, we sourced the data from one “main” study and extracted additional project information from linked studies as needed. To identify the main study, we prioritised journal articles, and in cases involving multiple reports or working papers, we used the most recent publication.

We extracted effects reported across different interventions, outcomes, and subgroups within each study. To address dependent effect sizes, we applied data processing and selection techniques. Several criteria were used to select a single effect estimate per outcome per study, as detailed in Appendix 8. Alternatively, we used robust variance estimation analyses (RVE; Fisher & Tipton, 2015; Hedges et al., 2010) to include all available data, even when it was statistically dependent. We considered this approach when it was substantively relevant and when we reached the minimum degrees of freedom required to provide valid inferences.

Dealing With Missing Data

In cases of missing or incomplete data, we made every effort to contact study authors to obtain the required information. If the necessary data could not be obtained, we reported the study’s characteristics but noted that it could not be included in the meta-analysis due to missing data.

Following the recommendations of Mullan et al. (2009) on collating data for SRs, we documented the number of studies for which we contacted authors, the information requested, the method for eliciting information, and the responses received, if any. When relevant, we also reported the impact of the information obtained from authors on the results, including sensitivity analyses. This information is presented in the Quantitative synthesis section.

Assessment of Reporting Biases

Publication bias is a type of reporting bias that arises when the publication of studies depends on the magnitude and/or direction of their results. The implications of this bias can range from not being able to accurately describe the evidence available to potentially introducing bias in favour of larger and statistically significant findings (De Vito & Goldacre, 2019). To reduce the possibility of publication bias, the CCB EGM searched for and included unpublished studies; hence we also included studies regardless of their publication status in this review.

Following Higgins et al. (2020), we visually inspected funnel plots for each outcome with at least 10 estimates reporting such a measure. In addition, if a meta-analysis was feasible, we tested for the presence of publication bias using contour-enhanced funnel graphs (Peters et al., 2008) and statistical tests (Egger et al., 1997) for outcomes for which we identified at least 10 estimates, as suggested by Sterne et al. (2011).

Data Synthesis

We conducted meta-analyses based on outcomes that were sufficiently similar. Studies were combined in a meta-analysis if there were at least two effect sizes that shared the same intervention type, had a comparable outcome and had similar comparison group conditions, following the approach described by Higgins et al. (2024). Our main analysis approach was using inverse-variance weighted, random effects models to account for heterogeneity across interventions and contexts (Higgins et al., 2020). These models are preferred when the effects of interventions are likely to vary across contexts and populations (Borenstein et al., 2009). We used robust variance estimation (RVE) models when we identified different outcome measures related to the same substantive construct (e.g., “income”, which could be measured through total or per capita income, income from specific activities, profits, etc.).

Meta-analyses were conducted using R software (R Core Team, 2022), specifically the metafor package (Viechtbauer & Cheung, 2010) for independent effects and the robumeta package (Fisher et al., 2023) for RVE analyses. If there were too few studies, or if the included studies were too heterogeneous in terms of outcomes, we instead provided a narrative discussion of individual effect sizes along the causal chain. Additionally, we highlight and provide potential explanations for existing gaps in the evidence.

Subgroup Analysis and Investigation of Heterogeneity

In our analysis, we examined and discussed the distribution of average effects across outcome types. We also statistically assessed heterogeneity by calculating the Q statistic, I², and τ² to provide an estimate of the amount of variability in the distribution of effect sizes (Borenstein et al., 2009). We followed Higgins’ (2003) categorisation of I² values:

• • Low heterogeneity for I² values between 0 and 30

• • Moderate heterogeneity for I² values between 30 and 50

• • Substantial heterogeneity for I² values between 50 and 90

• • Considerable heterogeneity for I² values between 90 and 100

We complemented this assessment with a graphical analysis using forest plots and, whenever feasible, conducted moderator analyses using meta-regression analysis to investigate sources of heterogeneity. Following the PROGRESS-PLUS approach (Lipsey, 2009), we assessed moderators falling into three broad categories of extrinsic, methodological, and substantive characteristics. Examples of these categories are listed below, while the full list of moderators considered, including their source and treatment, is presented in Appendix 9:

• • Extrinsic characteristics, such as the funder of the study (e.g., NGO, private sector, and government investments), publication type, and publication date.

• • Methodological characteristics, such as study design, risk of bias, length of follow-up, and types of outcome measures.

• • Substantive characteristics, such as participant characteristics (e.g., gender, age, socio-economic status, and education), context (e.g., geographical setting), intervention type, intervention features, and type of intervention implementers.

When feasible, we used random effects meta-regression to investigate the association between moderator variables and heterogeneity of treatment effects (Borenstein et al., 2009) and subgroup analyses to investigate heterogeneity by treatment subgroups (e.g., men and women, and poor and non-poor).

Sensitivity Analysis

When possible, we conducted sensitivity analyses to determine whether the results of the meta-analysis were sensitive to the removal of individual studies. We assessed their sensitivity to outliers with the potential to influence the average effect. Specifically, we used studentised residuals to identify studies that may have had unusually large effect estimates (Viechtbauer & Cheung, 2010). Studies with a studentised residual exceeding the 100 × (1 − 0.05/(2 × k))th percentile of a standard normal distribution were considered as potential outliers. We then tested if the outlier was influential, that is, if its removal affected the interpretation of the average effect in terms of its magnitude, direction or statistical significance. We present meta-analysis results after removing the influential outliers.

Additionally, when relevant, we evaluated the impact of considering studies with a high risk of bias by removing these studies from the analysis and comparing the findings to the main results. This sensitivity analysis is presented alongside the reported meta-analysis results in the Quantitative synthesis section.

Treatment of Qualitative Evidence

To address research question 2 on enablers, barriers and trade-offs, we identified additional information from included projects and extracted data from qualitative studies, process evaluations, descriptive quantitative studies, and project documents identified through the search approach outlined in the Searching other resources section. The extracted data focused on the contexts of implementation, barriers and facilitators affecting intervention effectiveness, and unintended consequences of the interventions. A trained reviewer performed the data extraction, and about 15% of the extractions were then checked for accuracy and agreement by a second reviewer.

We assessed the quality of qualitative studies, process evaluations, and descriptive quantitative studies identified using a mixed-methods appraisal tool (presented in Appendix 10), which was developed by Langer et al. (2016) and applied by Snilstveit et al. (2018). This tool builds on the Critical Appraisal Skills Program checklist (CASP, 2011) and the mixed-methods appraisal tool developed by Pluye et al. (2011). The critical appraisal tool used is presented in Appendix 10.

With this appraisal tool, we evaluated the adequacy of reporting, data collection, presentation, analysis, and the conclusions drawn in each study. Appendix 10 provides further details of each domain assessed. Each appraisal domain was rated as low, medium, or high trustworthiness. We determined an overall appraisal rating for a study based on the ratings given to each domain. Mixed-methods studies with a descriptive quantitative component were given two appraisal ratings, one for the qualitative data and one for the descriptive quantitative data. However, if the quantitative method is quasi-experimental or experimental, then it was not appraised using this tool. We filtered out particularly low-quality studies using a fatal flaw approach, as outlined by Dixon-Woods et al. (2004). This means that studies that failed to meet specific assessment criteria were rated as critical and not used in our synthesis. The exception were mixed-methods studies in which the other quantitative or qualitative component had a higher rating. In these cases, the data associated with the component with the higher rating was coded. Project documents, which are several types of documents presenting information on the projects but no analysis of project impacts, did not undergo critical appraisal because they usually provide factual information about the interventions, such as their design and available resources.

To identify implementation and contextual factors that could influence project success (Thomas et al., 2014), we conducted a thematic synthesis of qualitative evidence. Appendix 10 presents more details of this approach.

Treatment of Cost Data and Economic Evaluations

To address research question 3, we collected and analysed cost data reported in the set of included studies or additional studies identified through the second search of references. We employed established approaches for economic evidence synthesis (Shemilt et al., 2008), including both full economic evaluations (including cost-benefit, cost-effectiveness, and cost-utility analyses) and partial economic evaluations (such as cost analyses, cost-comparison studies, and cost-outcome descriptions), along with any supplementary documentation containing cost information for the interventions under review.

The review team critically assessed studies regarding their cost and/or effectiveness components. General cost information was synthesised through narrative analysis. When studies reported intervention costs and effects separately, the team extracted data on resources, unit costs, and/or total costs to examine both components, prioritising comparable outcomes where feasible. We did not include non-significant impacts in the cost-effectiveness analysis when the included studies did not find statistically significant effects (Dhaliwal et al., 2013).

Due to quality concerns in this literature, we developed a Cost Information Reliability Rating system, drawing from Acharya et al. (2024) to help assess the trustworthiness of economic evidence. The five-tier framework gives a benchmark for evaluating how thoroughly and accurately project costs are captured across studies (Appendix 11 includes the full tool used). By ranking studies from those with comprehensive ingredient-based costing (Gold Standard) to those with only basic budget figures (Below Expected Standards), we can be transparent about the strength of the evidence used. It also highlights where better economic evaluation methods are needed in land management research.

Summary of Findings and Assessment of the Certainty of the Evidence

Based on the review findings, we present a discussion of the completeness of the body of evidence in addressing the research questions. We also emphasise the main gaps in the evidence (e.g., related to understudied outcomes or missing data from studies) and draw implications for generalising the review results.

Results

We first describe the evidence base used in this SR by providing an overview of the characteristics and distribution of the included studies and projects ⁴ across the three intervention types covered in the review. We also discuss the risk of bias of the effects of relevant included studies. We then report the quantitative and qualitative synthesis findings. The quantitative synthesis presents the appraisal and synthesis results of these included effects by each outcome measure considered within the intervention and outcome types. For the qualitative synthesis, we discuss the number and type of additional studies found, appraisal results, and findings in terms of barriers, facilitators, unintended consequences, trade-offs and synergies between environmental and human well-being outcomes of the included projects.

Description of Studies

Results of the Search

As this review is based on studies identified in the CCB EGM, we did not search for additional quantitative evidence within academic and grey literature sources, nor perform citation tracking to address research question 1. We searched for qualitative and cost-related evidence to address research questions 2 and 3, as presented in the Qualitative synthesis and Cost data synthesis sections.

Included Studies

Figure 2 illustrates the process to identify the relevant studies for this SR. Within the CCB EGM, most of the included IEs focused on land and forests (832 studies). We identified 436 IEs evaluating the effects of the three considered land management intervention types on any environmental or human well-being outcomes considered in the EGM. Out of these IEs, 58 (13%) reported effects on both environmental and human well-being outcomes. These 58 papers met our PICOS inclusion criteria (Population, Intervention, Comparison, Outcomes, and Study design). Appendix 12 details the distribution of these IEs across intervention types.

Figure 2.

Identification of Included Studies in This SR

These 58 studies evaluated 60 unique projects across the three intervention types, including one baseline report (Starosta et al., 2017) and two protocols (Abraham, 2014; Huntington et al., 2015). Six additional papers are included as linked studies reporting additional outcome data about the effects of an intervention (i.e., data on outcomes that are not reported by the main identified study). ⁵ The number of projects was higher than that of unique studies, as three studies reported on multiple projects. Also, six projects were evaluated by more than one study.

Regarding publication years, 38% of the included studies (n = 22) were published in the last five years (2020–2024). In the same period, an additional 164 studies in the EGM evaluated one or more of the same three land management intervention types but were excluded from this review because they measured only one of environmental or human well-being outcome groups, but not both.

Population

The projects reviewed were implemented in 26 low-and middle-income countries (L&MICs; Figure 3). They include 24 projects in Sub-Saharan Africa, 18 projects in Latin America and the Caribbean, 14 in East Asia and the Pacific, three in South Asia, and one from Europe and Central Asia. The individual countries with the largest number of included projects are China (n = 7), Kenya (n = 7), Brazil (n = 5), Mexico (n = 5), Ethiopia (n = 3), Tanzania (n = 3) and Peru (n = 3). These projects targeted rural or peri-urban populations (at various units of analysis such as communities, households or individuals) engaged in small-scale land and forest natural systems and productive areas (pastoralists, herders, agro-pastoralists and farmers).

Figure 3.

Distribution of Projects by Country.

Note. The figure shows the number of projects in each country. HICs: high income countries

Interventions

Figure 4 illustrates that across the three land management interventions, community-based decentralised land management and monitoring is the largest category (n = 32), while protected areas is the intervention with the fewest projects (n = 10). A handful of these projects (n = 4) involved more than one intervention. One project (MRHESM) combined land rights and protected areas, two projects (Law70 and CBINReMP) associated land rights and community-based decentralised land management and monitoring, while one project (CBET) included both community-based decentralised land management and monitoring and protected areas. Because we did not identify many projects with more than one intervention type, we focused on these three categories individually, considering the combined projects within each category. Appendix 13 provides further information of each project included in the review and the full list of included impact evaluations.

Figure 4.

Distribution of Projects by Intervention Type

The community-based or decentralised land management and monitoring interventions reviewed were mainly implemented in Sub-Saharan Africa and Latin America and the Caribbean. While these interventions usually focused on management and monitoring activities, 13 projects included additional types of activities. For example, the Programa Municipios Verdes programme in Brazil combined municipal-level land management and monitoring, delivering information and training as well as in-kind transfers and equipment, enforcing fines for illegal deforestation and providing access to credit.

Interventions related to land rights were most implemented in Latin America and the Caribbean, Sub-Saharan Africa and East Asia and the Pacific. Around a third of these interventions provided additional activities. For example, the Land Tenure Assistance (LTA) project in Tanzania combined land rights with the provision of information and support on the land formalisation process for participants.

The review included protected areas interventions that were mainly implemented in Latin America and the Caribbean and East Asia and the Pacific. A third of these interventions considered additional activities, such as the Protected Areas policy in Brazil, which combined protected areas, with economic compensations to landowners.

Outcomes

We found an uneven distribution of outcome categories across our body of evidence. Within environmental outcomes (Figure 5), we observe clusters of evidence on intermediate outcomes, mainly related to natural resource use and management measures (n = 32) and land cover outcomes (n = 34). This SR covers less information on the effects of included projects on biodiversity and climate mitigation outcomes. We did not find studies reporting on greenhouse gas (GHG) emissions.

Figure 5.

Distribution of Projects by Environmental Outcome Categories

Across intermediate human well-being outcomes (Figure 6), the category that was mostly reported was practice and technology adoption (n = 17). For human well-being and poverty alleviation outcomes, the most common outcomes evaluated were income, assets and basic materials (n = 35) and employment and livelihood measures (n = 24), while the least reported were outcomes related to clean air (n = 1) and water access (n = 3).

Figure 6.

Distribution of Projects by Human Well-Being Outcome Categories

Study Design

Most of the included projects were evaluated using quasi-experimental designs (QEDs; n = 54) compared to experimental designs (RCTs; n = 6). Among QEDs, the methods most frequently employed were statistical matching (n = 29), followed by difference in differences and fixed effects models (n = 19), and instrumental variable models (n = 10; Figure 7).

Figure 7.

Distribution of Projects by Design.

Note. As projects could be evaluated by more than one study, and some studies used multiple methods, the number of methods is greater than the number of projects

When looking at the study designs used by the intervention category, RCTs were mostly implemented in community-based or decentralised land management and monitoring projects. Only one land rights project, the Land Tenure Assistance (LTA) in Tanzania, was evaluated using an experimental design, and none of the protected areas reviewed was based on an experimental design.

Excluded Studies

Based on the CCB EGM, we identified 79 studies with IEs evaluating one or more of the three intervention types and reported both environmental and human well-being outcomes. Nineteen studies were excluded for the following reasons (Appendix 14 provides information on these excluded studies):

• • Seventeen studies were excluded as they did not provide the effects of at least one of the three included intervention types on both environmental and human outcomes. For example, a study reported the effects of multiple intervention types, and for the interventions reviewed, authors only provided the effects of environmental or human well-being outcomes.

