Highlights

• Locally led adaptation (LLA) is an emerging priority among international climate and development donors and community-based organizations alike. Supporters of locally led adaptation can leverage the monitoring, evaluation, and learning (MEL) process to promote local agency and effective, equitable adaptation.
• Monitoring, evaluation, and learning (MEL) is an important process for managing the complexity, uncertainty, and context-specificity of any adaptation intervention, including those that are locally led.
• Different MEL approaches and methods are required that balance power, promote mutual accountability, value local knowledge and priorities, and create value for local actors.
• This paper recommends a systemic shift toward MEL that is locally led, context-aware, and itself adaptive. It provides operational steps throughout the MEL cycle that funders, intermediary organizations, and other institutions seeking to support locally led adaptation can take.

Context

In its 2019 flagship report, the Global Commission on Adaptation called for an increase in decentralization of adaptation finance to the local level. In 2020, partners of the Global Commission on Adaptation developed principles for locally led adaptation. These principles serve as foundational guidance for supporting and enabling locally led adaptation, and call for actions such as devolution of decision-making, patient and predictable financing, flexible programming, and redressing of social inequalities. This paper is intended to support implementation of these principles by exploring the role of MEL in supporting or discouraging locally led adaptation and to discuss MEL practices and approaches that align with these principles of locally led adaptation.

A growing body of research and practice is grappling with the opportunities and challenges of MEL for adaptation. Emerging evidence and practice centers on the MEL’s role in building social capital and equalizing the power dynamics that underpin the use and governance of knowledge generated through MEL. Grounded in the principles of locally led adaptation, this paper builds on these fields of research and practice to discuss how MEL can support local agency and reflect local priorities and expertise in the interest of more effective and equitable locally led adaptation interventions.

About This Working Paper

This paper presents a review of opportunities and challenges in MEL for locally led adaptation. It is based on a review of MEL approaches, practices, and literature, as well as consultations with a range of actors involved in locally led adaptation MEL.

The paper proposes key considerations relevant throughout the MEL cycle that align with the principles for locally led adaptation:

• Recognition of and response to structural inequalities
• Promotion of local agency in decision-making
• Understanding climatic and contextual uncertainty and complexity
• Prioritizing learning processes
• Generating value for local stakeholders

To operationalize these considerations, the paper discusses approaches, methods, and tools specific to each phase of the MEL cycle.

Conclusions and Recommendations

MEL of locally led adaptation requires a long-term systemic shift from traditional MEL to MEL that is locally led, balancing asymmetries in power and accountability. A range of MEL practices and tools are available in the short term to funders, intermediary organizations, and MEL practitioners to support locally led adaptation. However, limitations and practical constraints come with recommended changes to MEL practice, including changes in resources, time, and standard processes. Practical steps throughout the MEL cycle help navigate trade-offs and strengthen locally led adaptation interventions.

To support locally led adaptation through MEL, funders, intermediary organizations, and MEL practitioners should do the following:

• Understand and respond to structural inequalities, including how power dynamics affect the MEL process and whose objectives it serves, and whether different worldviews and definitions of resilience are equally valued. This is critical to ensure that MEL reflects the realities and priorities at the local level and that results are not inaccurate or biased, and to encourage the systemic change required for locally led adaptation.
• Embrace design of MEL systems that give equal or greater priority to downward accountability and learning compared with upward accountability. Distinct processes for accountability and learning can address tension between these objectives.
• Ask how MEL processes and outputs create value for local actors.
• Take a local demand–driven approach to building capacity for self-directed MEL. If MEL intends to support locally led adaptation, local actors themselves should determine what capacity, external expertise, and access to information they need.
• Enlist appropriate approaches and methods to navigate and better understand complexity and uncertainty.
• Create locally appropriate and context-specific indicator frameworks and adaptation metrics. Adaptive capacity is particularly useful as a starting point in defining a set of indicators that apply across scales and contexts.
• Adopt MEL technologies and process innovations as appropriate to increase local ownership.
• Develop MEL systems to support adaptive management, experimentation, and learning from failure.
• Collaborate with knowledge brokers who can translate terminology and concepts between external and local actors to enable ownership and contribution of local partners.
• Ensure that learning is applied, documented, and shared horizontally at the local level and vertically to national and international levels as appropriate, while prioritizing the knowledge needs and gaps of local actors as a primary audience group.

As locally led adaptation progresses, so should theory, practice, and learning about MEL for locally led adaptation. This paper provides a high-level and preliminary analysis of MEL for locally led adaptation, and encourages continued learning from local practice and local expertise in adaptation MEL.

As a process involving power and decision-making, monitoring, evaluation, and learning (MEL) can either perpetuate structural inequity or actively work against it (Emerson 2020). Conventional MEL risks further entrenching the inequities that make certain populations disproportionately vulnerable to climate change. However, there is a paucity of documented experience that addresses the entire cycle of adaptation MEL in a way that intentionally redresses power imbalances between donors and local partners, addresses biases in the knowledge and values that are privileged, and promotes downward accountability to local actors.

This paper discusses the role of MEL in encouraging equitable distribution of resources and agency through locally led adaptation (LLA). It provides an assessment of approaches and practices throughout the MEL cycle that help balance power and accountability, and discusses the advantages and limitations of these practices.

The MEL approaches and practices outlined in this paper are targeted primarily to donor and intermediary institutions and individuals involved in designing or delivering MEL in the context of locally led adaptation or where local ownership is a development priority. Since these groups currently have the power to direct resources to MEL, and influence MEL design and outcomes, they have a critical role in leveraging MEL to support LLA. Local actors, however, are facing the very risks LLA interventions seek to address, and are therefore most directly affected by MEL practices and approaches. As funders and implementers of climate adaptation interventions invest in and prioritize locally led adaptation, assessment of the implications of MEL for LLA can inform approaches and practices that build social capital and encourage equitable distribution of power.

The conclusions and recommendations of this paper are intended to provide practical suggestions for donor and intermediary institutions and MEL practitioners to better support locally led adaptation through MEL, and fundamentally rethink the role of MEL to support their climate resilience and social justice objectives. The advancements in MEL discussed in this paper represent an evolution of good practice in the interest of more effective and equitable adaptation outcomes.

The paper draws attention to the critical role of MEL in supporting locally led adaptation and offers good practices and lessons learned from relevant literature and practice. Section 2 presents key considerations for supporting locally led adaptation that are relevant throughout the MEL cycle, using a set of principles for LLA that were developed for the Global Commission on Adaptation as a conceptual framework. Section 3 walks through each phase of the MEL cycle and assesses specific approaches, methods, and tools that align with the principles of locally led adaptation. This assessment is based on the relevant bodies of research and practice on adaptation MEL, community-based adaptation, participatory MEL, and decolonization of MEL, as well as a set of case studies and consultations with stakeholders of MEL for locally led adaptation. Section 4 synthesizes the key findings from this assessment and presents conclusions and recommendations for MEL to better support and enable locally led adaptation.

Locally led adaptation recognizes that people closest to the effects of climate change, especially those facing structural marginalization, require the financing and decision-making power to ensure that adaptation investments reflect their priorities. It entails investing in solutions whereby local actors have the agency to make and influence these investment decisions and wield the innovation, flexibility, knowledge, and sense of urgency required to confront the crises of climate change and socioeconomic injustice (Conde et al. 2005).

Locally led adaptation is an emerging priority of some governments, bilateral and multilateral donors, and other institutions. Examples include programs such as the Least Developed Country (LDC) Initiative for Effective Adaptation and Resilience, County Climate Change Funds in Kenya, Enhancing Direct Access programs within funds like the Adaptation Fund and the Green Climate Fund, and localization movements in the humanitarian and development sectors (LIFE-AR 2019; Crick et al. 2019; GCF 2019; Adaptation Fund 2020a). Institutions from grassroots networks to international nongovernmental organizations (NGOs) and funders are calling for the systemic changes required to channel more finance for adaptation to the local level and increase the agency and decision-making authority of local-level actors. In support of these efforts, principles for LLA have been established, and research and guidance are emerging to create enabling environments for LLA.

As adaptation and climate-resilient development investments aim to support local leadership, the process of designing, monitoring, and learning from these efforts must also support local leadership. Locally led adaptation is an approach that challenges some of the underlying assumptions and structures prevalent in international development, emphasizing that to be effective, local actors most directly impacted by climate change must drive the adaptation investments that affect them. This paper defines MEL and LLA terminology as described in Box 1.

Several shortcomings of common MEL approaches remain that must be addressed to support LLA. MEL that prioritizes upward accountability can reinforce asymmetrical power relations between donors and local actors (Ramasobana et al. 2020). Such requirements of upward accountability often create structures that do not support the learning and adaptive management essential to locally led adaptation (Spearman and McGray 2011). Restricted interpretations of MEL lead to problematic practices, such as a tendency to prioritize products and outputs over the process, and to underestimate the relevance of local knowledge and experience. This risks a process that disempowers or devalues local actors and is not critical of who is affected by the MEL process and how (Pauw et al. 2020; Christiansen et al. 2018; Holzapfel 2016). If MEL systems are not designed to create value and ownership for local partners, they risk being extractive and disrespectful of local actors’ time and knowledge (McCreless 2015; CARE 2014). Institutional standards and requirements for MEL often prioritize upward accountability, creating a structure that does not support learning and adaptive management essential to locally led adaptation (Spearman and McGray 2011). These examples of MEL practices and approaches not only fail to support local actors but also undermine the goals of donors and intermediaries to support local priorities.

MEL is a social undertaking. It is applied to social challenges, and it can reinforce or confront societal values and social dynamics such as power and legitimacy. If not approached with the intention to support LLA, MEL practices may conflict with the objectives of locally led adaptation. Traditional MEL is not neutral in its approach to knowledge creation or dissemination. The evidence and knowledge generated in contexts of adaptation are valuable, but communities affected by adaptation interventions do not always experience this value. The theories of change and indicators guiding MEL do not often reflect local input, let alone local ownership. MEL tends to rely on external experts rather than supporting local capacity to implement MEL. These outputs and inputs of the MEL process also have socioeconomic, political, and environmental implications and are therefore political resources with financial consequences (Chilisa et al. 2016; Frehiwot 2019; Kawakami et al. 2008).

While many conventional MEL approaches and practices may not align with the principles of LLA, the generalized MEL cycle presents important opportunities to support LLA. MEL involves power, decision-making, trust, and communication among participants. These features serve as entry points to support locally led adaptation, including through collaborative, locally led decision-making, integration of social and gender equity considerations, building trust, social capital and local ownership, and evidence and learning about LLA. MEL that leverages these opportunities is more likely to support effective, equitable LLA, including by grounding the design of adaptation in local realities, generating more complete evidence, and mitigating climatic and programmatic risk through iterative learning and adaptive management (Faulkner et al. 2015).

Eight foundational principles outline the basic requirements for finance for adaptation that is accessible to and owned by appropriate local actors, and provide a framework for assessing how MEL practices support locally led adaptation. These principles were developed for the Global Commission on Adaptation by the International Institute for Environment and Development (IIED) in partnership with World Resources Institute (Soanes et al. 2021). They build on a decade of foundational work carried out by IIED, with Slum Dwellers International, Huairou Commission, the International Center for Climate Change and Development, and many others regarding financing for adaptation and resource access in communities vulnerable to climate change. The principles are grounded in the recommendations of the Global Commission on Adaptation’s Adapt Now report to increase the volume of devolved and decentralized funding available to local actors to identify, prioritize, design, implement, monitor, and learn from climate adaptation solutions (Global Commission on Adaptation 2019).

These principles serve a basis for understanding the potential of MEL to support locally led adaptation. Table 1 summarizes the eight principles and is followed by a discussion of overarching considerations for MEL to support these principles of LLA.

Putting these principles of LLA into practice through MEL entails several key considerations, drawn from relevant MEL research and practice. The remainder of this section discusses tenets for supporting LLA that are relevant throughout the stages of MEL: design and planning, monitoring, evaluation, and learning.

Recognizing and addressing how structural inequalities affect MEL reduces bias. Structural inequalities, such as those related to gender, race or ethnicity, and socioeconomic status, can manifest in different ways in MEL. Unequal value may be attributed to the different worldviews and perspectives, biasing outputs of the MEL process toward the worldviews and perspectives of participants with the most power (Segone 2012; Joyce 2020). For example, if a planning workshop does not provide a safe and inviting environment for participants with firsthand adaptation experience to speak up, then the outcomes of the workshop, such as a theory of change or indicator framework, will not reflect a complete understanding of challenges and possible solutions. Structural inequalities may be present between local actors and external actors (such as donors and MEL practitioners), among actors at the local level, between local government and civil society, or between women and men (Morchain et al. 2019). As such, it is important for LLA interventions to deliberately encourage equity.

Steps can be taken throughout the MEL cycle to mitigate the effect of structural inequalities on MEL, including conducting gender and social equity assessments, ensuring balanced and representative decision-making structures, and including indicators to understand the equity of decision-making processes. In applying Oxfam’s Vulnerability and Risk Assessment methodology in Malawi, Botswana, and Namibia, Morchain et al. (2019) acknowledged how their position as academics would bias the outcomes of the assessment. They broadened the framing of the assessment to include development priorities, in addition to climate priorities, and used translators and breakout groups to mitigate the effect of preexisting cultural norms and power dynamics in group discussions (Morchain et al. 2019).

The importance of local agency applies to decisions made through the MEL cycle. For an intervention to be genuinely locally led, local actors must have agency over their priorities and how adaptation takes place. Extended to MEL, local actors would have agency over the priorities of the MEL system and how MEL takes place in support of the intervention. Ensuring local agency in MEL starts with local ownership to help shape the overall purpose of the MEL system and data collection analysis methods and tools, and actively centering learning processes and products on the needs and demands of local actors (Silva Villanueva 2011; Faulkner et al. 2015).

Increasing access to information by tailoring knowledge products and tools to local audiences, and enhancing capacity to engage in MEL, supports local agency. Local universities and civil society organizations have a role to play as climate knowledge brokers, transforming data and information into knowledge for practical adaptation interventions, often working with local actors to coproduce knowledge on adaptation experience. Adaptation at Scale in Semi-arid Regions is a university network consortium that translates the experiences and input of communities on the front lines of climate change, giving them the opportunity to influence adaptation policy responses. Similarly, the University of Arizona’s Cooperative Extension system provides insights on context-specific local adaptation, researching adaptation processes and disseminating knowledge about local adaptation interventions to help both local government and local actors, particularly farmers, make informed decisions about adapting to changing climatic conditions (Brugger and Crimmins 2015). In an example of local government tailoring adaptation knowledge to its constituents, the Planning Institute of Jamaica shared lessons learned about resilient agriculture by tailoring knowledge products and communications to different local audiences to support decision-making, including local farmers and government utility managers (Adaptation Fund 2020b).

MEL is a process for navigating the complexity, uncertainty, and context-specificity of LLA. Given the unpredictable nature of climate change and the complexity of social, economic, and ecological factors that affect adaptation, it can be hard to know what the outcomes of LLA solutions will be. MEL can provide a process for innovation, experimentation, and adaptive learning to help manage this complexity and uncertainty. Developing a participatory theory of change helps generate a shared and iterative understanding of local context and the many different factors that may affect the outcomes of the intervention. In monitoring, it becomes important to develop indicator frameworks that integrate social, environmental, and economic factors (such as those based on socioecological systems), and reflect the connections and feedbacks linking human and natural systems (Olsson and Galaz 2012). At the learning stage there should be a focus on learning from unintended consequences and failures as well as successes by drawing on and combining several sources of knowledge and experience, including expert, scientific, and local or indigenous knowledge (Hulme 2015).

Creating value for local actors avoids extractive MEL. Even participatory MEL processes can be extractive of local actors’ time, knowledge, resources, and expertise if they do not explicitly create value for them (Wilmsen 2008). Mechanisms for downward accountability can be built into MEL processes through local actors’ active participation and enabling them to assess donor performance (Ebrahim 2003; Van Zyl and Claeyé 2019). Indicator frameworks can support mutual accountability by including indicators that reflect local priorities and definitions of resilience, as well as indicators of agency and social inclusion in the adaptation process (Estrella and Gaventa 1998; Fisher 2014) Cocreation of methods can help ensure that evaluations are useful to local actors, donors, and evaluators alike. Tailoring evaluations to locally determined priorities can have the added benefit of incentivizing participation and interest in the evaluation and the adaptation intervention going forward (Dunkley and Franklin 2017; Fitzpatrick 2012). Learning questions and processes are another opportunity to prioritize the learning goals of local actors over those of external actors (Faulkner et al. 2015).

Effective learning processes support effective locally led adaptation outcomes. Learning is essential to embracing and understanding the complexity of adaptation, and is the process linking data and evidence to the resilience outcomes and other changes that adaptation interventions seek to create (Silva Villanueva 2011; STAP 2017). An adaptation strategy in its own right, learning is especially relevant for locally led adaptation because of its role in building adaptive capacity at the local level (Baird et al. 2014). Local actors learn from both successful and failed adaptation interventions through self-determined experimentation with different coping strategies, or through purposeful social learning processes, to prepare adequate responses and coping strategies to climate risks and intervention outcomes (Tschakert and Dietrich 2010). A fit-for-purpose learning process will reflect different learning needs, goals, and ways of learning among stakeholder groups, and legitimize the diverse types of evidence and knowledge that support them (Alessa et al. 2016).