• • Two studies did not have an includable study design.

We did not include the 378 studies in the CCB EGM that evaluated the effects of these land management interventions on either environmental or human well-being outcomes. These studies are unlikely to inform on how these interventions have trade-offs or synergic environmental and human well-being outcomes.

Risk of Bias in Included Studies

We conducted risk of bias assessments for 1,205 estimates reported in evaluations of included projects. ⁶ In this section, we present summary statistics and key sources of biases present in this body of evidence. Eighty-six percent of these estimates were identified using quasi-experimental study designs (QEDs; Figure 8), while 14% came from randomised control trials (RCTs; Figure 9). Overall, 52% of included estimates were rated as having a high risk of bias; 28% were considered to have some concerns related to their risk of bias, and the remaining 20% were assessed as having a low risk of bias.

Figure 8.

Risk of Bias of Included Estimates Based on QEDs

Figure 9.

Risk of Bias of Included Estimates Based on RCTs

When assessing individual risk of bias domains, the most common source of bias was confounding, driven by QEDs. Within studies using QEDs, the risk of confounding bias was largely prevalent in studies based on statistical matching models, as authors did not often report sensitivity analyses to evaluate hidden biases, such as Rosenbaum’s test. These studies also regularly relied on endline time-variant covariates to match treated and control group observations estimations. Similarly, instrumental variables selected were often not exogenous. Across RCT studies, reporting bias was the most prevalent, as pre-analysis plans (PAPs) were not usually available, and when they were, not all of the outcomes discussed in the PAPs were then reported in the final evaluation report (and their absence was not justified).

By intervention category, 16% of estimates of community-based or decentralised land management and monitoring projects were assessed as having a low risk of bias. This is in line with estimates of land rights interventions and estimates from protected areas (15% and 20% respectively).

In each meta-analysis, the forest plot shows the estimates’ level of risk of bias (low, some concerns for risk of bias, or high). In the reporting of the meta-analysis results, we highlight the number of estimates which have a high risk of bias. When possible, we also conducted the meta-analysis excluding estimates considered to have a high risk of bias and describe if and how the overall weighted average effect changes.

Synthesis of Results

Quantitative Synthesis

We present the results of the quantitative synthesis for each of the three intervention types reviewed: community-based decentralised land management and monitoring, land rights and protected areas. We included project effects from IEs reporting both environmental and human well-being outcomes. By intervention, we conducted meta-analyses for all outcome measures with sufficient and comparable data available. ⁷ For each meta-analysis, we also explored 18 moderators to help explain differences in the average effect, including contextual factors (such as region of project, country-level biodiversity levels and exposure to climate change), target populations, as well as study characteristics (overall risk of bias, study design and year of publication).

Table 1 displays all the meta-analysis results by intervention category after removing influential outliers, along with the number of effects (n) included in each meta-analysis and the associated 95% confidence interval and p-value. SMDs represent the overall average weighted effect of an intervention type on a specific outcome. For some outcome measures, we reversed the direction of the estimates so the interpretation across all meta-analyses would be the same: higher effects represent an improvement in environmental or human well-being outcomes. Outcomes highlighted in green have a positive and statistically significant SMD at least at a 5% significance level, indicating that these effects are unlikely to be due to chance. Outcomes highlighted in orange have a negative and statistically significant SMD at least at a 5% significance level, indicating an adverse effect.

Table 1.

Summary of Meta-Analysis Results by Intervention Type

Notes. N = number of independent project estimates included in the analysis; SMD = standardised mean difference. 95% CI = confidence interval. Outcome measures highlighted in green have a positive and statistically significant SMD at least at a 5% significance level. Outcome measures highlighted in orange have a negative and statistically significant SMD at least at a 5% significance level. Outcome measures denoted with * indicate that the effect sizes were reversed, meaning that higher effects represent a “good” outcome (e.g., a higher effect represents a smaller area deforested or lower multi-dimensional poverty). Appendix 15 provides detailed information on each meta-analysis.

Due to the large number of meta-analyses conducted, this section provides a summary of the findings by intervention type and outcome category. At the beginning of each intervention type, we present a snippet of Table 1 and a high-level overview of the results.

The results need to be interpreted with caution. The number of included effects is often low, and a significant percentage of included effects were considered to have a high risk of bias. However, the absence of available evidence does not necessarily mean that these interventions are ineffective. The results reflect the available reported effects of these projects and provide important learnings that could be useful across contexts.

In addition, Appendix 15 provides a table listing all outcomes considered for each intervention type (including those we could not meta-analyse), in addition to further details of each meta-analysis conducted, including information of included projects, a forest plot summarising the results, discussion of outliers, and the results of moderator, subgroup, sensitivity and publication bias analyses. When a single project reported an effect on a relevant outcome, we also present a narrative description of this result.

Community-Based or Decentralised Land Management and Monitoring

This intervention involves transferring the management and decision-making authority over natural resources from national to regional and/or local actors. Community participation may take the form of fully or partially decentralised land management, monitoring of external managerial authorities, or reporting on natural resource use. We synthesised the effects of 28 projects.

Table 2 shows that community-based or decentralised land management and monitoring improved short-term environmental outcomes. These improvements included a reduction in the area used for agriculture and forestry products, increased tree planting and preservation, a rise in investment in forests, and enhanced monitoring of local natural resources. On human well-being outcomes, these projects increased participants’ income, which may have resulted from improved governance of natural resources and greater adoption of improved practices and technology. Longer-term environmental and human well-being impacts remain unclear. Limited evidence was available on environmental outcomes related to biodiversity, sustained changes in forest cover and climate mitigation. Similarly, the evidence was inconclusive on human well-being outcomes such as productivity, food security and multidimensional poverty.

Table 2.

Meta-Analysis Results for Community-Based or Decentralised Land Management and Monitoring Interventions

Notes. N = number of independent project estimates included in the analysis; SMD = standardised mean difference. 95% CI = confidence interval. Outcome measures highlighted in green have a positive and statistically significant SMD at least at a 5% significance level. Outcome measures denoted with * indicate that the effect sizes were reversed, meaning that higher effects represent a “good” outcome (e.g., a higher effect represents a smaller area deforested or lower multi-dimensional poverty). Appendix 15 provides detailed information on each meta-analysis.

Natural Resource Use and Management (Intermediate Environmental Outcomes)

We conducted meta-analyses for eight outcome measures related to natural resource use and management, each of which included between two to six project estimates. We found statistically significant effects for four of these measures: investment in forests, area used for agriculture and forestry products, tree planting and preservation, and natural resources monitoring.

Specifically, we observed that decentralised land management and monitoring led to a reduction in the area used for agriculture and forestry products. These estimates were reversed, meaning that a larger average effect represents a reduction in the area used for agriculture and forest products $\left(\widehat{SMD}=0.13,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.04;\hspace{0.17em}0.22\right],p<0.01\right)$ . In turn, we identified positive effects of these projects on investment in forests ( $\widehat{SMD}=0.40,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.14;\hspace{0.17em}0.65\right],p<0.01$ ), tree planting and preservation $\left(\widehat{SMD}=0.41,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.20;\hspace{0.17em}0.62\right],p<0.01\right)$ and on natural resources’ monitoring measures $\left(\widehat{SMD}=0.25,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.11;\hspace{0.17em}0.40\right],p<0.01\right).$ When feasible, we explored moderators and subgroup analyses but found that none were significantly driving these average effects. For the other measures in this group, including investments in natural resources and conservation, area deforested, and intensity of grazing, we did not find a statistically significant overall effect.

Land and Water Cover (Intermediate Environmental Outcomes)

We identified evidence to conduct four meta-analyses on land cover outcomes. None of these average effects were statistically significant. Twelve projects reported effects on measures of forest and vegetation area. While the overall effect was positive, very small and not statistically significant $\left(\widehat{SMD}=0.07,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.05;\hspace{0.17em}0.18\right],p=0.28\right).$ We found that evaluations published in a later year reported a lower effect than valuations published earlier $\left(p<\hspace{0.17em}0.01\right)$ . Controlling for potential confounding variables also reduced the estimated effect $\left(p<\hspace{0.17em}0.01\right)$ . These findings suggest that the overall effect presented in this meta-analysis may represent an upper bound if not appropriately adjusting for control variables. Likewise, projects from East Asia and the Pacific (CBGMP and FCRP) present a larger effect $\left(p=0.02\right)$ relative to projects implemented in Africa.

Eight projects evaluated the effect of decentralised land management specifically on forest area measures; however, the overall effect of this meta-analysis is negative, very small and not statistically significant $\left(\widehat{SMD}=-0.01,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.08;\hspace{0.17em}0.06\right],p=0.72\right).$ The average effect considering estimates from reports or working papers is lower compared to journal articles $\left(p=0.02\right)$ . This could potentially suggest a publication bias (i.e. evaluations may be more likely to be published if they report larger and statistically significant effects). However, we could not formally test for publication bias as eight studies inform this meta-analysis. Due to the small number of estimates informing the other two measures synthesised, the share of total area dedicated to forests and grassland area, we did not explore moderator, subgroup or publication bias analyses.

Environment Status and Health (Biodiversity Outcomes)

We conducted four meta-analyses for this category, each including from 2 to 11 project estimates. None of these overall effects were statistically significant.

Natural resources’ quality included, for example, measures related to whether land was eroded, vegetation index, and normalised differential vegetation index (NDVI). While we found a positive, moderate and not statistically significant overall effect $\left(\widehat{SMD}=0.22,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.02;\hspace{0.17em}0.46\right],p=0.07\right),$ the average effect was significantly larger for projects implemented in settings with higher exposure to climate change $\left(p=0.02\right)$ , according to the 2023 World Risk Report scores.

Due to the small number of estimates informing the other three measures synthesised, participants’ perception of forest resources’ quality, land resources’ quality and natural capital, we did not explore moderator, subgroup or publication bias analyses.

Species Complexity (Biodiversity Outcomes)

We meta-analysed one outcome measure in this category. Two projects evaluated the effect of decentralised land management on the prevalence of invasive plant species per area in Namibia and Sri Lanka. Because these estimates were reversed, a larger average effect represents a lower prevalence of invasive plant species. However, we found a positive, moderate and not statistically significant average effect $\left(\widehat{SMD}=0.23,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.26;\hspace{0.17em}0.72\right],p=0.35\right)$ . Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Carbon Storage and Sequestration (Climate Mitigation Outcomes)

We conducted one meta-analysis for this outcome category, covering two projects implemented in Namibia that reported measures of natural carbon stocks (plant biomass). The overall effect was positive, small and not statistically significant $\left(\widehat{SMD}=0.12,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.5;\hspace{0.17em}0.74\right],p=0.7\right)$ . Due to the small sample size for this meta-analysis, we did not conduct moderator, subgroup or publication bias analyses.

Knowledge Acquisition (Intermediate Human Outcomes)

We conducted two meta-analyses of the effects of decentralised land management and monitoring on two outcome measures of knowledge acquisition: conservation attitudes and education attained. We found that both overall effects were statistically significant. However, both analyses are also based on a small number of projects, 2 and 5, respectively.

The effect on conservation attitudes is large $\left(\widehat{SMD}=0.44,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.30;\hspace{0.17em}0.57\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ , but the small number of estimates included is insufficient to conduct moderator analysis. In turn, the effect on the education attained is small $\left(\widehat{SMD}=0.17,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.03;\hspace{0.17em}0.3\right],p=0.02\right)$ . When considering only the two estimates categorised as having low risk of bias, the overall effect was no longer statistically significant $\left(\widehat{SMD}=0.15,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.29;\hspace{0.17em}0.58\right],p=0.52\right)$ , meaning that estimates with high risk of bias drive the original effect. None of the other moderators contributed to explaining the variation in this average effect.

Practice and Technology Adoption (Intermediate Human Outcomes)

As for knowledge acquisition, the meta-analyses indicate positive and statistically significant effects on practice and technology adoption outcomes. We found evidence across two outcome measures (adoption of practices or technologies and use of household technologies). These analyses were based on three project estimates. Three studies evaluated the effects of three projects on the adoption of practices or technologies for agriculture (for example, adoption of pasture rotational plan, adoption of inorganic fertiliser and adoption of herding and cattle husbandry practices). We found a moderate $\left(\widehat{SMD}=0.25,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.09;\hspace{0.17em}0.42\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ . Similarly, the effect of three projects reporting the effects on the use of household technologies (such as electric cookers, biogas or wood stoves) was small $\left(\widehat{SMD}=0.15,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.08;\hspace{0.17em}0.21\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ .

Across these meta-analyses, the small number of project estimates precluded moderator analysis and publication bias analysis. All estimates were assessed as high risk of bias in the meta-analysis of the effects on the use of household technologies. Removing the estimate considered as high risk of bias, the effect on the adoption of practices or technologies remains moderate and statistically significant $\left(\widehat{SMD}=0.32,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.18;\hspace{0.17em}0.47\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$

Land Rights and Tenure (Intermediate Human Outcomes)

We conducted meta-analyses for two outcomes related to land rights and tenure. We first synthesised four projects on any measure of land rights, tenure or access, including ownership of land and other measures linked to access to land. The second meta-analysis combined two project estimates on the ownership of land. For both measures, we found that the effect is not statistically significant. Due to the small sample sizes, we did not conduct moderator, subgroup or publication bias analyses.

Productivity (Human Welfare to Support Adaptive Capacities)

We identified one measure from this category. The meta-analysis includes the effects of eight projects on agricultural, livestock or forestry productivity measures. The average effect is positive, very small and not statistically significant. However, relative to journal articles, reports or working papers show a smaller effect $\left(p=0.01\right)$ . This could be a potential sign of publication bias, but we were not able to formally test this due to the insufficient number of projects included. Likewise, when considering only estimates with a low risk of bias, the average effect is significantly larger compared to those with a high risk of bias $\left(p=0.14\right)$ , suggesting that in this case, evaluations that did not appropriately address common biases may be capturing a lower bound of the real effect of decentralised management interventions.

Income, Assets and Basic Materials (Human Welfare to Support Adaptive Capacities)

Including between 7 and 14 projects, we meta-analysed three measures relevant to this category: any measure of income, income, and any measure of assets. The overall effect was statistically significant in the first two meta-analyses, but not significant for measures of assets.

Measures of income included, for example, total income, total expenditure, profit, savings, and income from specific productive activities. We found a positive, small and statistically significant effect $\left(\widehat{SMD}=0.11,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.04;\hspace{0.17em}0.18\right],p<0.01\right)$ . Similarly, when we focused only on income (such as total income, excluding measures such as savings and profits), the overall effect was of a similar magnitude $\left(\widehat{SMD}=0.10,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.04;\hspace{0.17em}0.15\right],p<\hspace{0.17em}0.01\right).$ Relative to the six projects implemented in Africa, the two estimates from East Asia and Pacific were, on average, higher $\left(p=0.01\right)$ . When we tested for publication bias analysis for the income meta-analysis, we did not identify any funnel plot asymmetry, indicating that publication bias is unlikely.

We were also able to rerun these analyses, excluding the estimates assessed as having a high risk of bias. The overall effect on any measure of income is no longer statistically significant $\left(p=0.12\right)$ . However, the average effect on income remains statistically significant and has a similar magnitude $\left(\widehat{SMD}=0.12,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.04;\hspace{0.17em}0.21\right],p=0.01\right)$ , suggesting that while small, this finding is methodologically reliable.

Employment and Livelihoods (Human Welfare to Support Adaptive Capacities)

We synthesised the effects on four measures of employment and livelihoods: access to services, access to credit, the diversity of income sources, and the share of total income from forest products, agriculture or livestock. The overall effect was statistically significant across all measures; however, the available evidence in each of these meta-analyses consisted of fewer than five project estimates.