The process of learning, versus knowledge products such as reports or briefs, is therefore a key consideration for LLA and is most effectively embedded throughout the MEL cycle. Supporting adaptive capacity through learning does require resources and time from all local and external partners involved and should be adequately planned and budgeted for (Tschakert and Dietrich 2010).

Applying these key considerations often entails practical constraints and challenges. The benefits of MEL processes that bring in a wider range of participants and introduce new elements may require additional time, resources, and capacity. Oxfam International recommends allocating 10–13 percent of a total budget to MEL (Carmona et al. 2018). Local and external partners alike may feel the burden of additional time, resources, and capacity. A challenge for those responsible for designing the MEL system is to balance the priority of local agency with the risk of overburdening local partners. Surveys or baseline studies that take more time than necessary, for example, can exhaust partners and make them less willing to engage in the MEL process (Flatters 2017). Emphasizing local agency to determine roles and responsibilities in MEL and adhering to the “do no harm” principle are strategies to help mitigate this risk.

Drawing on examples and case studies, this section reviews approaches, methods, and tools specific to the four stages of the MEL cycle: design and planning, monitoring, evaluation, and learning. It includes discussion of lessons learned and benefits and challenges to implementing these practices in support of LLA.

3.1. Design and Planning

The methods and tools presented below support design and planning of a MEL system and have implications for the planning and implementation of adaptation interventions and the extent to which they are locally led.

Hiring local experts in senior MEL roles is an opportunity to support LLA. MEL systems that are designed and led by external experts can discount local knowledge and expertise. Such a practice perpetuates power imbalance and is less likely to result in comprehensive MEL systems (Estrella and Gaventa 1998; Van Zyl and Claeyé 2019). Locally available skills and expertise can be appraised in the planning phase of the MEL cycle. Additional resources may be required to engage local or national knowledge brokers and, where MEL experts are not available locally, to strengthen the capacity of local actors to lead MEL processes.

Theories of change can be used as a tool to ensure that adaptation interventions reflect local priorities and realities. Developing a theory of change is a process rather than a static product and can support LLA design by drawing attention to the assumptions and evolving understanding linking intervention activities and outcomes (Valters 2015). Local input to the theory of change process is most consequential if integrated from intervention inception. This may require a structural change to adaptation programming, however, as theories of change are often defined and designed before local partners are engaged. Routinely revisiting a theory of change throughout an intervention to test progress against locally determined outcomes and to integrate local knowledge and learning provides a deeper understanding of the link between climate change and the results of the intervention (NEF 2017).

Tools to build a detailed understanding of local stakeholder and contextual dynamics include political economy analysis, stakeholder mapping, and community-based climate vulnerability assessment. These three common tools can support assessment of relevant stakeholders, the nature of their vulnerabilities, and sociopolitical, economic, and environmental factors at play in a particular context. Political economy analysis (PEA) is concerned with how power and resources are distributed and contested. A useful PEA informs where locally driven opportunities for change may emerge and where constraints may need to be addressed, including why institutions matter (Teskey 2020). Stakeholder analysis and mapping is a way to identify an intervention’s key stakeholders, assess their interests, needs, and incentives and clarify how these may affect and inform an intervention’s design and delivery (Rai et al. 2015). Early engagement with diverse stakeholders creates space for them to influence the intervention design and delivery process (Leventon et al. 2016). Community-based climate vulnerability assessment integrates local knowledge and engages communities in the formulation of LLA plans. It provides a clear starting point for the definition of appropriate intervention and wider climatic indicators (ADB 2018).

Creating a climatic baseline and climatic monitoring system is key to understanding the outcomes of LLA. Once a detailed understanding of local stakeholder dynamics has been established, a corresponding and locally specific understanding of the climatic conditions and dynamics affecting those local stakeholders also needs to be developed where feasible. This is often described as a “declining climatic baseline.” Defining and measuring LLA results in the context of these climate dynamics helps actors plan, understand, and learn from an LLA intervention. This may require external expertise and can be constructed in collaboration with local stakeholders. Establishing a climatic baseline and then monitoring the climatic context in which an LLA intervention is delivered, in parallel to monitoring the results of the intervention itself, informs learning and adjustments required to continue the intervention in the face of climate changes. This is not a simple undertaking due to the temporal and spatial unpredictability of climatic shocks and stresses. For example, flood events are hard to predict and have locally specific impacts. Historical weather and climate records can provide useful climatic baseline data. Geographic information systems (GISs) and other technologies are increasingly being used to overlay and illustrate climatic data baseline and subsequent climatic changes.

3.2. Monitoring

Monitoring can support LLA by providing information for agile course correction, mutual accountability, and learning about the process, context, and expected outcomes (Warner 2017). Some monitoring methods and technologies described below can reduce the burden on participants and ensure that information collected is available and useful to populations who have limited access to climate information. While these examples of technological solutions can support more equitable access to information and influence in the monitoring process, it is important to recognize that not all populations have equal access to technology, and that technology alone cannot address challenges such as local agency in adaptation that are rooted in an imbalance of power.

The concept of adaptive capacity is a useful starting point for defining appropriate indicators and adaptation metrics. Adaptation is often assessed through concepts such as risk, vulnerability, and resilience, or through proxies that are expected to lead to adaptation, such as adaptive capacity (Leiter and Pringle 2018). The UK-funded Building Resilience and Adaptation to Climate Extremes and Disasters (BRACED) program created a set of locally defined and context-specific subindicators based on adaptive capacity, including indicators related to new knowledge and skills, new attitudes and behaviors, and shifting institutional relationships, which ultimately supported new, locally driven policies and practices (BRACED 2015).

Monitoring frameworks offer potential to enable adaptive management and prioritize learning. Adaptive management enables experimentation and innovation, and sometimes expects that interventions fail. Monitoring systems that support adaptive management serve multiple functions: they generate evidence on an intervention’s management and quality of delivery, allow for comparison between different types of interventions, focus on near-real-time learning, monitor unintended consequences and failures as well as successes, and ensure shared understanding among MEL participants of how evidence will be applied to support positive change. The Global Learning for Adaptive Management initiative is supporting the wider uptake of adaptive management principles and practices, including in climate change adaptation. This includes a focus on learning about effective MEL for adaptive management. The key is to design LLA monitoring frameworks not just for data collection but also for intentional and collective reflection on the data to support ongoing and real-time learning, course correction, and decision-making (IDS 2020).

Creating open data governance structures can support locally led decision-making. Climate science, earth observation data, and lessons learned about LLA are more likely to be used by local decision-makers if they are accessible through appropriate and demand-driven platforms (Acclimatise 2018). Solutions to data accessibility and application challenges involve open climate data, collaboration, and investments in education and long-term alliances for the codesign and coproduction of knowledge. The principles of open access, privacy, and appropriately packaged and accessible data apply across scales from national and international GIS initiatives down to climate data generated at the local level.

Sharing information about LLA also supports local ownership, decision-making, and social capital. The Mercy Corps Pastoralist Areas Resilience Improvement through Market Expansion (PRIME) project in Ethiopia uses a technology-driven approach to inclusive decision-making and bottom-up data collection. Its online platform, Kiprojects, allows local NGO and program staff to suggest and submit new activities for rapid approval. Staff can also make real-time learning about project progress easily accessible for other team members across Ethiopia (Desai et al. 2018).

Free, prior, and informed consent (FPIC) is another data governance structure that specifically supports the protection of local and indigenous knowledge shared in the context of MEL. FPIC is a principle that informs the right to consultation and is enshrined in the UN Declaration on the Rights of Indigenous Peoples and the International Labour Organization Indigenous and Tribal Peoples Convention 169 (FAO 2016).

Locally led data collection, data analysis, and learning can be enabled and enhanced through appropriate information and communications technologies (ICT). Although technology cannot solve all problems related to accessibility and inclusion, data collection, analysis, and visualization science and technology innovations can support MEL of LLA by making it more feasible to engage populations who are traditionally hard-to-reach or not given opportunities to express their views, as well as to directly engage local actors in large numbers. Remote sensing, use of social media for data analysis and engagement, electronic financial transactions, and mobile applications and devices are some examples that can support MEL. Locally driven demands for accountability and social activism by civil society encourage uptake of locally accessible ICT tools and platforms, such as through mobile phones. A prominent example is the Slum/Shack Dwellers International initiative Know Your City, described in Box 2.