We found that the overall effect of these projects on access to services, access to credit and diversity of income sources are all positive, statistically significant and moderate. Access to services measures relate to access to credit, access to a savings account, and access to local infrastructure from four projects $\left(\widehat{SMD}=0.27,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.11;\hspace{0.17em}0.42\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ . In the second meta-analysis, we only focused on measures of access to credit $\left(\widehat{SMD}=0.29,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.13;\hspace{0.17em}0.44\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ . We found the effects of four projects on diversity of income sources, which are measures such as the number of crops cultivated and the number of income sources diversity, is positive, small and statistically significant ( $\widehat{SMD}=0.13,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.02;\hspace{0.17em}0.25\right],p=0.03).$ The overall effect on the share of total income from forest products, agriculture or livestock is positive, moderate and statistically significant ( $\widehat{SMD}=0.24,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.07;\hspace{0.17em}0.41\right],p=0.01)$ . Due to the small sample size for this meta-analysis, we did not conduct subgroup or publication bias analyses. We did not find that any moderators contributed to explaining the variation in effect sizes in this meta-analysis.

Food Security (Human Welfare to Support Adaptive Capacities)

We identified evidence to conduct five meta-analyses on food security outcomes. None of these average effects were statistically significant. Six projects reported effects on measures of any measure of food security, which included duration of food sufficiency, Food Insecurity Experience Scale (FIES), Households Dietary Diversity Score (HDDS), and other measures of food consumption. Two projects focused on the effect on HDDS, four provided effects on food consumption measures and four projects on food accessibility. We were not able to formally test for publication bias, moderator and subgroup analyses due to the limited number of studies in these meta-analyses.

Social Selations (Human Welfare Support Adaptive Capacities)

We conducted meta-analyses for five outcome measures related to social relations. The social relations outcomes measures were the following: household involvement in environmental protection, resources’ disputes, trust, extent of social networks and social capital. We did not find statistically significant effects across these outcomes, except for household involvement in environmental protection. For this measure, the effect was moderate, positive and statistically significant ( $\widehat{SMD}=0.27,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.16;\hspace{0.17em}0.36\right],p\hspace{0.17em}<\hspace{0.17em}0.01$ ). Because two or four projects informed these analyses, we were not able to conduct moderator, subgroup or publication bias analyses.

Governance and Empowerment (Human Welfare to Support Adaptive Capacities)

For governance and empowerment outcomes, we meta-analysed six measures: any measure of women’s empowerment, any measure of governance, measures of governance indices, accountability, democracy and transparency. The overall effects were statistically significant in all these analyses except for women’s empowerment.

We found a large, statistically significant effect when we combined the estimates of nine projects linked to any measure of governance $\left(\widehat{SMD}=0.41,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.20;\hspace{0.17em}0.62\right],p<0.01\right)$ . This included measures of accountability, democracy, leadership and transparency linked to governance of natural resources. In turn, the analysis on measures of governance indices covered two projects and combined several aspects of governance, such as accountability, democracy, leadership and transparency, into a single measure. This effect is also large and statistically significant $\left(\widehat{SMD}=0.7,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.22;\hspace{0.17em}1.17\right],p<0.01\right)$ .

For the other three meta-analyses, the overall effect was positive and moderate: accountability $\left(\widehat{SMD}=0.26,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.17;\hspace{0.17em}0.35\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right),$ democracy $\left(\widehat{SMD}=0.49,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.31;\hspace{0.17em}0.68\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ and transparency $\left(\widehat{SMD}=0.28,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.13;\hspace{0.17em}0.43\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ . When only considering estimates with low risk of bias for accountability, the overall effect remains statistically significant, positive and moderate $\left(\widehat{SMD}=0.25,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.15;\hspace{0.17em}0.34\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$

Water Access (Human Welfare to Support Adaptive Capacities)

We conducted one meta-analysis for this outcome category. We synthesised the effects of two projects on measures of access to water, which were implemented in Sri Lanka and Mozambique. The overall average effect is negative, small and not statistically significant $\left(\widehat{SMD}=-0.06,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.7;\hspace{0.17em}0.58\right],p=0.86\right)$ . Due to the small sample size for this meta-analysis, we did not conduct moderator, subgroup or publication bias analyses.

Climate Risk Exposure and Resilience (Human Welfare to Support Adaptive Capacities)

We synthesised the available evidence on the effects on climate risk exposure and resilience. We first meta-analysed the available evidence from four projects on forest fire exposure. The second meta-analysis combined the two project estimates on any measure of resilience to shocks. Measures included households’ income level, if affected by frost, drought, crop disease or economic shocks, perceived improved resilience to climate change and a resilience index based on six household-level questions. For both measures, we found that the effect is not statistically significant. Due to the small sample size for this meta-analysis, we did not conduct moderator, subgroup or publication bias analyses.

Multi-Dimensional Poverty (Human Welfare to Support Adaptive Capacities)

We meta-analysed one outcome measure in this category. Two projects evaluated the effect of decentralised land management on measures of multi-dimensional poverty in Mexico and Nepal. The effect sizes were reversed, meaning that a larger effect represents lower multi-dimensional poverty. We found a negative, very small and not statistically significant average effect $\left(\widehat{SMD}=-0.01,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.26;\hspace{0.17em}0.23\right],p=0.91\right)$ . Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Land Rights

This intervention involves formal registration of land, either through the conversion of communal or non-demarcated rural land to freehold title, or the statutory recognition of customary or communal rural land rights. The meta-analyses were based on the effects of 15 land rights projects.

Table 3 shows that land rights improved agricultural and livestock productivity and the value of outputs per area. These projects also led to a greater diversity of income sources and greater investments in natural resources or conservation. The provision of land rights increased out-migration for employment. We did not find any other statistically significant effects in the meta-analysis of the other environmental or human well-being outcomes.

Table 3.

Meta-Analysis Results for Land Rights Interventions

Notes. N = number of independent project estimates included in the analysis; SMD = standardised mean difference. 95% CI = confidence interval. Outcome measures highlighted in green have a positive and statistically significant SMD at least at a 5% significance level. Outcome measures denoted with * indicate that the effect sizes were reversed, meaning that higher effects represent a “good” outcome (e.g., a higher effect represents a smaller area deforested or lower multi-dimensional poverty). Appendix 15 provides detailed information on each meta-analysis.

Natural Resource Use and Management (Intermediate Environmental Outcomes)

We conducted meta-analyses for four outcome measures related to natural resource use and management. We observed a positive, very small and statistically significant effect of land rights on investment in natural resources or conservation $\left(\widehat{SMD}=0.05,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.02;\hspace{0.17em}0.08\right],p<0.01\right)$ . In the moderator analyses, relative to the two effects from journal articles, the two effect sizes from conference proceedings were, on average, higher $\left(p=0.04\right).$ Four estimates remained after excluding high risk of bias estimates. The overall weighted average effect maintained its magnitude and statistical significance $\left(\widehat{SMD}=0.04,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.01;\hspace{0.17em}0.07\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$

For the other measures in this group, investment in forests, area used for forest crops, grazing or agriculture, and area deforested, we did not find a statistically significant overall effect. However, for the area deforested, studies published in a later year showed a lower effect than evaluations published earlier $\left(p<\hspace{0.17em}0.02\right).$ Similarly, the average effect was smaller for countries with a higher number of internally displaced people, according to IDMC (2023), notably in Brazil (TL) and Mexico (PROCEDE; $p=0.04)$ . When considering the four estimates assessed as having low risk of bias, the overall effect was no longer statistically significant $\left(p=0.08\right).$ Publication bias analysis was not possible for any of these meta-analyses.

Land and Water Cover (Intermediate Environmental Outcomes)

Based on the available evidence, we conducted three meta-analyses of the effects of land rights on three outcome measures of land and water cover: forest or vegetation area, forest area and share of total area dedicated to forests. For all outcomes, we found that the overall effect of included project estimates is not statistically significant. When feasible, we explored moderators and subgroup analyses but found that none were significantly driving these average effects. Due to the small sample size for this meta-analysis, we did not publication bias analyses.

Environment Status and Health (Biodiversity Outcomes)

We meta-analysed one outcome measure in this category. Two projects evaluated the effect of decentralised land management on natural resources’ quality in China and Liberia. Measures included the Enhanced Vegetation Index (EVI, CFTR) and whether perceived forest quality improved (LSLA). We found a negative, small and not statistically significant overall effect $\left(\widehat{SMD}=-0.12,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.24;\hspace{0.17em}0.01\right],p=0.06\right)$ . Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Practice and Technology Adoption (Intermediate Human Outcomes)

On this outcome, there is evidence on the effects of six land rights projects on the adoption of practices and technologies for agriculture (adoption of fertilisers and pesticides). The overall effect is positive, very small and not statistically significant $\left(\widehat{SMD}=0.06,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.01;\hspace{0.17em}0.13\right],p=0.08\right).$ We found that effect sizes increased with higher exposure to climate change $\left(p<0.01\right)$ , according to the 2023 World Risk Report scores (Bündnis Entwicklung Hilft/IFHV, 2023). Colombia had the highest scores, followed by China. Also, we observed that the effects were on average greater for projects which combined several activities (LTA and Law70, $p=0.04$ ). We did not find that any other moderators contributed to explaining the variation in effect sizes in this meta-analysis. Four remain after excluding high risk of bias estimates. The overall effect remains not statistically significant $\left(\widehat{SMD}=0.06,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.02;\hspace{0.17em}0.15\right],p=0.15\right).$ We were not able to do any additional subgroup and publication bias analyses due to the sample size of the included project estimates.

Land Rights and Tenure (Intermediate Human Outcomes)

We conducted meta-analyses for two outcome measures related to land rights and tenure. The first meta-analysis combined the two project estimates on the ownership of land. We then synthesised the available evidence from six projects on any measures of land rights, tenure or access. Outcome variables related to participation in rental markets and perceptions of land insecurity. For both measures, we found that the effect is not statistically significant. Due to the small sample size for this meta-analysis, we did not conduct moderator, subgroup or publication bias analyses.

Productivity (Human Welfare to Support Adaptive Capacities)

We conducted two meta-analyses of the effects of land rights on two outcome measures of productivity: agriculture or livestock productivity and value of output per area. For both outcomes, we found that the overall effect was statistically significant.

For agriculture or livestock productivity, we found a positive, small and statistically significant overall effect $\left(\widehat{SMD}=0.10,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.02;\hspace{0.17em}0.18\right],p=0.02\right)$ . Studies with a later year of publication report had on average lower effect sizes $\left(p=0.04\right).$ The included studies were published between 2016 and 2023. We did not find that any other moderators contributed to explaining the variation in effect sizes in this meta-analysis. Only keeping estimates with a low risk of bias or with some concerns for risk of bias, three estimates remained in the meta-analysis. The overall effect becomes not statistically significant $\left(\widehat{SMD}=0.12,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.01;\hspace{0.17em}0.25\right],p=0.08\right).$ We synthesised the effects of two projects on the value of outputs per area in Zambia and Colombia. The overall effect was positive, very small and statistically significant $\left(\widehat{SMD}=0.05,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.001;\hspace{0.17em}0.1\right],p=0.03\right).$ When feasible, we explored moderators and subgroup analyses but found that none were significantly driving these average effects. Due to the small sample size for this meta-analysis, we did not publication bias analyses.

Income, Assets and Basic Materials (Human Welfare to Support Adaptive Capacities)

We meta-analysed three measures relevant to this category: any measure of income (8 projects), income (3 projects), and any measure of assets (8 projects). The overall effect was not statistically significant in all the meta-analyses. Across the three meta-analyses, when feasible, we explored moderators and subgroup analyses but found that none were significantly driving these average effects. Due to the small sample size for this meta-analysis, we did not perform publication bias analyses.

Employment and Livelihoods (Human Welfare to Support Adaptive Capacities)

We meta-analysed four measures relevant to this category: access to credit, diversity of income sources, share of total income from forest products, agriculture or livestock and migration for employment. The overall effect was statistically significant for the meta-analysis combining estimates of diversity of income sources and migration for employment. The result was not statistically significant for access to credit and share of total income from forest products, agriculture or livestock.

For diversity of income sources, we found a positive, very small and statistically significant overall effect $\left(\widehat{SMD}=0.09,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.02;\hspace{0.17em}0.15\right],p=0.01\right).$ Project effects on migration for employment were reversed as it was considered a deterioration of employment and livelihood outcome if household members had to migrate outside of the area to find employment. We found that land rights slightly increased out-migration for work $\left(\widehat{SMD}=-0.04,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.07;\hspace{0.17em}-0.02\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right)$ . Because two, three or four projects inform these analyses, we were not able to conduct moderator, subgroup or publication bias analyses.

Food Security (Human Welfare to Support Adaptive Capacities)

We identified evidence to conduct three meta-analyses on food security outcomes. None of these average effects were statistically significant. Four projects reported effects on measures of any measure of food security, which included duration of food sufficiency, Food Insecurity Experience Scale – FIES, Households Dietary Diversity Score – HDDS, and other measures of food consumption. Two projects provided effects on food consumption measures, and four projects on food accessibility. We did not test for publication bias, moderator and subgroup analyses as too few estimates inform these meta-analyses.

Social Relations (Human Welfare to Support Adaptive Capacities)

We synthesised the available evidence on this outcome measure in this category. We found effects of two land rights projects on measures resources’ disputes in Tanzania and Ethiopia. When necessary, the effect sizes here are reversed meaning that a higher effect represents lower resources’ disputes. The overall effect was positive, very small and not statistically significant $\left(\widehat{SMD}=0.02,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.08;\hspace{0.17em}0.13\right],p=0.66\right).$ Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Governance and Empowerment (Human Welfare to Support Adaptive Capacities)

We meta-analysed one outcome measure in this category. Two projects evaluated the effect of land rights on measures of women’s empowerment in Ethiopia and Tanzania. We found a positive, very small and not statistically significant $\left(\widehat{SMD}=0.09,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.03;\hspace{0.17em}0.22\right],p=0.16\right)$ . Measures used were the following: the percentage of female watershed committee members (CBINReMP) and a binary variable indicating if land-related decision-making power is exclusively held by a male household head (reversed, LTA). Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Protected Areas

Protected areas are regulatory frameworks such as national parks or reserves, where access and use of resources are either fully restricted or regulated to achieve the long-term conservation of nature with associated ecosystem services and cultural values. We included the effects of 11 protected areas.

Table 4 shows that protected areas improved a few human well-being outcomes, including agricultural or livestock productivity, and income and assets. However, the available evidence is limited. While we synthesised a handful of environmental outcomes, we did not identify statistically significant effects on any of these measures.

Table 4.

Meta-Analysis Results of Protected Areas Interventions

Notes. N = number of independent project estimates included in the analysis; SMD = standardised mean difference. 95% CI = confidence interval. Outcome measures highlighted in green have a positive and statistically significant SMD at least at a 5% significance level. Outcome measures denoted with * indicate that the effect sizes were reversed, meaning that higher effects represent a “good” outcome (e.g., a higher effect represents a smaller area deforested or lower multi-dimensional poverty). Appendix 15 provides detailed information on each meta-analysis.