3.3. Evaluation

There is increasing recognition that conventional evaluations (often characterized as project-focused, ex-post, and designed and delivered by external international evaluation teams) are not fit for purpose or supportive of LLA (IPCC 2014). LLA is inherently subjective and rooted in local context. There are clear benefits for both local participants and funders in tailoring evaluations to ensure that resources are aligned with local needs and desired outcomes. This often requires flexible evaluations and cocreation of evaluation processes. Imposing externally constructed outcomes for adaptation interventions risks misaligning resources with what is needed and valued at a local level, and evaluation methods that will not effectively measure LLA outcomes.

The approaches outlined below draw primarily from influences of participatory action research, developmental evaluation, and realist evaluation. They describe methods that are tailored-for-purpose, context-specific, and use evidence generated from evaluations for the benefit of local stakeholders (Pawson and Tilley 1997; Patton 2008).

Several conventional evaluation methods are closely aligned with the principles of local agency and prioritize learning.

• Tracking Adaptation and Measuring Development (TAMD) is a standard methodology employed where participatory evaluation processes are the priority, including participatory data collection and indicator development that ensures local agency in the evaluation process (Brooks et al. 2013).
• Most Significant Change (MSC) is a flexible participatory monitoring or evaluation technique in which evaluation practitioners and local stakeholders collect narratives of change following an intervention to understand and highlight the most significant changes resulting from an intervention. MSC narratives and the value attributed to them are validated by other participant stakeholders in the community (Davies and Dart 2005).
• Appreciative inquiry is a change management process used as an evaluation tool focused on the strengths of an intervention, learning what is working well and investing in the factors that sustain successful outcomes. The inquiry itself is a collaborative effort, capturing the positive features of an intervention and encouraging continuous positive change (Acosta and Douthwaite 2005).
• Climate Resilience Evaluation for Adaptation through Empowerment (CREATE) is an evaluation method developed by the International Union for Conservation of Nature for policymakers, field practitioners, or local actors. It is based on community-based vulnerability assessments but has been adapted as a tool for community self-evaluation. CREATE is flexible, designed for learning and experimentation, and aims to assess vulnerabilities and adaptive capacities, with the added value of supporting local actors in identifying pilot activities and strategic interventions as well as short-, medium-, and long-term adaptation activities (Shott and Mather 2012).

The climate-smart villages approach described in Box 3 provides a case of participatory, locally driven evaluation tailored to hyperlocal (village) contexts and scaled up for global learning through an accessible database.

Subjective evaluations and self-assessments integrate local stakeholders’ values, perspectives, and perceptions of risk. Béné et al. (2019) found that subjective understanding of resilience and a sense of self-efficacy have a strong positive impact on the households’ ability to effectively recover from shocks. These results suggest that local actors who have a clearer understanding of their resilience are better able to cope with effects of climate change (Béné et al. 2019).

Use of subjective measures of climate resilience is an emerging approach that offers a simple, efficient, and locally driven alternative to predefined resilience indicators or indices. Subjective measures are used in contexts where capturing real experiences from the bottom up is a priority, when local stakeholder experiences are assessed and compared over time, and where additional methods of measuring resilience complement subjective definitions (Jones and Tanner 2017). The Subjectively Evaluated Resilience Score was used for household resilience surveys in Uganda to understand the resilience capacity of local actors and establish a baseline for an ongoing impact evaluation (Jones 2019). Similarly, Reckien et al. (2013) developed vulnerability maps based on interviews with five socioeconomic groups in South New Delhi. Local actors’ subjective experiences of physical climate impacts and abstract climate concepts were used to determine potential causes of vulnerability and future adaptation options. The vulnerability maps indicated that lower-income groups were more affected by climate-related impacts and would benefit most from infrastructure investments. Subjective measures of resilience and vulnerability create space for a context-specific and localized assessment of an adaptation intervention according to the needs and logic of the community whose resilience is in question.

Culturally responsive evaluations can amplify local voice and support equitable outcomes. Hood et al. (2015) provide comprehensive guidelines for conducting culturally responsive evaluation. The guidelines recognize the centrality of culturally defined values and beliefs to any evaluation. In culturally responsive evaluation, outcomes are valid if evidence generated is supported by the cultural and local context, as opposed to validity defined by conventional definitions of research rigor. Culturally responsive evaluation specifically aims to address bringing equity to evaluations through its focus on historically marginalized groups (Hood et al. 2015).

Johnston-Goodstar (2012) suggests that evaluation advisory groups (EAGs) are ideally suited to bringing evaluators and indigenous communities together to discuss the evaluation process. Using language and tools acceptable to indigenous or context-specific worldviews, and centering indigenous voices, EAGs present an opportunity to examine differences in values, norms, and assumptions, and reflect on power relations that might influence the outcome of an evaluation or the evaluation process. For example, the Swinomish Indian Tribal Community in the U.S. state of Washington informed the design of evaluation metrics for a community planning intervention following a process similar to EAGs. After consultations with tribal elders had established a mutual understanding of the evaluation process, “climate change health” metrics were integrated into a conventional evaluation and planning process. Cocreating an evaluative process was also a valuable decision-support and learning tool for the Swinomish community and in other evaluations based on indigenous knowledge (Donatuto et al. 2020; Kerr 2012).

Although not uniquely tailored to locally led action and participation, the UN Children’s Fund guide to evaluation for equitable development draws on decolonial and feminist approaches to evaluation, indigenous knowledge, and human rights practice and could be adapted to the LLA context (Segone 2012). Another resource is the Equitable Evaluation Project, a peer-to-peer platform used to encourage evaluators to reflect on the cultural considerations and racial bias in evaluation processes, tools, methods, and offer approaches that can create positive social change and equitable outcomes (Equitable Evaluation 2017).

3.4. Learning

Common methods of integrating learning into adaptation interventions include regular learning reviews, use of online knowledge-sharing platforms, facilitated learning events or dialogues, and critical reflection through formal evaluation (Harvey et al. 2017). Below we assess a selection of less common approaches, methods, and tools for learning that are especially relevant to the LLA context.

Distinct processes for accountability and learning can address tension between these objectives. Reporting on target outputs for upward accountability purposes may discourage a culture of learning from failure and flexibility to adapt, and can consume intervention resources like team member time (Spearman and McGray 2011). If integrated from the start of an intervention, a two-track model with dedicated workstreams and resources for separate accountability and learning functions can encourage learning by removing the disincentive to report failure (Fisher and Anderson 2018; Smith 2020). The BRACED program has a dedicated “Fund Manager” who accounts for the effectiveness of the development assistance funding that supports the program, as well as a dedicated “Knowledge Manager” who is responsible for evidence-generation and learning about resilience (BRACED and Bond 2019). Donors can also allocate specific resources for learning or innovation funds to ensure adequate resources and commitment to learning objectives (Harvey et al. 2017). For example, the Collaborative Adaptation Research Initiative in Africa and Asia (CARIAA) had a designated “Opportunities and Synergies Fund,” which supports learning and research-into-use for adaptation interventions.

Social learning supports collaborative decision-making for adaptation contexts (Cundill and Rodela 2012). Social learning can support LLA as a process that entails active engagement and agency, recognizing that information, consultation, and participation are insufficient for effective adaptation (Collins and Ison 2009; Cundill et al. 2014).

Social learning is still somewhat ambiguously defined, posing challenges for implementation (Medema et al. 2014). CARIAA addressed this challenge by focusing on creating an enabling environment for social learning, rather than developing a prescriptive learning approach. The enabling conditions the initiative emphasized align with others identified in this paper, including flexibility in resource allocation and scope and separating MEL’s learning and accountability functions (Ensor and Harvey 2015). Initial findings from community adaptation interventions indicate that social learning can bring economic benefits and community cohesion if they are based on sustained engagement with relevant local stakeholders, capacity development for learning, reflection and cocreation of new knowledge, and challenging institutional barriers (Van Epp and Garside 2019).

Prioritizing self-directed learning and creating a learning culture can promote sustained local ownership of LLA (Smith 2020). If not intentionally locally driven, learning can be top-down and not necessarily meet the learning needs of local actors (Valters 2015). One of many examples of self-directed learning is the practice by farmers in Western Kenya of integrated soil fertility management. Multiple years of group learning led to scaling-out of farming practices and expansion of scope to include additional income-generating activities, suggesting that the advantages of locally owned learning may be worth trade-offs such as additional investment of time, resources, and capacity building (Ramisch et al. 2006).

In South Sulawesi Province, Indonesia, the Climate Adaptation through Sustainable Urban Development research project tested various qualitative and quantitative participatory evaluation methods to understand how well its water resilience project was creating a learning culture. The “Factors of Success” method, a collaborative group exercise that identifies and maps ideas of project success over time, and the “Obstacles and Enablers” method, in which participants reflect on potential “obstacles” to project success as well as potential “enablers” to overcoming these obstacles, were two qualitative methods that supported critical reflection and anticipatory learning. They could be integrated into a MEL system to promote a learning culture and participatory learning about LLA (Larson et al. 2016).