Natural Resource Use and Management (Intermediate Environmental Outcomes)

Based on the available evidence, we conducted two meta-analyses of the effects of protected areas on two outcome measures of natural resource use and management: the occurrence of any deforestation and the use of natural resources. For the occurrence of any deforestation, we found that the overall effect was statistically significant but very small, meaning that protected areas slightly reduced deforestation $\left(\widehat{SMD}=0.05,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.02;\hspace{0.17em}0.07\right],p<0.01\right).$ For the second outcome, measures relate to the number of livestock animals grazing, the weight of fuelwood and wild plants collected, and we found that the overall effect of protected areas was not statistically significant. From the moderators tested in the first meta-analysis, we identified that estimates assessed with a low risk of bias or with some concerns with risk of bias are, on average, greater relative to estimates with a high risk of bias (both $p<0.01$ ). Within the other moderators and subgroup analyses, none were significantly driving these average effects. Due to the small sample size for this meta-analysis, we did not conduct publication bias analyses.

Land and Water Cover (Intermediate Environmental Outcomes)

Based on the available evidence, we conducted a meta-analysis of the effects of protected areas on one outcome measure of land and water cover: forest area. We found that the overall effect of included project estimates is not statistically significant. This meta-analysis is based on five estimates.

The overall effect on forest area is positive, very small and not statistically significant $\left(\widehat{SMD}=0.07,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.02;\hspace{0.17em}0.16\right],p=0.15\right)$ . Two remain after excluding high risk of bias estimates. The overall weighted average effect becomes statistically significant and remains positive and very small $\left(\widehat{SMD}=0.07,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.05;\hspace{0.17em}0.08\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$

From the moderator analysis, we did not find any statistically significant results. Publication bias and any additional subgroup analyses are not possible for both outcomes, as they are based on only a few estimates.

Environment Status and Health (Biodiversity Outcome)

We meta-analysed one outcome measure in this category. Two projects evaluated the effect of protected areas on natural resources’ quality in Mexico and China. We found a positive, very small and not statistically significant $\left(\widehat{SMD}=0.02,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.001;\hspace{0.17em}0.05\right],p=0.10\right)$ . Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Productivity (Human Welfare to Support Adaptive Capacities)

We identified two measures from this category. The meta-analysis includes the effects of two projects on agricultural or livestock productivity measures. The average effect is positive, very small and statistically significant $\left(\widehat{SMD}=0.16,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0;\hspace{0.17em}0.32\right],p=0.04\right).$ The second measure is production value, and the overall effect was not statistically significant. With only three and two project estimates included in these meta-analyses, we were not able to conduct moderator, subgroup or publication bias analyses.

Income, Assets and Basic Materials (Human Welfare to Support Adaptive Capacities)

We meta-analysed four measures relevant to this category: any measure of income, income, any measure of assets and measures of income-related poverty. The overall effect was statistically significant for the meta-analysis of any measure of income, income and any measure of assets. The result was not statistically significant for measures of income-related poverty.

For any measures of income, we found a positive, small and statistically significant overall effect $\left(\widehat{SMD}=0.11,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.001;\hspace{0.17em}0.23\right],p=0.04\right).$ Out the five included effect sizes, three remain after excluding high risk of bias estimates. The statistical significance and magnitude of the overall effect do not extensively change $\left(\widehat{SMD}=0.17,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.06;\hspace{0.17em}0.28\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$ For income, the overall effect was moderate and statistically significant $\left(\widehat{SMD}=0.20,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.09;\hspace{0.17em}0.32\right],p<0.01\right)$ . Two estimates remain after excluding high risk of bias estimates, and the overall effect remains statistically significant, positive and moderate $\left(\widehat{SMD}=0.25,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.11;\hspace{0.17em}0.39\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$ For any measures of assets, the overall effect was positive, very small and statistically significant $\left(\widehat{SMD}=0.03,95\%\hspace{0.17em}CI\hspace{0.17em}\left[0.02;\hspace{0.17em}0.05\right],p\hspace{0.17em}<\hspace{0.17em}0.01\right).$

When feasible, we explored other moderators and subgroup analyses but found that no others were significantly driving these average effects. Due to the small sample size for this meta-analysis, we did not carry out publication bias analyses.

Employment and Livelihoods (Human Welfare to Support Adaptive Capacities)

We synthesised the effects of two projects on employment at the group level (number of ecotourism operators in CNRA and the total percent of the population that is employed in GPNR) in China. The meta-analysis forest plot, where the overall effect was positive, very small and not statistically significant $\left(\widehat{SMD}=0.03,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.25;\hspace{0.17em}0.32\right],p=0.83\right).$ Because two projects inform this analysis, we were not able to conduct moderator, subgroup or publication bias analyses.

Health and Well-Being (Human Welfare to Support Adaptive Capacities)

We conducted one meta-analysis for this outcome category. We synthesised the effects of two projects on measures of health quality, which were implemented in Peru and China. The overall average effect is negative, very small and not statistically significant $\left(\widehat{SMD}=-0.07,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.86;\hspace{0.17em}0.72\right],p=0.86\right)$ . Due to the small sample size for this meta-analysis, we did not conduct moderator, subgroup or publication bias analyses.

Multi-Dimensional Poverty (Human Welfare to Support Adaptive Capacities)

We meta-analysed one outcome measure in this category. Four projects evaluated the effect of protected areas on measures of multi-dimensional poverty in Mexico, China and Costa Rica. The effect sizes were reversed, meaning that a higher effect represents lower multi-dimensional poverty. We found a positive, small and not statistically significant average effect $\left(\widehat{SMD}=0.11,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.05;\hspace{0.17em}0.27\right],p=0.17\right)$ . Because two projects inform this analysis, we were not able to conduct moderator or publication bias analyses. Out of the four included effect sizes, two remain after excluding estimates with a high risk of bias. The overall weighted average effect remains not statistically significant $\left(\widehat{SMD}=0.12,95\%\hspace{0.17em}CI\hspace{0.17em}\left[-0.2;\hspace{0.17em}0.44\right],p=0.45\right).$

Environmental and Human Well-Being Effects Across Projects

While meta-analyses offer a rigorous synthesis of effects across all included projects, they do not capture the within-project alignment or divergence between environmental and human well-being outcomes. We provide summary statistics on the percentage of projects reporting effects on environmental and human well-being outcomes, disaggregated by statistical significance and direction of effect. While this is beyond the quantitative synthesis analysis, it shows that 77% of projects reported at least one positive and statistically significant estimate on both types of outcomes (Table 5).

Table 5.

Distribution of Projects by Statistical Significance and Direction of Environmental and Human Well-Being Outcomes

Percentage of projects reporting
Only positive and statistically significant effects on BOTH environmental and human well-being outcomes	14%
At least one positive and statistically significant effect on an environmental outcome and at least one positive and statistically significant effect on a human well-being outcome	63%
Only not statistically significant effects on BOTH environmental and human well-being outcomes or combination of not statistically significant effects on one group of outcomes and both positive and negative statistically significant effects on the other group of outcomes.	20%
Combination of positive and negative statistically significant effects on BOTH environmental and human well-being outcomes	3%

Note. for 4 projects, we did not have sufficient data to derive effect sizes across both environmental and human well-being outcomes.

Fourteen percent of projects reported only positive and statistically significant estimates for environmental and human well-being outcomes. For example, the CERP project in the Democratic Republic of the Congo led to greater tree plantation (natural resource use and management outcome) and improved the households’ food diversity consumed (food security outcome).

In 63% of projects, we identified at least one positive and statistically significant estimate for an environmental outcome and at least one positive and statistically significant estimate for a human well-being outcome. For example, in the NPAA project (protected areas in Peru), across some specific types of protected areas (for example, protected areas established before 1990), deforestation decreased, and total expenditure rose. In others (for example, national parks), however, the project resulted in greater deforestation.

Qualitative Synthesis

This section presents the findings relevant to our third research question, which focuses on barriers, enablers, and trade-offs between environmental and human well-being outcomes that impact the effectiveness of the specified projects. Further details of this process can be found in Appendix 16. Through this process, we identified 112 documents that offered qualitative or descriptive evidence on factors and conditions that can affect the success of projects, covering 47% of projects reviewed. This included three impact evaluations that provided relevant information on these factors, either through embedded qualitative components in mixed-methods approaches or within their accounts of intervention implementation. Eighteen documents from the search, such as project documents, were also reviewed and extracted for factual information on project background and activity details as relevant. In total, we reviewed 39 mixed-methods studies, 42 qualitative primary studies, 13 quantitative primary studies, and 18 additional documents. Appendix 17 provides the full list of included qualitative studies. We appraised all these studies, except for the 18 descriptive documents, as these are not research but factual information about projects. Several mixed-methods studies received two critical appraisal ratings; one based on the qualitative research methods used and one based on the quantitative research approach. If the mixed-methods studies used rigorous quasi-experimental or experimental quantitative methods, only the qualitative methods were appraised. Several of these studies covered the same projects.

Using our critical appraisal tool, 13 studies were assessed as high quality (Table 6). These showed sound research design, suitable sampling, methodological rigour, and produced well-contextualised and credible findings. Eighty-one studies were rated as moderate quality; these generally lacked reflectiveness about how contextual factors might have affected the research output. Twenty-one were categorised as low quality, also primarily due to shortcomings in research reflectiveness and at least one other section of the tool, but without a clear trend. Eleven studies were judged to be of critical quality, exhibiting significant flaws and not complying with minimum methodological criteria ensuring reliability and empirical grounding. Five of these were rated as flawed due to the research not being rigorously implemented which meant that the study did not provide a clear account or description of data collection processes, the data collection was not demonstrated to target depth, detail and richness of information, there was no evidence for how descriptive analytical categories were generated and used, and the data presentation did not distinguish between data, analytical frame and interpretation. Consequently, they were excluded from the analysis of qualitative evidence. In total, 126 critical appraisal ratings were provided. Overall, this qualitative body of evidence shows considerable reliability, as most of these ratings were considered high or moderate.

Table 6.

Overview of Intervention Types by Qualitative Appraisal Scores

Appraisal score	Intervention categories	No. of projects	No. of unique studies
13 High quality ratings (8 qualitative and 5 descriptive quantitative ratings)	• Decentralised mgmt. • Land rights • Decentralised mgmt. and protected areas	8	11
79 moderate quality ratings (48 qualitative and 31 descriptive quantitative ratings)	• Decentralised mgmt. • Land rights • Decentralised mgmt. and protected areas • Land rights and decentralised mgmt. • Protected areas	24	69
21 Low quality ratings (17 qualitative and 4 descriptive quantitative ratings)	• Decentralised mgmt. • Land rights	9	18

The remainder of this section is structured around themes that emerged from our analysis of the data around five categories: enablers, barriers, synergies, trade-offs, and unintended consequences. In this section, we present themes that were identified in more than a single project. Within each category, we present illustrative examples drawn from multiple projects to highlight recurring patterns and cross-cutting issues. Table 7 summarises the themes identified across factors, and Appendix 18 provides further details of this coding.

Table 7.

Descriptions and Summary of Themes From Qualitative Evidence

Factor	Theme	Short description	No. of projects and studies
Enablers	1. Embedding mechanisms for improving livelihoods	Programmes focusing on improving livelihoods, reported to increase acceptance and reduce resistance to programme.	11 projects 21 studies
	2. Efficient programme collaboration and management	Collaboration within communities and effective program management, supported by active leaders and efficient training strategies, are key enablers.	10 projects 19 studies
	3. Supportive communities	Supportive community norms, values, and active participation can increase participation, engagement and behaviour adoption.	5 projects 7 studies
	4. Increased participation of women	Involving women in design and implementation of programmes can boost participation, bring valuable insights, and empower communities.	3 projects 5 studies
	5. Community oversight and anti-corruption measures	Strong governance and accountability in monitoring elected officials were reported as more efficient interventions with greater programme satisfaction.	2 projects 2 studies
Barriers	1. Ambiguity, weak governance, and corruption in the management of the intervention	Political instability, corruption, and weak governance can hinder community participation and sustainable development, reportedly leading to unclear processes and mismanagement of funds.	17 projects 34 studies
	2. Social inequalities and exclusion of vulnerable populations	Social barriers to conservation include gender disparities, unequal access to information, cultural practices, and challenges faced by marginalised groups, which can hinder and undermine efforts to improve conditions.	7 projects 17 studies
	3. Inadequately trained project implementers and community participants	Inadequate training, high staff turnover, and low motivation among project staff can hinder the effectiveness and delivery of interventions.	7 projects 13 studies
	4. Lack of material, financial, and human resources	Many interventions faced insufficient funding and a lack of resources, hindering project completion or sustainable support after programme completion.	11 projects 12 studies
	5. Disconnect between design and field realities	The implementation of the programs faced challenges due to disconnects between design and local realities, including not considering low literacy or local conservation awareness, inadequate infrastructure, and insufficient methods to prevent misinformation.	15 projects 33 studies
	6. Environmental/climatic challenges	Prolonged droughts and climate change are disrupting development and conservation efforts, which can lead to resource conflicts, weakened governance, and a shift from sustainable management to crisis response.	8 projects 13 studies
	7. Population growth and migration can strain natural resources	The influx of migrants seeking land and population growth can lead to forest degradation, resource depletion, and disrupted local land management.	3 projects 3 studies
Synergies	1. Project activities aligned with community livelihoods	Aligning intervention components with existing local livelihoods was reported to create synergies that enhanced the effectiveness of the interventions.	2 projects 2 studies
Trade-offs	1. Wildlife conservation vs human safety and food security	Several interventions reported issues related to safety and food security as a trade-off from the wildlife conservation efforts.	4 projects 6 studies
	2. Communities’ livelihoods vs conservation of land, forests or other natural resources	Several interventions reported trade-offs where either livelihoods were improved at the expense of conservation or conservation was enhanced at the expense of livelihoods.	5 projects 9 studies
Unintended consequences	1. Worsened deforestation and degradation	Some conservation efforts reported unintentionally causing deforestation and land degradation due to insufficient economic incentives, weak enforcement, and displacement of grazing pressure.	8 projects 8 studies
	2. Increased land and agrarian conflicts	Interventions can sometimes exacerbate land boundary conflicts and agrarian issues for less privileged groups by disrupting traditional land use and excluding indigenous people and local farmers from decision-making and benefit sharing.	5 projects 6 studies
	3. Further marginalisation of ordinary community members and women	The interventions aimed to enhance livelihoods and protect forests, but local communities were often less informed, leading to marginalisation.	2 projects 2 studies

Enablers

We define enablers as factors that help contribute to achieving project goals and improving the effectiveness of the projects. We identified five common themes for community-based or decentralised land management and monitoring interventions. For protected area interventions and land rights, no enablers were reported across more than one project.

Embedding Mechanisms for Improving Livelihoods

The most commonly identified enabler was projects establishing mechanisms to improve people’s livelihoods, which was reported in 11 projects across 21 studies on community-based or decentralised management interventions. In cases where the main aim was to improve livelihoods, finding the correct mechanism for this seems to enable acceptance and participation in projects. Several strategies were implemented, such as revenue sharing from projects, proposing ways to help protect farms from damage, or providing employment opportunities, all aimed at offering participants an additional or alternative source of income (Asian Development Bank, 2012; Coppock et al., 2020; Galvin et al., 2021; Namanha, 1999; Waruingi et al., 2021). This enabler was found to reduce resistance to the projects.

For example, the Kapit-Bisig Laban sa Kahirapan Comprehensive Integrated Delivery of Social Services (KALAHI-CIDSS) project in the Philippines had very large participation by the poorest in the community as one component of this community-based land management project provided participants with subproject construction work, allowing them to earn additional income for the daily needs of their families (Asian Development Bank, 2012). This was also the case for the Plantation Establishment and Livelihood Improvement Scheme (PELIS) project in Kenya, which was reported to have influenced households’ participation through providing forest benefits, such as providing income from PELIS plots and the value of forest products extracted, such as fuelwood and bamboo shoots (Waruingi et al., 2021).

Efficient Programme Collaboration and Management

Efficient programme collaboration and management were also identified as common enablers, where 10 projects across 19 studies on community-based or decentralised management interventions reported this enabler. Several types of collaboration and knowledge sharing have been reported, such as collaboration within communities or community-level land management groups. Other factors reported as important for efficient programme management include active and influential leaders, and sufficient and efficient training strategies for participants and project staff.

For instance, in the Amani Butterfly Project in Tanzania, farmers were found to be heavily dependent on others, including fellow farmers and project staff, for agricultural information and knowledge. Many of these farmers lived close to one another and were accustomed to a culture of sharing knowledge with each other, which was reported to be an important factor for the successful implementation of butterfly farming (Ndumbaro and Mutula, 2017). The Forest Co-management (FCM) initiative in Malawi effectively built capacity by offering comprehensive training that included hands-on institution building, community empowerment, and training for extension staff. The latter emphasised social facilitation, forest policy formulation, participatory management, and enterprise development. As a result, the capacity-building efforts significantly enhanced the organisational, institutional, and technical skills within the community (Zulu, 2013).

Supportive Communities

Communities exhibiting supportive norms, values, and attitudes aligned with the aim of the project can improve participation, participants’ efforts, and adoption of behaviours related to the project, potentially leading to its positive outcomes. This enabler was reported by 5 projects across 7 studies on community-based or decentralised land management interventions.

In Tanzania, the local community had rituals that held cultural and religious value. These rituals strengthened the connection between people and the forest, promoting sustainable forest management and were, therefore, reported to work as an enabler for the Joint Forest Management (JFM) project (Elisha et al., 2023). Farmers participating in the Community Based Rangeland and Livestock Management (CBRLM) project in Namibia expressed their enthusiasm for the initiative, noting that the loss of grassland had made it necessary for them to regrow the vegetation. This need was found to act as a motivating factor for participants to embrace the project (Coppock et al., 2020).

Increased Participation of Women

Focusing on women when designing and implementing programmes can improve participation, bring more knowledge and workforce to the programme, and can have the added benefit of empowering women in communities. Three projects across five studies on community-based or decentralised land management interventions reported this as an enabler.

Herders and women were brought into the Northern Rangelands Trust (NRT) project in Kenya. Traditionally, the elders led the grazing committees. This change was found to help reinforce traditional practices and integrate scientific knowledge. Women played an important role in improving grasslands through clearing invasive species and helping control erosion (Galvin et al., 2021). Similarly, the Kapit-Bisig Laban sa Kahirapan Comprehensive Integrated Delivery of Social Services (KALAHI-CIDSS) project in the Philippines focused on actively involving women, who served as community volunteers. Many participants believed that women are better than men at paperwork, monitoring construction materials from suppliers, and working with lighter construction materials from suppliers. Women also provided some construction labour and prepared food for construction workers. They also often had more time to dedicate to the project compared to men, as their husbands would often work outside the village (Asian Development Bank, 2012).

Community Oversight and Anti-Corruption Measures

Strengthening governance through community monitoring and oversight can improve programme efficiency and increase participant satisfaction. This enabling factor was identified in two studies, each examining a separate community-based or decentralised land management and monitoring intervention. In the Philippines, Reid (2011) reported that the KALAHI-CIDSS project achieved better targeting and more effective service delivery by incorporating barangay assemblies and volunteer committees designed to enhance the scrutiny of elected officials. These mechanisms were reported to reduce the misuse of discretionary funds by mayors, contributing to increased transparency and accountability. Similarly, Persha and Meshack (2016) found that, on average, households participating in Tanzania’s JFM reported higher satisfaction with village environmental and natural resource committees. These institutions were associated with key attributes of good governance, including a lack of corruption, transparency, and strong oversight roles carried out at the community level.

Barriers

There may be factors or conditions that hinder the effective implementation, reach, or impact of an intervention. These can act as barriers and prevent the project from achieving its intended goals or outcomes. Seven overarching barriers have been identified across multiple studies and projects, as summarised in Table 8.

Table 8.

Summary of Barrier Themes by Intervention Category

Intervention categories covered by theme	Theme	No. of projects and studies
• Decentralised mgmt. • Decentralised mgmt. & protected areas • Land rights	Ambiguity, weak governance, and corruption in the intervention management	17 projects 34 studies 2 other documents
	Social inequalities and exclusion	7 projects 17 studies
	Disconnect between designs and field realities	15 projects 33 studies 1 other document
• Decentralised mgmt. • Land rights	Inadequately trained project implementers and community participants	7 projects 13 studies
	Lack of material, financial, and human resources	11 projects 12 studies 1 other document
• Decentralised mgmt. • Decentralised mgmt. & protected areas	Environmental/climatic challenges	8 projects 13 studies
• Decentralised mgmt.	Population growth and migration are straining natural resources	3 projects 3 studies

Ambiguity, Weak Governance, and Corruption in the Intervention Management

Political instability, corruption, and weak governance structures were common barriers, undermining community participation, sustainable resource management, and development outcomes, as suggested by 34 studies and two other documents across 17 projects. Roughly two-thirds of the studies (30 studies and 2 other documents across 15 projects) focused on community-based or decentralised land management or monitoring interventions. The remaining one-third included studies on land rights (2 studies, 1 project) and an intervention combining community-based or decentralised land management or monitoring and protected areas (2 studies, 1 project).

These barriers can either be related to a higher level or exogenous issues, such as weak governance or corruption in government, or a lower level or endogenous community-level factors, such as corruption from programme implementation staff. There are multiple examples of this barrier on the higher level. In Uganda’s National Forestry Authority (NFA), most revenue generated from forest produce was not reinvested into the forestry sector, reflecting financial mismanagement at the central government level (Turyahabwe, 2006). Similarly, Uganda’s Collaborative Management Agreements (CMA) reported cases of unclear revenue-sharing processes and limited community involvement in decision-making (Atuhaire, 2009).

There are also several examples of this barrier at the community-level. In Kenya’s NRT project, poor accountability, unclear committee roles, and weak communication between leaders and communities enabled elite capture and ranger misconduct, including selectively granting access to resources (Okwany and Owino, 2024; Ontiri and Robinson, 2016). In the Philippines, the KALAHI-CIDSS project also reported a few cases of misused project funds and materials by local councils (Beatty et al., 2017). In Brazil’s Federal Agrarian Reform, Pinheiro (2009) noted that landowners exploited judicial processes to delay, obstruct, or inflate the costs of land reform, leveraging a legal system that excluded rural workers from direct participation. This weakened institutional accountability limited the reform’s ability to reallocate land effectively. Tanzania’s JFM reported that most participating villages lacked signed Joint Management Agreements, which are essential for formalising communities’ legal rights to revenue-sharing and forest management (Persha and Meshack, 2016). This absence of legal agreements weakened accountability and limited both benefit sharing and legal recourse.

Social Inequalities and Exclusion of Vulnerable Populations

Social inequalities and exclusion of vulnerable populations were reported as a consistent barrier across 17 studies, spanning 7 projects. These projects were mainly community-based or decentralised land management or monitoring types (n = 5), and the remaining were land rights, and a multi-component having both protected areas and community-based or decentralised land management or monitoring interventions.

Leak (2017) and Li et al. (2023) reported that unequal access to information and communication technology (ICT) and language skills faced by local service providers limited their benefits from tourism business opportunities in the Community-Based Ecotourism (CBET) in China. Local elites dominated securing tourists by using their better access to acquiring English and communication skills, ICT tools and online booking platforms. In contrast, local service providers often lacked access to English, communication, and digital literacy skills learning, as well as resources to obtain ICT tools, to compete at the same level. In Namibia’s Kwandu Conservancy, women were reluctant to take on roles perceived as physically demanding or traditionally masculine, such as patrolling or forest-based work, and were often not elected to positions that offered stipends (Khumalo, 2012). Similar patterns were found in Kenya’s PELIS and NRT, and Tanzania’s JFM projects, where religious beliefs, cultural norms, and ritual practices hindered women’s participation in the project (Elisha et al., 2023; Robinson and Maganga, 2009; Samson et al., 2017; Waruingi et al., 2021). In these projects, women were also less informed about project updates, as outreach efforts tended to target them less. In China’s Collective Forest Tenure Reform (CFTR), households with lower socio-economic status were less likely to be reached or informed by local government actors, increasing the risk of losing land rights while receiving little or no benefit from the reform (Song and Cannon, 2011).

Inadequately Trained Project Implementers and Community Participants

Inadequately trained project implementers, including community participants who co-implemented projects, were reported to impede intervention delivery processes, as reported by 13 studies covering 7 projects. Many of these projects were community-based or decentralised land management or monitoring interventions (n = 6), with one focused on land rights. In Namibia’s Kwandu Conservancy, Kasaona (2006) noted that some of the community guards delayed damage assessments caused by wildlife, which undermined timely and accurate responses, partly due to inadequate training. Similarly, in Mongolia, Hess et al. (2010) reported that the Community-based Grasslands Management Project suffered from poor knowledge on tree planting, with communities often selecting species unsuited to the Gobi climate. Yiwen et al. (2020) also identified that forest group leaders in China’s Collective Forest Tenure Reform operated without adequate training, compensation, or continuity, resulting in inconsistent forest management.

Lack of Material, Financial, and Human Resources

Lack of material, financial, and human resources, such as inadequate funding, insufficient staffing, and the absence of post-project sustainability planning, can be critical barriers to interventions’ success. It was identified across 12 studies covering 10 distinct community-based or decentralised land management or monitoring interventions. There was also one other document covering a land rights intervention. In China’s CBET project, for example, many school-based “Eco-clubs”, aimed at raising ecological awareness, became inactive after the project ended, mainly due to a lack of resources acquired during the project planning and implementation phase (Su, Wall and Ma, 2014), while another study highlighted overreliance of this project on a single funding source as a key structural weakness (Yang, 2019). In Mexico’s Community Forestry Program (CFP), Serrano (2022) reported a lack of resources to reinvest profits into essential infrastructure, such as tools and machinery, as a major challenge, hindering long-term sustainability. Uganda’s Collaborative Management Agreements (CMAs) reported resource constraints in funding and staffing that reduced responsiveness to human-wildlife conflict incidents and prevented the implementation of effective mitigation strategies (Atuhaire, 2009).

Disconnect Between Designs and Field Realities

When implementing the programs, there were several instances of disconnect between the design and the realities on the ground. For example, top-down approaches limited local engagement and learning. Additionally, there was a lack of consideration at project design of low literacy levels and low conservation awareness within the local community. Other challenges included inadequate infrastructure in the implementation areas and insufficient methods to address or prevent misinformation, which served as barriers to participation. This barrier was reported in 15 projects across 33 studies and one other document. Ten of these projects and 20 studies and one other document were community-based or decentralised land management and monitoring interventions, three projects and 11 studies were land rights interventions, one project and one study referred to a protected areas intervention, and one project and one study addressed a combined community-based or decentralised land management and monitoring intervention.

The Kwandu Conservancy project in Namibia reported several of these barriers, such as women facing discrimination. Many women reported unfair treatment in the hiring process for guard positions, with almost all employees being male (Khumalo, 2012).

Additionally, there were significant issues with processing compensation claims for damages caused by wildlife. The guards responsible for verifying these claims often had to travel long distances, and by the time they arrived, the tracks of the animals had typically disappeared, making it impossible to confirm the claims if they were too late. Furthermore, there were frequent delays in disbursing payments for verified claims (Kasaona, 2006).

In Kenya’s community-based or decentralised land management and monitoring PELIS project, participation required enrolment and registration fees in addition to several direct and indirect costs of engaging in PELIS activities. As a result, it was mainly accessible to wealthier participants for participation, and few poorer households joined (Waruingi et al., 2021).

For the JFM community-based or decentralised land management and monitoring project in Tanzania, it was found that the villagers overall had much lower awareness about JFM and the associated forest policy and law changes compared to other stakeholders. This suggested that the local village leaders had not shared enough information about the project with the villagers. These low levels of knowledge about JFM meant that several individuals could not benefit from the local forests through the project and lowered participation levels (Robinson and Maganga, 2009).

Another example is the federal agrarian reform project in Brazil, which was a land rights initiative that faced issues related to bottlenecks in project rollout. For a long time, there was no electricity in the households in the area, reducing the appeal and viability of staying on the land (Cruz, 2008).

Environmental/Climatic Challenges

Environmental challenges, especially prolonged droughts, climate change and natural disasters, can significantly impact conservation projects. It has been identified as a barrier in eight projects across 13 studies. Seven projects and 10 studies were community-based or decentralised land management and monitoring interventions, while one project and three studies were combined community-based or decentralised land management and monitoring and protected areas interventions.

CBET in China was both a community-based or decentralised land management and monitoring, and a protected areas project. As a result of the 2008 earthquake and the related events, such as the destruction of major infrastructure, the isolation of the village, and the withdrawal of foreign investors, the CBET nearly collapsed (Zheng et al., 2021). Climate change and extreme weather have also caused fluctuations in the project’s local tourism revenue (Li et al., 2023). For the CBCA community-based or decentralised land management and monitoring project in Kenya, areas faced climate change, which has been reported as one of the contributing factors to the degradation of the ecosystem goods and services (Kombat, 2020).

Population Growth and Migration Can Strain Natural Resources

Population growth can place significant pressure on land and forest resources, which can lead to deforestation and unsustainable land use. This was identified as a barrier in three projects across three studies, all community-based or decentralised land management and monitoring interventions.

The JFM project in Tanzania reported an increase in immigration to project villages due to the grazing lands and fertile agricultural fields. This had consequences for forest conservation due to increased demand for forest resources from the enlarged population (Tarimo and Ringo, 2016). For the NRT project in Kenya, unplanned settlements and increased human populations were found to result in fragmented rangelands, which caused issues for livestock and wildlife movement. The pressure of scattered settlements also resulted in unreliable water sources (Galvin et al., 2021).

Synergies

Synergies refer to the interaction of multiple components that, when combined, produce an outcome greater than the sum of their individual effects. We only found one instance where more than a single project reported on the same synergy. Two studies, each focusing on a different community-based or decentralised land management and monitoring projects, noted that synergies occur when aligning the activities with existing community livelihoods, enhancing the overall effectiveness and sustainability of the projects (Li et al., 2023; Robinson et al., 2021).

Project Activities Aligned With Community Livelihoods

Robinson et al. (2021) noted that the NRT in Kenya promoted sustainable conservation practices through the establishment of grazing zones and schedules, which were well-aligned with the traditional livelihoods of livestock-raising households. The practices helped create a virtuous cycle that reduced degraded bare ground and improved forage availability, which in turn strengthened livestock health and increased livestock product yields. Li et al. (2023) found that the CBET project in China, which allowed participants to maintain their traditional livestock-based livelihoods, helped reduce their dependency on seasonal employment or migration during the off-tourist season, compared to those solely reliant on the tourism business.

Trade-Offs

Trade-offs occur when improving one outcome negatively impacts another. In this context, all trade-offs were between environmental and human well-being outcomes. Two key themes emerged from the analysis: wildlife conservation versus human safety and food security, and livelihoods of the communities or households versus conservation related to land, forests or natural resources. These themes relate three intervention types: community-based or decentralised land management or monitoring projects, protected areas projects, and projects combining the two previous interventions. No trade-offs were identified from the projects on land rights.

Wildlife Conservation vs Human Safety and Food Security

Wildlife conservation as a trade-off for human safety and food security was reported in four projects across six studies on community-based or decentralised land management or monitoring, and protected areas interventions. In these cases, increased wildlife, which was supported by the conservation projects, came at the cost of decreased food security and damaged properties. In the Kwandu community-based conservancy project in Namibia, elephants and bushpigs raided farms, ruining nearly all the farmers’ crops, leading to food insecurity (Khumalo and Yung, 2015). Similar issues were also reported for the protected areas project, the giant panda reserve, in China (Ma et al., 2017).