The case study in Box 4 uses an example from the Devolved Climate Finance mechanism piloted in Mali, Tanzania, Senegal, and Kenya to illustrate how bottom-up learning can support locally led adaptation investments and decision-making.

Learning through games can facilitate the communication of complex systems and support learning and dialogue. This is a method employed in support of climate resilience and disaster risk reduction by the International Federation of the Red Cross and Red Crescent Societies Climate Centre. The Climate Centre’s games use storytelling, active learning, emotional engagement, and problem-solving for learning about and managing climate risk and natural disasters (Solinska-Nowak et al. 2018). This unique method requires unique resources, including game facilitators, designers, and willing participants. Although there is insufficient evidence available to link learning through games to adaptation outcomes in the long term, anecdotal evidence suggests strong learning outcomes, especially when participants are involved in the game’s design (Bachofen et al. 2012).

Peer-to-peer learning supports local agency and social learning for LLA. One example of peer-to-peer learning for adaptation was an exchange the Adaptation Fund hosted in 2019 focused on resilient water and agriculture. Representatives of various national implementing entities of the Adaptation Fund learned directly from one of their peers in Chile about agricultural resilience to drought, soil erosion, wildfire, and unpredictable precipitation. Following the exchange, they applied lessons learned to enhance agricultural resilience in their respective contexts, three within two months of the exchange (Adaptation Fund 2019).

Knowledge exchange platforms are a vehicle for long-term, cross-scale learning and the building of an evidence base around effective and equitable LLA. Documentation, including through collaboration with knowledge partners such as local universities and dedicated platforms for sharing learning, is a simple but important method to support learning for LLA (Harvey and Fisher 2013). Global platforms appropriate for vertical knowledge exchange about LLA include the annual Community-Based Adaptation conference, the annual UN Framework Convention on Climate Change (UNFCCC) Conference of the Parties, and the annual Gobeshona conference in Bangladesh.

Alternative methods of disseminating best practice for local adaptation horizontally among local actors can include the use of digital media. For example, the North East Network—a knowledge broker for adaptation interventions in the Indian states of Assam, Nagaland, and Meghalaya—uses participatory video to disseminate local knowledge on sustainable natural resource management practices (CDKN 2018).

There is increasing recognition that adaptation happens at a local level, and that local actors therefore should have agency over the adaptation interventions affecting them. National governments, multilateral development banks, bilateral donors, and international NGOs are investing in locally led adaptation to achieve resilience objectives that prioritize the communities at the front lines of climate impacts.

We recommend 10 ways in which institutions and individual practitioners, especially funders and intermediary organizations, can begin to align MEL with the principles of locally led adaptation. The recommendations outline opportunities to promote local agency in decision-making about the MEL process and to ensure that local actors have influence in critical decisions throughout the MEL cycle on the purpose of the MEL system, the theory of change behind the intervention, learning goals and processes, metrics and indicators to assess progress, how data is collected and used, what external support is needed, and evaluation approaches and objectives. The following recommendations are listed in the general order of the MEL cycle, although many apply throughout MEL.

1. All actors in the MEL process should understand and respond to structural inequalities, including how power dynamics affect the MEL process and whose objectives it serves, and whether different worldviews and definitions of resilience are equally valued. This is critical to ensure that MEL reflects local realities and priorities and that results are not inaccurate or biased. Basing theories of change and indicators on subjective definitions of resilience and local and experiential knowledge provides a way for MEL to reflect local priorities and perspectives. By prioritizing goals of local actors and legitimizing local knowledge and experience, MEL systems should aim to redress power imbalances and be part of a funder’s strategy to ensure that investments reach communities and reduce social and gender inequalities.
2. Funders, intermediaries, and practitioners should embrace the design of MEL systems that give equal or greater priority to downward accountability and learning compared with upward accountability. Distinct processes for accountability and learning can address tension between these objectives. Funders, intermediary organizations, and MEL practitioners should allow local partners to determine learning goals and collaborate to decide which approaches will best support these goals. Emerging approaches and tools that can support learning for LLA include social learning, learning through games, peer-to-peer learning, and virtual learning.
3. Funders and intermediaries should ensure that MEL creates value for local actors. MEL processes and the data and learning they generate should prioritize the knowledge and learning needs of local actors equally with funders’ MEL goals, which frequently relate to measuring progress and performance.
4. Funders and intermediaries should take a local demand–driven approach to building capacity for self-directed MEL. For MEL to support locally led adaptation, local actors themselves should determine what capacity, external expertise, and access to information they need to lead MEL that supports their goals in the long term. Engage knowledge brokers in this process as needed.
5. MEL practitioners should adopt appropriate methods to navigate and better understand complexity and uncertainty with regard to climate dynamics and locally led adaptation contexts and settings. This entails creating shared and comprehensive understanding both of local stakeholder and contextual dynamics, on the one hand, and locally specific understanding of the climatic conditions affecting local stakeholders, on the other. Establishing a climatic baseline and monitoring system and employing social assessment tools support understanding of relevant stakeholder and contextual dynamics as well as the outcomes of an LLA intervention.
6. MEL practitioners should create locally appropriate and context-specific indicator frameworks and adaptation metrics. Adaptive capacity is particularly useful as an adaptation metric and as a starting point in defining a set of context-specific LLA indicators. Indicator frameworks should also look to better integrate social, economic, and environmental dimensions of LLA, recognizing the spatial and temporal interconnectedness of these systems in adaptation. In order to support LLA, metrics and indicators must reflect what local actors view as important to measure and as reflecting their definitions of vulnerability, adaptive capacity, and strengthened resilience.
7. MEL practitioners should leverage MEL technologies and process innovations as appropriate to increase local ownership, voice, participation, and representation. Data collection and data analysis technologies across mobile applications, remote monitoring systems, climate and digital advisory services, and other technologies can be used both to increase access to climate data and information to inform decision-making, and to facilitate locally driven data collection and governance platforms.
8. With support from funders, MEL practitioners should develop systems to support adaptive management, experimentation, and learning from failure. This will entail promoting a culture that accepts learning from failure, building in time and resources to support iterative learning from the outset of an intervention, and designing monitoring frameworks that enable adaptive management for course correction. Encouraging near-real-time learning and openness about what works, what doesn’t, and why can foster a nimble intervention that is more likely to achieve its adaptation goals and help mitigate risks associated with financing and programming requirements for locally led adaptation.
9. Funders and intermediaries should engage knowledge brokers as appropriate to enable ownership and contributions by local partners. Differences in cultural norms and terminology should not interfere with a funder’s ability to engage local actors, or with local actors’ ability to engage in the MEL process. Knowledge brokers may have a role to play in facilitating local agency throughout the MEL cycle—for example, in the design and theory of change development, defining indicators, analyzing monitoring data, and sharing evaluation findings and lessons learned.
10. All actors involved in MEL should work together to ensure that learning is applied, documented, and shared. To promote more effective, equitable LLA interventions and build up an evidence base around LLA in the long term, learning should be shared and applied both horizontally at the local level and vertically through global knowledge exchange platforms, such as the annual UNFCCC Conference of the Parties, the annual conference on Community-Based Adaptation, the UN General Assembly, and the annual Gobeshona conference. Evidence and learning generated through the MEL process should, however, first meet the knowledge needs and gaps of local actors as a primary audience group.

The changes needed to enable widespread locally led adaptation will take time, and the same applies to shifting MEL for LLA. MEL practice to support LLA also often entails significant changes or trade-offs compared with current adaptation MEL practice. However, there are meaningful, concrete steps we can take to have more supportive MEL while managing constraints.

The authors of this paper believe that both the systemic shift and the practical steps can be taken in parallel—combining some relatively simple and immediate practical changes to existing MEL systems with a longer-term journey to systemically shift how MEL supports LLA interventions. Many of the practical steps suggested can be made in isolation or retrofitted to existing MEL systems. The systemic shift in approach requires that those funding, designing, and delivering MEL systems reconsider the value and use of MEL, placing the core emphasis on local ownership, downward accountability, and locally driven learning from the outset.

In keeping with the principle of coordinated action and investment for locally led adaptation, horizontal and vertical learning and knowledge-sharing will support this journey. Leveraging global platforms to share learning and exchange knowledge about LLA interventions can support more effective LLA in the long term.

MEL that supports locally led adaptation will also support improved LLA outcomes, enabling more context-sensitive, agile, equitable, and sustainable efforts to build climate resilience at the local level.