Communities Livelihoods vs Conservation of Land, Forests or Other Natural Resources

Livelihoods vs conservation was reported as a trade-off in five projects across nine studies covering community-based or decentralised land management or monitoring, and combined community-based or decentralised land management or monitoring interventions and protected areas intervention. This trade-off was reported both as instances where communities or households’ livelihoods improved at the expense of land, forests or natural resource conservation, and when enhanced land, forest or natural resource conservation came at the expense of communities and households’ livelihoods. Land, forest and natural resource conservation was found to prevent communities from carrying out activities they previously relied on for their livelihoods, such as hunting, fishing, and collecting firewood.

This was the case for the community-based or decentralised land management or monitoring project, JFM, in Tanzania, where several sources of livelihoods were affected and reduced due to the land, forest and natural resources conservation. This included villagers being restricted from accessing the reserve after it became a national park, preventing them from collecting firewood. The villagers’ own land pieces were too small and could not support them by growing trees for firewood and other crops to meet their livelihoods (Francis et al., 2015). This generated tensions between economic needs and environmental protection.

On the other hand, conservation issues can also occur because of an increased focus on agricultural expansion rather than land, forest and natural resource conservation, and increased tourism in areas affecting plant diversity. In a community-based project in China, CBET, the communities participating in eco-tourism reported fewer perennial forbs than grazing grasslands. This means eco-tourism could negatively contribute to plant diversity and soil stability (Li et al., 2023).

Unintended Consequences

Unintended consequences occur as project results but were not originally planned, anticipated, or targeted by the intervention design. These consequences can occur within or outside the intervention areas and may emerge many years after the interventions have ended. Although unintended consequences can be either positive or negative, all cases identified across the reviewed qualitative studies pointed to negative consequences across three areas, including worsened deforestation and degradation, marginalisation of ordinary community members and women, and conflicts in participating communities.

Worsened Deforestation and Degradation

Land rights, and community-based or decentralised land management and monitoring interventions aim to promote reforestation and forest conservation by securing tenure for local communities and enhancing their participation, accountability, and monitoring capacity. However, these projects can inadvertently lead to deforestation and land degradation, as supported by eight studies covering eight separate projects.

The Community-Based Conservation Associations (CBCAs) project in Kenya aimed to promote sustainable forest management and improve livelihoods, but eventually led to degradation that, in some treated areas, persisted or even worsened (Lambini and Nguyen, 2022). This may be attributed to several factors reported in the study, including a lack of sufficient incentives or clear benefit-sharing mechanisms for households, which limited adherence to the adoption of intervention practices (Lambini and Nguyen, 2022). Senganimalunje et al. (2020)noted that although the Forest Co-Management (FCM) in Malawi aimed to enhance rural livelihoods and promote sustainable forest use, the project did not sufficiently build the capacity of communities to manage forest resources as cohesive units. Instead, destructive behaviours such as illegal extraction and charcoal burning continued, contributing to further forest degradation. This was partly driven by the introduction of commercial use fees and fines, which led some individuals to avoid payment by shifting extraction pressure to other areas rather than reducing exploitation. Similarly, the Community-based Integrated Natural Resources Management Project (CBINReMP) in Ethiopia experienced unintended spillover effects, where participants accelerated land degradation in areas adjacent to the treated sites, as grazing pressure shifted to other open lands after large parts of the watershed were closed off for restoration (IFAD, 2018).

Increased Land and Agrarian Conflicts

Conservation interventions, particularly those involving community participation, aim to address global environmental challenges through localised actions. However, six studies examining five distinct community-based or decentralised land management interventions reported an unintentional increase in conflicts over land boundaries, as well as land conflicts relating to resources for agricultural use affecting less privileged groups. Coppock et al. (2020) noted that the Community-Based Rangeland and Livestock Management (CBRLM) project in Namibia increased social conflict despite improvements in community governance and collective resource management. Unresolved disputes grew in number following the project’s completion, particularly over rangeland use and the management of community funds. Tensions arose both within communities, between participating and non-participating farmers, as well as between neighbouring communities, especially where project benefits were perceived as exclusionary or unevenly distributed. Farmers who did not adhere to communal grazing plans were sometimes labelled as poachers, deepening mistrust. Walker et al. (2024) reported an increase in conflicts, particularly livestock raiding and land or boundary disputes, which were significant sources of insecurity for communities involved in the Northern Rangelands Trust (NRT) projects in Kenya. These conflicts often stemmed from competition over scarce resources and inter-community tensions.

Further Marginalisation of Ordinary Community Members and Women

Two studies, each focusing on a different community-based or decentralised land management intervention, noted that communities were often less informed, inadequately engaged, or confused about the purpose and implementation of interventions, which ultimately weakened their participation and agency. Global Witness (2018) noted that in Liberia, the introduction of a community forestry system for the citizen monitoring (CM) project aimed to empower the whole communities, but instead led to the disempowerment of ordinary community members. Weak regulatory frameworks and unclear guidelines allowed commercial actors and local elites to dominate decision-making processes, prioritising logging interests over broader community needs. In Tanzania, Robinson and Maganga (2009) observed further marginalisation of women in the JFM project, driven by inadequate communication, failure to address existing gender inequalities, and the continued exclusion of women from forest-related decision-making.

Cost Data Synthesis

In the following section, we present a synthesis of the evidence to address review question 3, which concerns the cost-effectiveness of interventions. We describe the general findings of our cost evidence analysis and then summarise each of the projects for which we identified relevant data.

We found cost information for 42% of projects covered by this review, spread across 78 sources of information. Some projects reported a unit cost or number of households affected, while a few reported benefit-cost ratios using unit budget/expenditure and average household gains in net income terms. However, the overall quality of the cost analyses was generally insufficient to fully understand the cost-effectiveness of these interventions. A summary table of the sources and analysis for this section is provided in Appendix 19.

Extent and Quality of the Cost Evidence

After revision, we found four projects with sources that can be classified as having a certain degree of acceptable reliability, reporting project budget or expenditures: Livestock and Pasture Development Project (LPDP) in Tajikistan; Land Certification Program (LCP) and Community-based Integrated Natural Resources Management Project (CBINReMP) in Ethiopia; and Community Forestry Program (CFP) in Mexico. The information comes from project completion reports (two from IFAD and one from the World Bank) and an academic paper discussing findings from a World Bank project. Additionally, we found an investment memo projecting a detailed cost-benefit analysis for the Kapit-Bisig Laban sa Kahirapan Comprehensive Integrated Delivery of Social Service (KALAHI-CIDSS) project in the Philippines.

The remaining sources were not considered in the following analysis due to the absence of a systematic costing approach, lack of expenditure verification, missing cost components, insufficient cost details, undocumented cost allocation methods, or reliance on basic budget figures or actual spent budgets rather than allocations.

Comparative Analysis of Cost-Effectiveness

The empirical evidence from the four reviewed projects illustrated varying degrees of cost-effectiveness in rural development interventions across different regional contexts. The Pasture Development Project (LPDP) in Tajikistan, with a total cost of US$14.56 million, presents an interesting contrast between theoretical model projections and actual project-wide outcomes. While individual component models showed impressive benefit-cost ratios – pasture improvement models returned between US$2.30 and US$5.90 for every US$1 invested; alfalfa production generated US$5.90 per US$1 invested; and the Sustainable Pasture Management and Livestock Development Plan yielded US$6.30 per US$1 invested – the overall project achieved a more modest net present value (NPV) of US$702,150, translating to approximately US$185.70 per household or a 4.8% return on investment. This discrepancy highlights the challenges of scaling specific projects across diverse contexts. Nevertheless, the project achieved an Economic Internal Rate of Return (EIRR) of 23.9% (exceeding the ex-ante projection of 21%) and delivered substantial productivity gains, with milk production increasing by 21%, meat production by 46%, and livestock numbers growing by 8%, while animal mortality decreased by 1%. The project also created 808 permanent jobs and reached 106% of its target households (3,781 households). This case demonstrates that while theoretical models may project higher returns for specific interventions, actual project-wide implementation often yields more modest economic outcomes, though still delivering positive returns and significant social impacts (Bakertilly, 2019, IFAD, 2018).

The Ethiopian Land Certification Program (LCP) stands out as particularly cost-effective, with implementation costs of approximately US$1 per parcel – significantly lower than comparable programs in other regions (US$7-28 in Madagascar, US$7-10 in West Africa, and US$40 in Uganda). The program’s decentralised, participatory approach allowed for rapid implementation, with certificates issued to more than 20 million plots over a short period. Rather than relying on expensive technology or centralised processes, the programme used locally elected Land Administration Committees and field-based adjudication with neighbours present. The economic impact included a five percent increase in the probability of new land-related investment and measurable improvements in tenure security, with 35% more households confident of receiving compensation in case of land appropriation. Subjective assessments show that 88% of beneficiaries perceive certification to increase incentives for investment in trees, 86% for soil and water conservation structures, and 66% for sustainable management of common property resources. Moreover, 90% of households expressed interest in second-stage certification that would include a spatial reference (map or sketch) and were willing to pay modest sums for this enhancement, suggesting strong demand for the program’s services beyond the initial registration (Deininger et al., 2008).

Mexico’s Community Forestry Program (CFP), with a budget of US$18.44 million, demonstrated strong economic viability through a thorough analysis. The program assessed both financial returns and distributional impacts across four communities in Oaxaca. Forest production activities showed robust internal rates of return ranging from 16% to over 50%, while non-timber forest products such as resin extraction and water bottling achieved EIRRs of 25% and 26%, respectively. Beyond mere profitability, the analysis revealed impressive value capture by local communities, with three of the four communities retaining over 85% of the value-added, and the majority accruing to the “community-at-large” rather than individuals. The project also quantified value-added per hectare, revealing how technological advancement (such as kiln-drying in Pueblos Mancomunados) could generate twice the value-added per hectare compared to communities with more basic processing methods, despite vast differences in available forest area (World Bank, 2004).

Ethiopia’s Community-based Integrated Natural Resources Management Project (CBINReMP), with an investment of US$27.4 million, presented a detailed cost-benefit analysis for its income-generating activities (IGA) component targeting landless youth. The analysis tracked actual performance over three years (2008-2010) and projected costs and benefits for an additional seven years. Youth groups engaged in vegetable and fruit production (onions, potatoes, tomatoes) and cattle fattening demonstrated considerable profitability, achieving a benefit-cost ratio of 2.0 at a 12% discount rate and a positive Net Present Value of ETB 2,100,147. Notably, the analysis revealed that the IGAs remained profitable even when tested with discount rates up to 50%, indicating exceptional resilience and returns. However, it is important to note that this analysis focused solely on the IGA component rather than the entire project, and a supervision mission in April 2018 found that random samples of IGAs were not profitable, suggesting potential sustainability challenges or variability in outcomes across different groups (Federal Democratic Republic of Ethiopia, 2019).

Methodological Considerations in Assessing Cost-Effectiveness

The diverse analytical approaches employed across these four projects highlight important methodological considerations in assessing cost-effectiveness. The LPDP in Tajikistan utilised a cost-benefit analysis with a “with/without” comparison framework, providing a robust economic evaluation. In contrast, the Ethiopian LCP relied on a partial economic evaluation closer to cost-effectiveness analysis, emphasising cost minimisation while achieving specific outcomes. The Mexican CFP utilised an economic analysis that examined not only internal rates of return but also the distribution of value-added across stakeholders. At the same time, the Ethiopian CBINReMP presented a detailed cost-benefit analysis for specific project components.

These methodological differences reflect the inherent challenges in standardising cost-effectiveness assessments across diverse development interventions. While the benefit-cost ratios and EIRRs provide useful metrics for comparing financial returns, they often fail to capture the full spectrum of project benefits, particularly non-monetary outcomes like enhanced tenure security, improved natural resource governance, and institutional capacity building. Moreover, the reliability ratings assigned to these analyses – ranging from “reliable” for the LPDP to “marginal reliability” for CFP and CBINReMP – underscore the varying quality of economic data and analytical rigour across these assessments.

Scale, Scope, and Cost-Effectiveness

This comparative analysis critically considers the relationship between project scale, scope, and cost-effectiveness. The Ethiopian LCP, with its narrow focus and streamlined implementation approach, achieved remarkable cost-efficiency and scale. In contrast, more comprehensive projects like the LPDP in Tajikistan and the CBINReMP in Ethiopia, with their multiple components and broader objectives, demonstrated more variable cost-effectiveness across different project elements.

This pattern suggests that while targeted interventions may achieve greater cost-efficiency, more comprehensive approaches addressing multiple aspects of rural development may generate synergistic benefits that justify higher costs. The Mexican CFP represents an intermediate case, where a sectoral focus (forestry) combined with multiple intervention strategies (technical assistance, institutional strengthening, product diversification) achieved substantial economic returns while maintaining community control over resources and benefits.

Discussion

Summary of Main Results

Effects of Community-Based or Decentralised Land Management and Monitoring Interventions

This intervention type involves transferring the management and decision-making authority over natural resources from national to regional and/or local actors. We quantitatively synthesised the effects of 28 projects in 27 studies. The number of estimates informing each meta-analysis varied considerably, ranging from 12 projects for the synthesis of the average effects on forest or vegetation area to analyses informed by only two projects (e.g., natural resources monitoring or resources disputes). When examining low risk of bias estimates in the 51 meta-analyses, the average effect was no longer statistically significant for two outcomes (education attained and any measure of income). Most often, we could not repeat the meta-analysis without estimates with a high risk of bias because there were fewer than two estimates with a low risk of bias or some concerns for risk of bias. For another six outcomes, we conducted a meta-analysis excluding the high risk of bias estimates. The average effect remained not statistically significant for four outcomes and remained statistically significant for two outcomes (adoption of practices and technology and income). It is, therefore, necessary to be cautious when interpreting these results. Understanding the studies included in the meta-analyses and their potential biases is crucial.

On environmental outcomes, we found a reduction in the area used for agriculture and forestry products. There is evidence that participants have shifted partly away from agricultural and forestry harvesting, as these projects promote forest preservation and development (for example, the Community Forest User Groups Program in Nepal, Libois et al., 2021). These projects may have affected local actors’ behaviours through the projects’ positive effects on conservation attitudes. However, we were not able to identify other associated changes that may occur over a longer period (i.e., improved forest cover and biodiversity), or whether these changes persisted through time. Likewise, the analysis did not elucidate whether agricultural and forestry practices became more productive or whether the area allocated to forest or vegetation expanded. We did, however, find that decentralised land management improved investment in forest and local natural resources monitoring, potentially contributing to the sustainability of resources. Because of the small number of estimates reported for many environmental outcomes, we were not able to explore further the mechanisms at play or the effects of these projects on other biodiversity or climate mitigation outcomes.

The results suggest that decentralised management projects increased participants’ income, possibly driven by improvements in the governance of natural resources and greater adoption of improved practices and technology. There is evidence that giving communities control over natural resources contributed to changing participants’ conservation attitudes and facilitated their attainment of education. We also found that these projects provided greater access to services and further diversified income sources. Decentralised land management and monitoring projects were often accompanied by training and other support to strengthen the local management body. This assistance enabled target populations to access services and key inputs previously unavailable (for example, LPDP, Cavatassi and Mallia, 2018). These analyses did not conclusively indicate that participants experienced other human well-being benefits.

Effects of Land Rights Interventions

We defined land rights as a formal registration of land, either through the conversion of communal or non-demarcated rural land to freehold title or the statutory recognition of customary or communal rural land rights. The evidence of the effects of 15 land rights projects was based on 17 studies, and the meta-analyses included from 2 to 8 projects each.

We found an increase in agricultural and livestock productivity and the value of outputs per area, which could potentially relate to the positive effect found on the diversity of income sources and investments in natural resources or conservation. With legal rights to land access and use, increased productivity and value of production could allow participants to diversify and invest in other productive activities, including conservation efforts. Evidence suggests that land rights eliminated land conflicts, encouraging investments towards the development of diverse and long-term activities (for example, this was highlighted in the LTA project, Persha & Patterson-Stein, 2021). We also found that granting land rights increased out-migration from targeted areas for employment. However, this result is based on two projects. For example, in the case of the PROCEDE project (De Janvry et al., 2015), land security removed the need to demonstrate land use to retain access, allowing labour to be reallocated, enabling migration to pursue other opportunities. We did not identify statistically significant average effects on other environmental or human well-being outcomes.

Across the 25 meta-analyses conducted, in six cases, we had sufficient evidence to repeat the analysis without estimates appraised as having a high risk of bias. The average effect on agriculture and livestock productivity was no longer statistically significant. For another four outcomes, the average effect remained not statistically significant, but it became statistically significant for one outcome (investment in natural resources or conservation).

Effects of Protected Areas Interventions

Protected areas have defined boundaries, formally recognised under statutory civil law or traditional rules aiming to influence natural systems and human behaviour to protect, maintain, and restore ecosystems and biodiversity. We included effects of 11 protected areas projects from 13 studies, each meta-analysis covering estimates from between two and five projects.

The available evidence of the effects of protected areas in this review is limited, which constrained the analyses we were able to conduct. While we synthesised a handful of environmental outcomes, we did not identify statistically significant effects on any of these measures except for the occurrence of any deforestation. We found that protected areas slightly diminished deforestation. We also found a positive effect of protected areas on a few human well-being outcomes, including agricultural or livestock productivity, and income and assets, suggesting that the protection of areas can improve participants’ livelihoods. However, these findings are insufficient to fully understand the effects and underlying mechanisms associated with protected areas that could lead to these outcomes.

In seven out of the 14 meta-analyses, we conducted the analysis without estimates considered to have a high risk of bias. Regarding deforestation, the average effect became not statistically significant. In contrast, we found a statistically significant effect for both forest and vegetation area, and forest area. The interpretation of the results did not change for the remaining four outcome measures.

Factors That can Affect Project Effectiveness

Across all three intervention types, we assessed whether effect sizes in the meta-analyses varied according to specific factors. However, the evidence available did not support a detailed or robust moderator analysis to explain the observed heterogeneity in effect sizes. In addition, a small number of projects reported effects subgroup-disaggregated results, limiting our ability to investigate effects by targeted groups. For example, five projects presented effect sizes by poverty status of targeted populations, but differences in outcome measures prevented conducting a subgroup meta-analysis. Similarly, two projects reported effects disaggregated by gender, which was insufficient for subgroup analysis.

From the qualitative synthesis, we identified several factors – including enablers, barriers, synergies, trade-offs, and unintended consequences – that can affect the successful implementation of land management projects and, hence, increase the likelihood of improving indicators of impact. We found relevant information on factors in 47% of projects reviewed, mostly community-based or decentralised land monitoring and management interventions, with some contribution from land rights and protected area interventions. This evidence is of considerable quality, with over 80% of the qualitative studies rated as moderate or high quality.

Most of these factors are related to the project itself or characteristics of the communities where it is implemented. Five areas emerged as critical when considering the dynamics that a project is changing within a community to achieve its goals.

• • First, aligning intervention activities with existing community livelihoods or embedding alternative income-generating opportunities to support livelihoods emerged not only as an important enabler of the effectiveness of land management interventions but can also create virtuous circles for the sustainability of land and people’s livelihoods (Li et al., 2023; Robinson et al., 2021).

• • Second, aligning with the communities’ traditional values and integrating all groups from the community was highlighted as both an enabler and a barrier for local involvement. It is important to approach this constructively, recognising that values can evolve. Aligning with traditional values can coexist with supporting inclusion and gender equality. For example, involving women and elders in day-to-day activities and ensuring the community is informed of the project objectives, activities, and benefits can help enhance the participation and agency of the overall community and minimise the further marginalisation of groups traditionally excluded due to socioeconomic, religious, age, or cultural reasons. If current local values and norms in a community are not considered, it can lead to resistance to the programme, lower participation rates, and further exclusion of these marginalised or vulnerable groups, such as low participation among women due to programme activities being seen as traditionally masculine.

• • Third, providing knowledge-sharing and collaboration opportunities, including tailored training when required, was identified as a factor that can be an enabler when present and a barrier when absent. This can help implementers and participants be successfully and continuously involved in project activities and support rural participants in taking greater ownership of decision-making.

• • Fourth, lack of clarity on documentation and/or understanding of project processes, roles, and responsibilities, or inadequate governance and funding structures were identified as barriers to project success. These can lead to corruption, both at the project management level and in terms of how projects fit into regional/national dynamics. They can escalate into generating social conflicts due to perceived or real unfair distribution of costs and benefits, which could weaken social and network infrastructure during project implementation, creating risks for its long-term sustainability.

• • Finally, we identified trade-offs in terms of placing environmental and human livelihood goals against each other. Conservation interventions can potentially and inadvertently lead to deforestation, land degradation and biodiversity loss if they do not provide the right incentives for project adoption or anticipate behaviours that can occur because of the project, including, for example, the illegal extraction of natural resources due to crowding-out of conservation-friendly existing local norms. In addition, increases in wildlife populations due to conservation efforts can also come at the cost of reduced food security and property damage, potentially affecting people’s livelihoods (Francis et al., 2015; Khumalo & Yung, 2015; Li et al., 2023; Ma et al., 2017).

In turn, higher-level contextual features can also affect a project or its participants. While it can be difficult to anticipate or address these, project design can consider the likelihood that projects may face these or similar factors to understand their potential impact on the project and its participants. Climatic shocks and population growth and migration (e.g., unplanned settlements due to climate, conflict or food insecurity) are examples of these factors, which can be increasingly unexpected and have devastating effects on land use and people’s adaptation capacities.

Cost Evidence of Included Projects

Our review revealed methodological diversity in how development interventions approach cost-effectiveness analysis. Of the 25 projects with cost information (42% of our sample), only four provided sufficiently reliable economic data for meaningful assessment. This statistic is consistent with the broader international development field, in which Brown and Tanner (2019) reported that fewer than one in five impact evaluations integrated a cost assessment. These projects employed distinct analytical frameworks—from comprehensive cost-benefit analyses with “with/without” comparisons in Tajikistan’s LPDP to partial economic evaluations emphasising cost minimisation in Ethiopia’s LCP. This methodological heterogeneity reflects not merely inconsistent reporting standards, but also the inherent complexity of valuing diverse development outcomes across vastly different contexts. The challenge lies not only in calculating financial returns, but also in capturing critical non-monetary benefits such as enhanced tenure security, improved governance structures, and institutional capacity building that frequently constitute the enduring legacy of these interventions.

Beyond methodological considerations, our analysis reveals compelling patterns regarding implementation approaches and their relationship to cost-effectiveness. The Ethiopian Land Certification Program’s remarkable efficiency (US$1 per parcel compared to US$7–40 in other regions) indicates how locally-driven, participatory processes can dramatically reduce costs while maintaining quality outcomes. Similarly, Mexico’s Community Forestry Program illustrates that when communities retain control over resources and implementation, they can capture over 85% of value-added locally while achieving substantial returns (EIRRs of 16-50%). These findings suggest that cost-effectiveness in development may depend less on technical sophistication than on appropriate institutional design and community engagement. While targeted interventions generally achieved greater cost-efficiency, comprehensive approaches addressing multiple rural development aspects in some cases showed valuable synergistic benefits that potentially justified their higher implementation costs. This is an important consideration for policymakers weighing focused versus integrated project designs.

Overall Completeness and Applicability of Evidence

This review relied on evidence from the Climate Change and Biodiversity (CCB) EGM and focused on land management projects that reported both environmental and human well-being outcomes. Because we drew from a specific profile of studies from the CCB EGM (i.e., evaluations that reported both an environmental and a human well-being outcome), the representativeness of the sample of projects reviewed is partial compared to all projects included in the CCB EGM. This is most evident when looking at the distribution of projects reviewed across intervention categories (see for example, Figure 4). This is not expected to be consistent with the distribution of all interventions implemented in L&MICs.

Table 9 presents a summary of the extent of the evidence used in the quantitative synthesis to address research question 1. The table illustrates two main trends. First, the three intervention categories reviewed present dissimilar levels of evidence, with decentralised land management and monitoring reporting on most outcome categories, while protected areas interventions covered fewer outcome categories with sufficient evidence for synthesis.

Table 9.

Extent of Quantitative Evidence by Intervention Types and Outcomes

Notes. DEC = Decentralised land management and monitoring; LR = Land rights; PA = Protected areas. For each intervention (column), coloured cells denote the outcome categories for which we were able to conduct at least one meta-analysis.

It is surprising that only a few rigorous impact evaluations reported both environmental and human well-being effects of protected areas in L&MICs, despite their prevalence in environmental and international development efforts (18% of the world’s terrestrial and inland water areas are under some type of protected areas status, UNEP-WCMC and IUCN 2024). This may reflect a disciplinary gap in which researchers act in silos rather than examining socio-ecological systems (Cumming & Allen, 2017). Given the importance of addressing socio-environmental crises (i.e., poverty, climate change, biodiversity loss), it is crucial to bridge this gap.

The second trend highlighted in Table 9 presents a summary of the extent of the evidence used in the quantitative synthesis to address research question 1. The table illustrates two main trends. First, the three intervention categories reviewed present dissimilar levels of evidence, with decentralised land management and monitoring reporting on most outcome categories, while protected areas interventions covered fewer outcome categories with sufficient evidence for synthesis. Presents a summary of the extent of the evidence used in the quantitative synthesis to address research question 1. The table illustrates two main trends. First, the three intervention categories reviewed present dissimilar levels of evidence, with decentralised land management and monitoring reporting on most outcome categories, while protected areas interventions covered fewer outcome categories with sufficient evidence for synthesis. Is that intermediate environmental outcomes were the most commonly evaluated across the interventions. In contrast, biodiversity, climate mitigation and other longer-term human well-being outcomes were the least commonly reported. In fact, we were not able to explore four outcomes across any intervention: nutrition, habitat structural complexity, greenhouse gas (GHG) emissions, and clean air. One explanation for this gap in outcomes could be the relative difficulty of collecting reliable data on these outcomes compared to intermediate environmental and human well-being outcomes. For example, on nutrition, adequately collecting children’s weight-for-height or height-for-age requires in-person measures by trained staff.

Similarly, rigorously measuring the impact of projects on habitat structure, GHG emissions, and clean air outcomes can be complex. On one hand, evaluating a project implemented at the local level on GHG outcomes can be methodologically challenging if the treatment and control groups are in close proximity (i.e., risk of leakage). On the other hand, the resources and commitment required to evaluate projects with outcomes at multiple levels or units after several years are not always available.

Beyond the gaps on interventions and outcomes, we included only three projects in South Asia and one from Europe and Central Asia. The review did not include protected areas from Sub-Saharan Africa, South Asia or Europe and Central Asia. The distribution of the evidence does not overlap with the most climatically and environmentally vulnerable and exposed areas in the world, according to the World Risk Report (Bündnis Entwicklung Hilft/IFHV 2023). The report estimated that, between 2000 and 2023, South Asia was the most vulnerable region in the world to extreme natural events or negative impacts of climate change (Bündnis Entwicklung Hilft/IFHV 2023). The fact that the evidence is not primarily focused on the most vulnerable areas may be due to a lack of consensus on which regions are considered the most at risk. Additionally, local political and economic factors, such as ongoing conflicts, limited budgets, or sudden shocks, can restrict the number of projects implemented in these areas.

The results of the additional search for qualitative and cost information of included projects were also unevenly distributed. In the qualitative synthesis, we identified evidence on factors affecting effectiveness for 47% of the 60 included projects. Similarly, we found cost information for 42% of these 60 projects. We found that this cost evidence was based on inconsistent reporting standards. In both cases, the information was highly focused on decentralised land management and monitoring interventions.

When comparing the set of 378 studies included in the CCB EGM with the set of 58 studies included in this SR, we identified two main trends. First, regarding the types of interventions evaluated: the studies included in the SR evaluated a higher proportion of decentralised management interventions, whereas studies included in the EGM evaluated protected areas in a greater proportion. Second, the prevalence of outcome measures between the two sets of studies is generally the same, except for climate mitigation outcomes (greenhouse gas emissions and carbon storage and sequestration), which are most commonly reported in evaluations of protected areas. The number of evaluations covering climate mitigation outcomes in EGM studies is still low (15 studies), which was highlighted as one of the evidence gaps in the map. The predominance of evidence derived from a single intervention category (i.e., decentralised land management) posed challenges for directly comparing environmental and human well-being outcomes in this review.

Quality of the Evidence

Across all intervention types, the available quantitative evidence is subject to risks of bias. Based on study methods, analysis, and reporting, 52% of estimates were rated as having a high risk of bias. Most impact evaluations used a quasi-experimental design, and among these, the most common sources of bias were related to confounding and selection bias. This suggests that, methodologically, this body of evidence did not follow the gold standards for conducting or reporting impact evaluations. High risk of bias estimates may not precisely reflect the true project effects.

Missing information on context, intervention design and implementation limited our analyses. For example, for seven interventions, the number of months during which participants were exposed to the intervention was not specified. Additionally, we had to exclude seven projects from the meta-analyses as they did not report the required information to compute effect sizes. Lastly, the limited number of estimates per meta-analysis also prevented robust moderator analyses. We were unable to conduct a detailed examination of how effect sizes vary by moderators and across subgroups.

Most qualitative evidence used to identify success factors was rated as high or moderate quality (10% and 64%, respectively), indicating that the evidence showed considerable reliability. However, this was not the case for the additional cost evidence for included projects. In the few cases where cost evidence was available, it was mostly considered unreliable. Out of the 25 projects where we found cost information, only four included data classified as having a certain degree of acceptable reliability, reporting project budget or expenditures.

Potential Biases in the Review Process

The evidence base for the review was established using the CCB EGM, with the search finalised in March 2024. Given the rapid rise in publications, as highlighted in the CCB EGM report (Marion et al., 2024), it is likely that including more recent literature will influence the review findings and improve the robustness of the analyses.

Likewise, the CCB EGM only used search terms in English. This implies that the CCB EGM may have missed evidence in other languages. To increase discoverability, databases usually include titles and abstracts in English, even if the reports are in another language. Moreover, evidence from epidemiology indicates that using English search terms does not prevent the successful finding of relevant non-English publications (Nussbaumer-Streit et al., 2020). While the extent of any omissions is uncertain, it is likely minimal as the CCB EGM included 14 studies published in Chinese, Indonesian, French, Portuguese and Spanish.

We conducted meta-analyses for a limited number of environmental and human well-being outcomes, and in many cases, these were based on a small number of estimates and should thus be considered exploratory. This is likely due to the combination of having an evidence base of 60 projects across 25 outcomes. At best, 23% of the projects (n = 14) reported on the same outcome measure. Likewise, it was not possible to identify L&MICs-wide representative effects of land management interventions across outcomes along the causal chain. In addition, the small number of estimates precluded us from testing the presence of publication bias and performing complete moderator analyses to detect potential sources of variation in the effects. However, even based on a small number of estimates, meta-analyses are preferable to ad hoc summaries (Borenstein et al., 2021; Valentine et al., 2010). Because they establish the direction, magnitude and statistical significance of overall effects, they provide more credible and transparent evidence-based insights on effects than narrative summaries.

Another limitation regarding the review process is that the quality of the quantitative evidence raises concerns about internal validity. All the meta-analyses included at least one estimate that either did not provide sufficient information to assess the risk of bias or was assessed as having a high risk of bias. Moreover, in a few meta-analyses (n = 6), the overall effect changed its statistical and/or substantive interpretation after excluding estimates considered to have a high risk of bias.