Acclimatise. 2018. “Applying Earth Observation Data to Support Robust Investment Decisions in the Face of a Changing Climate.” Acclimatise Briefing Note, 2018 Eye on Earth (EoE) Symposium. http://www.acclimatise.uk.com/wp-content/uploads/2019/02/EoE-briefing-note_FINAL.pdf.

Acosta, A.S., and B. Douthwaite. 2005. “Appreciative Inquiry: An Approach for Learning and Change Based on Our Own Best Practices.” ILAC Brief 6 (July). https://cgspace.cgiar.org/handle/10568/70175.

Adaptation Fund. 2019. “Adaptation Fund Chile Country Exchange: NIEs Apply Lessons Learned.”

Adaptation Fund. 2020a. “Climate Adaptation Finance: Direct Access.” June. https://www.adaptation-fund.org/wp-content/uploads/2019/11/Direct-Access-June-2020.pdf.

Adaptation Fund. 2020b. “Jamaica: Knowledge Management with the Audience in Mind.” https://www.adaptation-fund.org/jamaica-knowledge-management-with-the-audience-in-mind/.

ADB (Asian Development Bank). 2018. “Community-Based Climate Vulnerability Assessment and Adaptation Planning: A Cook Islands Pilot Project.” Manilla: ADB. https://www.preventionweb.net/files/27076_climatechangeassessmentcoo.pdf.

Aggarwal, P.K., A. Jarvis, B.M. Campbell, R.B. Zougmoré, A. Khatri-Chhetri, S. Vermeulen, A.M. Loboguerrero Rodriguez, et al. 2018. “The Climate-Smart Village Approach: Framework of an Integrative Strategy for Scaling Up Adaptation Options in Agriculture.” Ecology and Society 23 (1).

Alessa, L., A. Kliskey, J. Gamble, M. Fidel, G. Beaujean, and J. Gosz. 2016. “The Role of Indigenous Science and Local Knowledge in Integrated Observing Systems: Moving toward Adaptive Capacity Indices and Early Warning Systems.” Sustainability Science 11 (1): 91–102.

Antonio, D., J. Makau, and S. Mabala. 2013. “Innovative Pro-poor Land Tools under Implementation: Piloting the STDM In Uganda.” GIM International: The Worldwide Magazine for Geomatics 27 (4).

Bachofen, C., P. Suarez, M. Steenbergen, and N. Grist. 2012. “Can Games Help People Manage the Climate Risks They Face? The Participatory Design of Educational Games.” Red Cross/Red Crescent Climate Centre Working Paper Series 3.

Baird, J., R. Plummer, C. Haug, and D. Huitema. 2014. “Learning Effects of Interactive Decision-Making Processes for Climate Change Adaptation.” Global Environmental Change 27 (July): 51–63. doi:10.1016/j.gloenvcha.2014.04.019.

Béné, C., T. Frankenberger, T. Griffin, M. Langworthy, M. Mueller, and S. Martin. 2019. “‘Perception Matters’: New Insights into the Subjective Dimension of Resilience in the Context of Humanitarian and Food Security Crises.” Progress in Development Studies 19 (3): 186–210.

Bolnick, J., S. Dobson, S. Patel, A. Kallergis., and N. MacPherson. 2018. “Know Your City: Slum Dwellers Count.” Cape Town: Slum Dwellers International. https://knowyourcity.info/wp-content/uploads/2018/02/SDI_StateofSlums_LOW_FINAL.pdf.

BRACED (Building Resilience and Adaptation to Climate Extremes and Disasters). 2015. “BRACED Programme Monitoring & Evaluation (M&E) Guidance Notes: Note 6, Project Baselines.” https://www.itad.com/wp-content/uploads/2020/02/BRACED-M-E-Guidance-Notes-v1-1-December-2015-2-1.pdf.

BRACED and Bond. 2019. “Learning for Climate Resilience Programming: BRACED & Bond Resilience Learning Group Workshop Lessons.” March. https://www.bond.org.uk/sites/default/files/learning_for_climate_resilience_programming_rep4.pdf.

Brooks, N., S. Anderson, I. Burton, S. Fisher, N. Rai, and I. Tellam. 2013. “An Operational Framework for Tracking Adaptation and Measuring Development (TAMD).” London: International Institute for Environment and Development. https://www.researchgate.net/publication/262684281.

Brugger, J., and M. Crimmins. 2015. “Designing Institutions to Support Local-Level Climate Change Adaptation: Insights from a Case Study of the U.S. Cooperative Extension System.” Weather, Climate, and Society 7 (1): 18–38. doi:10.1175/WCAS-D-13-00036.1.

CARE. 2014. Participatory Monitoring, Evaluation, Reflection and Learning for Community-Based Adaptation: A Manual for Local Practitioners. Geneva: CARE International. https://careclimatechange.org/tool-kits/pmerl/.

Carmona, E.F., S. Thomas, H. Jeans, G. Castillo, A. Pretari, C. Geraets, et al. 2018. Monitoring, Evaluation and Learning for Resilience: A Companion Guide. Oxford, UK: Resilience Knowledge Hub and Oxfam. https://oxfamilibrary.openrepository.com/bitstream/10546/620498/1/gd-monitoring-evaluation-learning-resilience-190618-en.pdf.

CDKN (Climate and Development Knowledge Network). 2018. “Local Knowledge and the Role of Knowledge Brokers in India.” https://cdkn.org/2018/11/feature-local-knowledge-and-the-role-of-knowledge-brokers-in-india/?loclang=en_gb.

Chilisa, B., T.E. Major, M. Gaotlhobogwe, and H. Mokgolodi. 2016. “Decolonizing and Indigenizing Evaluation Practice in Africa: Toward African Relational Evaluation Approaches.” Canadian Journal of Program Evaluation 30 (3).

Christiansen, L., G. Martinez, and P. Naswa, eds. 2018. Adaptation Metrics: Perspectives on Measuring, Aggregating and Comparing Adaptation Results. Copenhagen: UN Environment Programme–DTU Partnership.

Cole, N.L. 2020. “How Sociologists Define Human Agency.” ThoughtCo, August 28. thoughtco.com/agency-definition-3026036.

Collins, K., and R. Ison. 2009. “Jumping Off Arnstein’s Ladder: Social Learning as a New Policy Paradigm for Climate Change Adaptation.” Environmental Policy and Governance 19 (6): 358–73.

Conde, C., K. Lonsdale, A. Nyong, and I. Aguilar. 2005. Engaging Stakeholders in the Adaptation Process—Adaptation Policy Frameworks for Climate Change: Developing Strategies, Policies and Measures. Cambridge: Cambridge University Press.

Crick, F., C. Hesse, V. Orindi, M. Bonaya, and J. Kiiru. 2019. “Delivering Climate Finance at the Local Level to Support Adaptation: Experiences of County Climate Change Funds in Kenya.” Working paper. Nairobi: Ada Consortium. https://pubs.iied.org/G04415/.

Cundill, G., and R. Rodela. 2012. “A Review of Assertions about the Processes and Outcomes of Social Learning in Natural Resource Management.” Journal of Environmental Management 113 (December): 7–14. doi:10.1016/j.jenvman.2012.08.021.

Cundill, G., S. Shackleton, L. Sisitka, M. Ntshudu, H. Lotz-Sisitka, I. Kulundu, and N. Hamer. 2014. Social Learning for Adaptation: A Descriptive Handbook for Practitioners and Action Researchers. International Development Research Center, Rhodes University, and RULIV (Promoting Rural and Urban Livelihoods). https://www.weadapt.org/sites/weadapt.org/files/legacy-new/knowledge-base/files/52e626a294068handbook-final-23-jan-2014.pdf.

Davies, R., and J. Dart. 2005. The “Most Significant Change” (MSC) Technique: A Guide to Its Use. https://www.betterevaluation.org/en/resources/guides/most_significant_change.

DCF Alliance. 2019. “The Devolved Climate Finance Mechanisms: Principles, Implementation and Lessons from Four Semi-arid Countries.” Working paper. https://pubs.iied.org/G04424/.

Desai, H., G. Maneo, E. Pellfolk, and A. Schlingheider. 2018. Managing to Adapt: Analysing Adaptive Management for Planning, Monitoring, Evaluation, and Learning. Oxfam Research Reports. https://www.alnap.org/system/files/content/resource/files/main/rr-managing-to-adapt-pmel-220318-en.pdf.

Donatuto, J., L. Campbell, and W. Trousdale. 2020. “The ‘Value’ of Values-Driven Data in Identifying Indigenous Health and Climate Change Priorities.” Climatic Change 158 (2): 161–80.