Agreements and Disagreements With Other Studies or Reviews

As discussed in the Background section, five reliable systematic reviews from the CCB EGM, appraised as having high- or medium-confidence, were relevant to this review (Geldmann et al., 2013; Lawry et al., 2014; Pullin et al., 2013; Samii et al., 2015; Waddington et al., 2019). The main strength of our review is its inclusion of recent studies up to March 2024, offering the most up-to-date synthesis of land management policies and programmes in L&MICs.

Findings in these reviews broadly align with ours. For instance, some reviews show that community-based or decentralised land management and monitoring, and land rights interventions can improve certain human well-being outcomes, such as productivity and income, though they also highlight a limited evidence base (Lawry et al., 2014; Samii et al., 2015; Waddington et al., 2019). Some also echo the importance of key factors to programme success, such as governance quality, community engagement, legal recognition of community rights to manage and benefit from resources, and inclusive intervention designs (Lawry et al., 2014; Pullin et al., 2013; Samii et al., 2015; Waddington et al., 2019). Persistent challenges include the insufficient evidence to draw robust conclusions on environmental (Geldmann et al., 2013; Samii et al., 2015) and human well-being outcomes (Lawry et al., 2014; Pullin et al., 2013; Samii et al., 2015; Waddington et al., 2019), a concern echoed in our synthesis.

Our review differs in two key ways. First, each of the five earlier reviews focused on only one of three land intervention types examined in this review, except for Waddington et al. (2019), which included two. Second, unlike our review, none of them restricted the evidence base to studies reporting on both environmental and human well-being outcomes. This means that the evidence used in those reviews differs from ours, particularly in its ability to explore trade-offs or synergies between environmental and human well-being outcomes. Our review identified qualitative evidence of synergies when interventions align with existing community livelihoods, and trade-offs when efforts to conserve wildlife and protect forests come at the expense of local livelihoods, food security, or human safety.

Geldmann et al. (2013) and Pullin et al. (2013) narratively synthesised the effects of protected areas. Both reviews included quantitative studies with experimental and quasi-experimental designs evaluating interventions in L&MICs but, contrary to our review, also included interventions in HICs. Geldmann et al. (2013) focused on habitat cover and species populations. Their descriptive synthesis of the evidence from 118 studies showed that protected areas may conserve forest habitat, but lacked clear evidence on species populations due to limited data. While our review adds weight to the need for more evidence to draw robust conclusions on the effects of protected areas, it extends Geldmann et al. (2013) by evaluating both environmental and human well-being outcomes of protected areas. Like their review, we found environmental effects were not statistically significant, often due to small sample sizes and limited data, except for a slight decrease in deforestation.

Pullin et al. (2013), through a mixed-methods systematic review, synthesised 79 quantitative and 34 qualitative studies to examine the effects of protected areas on human well-being, including social capital, access to ecosystem services and goods, human rights, and livelihood strategies. Due to concerns about study heterogeneity and methodological limitations, they found no clear quantitative effects. Our review strengthens the evidence by showing a statistically significant reduction in deforestation and benefits for agricultural and livestock productivity, income and assets. Their qualitative synthesis highlighted inclusive and rights-based governance as key to the sustainability of protected areas, while noting tensions related to external regulations and enforcement. They identified implementation challenges, such as a lack of compensation and delays, along with potential ‘implementation solutions’ such as providing culturally sensitive support and enabling secure land tenure. While applying a different conceptual framework, our review’s qualitative findings found largely similar enablers and barriers to intervention effects across all three land management types, including protected areas, which aligns with the qualitative findings of Pullin et al. (2013).

Lawry et al. (2014) conducted a mixed-methods systematic review of land rights interventions in rural areas in L&MICs across Africa, East Asia, Latin America and South Asia. The authors included 20 quasi-experimental and experimental studies and nine qualitative studies. Based on the limited evidence base, they reported positive effects on land-related investments, productivity, and income, with the strongest benefits observed for interventions providing formal, documented property rights, such as land titling. Evidence on access to credit was inconclusive due to high heterogeneity. Our meta-analysis also found improvements in productivity and diversified income sources, but not income itself. We further found that the interventions can promote investment in natural resources and conservation. Their qualitative findings highlighted that while land rights interventions can deliver economic benefits, poorly designed or implemented interventions may reduce access for women and other vulnerable groups, particularly where existing inequalities are not addressed. While our review echoed their qualitative findings, we extend this finding by documenting similar risks of marginalisation not only in land rights interventions but also in community-based or decentralised land management interventions.

Samii et al. (2015) identified eight quantitative and four qualitative studies of decentralised forest management programmes in seven L&MICs across Sub-Saharan Africa, South Asia, and Latin America. They reported mixed, context-dependent effects on forest cover and human welfare outcomes, and inconclusive findings on ‘win-win’ outcomes, mainly due to a limited evidence base. While our review did not observe a statistically significant effect on deforestation, we identified reduced land used for harvesting agricultural and forestry products, and increased conservation investment. We further build on the previous findings by identifying statistically significant positive effects on income, diversified livelihood, household involvement in environmental protection, and governance of natural resources. Samii et al. (2015) also noted that while the programmes may boost income, poorer households could be negatively affected if intervention designs were not equitable or democratic. Our qualitative findings align with these results, showing that benefits can be unevenly distributed and that marginalised groups may be excluded or harmed in the absence of inclusive planning and strong support mechanisms.

A mixed-methods systematic review by Waddington et al. (2019) included quantitative impact evaluations for 35 citizen engagement interventions in L&MICs, covering community-based or decentralised land management and monitoring interventions, and protected areas. The authors reported small but positive effects of the above interventions on income/expenditure and agricultural yields, but noted that the evidence base was limited, with most studies at high risk of bias, preventing strong conclusions on effectiveness or the testing of key mechanisms quantitatively. Similarly, our review identified small but positive effects of community-based or decentralised land management and monitoring interventions on income and expenditure, but we did not identify statistically significant effects on agricultural, livestock, or forestry productivity. We found, however, positive effects on other broader outcomes beyond the findings of Waddington et al. (2019), such as increased forest investment, improved conservation attitudes, further access to services and credit, and stronger local accountability and governance.

The qualitative synthesis in Waddington et al. (2019) identified key mechanisms, barriers, and enablers influencing intervention success. Mechanisms contributing to success included strengthening downward accountability to communities and enhancing local capacity for collective action. Identified barriers included a lack of government or local official buy-in and the risk of elite capture. The review also noted enablers such as the legal recognition of community rights over resource management and benefits, as well as the availability of alternative livelihood opportunities. These findings align with our qualitative findings, which identified similar factors but extended the analysis beyond community-based or decentralised land management alone, also drawing on evidence from land rights and protected area interventions.

Authors’ Conclusions

Implications for Practice and Policy

Based on the SR’s scope, at the local level, land management interventions should be considered as one of the possible strategies to promote sustainable use of land resources and improve human livelihoods. This review further confirms and expands past synthesis findings. In some cases, land management encouraged behavioural shifts towards land resource conservation, ultimately resulting in improved natural resource outcomes. Also, evidence suggests that these interventions can lead to higher productivity and income. Over time, these interventions may have significant implications for local ecosystems and populations in the context of climate change and land resource depletion. For example, the Plantation Establishment and Livelihood Improvement Scheme in Kenya enhanced household participation and well-being by offering benefits that promoted the sustainable use of land resources (such as creating opportunities to extract forest products and providing plots for growing crops), which, in turn, increased participants’ income (Waruingi et al., 2021).

Given the interrelation between environmental factors (such as biodiversity and climate) and human well-being issues (including poverty, displacement, conflict, and hunger), examining the impacts of land management interventions on these outcomes in isolation may fail to uncover the complex interactions between the environment and the livelihoods and welfare of populations. Therefore, investments in land management policies should be based on a thorough understanding of the local context to effectively assess potential trade-offs and shifts in local incentives and dynamics at the problem identification and intervention design stages.

For example, trade-offs between environmental conservation and sustaining livelihoods from land management interventions should be considered when designing land projects and policies. Conservation efforts, integral in establishing protected areas, can severely restrict access to natural resources, which are often essential for local communities’ livelihoods. For example, the Sichuan giant panda reserves in China, wildlife raided farms, ruining nearly all the farmers’ crops, leading to food insecurity (Ma et al., 2017). However, across the evidence of the effects of protected areas, IEs most often only evaluated environmental outcomes, which explains the low number of evaluated protected areas in this SR. A recent evaluation of BMZ-supported protected areas also found that while environmental goals are prioritised, livelihood impacts are often inadequately addressed (Sting et al., 2024). There are growing calls to integrate social and economic considerations within land management interventions more effectively (Elias et al., 2022). Providing clear and long-term mechanisms for replacing, diversifying, or enhancing income-generating activities can help ensure interventions create virtuous cycles.

Land management projects and policies should, foremost, be grounded on sound knowledge of the local setting and populations, and on the best evidence that is available. However, areas with inconclusive evidence can also be taken as an opportunity for learning that can be filled out through strategic programming. Projects that incorporate sufficient flexibility for continuous learning can have several benefits. First, learning cycles during a project’s lifetime can help identify and adapt aspects that have not been implemented appropriately or that were not accounted for at the design stage. Second, if paired with a testing approach, projects can explore the implementation of variations of activities to identify strategies that work best in specific settings, and for groups and members of communities from diverse landscapes. Otherwise, programme design faces the risks of ignoring previous findings and learnings and risks promoting the latest trends in this field (Redford et al., 2013).

For example, funding of phased-in land interventions, accompanied by a suitable experimental evaluation design and cost-analysis plan, may represent an operative way to identify the most cost-effective interventions tailored to local contexts and times. When implementing a phased-in approach, the intervention is randomly assigned to participants over time, and those targeted later can serve as an adequate control group in an impact evaluation of the programme. This design approach enables the removal of ethical issues linked to restricting access to interventions and, if the evaluation is conducted transparently and rigorously, permits attribution of outcome changes to the intervention’s activities (Sturdy & Khatua, 2024). It allows for adaptive implementation of the intervention, recognising the dynamic changing nature of the problems to be addressed and the settings.

Implications for Research

The review leaves key research questions unanswered, underscoring the need for future evaluations to generate reliable, comparable evidence, which will be essential for addressing climate, biodiversity, and human well-being challenges.

There is a need to embed an evaluative lens and rigorous evaluations at the project design stage to build up the body of evidence of land management interventions aimed at achieving environmental and human well-being goals. Conducting a rigorous impact evaluations involve registering a pre-analysis plan, employing robust counterfactual designs and collecting both comparable outcome data for treatment and control groups at baseline and follow-up. It also requires transparent reporting on assignment mechanisms, baseline balance, attrition, implementation fidelity, spill-over effects, potential biases, and external validity. Along with bridging the methodological approaches for measuring environmental and human well-being outcomes in future research, new research can help alleviate some of the gaps identified in this review. It is also critical that these evaluations are adapted to the local context and specific research questions (Pynegar et al., 2025). Incorporating process evaluation into impact evaluation can help to better understand how and why interventions work or fail by examining implementation processes and contextual factors (Dixon & Bamberger, 2021).

Theory and empiricism need to be at the centre of rigorous evaluations of land management policies and projects in L&MICs. This can help ensure that the identification of problems and related research entail a careful analysis of both environmental and human well-being aspects at play. Such analysis is crucial for appropriately designing and further estimating project effects, particularly, but not exclusively, at the final stages of the causal chain or related to complex measures such as GHG emissions or multi-dimensional poverty (Leisher et al., 2024; also suggested in Sting et al., 2024). Evaluations grounded in theory and empirical evidence can help identify and prioritise efforts on areas facing greater climate risk and vulnerability, which will potentially have the greatest impacts.

More decisive step-by-step intervention designs towards the identification of outcomes throughout project implementation (i.e., short-, medium- and long-term) can strengthen a culture of collective learning-by-doing for all involved, including rural communities. This cultural shift can enable testing of a range of evaluation approaches and methods (e.g., mixed-methods approaches) and further identification of the most suitable ones to track (monitor) and demonstrate (evaluation) incremental transformative change. Step-by-step learning and programming can be supported by more agile evaluation approaches, including rapid cycle evaluation and A/B testing, although this can require sophisticated implementation, management, and logistics capabilities.

Exploiting the expanding sources of satellite (also known as remote-sensing) or GIS data for measuring climatic, human well-being, and biodiversity outcomes is a practical approach that could significantly facilitate access to high-quality data in terms of temporal and spatial resolution to cover the evidence gaps around these outcomes (3ie 2025). In a context of declining funding for climate and international development research, it is essential to identify efficient ways to conduct quality and valuable research.

Further exploration of the relationship between land, biodiversity conservation, and food security at the local level is needed. Although climate change and biodiversity loss require urgent action, further research can help model, explore and anticipate the potential detrimental effects that these land management interventions can have on food security and identify long-term approaches. In turn, this would avoid driving further environmental degradation or exacerbating inequality. These effects are highly specific to the local environment and socio-economic contexts that imply different vulnerabilities for different members of society.

Future evaluations should consider adopting equity, diversity and inclusion dimensions in their design and reporting. The increased integration of these approaches into land management interventions, along with a focus on indigenous peoples and local communities (IPLC) should be paired with solid methodological designs and data collection processes to track equity trends and understand projects’ effects over diverse groups within communities. A practical step for future research would be to collect more and better disaggregated data for different groups, including women and other minority groups. Hankivsky et al. (2018) and Welch et al. (2016 and 2017) offer established guidelines for equity-sensitive research.

Land management interventions should be carefully planned to conduct cost-effectiveness assessments at the design stage. Projects that only evaluate their impact may miss the opportunity to understand their scalability and offer a comparison point for similar interventions. While there are no established guidelines, 3ie’s cost-effectiveness handbook (Acharya et al., 2024) can be a starting point to guide implementers and evaluators on how to design, implement, and report cost-effectiveness metrics in impact evaluations.

Appendix.

Abbreviations

DEFRA

Department for Environment, Food and Rural Affairs

EGM

Evidence Gap Map

EIRR

Economic Internal Rate of Return

EVI

Enhanced Vegetation Index

FIES

Food Insecurity Experience Scale

GBF

Kunming-Montreal Global Biodiversity Framework

GHG

Greenhouse Gas

HDDs

Households Dietary Diversity Score

HICs

High Income Counties

IEs

Impact Evaluations

IPBES

Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services

IPCC

Intergovernmental Panel on Climate Change

IPLC

Indigenous Peoples and Local Communities

IUCN

International Union for Conservation of Nature

L&MICs

Low-or Middle-Income Countries

NbSI

Nature-Based Solutions Initiative

NDVI

Normalised Differential Vegetation Index

NPV

Net Present Value

OECD

Organisation for Economic Co-operation and Development

PAPs

Pre-Analysis Plans

PES

Payment for Environmental Services

SDs

Standard Deviations

SDGs

Sustainable Development Goals

SECP

Stakeholder Engagement and Communication Plan

SRs

Systematic Reviews

SMD

Standardised Mean Difference

UNFCCC

United Nations Framework Convention on Climate Change

ORCID iDs

Pierre Marion https://orcid.org/0009-0000-5371-8444

Sanghwa Lee https://orcid.org/0000-0003-4500-2387

Lucas Sempé https://orcid.org/0000-0002-0978-6455

Claudia Romero https://orcid.org/0000-0003-4589-4231

Data Availability Statement

The data and analytic codes used in this review analyses will be made available in 3ie’s Dataverse, including the quantitative, qualitative and cost data referenced in this report: https://dataverse.harvard.edu/dataverse/3ie.