Dunkley, R.A., and A. Franklin. 2017. “Failing Better: The Stochastic Art of Evaluating Community-Led Environmental Action Programs.” Evaluation and Program Planning 60 (February): 112–22. doi:10.1016/j.evalprogplan.2016.11.005.

Ebrahim, A. 2003. “Accountability in Practice: Mechanisms for NGOs.” World Development 31 (5): 813–29. https://www.alnap.org/system/files/content/resource/files/main/575-cached.pdf.

Ensor, J., and B. Harvey. 2015. “Social Learning and Climate Change Adaptation: Evidence for International Development Practice.” WIRES Climate Change 6 (5): 509–22. doi:10.1002/wcc.348.

Emerson, R.O. 2020. “Power Dynamics in International Development Evaluations: A Case Study of the Girls Education Challenge Programme.” African Evaluation Journal 8 (1): 1–11.

Equitable Evaluation. 2017. “Equitable Evaluation Project Framing Paper.” July. https://coloradohealth.org/sites/default/files/documents/2019-10/Public%20for%20Web%20Equitable%20Evaluation%20Framing%20Paper%20July%202017%20.pdf.

Estrella, M., and J. Gaventa. 1998. “Who Counts Reality? Participatory Monitoring and Evaluation: A Literature Review.” Institute of Development Studies Working Paper 70. https://www.ids.ac.uk/publications/who-counts-reality-participatory-monitoring-and-evaluation-a-literature-review/.

Eyben, R., I. Guijt, C. Roche, and C. Shutt. 2015. The Politics of Evidence and Results in International Development: Playing the Game to Change the Rules? Rugby, UK: Practical Action.

FAO (Food and Agriculture Organization of the United Nations). 2016. Free Prior and Informed Consent: An Indigenous Peoples’ Right and a Good Practice for Local Communities—Manual for Practitioners. Rome: FAO. http://www.fao.org/3/a-i6190e.pdf.

Faulkner, L., J. Ayers, and S. Huq. 2015. “Meaningful Measurement for Community-Based Adaptation: Meaningful Measurement for Community-Based Adaptation.” New Directions for Evaluation, no. 147 (September): 89–104. doi:10.1002/ev.20133.

Fisher, S. 2014. “The Emerging Geographies of Climate Justice.” Geographical Journal 181 (1): 73–82. https://rgs-ibg.onlinelibrary.wiley.com/doi/10.1111/geoj.12078.

Fisher, S., and S. Anderson. 2018. “Developing Meaningful Local Metrics for Climate Adaptation: Learning from Applying the TAMD Framework at Local Scales.” In Adaptation Metrics: Perspectives on Measuring, Aggregating and Comparing Adaptation Results, edited by L. Christiansen, L., G. Martinez, and P. Naswa. Copenhagen: UN Environment Programme–DTU Partnership.

Fitzpatrick, J.L. 2012. “An Introduction to Context and Its Role in Evaluation Practice.” New Directions for Evaluation 135: 7–24.

Flatters, G. 2017. “Focus on the Right Users to Avoid an M&E Apocalypse.” Vimeo. London: Open Data Institute. https://vimeo.com/209906761.

Frehiwot, M. 2019. “Made in Africa Evaluation: Decolonizing Evaluation in Africa.” https://www.researchgate.net/publication/337768900.

Global Commission on Adaptation. 2019. “Adapt Now: A Global Call for Leadership on Climate Resilience.” GCA and World Resources Institute. https://cdn.gca.org/assets/2019-09/GlobalCommission_Report_FINAL.pdf.

GCF (Green Climate Fund). 2019. “GCF in Brief: Enhancing Direct Access.” https://www.greenclimate.fund/document/gcf-brief-enhancing-direct-access.

Harvey, B., and C. Fisher. 2013. “Mobilising Knowledge for Climate Change Adaptation in Africa: Reflecting on the Adaptive Management of Knowledge Networks.” Knowledge Management for Development Journal 9 (1): 37–56. http://journal.km4dev.org/.

Harvey, B., T. Pasanen, A. Pollard, and J. Raybould. 2017. “Fostering Learning in Large Programmes and Portfolios: Emerging Lessons from Climate Change and Sustainable Development.” Sustainability 9 (2): 315. doi:10.3390/su9020315.

Holzapfel, S. 2016. “Boosting or Hindering Aid Effectiveness? An Assessment of Systems for Measuring Donor Agency Results.” Public Administration and Development 36 (1): 3–19.

Hood, S., R.K. Hopson, and K.E. Kirkhart. 2015. “Culturally Responsive Evaluation.” In Handbook of Practical Program Evaluation 281, edited by K.E. Newcomer, H.P. Hatry and J.S. Wholey. Hoboken, NJ: John Wiley & Sons.

Hulme, M. 2015. “Knowledge Pluralism.” In Research Handbook of Climate Governance, edited by K. Bäckstrand and E. Lövbrand, 555–65. Glos, UK: Elgar. https://www.mikehulme.org/wp-content/uploads/2014/03/Hulme20141229KB_revised.pdf.

IDS (Institute for Development Studies). 2020. “The Global Learning for Adaptive Management Initiative (GLAM).” IDS Projects. https://www.ids.ac.uk/projects/the-global-learning-for-adaptive-management-initiative-glam/.

Jarvis, A., A. Eitzinger, M. Koningstein, T. Benjamin, F.C. Howland, N. Andrieu, J. Twyman, and C. Corner-Dolloff. 2015. “Less Is More: The 5Q Approach.” Cali, Colombia: Centro Internacional de Agricultura Tropical (CIAT). http://dapa.ciat.cgiar.org/.

Johnston-Goodstar, K. 2012. “Decolonizing Evaluation: The Necessity of Evaluation Advisory Groups in Indigenous Evaluation.” New Directions for Evaluation, no. 136 (December): 109–17. doi:10.1002/ev.20038.

Jones, L. 2019. “Resilience Isn’t the Same for All: Comparing Subjective and Objective Approaches to Resilience Measurement.” WIRES Climate Change 10 (1): e552. doi:10.1002/wcc.552.

Jones, L., and T. Tanner. 2017. “‘Subjective Resilience’: Using Perceptions to Quantify Household Resilience to Climate Extremes and Disasters.” Regional Environmental Change 17 (1): 229–43.

Joyce, M. 2020. “Impact Measurement: A Cautionary Tale.” https://medium.com/@dobiggood/impact-measurement-a-cautionary-tale-d40991561489.

Kawakami, A.J., K. Aton, F. Cram, M. Lai, and L. Porima. 2008. “Improving the Practice of Evaluation through Indigenous Values and Methods: Decolonizing Evaluation Practice—Returning the Gaze from Hawai’i and Aotearoa.” In Fundamental Issues in Evaluation, edited by N.L. Smith and P.R. Brandon, 219–42. New York: Guilford.

Kerr, S. 2012. “Kaupapa Māori Theory-Based Evaluation.” Evaluation Journal of Australasia 12 (1): 6–18. https://journals.sagepub.com/doi/abs/10.1177/1035719X1201200102.

Larson, S., D.G.C. Kirono, G. Tjandraatmadja, and R. Barkey. 2016. “Monitoring and Evaluation Approaches in Water Resources Project Design: Experiences from an Urban Water System Climate Change Adaptation Project in Indonesia.” Water Policy 18 (3): 708–26. doi:10.2166/wp.2015.144.

Leiter, T., and P. Pringle. 2018. “Pitfalls and Potential of Measuring Climate Change Adaptation through Adaptation Metrics.” In Adaptation Metrics: Perspectives on Measuring, Aggregating and Comparing Adaptation Results, edited by L. Christiansen, G. Martinez, and P. Naswa, 29–48. Copenhagen: UN Environment Partnership–DTU Partnership.

Leventon, J., L. Fleskens, H. Claringbould, G. Schwilch, and R. Hessel. 2016. “An Applied Methodology for Stakeholder Identification in Transdisciplinary Research.” Sustainability Science 11: 763–75. https://link.springer.com/article/10.1007/s11625-016-0385-1.

LIFE-AR (Least Developed Countries Initiative for Effective Adaptation and Resilience). 2019. “LDC 2050 Vision: Towards a Climate-Resilient Future.” September. http://www.ldc-climate.org/wp-content/uploads/2019/12/LDC-Group-Vision.pdf.

McCreless, M. 2015. “A Client-centric Approach: Impact Evaluation That Creates Value for Participants.” Working paper. June. Root Capital. https://rootcapital.org/wp-content/uploads/2018/02/2015-june_client_centric_approach_final.pdf.

Medema, W., W. Arjen, and A. Adamowski. 2014. “Multi-loop Social Learning for Sustainable Land and Water Governance: Towards a Research Agenda on the Potential of Virtual Learning Platforms.” NJAS: Wageningen Journal of Life Sciences 69 (June): 23–38. doi:10.1016/j.njas.2014.03.003.

Morchain, D., D. Spear, G. Ziervogel, H. Masundire, M.N. Angula, J. Davies, C. Molefe, and S. Hegga. 2019. “Building Transformative Capacity in Southern Africa: Surfacing Knowledge and Challenging Structures through Participatory Vulnerability and Risk Assessments.” Action Research 17 (1): 19–41. doi:10.1177/1476750319829205.

NEF (Near East Foundation) 2017. “Decentralizing Climate Funds (DCF): Incorporating Tools to Measure Resilience into Mali’s Local Planning Systems.” Policy brief. https://www.neareast.org/download/materials_center/DCF_RA_Tools_Mali_Brief_En.pdf.

Olsson, P., and V. Galaz. 2012. “Socio-ecological Innovation and Transformation.” In Social Innovation: Blurring Boundaries to Reconfigure Markets, edited by A. Nicholls and A. Murdoch, 223–47. London: Palgrave Macmillan. https://www.stockholmresilience.org/research/research-videos/2012-04-20-social-ecological-innovations.html.

Patton, M.Q. 2008. Utilization-Focused Evaluation. 4th ed. Thousand Oaks, CA: Sage.

Pauw, W.P., C. Gruening, and C. Menzel. 2020. “Number of Beneficiaries as an Indicator for Adaptation: Do the Numbers Add Up?” GCF Monitor, no. 2.

Pawson, R., and N. Tilley. 1997. “An Introduction to Scientific Realist Evaluation.” In Evaluation for the 21st Century: A Handbook, edited by E. Chelimsky and W.R. Shadish, 405–18. Thousand Oaks, CA: Sage.

Rai, N., S. Acharya, R. Bhushal, R. Chettri, M. Shamshudoha, M. Kallore, N. Kaur, et al. 2015. “Political Economy of International Climate Finance: Navigating Decisions in PPCR and SREP.” IIED Working Paper. London: International Institute for Environment and Development.

Ramasobana, M., C. Morkel, and M. Frehiwot. 2020. “What Is Meant by Transforming Evaluations for Africa?” Twende Mbele Report, July. CLEAR-AA, Tiyimele Consultants, and the Institute of African Studies. http://www.twendembele.org/reports/what-is-meant-by-transforming-evaluations-for-africa/.

Ramisch, J. J., M.T. Misiko, I.E. Ekise, and J.B. Mukalama. 2006. “Strengthening ‘Folk Ecology’: Community-Based Learning for Integrated Soil Fertility Management, Western Kenya.” International Journal of Agricultural Sustainability 4 (2): 154–68. doi:10.1080/14735903.2006.9684798.

Reckien, D., M. Wildenberg, and M. Bachhofer. 2013. “Subjective Realities of Climate Change: How Mental Maps of Impacts Deliver Socially Sensible Adaptation Options.” Sustainability Science 8 (2): 159–72.

Segone, M., ed. 2012. Evaluation for Equitable Development Results. New York: UNICEF.

Shott, J., and R. Mather. 2012. “Climate Resilience Evaluation for Adaptation through Empowerment (CREATE): Adaptation Made Easy for Everyone.” International Union for Conservation of Nature. https://www.iucn.org/sites/dev/files/import/downloads/create_factsheet_final.pdf.

Silva Villanueva, P. 2011. “Learning to ADAPT: Monitoring and Evaluation Approaches in Climate Change Adaptation and Disaster Risk Reduction—Challenges, Gaps and Ways Forward.” Strengthening Climate Resistance Discussion Paper. Institute for Development Studies. https://www.ids.ac.uk/download.php?file=files/dmfile/SilvaVillanueva_2012_Learning-to-ADAPTDP92.pdf.

Smith, B. 2020. “Closing the Learning Loop in Locally Led Adaptation.” IIED Briefing, July.

Soanes, M., S. Addison, and C. Shakya. 2020. “Calling for Business Unusual: Why Local Leadership Matters.” IIED Briefing, October. https://pubs.iied.org/pdfs/17767IIED.pdf.

Soanes, M., A. Bahadur, C. Shakya, B. Smith, S. Patel, C. Rumbaitis del Rio, T. Coger, A. Dinshaw, S. Patel, S. Huq, and M. Musa. 2021. “Principles of Locally Led Adaptation: A Call to Action.” IIED Issue Paper, January.

Solinska-Nowak, A., P. Magnuszewski, M. Curl, A. French, A. Keating, J. Mochizuki, Wei Liu, et al. 2018. “An Overview of Serious Games for Disaster Risk Management: Prospects and Limitations for Informing Actions to Arrest Increasing Risk.” International Journal of Disaster Risk Reduction 31 (October): 1013–29. doi:10.1016/j.ijdrr.2018.09.001.

Spearman, M., and H. McGray. 2011. “Making Adaptation Count: Concepts and Options for Monitoring and Evaluation of Climate Change Adaptation.” Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ). http://star-www.giz.de/dokumente/bib-2011/giz2011-0219en-monitoring-evaluation-climate-change.pdf.

STAP (Scientific and Technical Advisory Panel). 2017. “Evaluation of Climate Change Adaptation.” Global Environment Facility. https://www.researchgate.net/publication/319086400_Strengthening_Monitoring_and_Evaluation_of_Climate_Change_Adaptation_A_STAP_Advisory_Document.

Teskey, G. 2020. “What Is Political Economy Analysis (PEA) and Why Does It Matter in Development?” From Poverty to Power (blog), September 2. https://oxfamblogs.org/fp2p/what-is-political-economy-analysis-pea-and-why-does-it-matter-in-development/.

Tschakert, P., and K.A. Dietrich. 2010. “Anticipatory Learning for Climate Change Adaptation and Resilience.” Ecology and Society 15 (2). https://www.jstor.org/stable/26268129.

Valters, C. 2015. “Theories of Change: Time for a Radical Approach to Learning in Development.” London: Overseas Development Institute. https://www.odi.org/sites/odi.org.uk/files/odi-assets/publications-opinion-files/9835.pdf.

Van Epp, M., and B. Garside. 2019. “Towards an Evidence Base on the Value of Social Learning–Oriented Approaches in the Context of Climate Change and Food Security.” Environmental Policy and Governance 29 (2): 118–31.

Van Zyl, H., and F. Claeyé. 2019. “Up and Down, and Inside Out: Where Do We Stand on NGO Accountability?” European Journal of Development Research 31 (3): 604–19.

Warner, A.T. 2017. “What Is Monitoring in Humanitarian Action? Describing Practice and Identifying Challenges.” ALNAP Scoping Paper. Active Learning Network for Accountability and Performance in Humanitarian Action/Overseas Development Institute.

Wilmsen, C. 2008. “Extraction, Empowerment, and Relationships in the Practice of Participatory Research.” In Towards Quality Improvement of Action Research, edited by B. Boog, M. Slager, J. Preece, and J. Zeelen, 135–46. Leiden, the Netherlands: Brill/Sense. doi:10.1163/9789087905941_011.

We are pleased to acknowledge our institutional strategic partners that provide core funding to WRI: the Netherlands Ministry of Foreign Affairs, Royal Danish Ministry of Foreign Affairs, and Swedish International Development Cooperation Agency.

The authors would like to give special thanks to our colleagues at the Least Developed Countries University Consortium on Climate Change (LUCCC), Luis Artur, David Mfitumukiza, and Sidat Yaffa, as well as our colleagues at Women’s Climate Centers International (WCCI), Rosemary Atieno, Godliver Kobugabe, Tracy Mann, Comfort Mukasa, and Rose Wamalwa for the expertise, insights, and examples they provided to inform this paper and review drafts. We would like to thank Christina Chan, Ayesha Dinshaw, Saleemul Huq, Nancy MacPherson, Arghya Sinha Roy, Saul Pereya, Cristina Rumbaitis del Rio, and Barry Smith for taking the time to review and strengthen the paper. We are grateful to Mahamat Assouyouti, Cristina Dengel, Susannah Fischer, Vincent Gainey, Kazi Eliza Islam, Timo Leiter, Heather McGray, and Nina Ullery for providing inputs and sharing their experience in the development of the paper. Thanks to Isabella Suarez for providing research support. Thank you to partners from the International Centre for Climate Change and Development, LUCCC, Slum/Shack Dwellers International, Huairou Commission, and WCCI, who convened at the Gobeshona 6 conference and provided inspiration and direction for the paper.

Tamara Coger is a Senior Associate at WRI.
Contact: tamara.coger@wri.org

Sarah Corry is a consultant at Sophoi.
Contact: sarah@sophoi.co.uk

Robbie Gregorowski is a Director at Sophoi.
Contact: robbie@sophoi.co.uk