Opportunities to strengthen Africa’s efforts to track national-level climate adaptation

Abstract

Tracking progress towards the Global Goal on Adaptation requires documentation of countries’ intentions, against which future progress can be measured. The extent to which existing national policy documents provide adequate baselines is unclear. We evaluated the adequacy of African Nationally Determined Contributions (NDCs) (N = 53) and National Adaptation Plans (NAPs) (N = 15) against three criteria—coverage, consistency and robustness—mapped to the adaptation cycle. Fifty-three percent of NAPs and 8% of NDCs cover all elements needed for providing sufficient baselines for tracking adaptation progress. Only 40% and 9% of the NAPs and NDCs, respectively, provide consistent links between climate risk assessment, planning, implementation and tracking. No document provided fully robust indicators to operationalize tracking. Notable efforts towards adequacy exist, especially in NAPs. The findings illustrate continental-scale advances and shortcomings for tracking progress, and emphasize opportunities in upcoming NDC revisions and NAP processes to enhance their coverage, consistency and robustness for future adaptation tracking.

Main

Tracking adaptation implementation and effectiveness is needed to enhance financial and technical support for climate action^1,2,3 and mitigate risks of maladaptation⁴. The Paris Agreement established the Global Goal on Adaptation (GGA), aiming to enhance resilience, increase adaptative capacity and reduce vulnerability to climate change⁵. It also introduced the Global Stocktake (GST) to assess progress towards these objectives. The Glasgow–Sharm el-Sheikh work programme (2022–2023) developed the GGA framework⁶. The first GST², syntheses of adaptation science⁷ and government reports^8,9,10,11 highlight limited evidence and ability to document adaptation progress and called for continued development of methods to track progress. Capitalizing on this momentum and on the substantial work on adaptation tracking principles and frameworks^{3,12,13,14,15,16,17}, the UAE-Belém Work Program, established by the United Nations Framework Convention on Climate Change (UNFCCC) in 2023, seeks to define indicators for assessing the GGA and the targets agreed upon in the framework.

Countries use various instruments to report planned adaptation targets, actions and support needed to the UNFCCC. Nationally Determined Contributions (NDCs)—pledges for national climate action—are political documents that outline country priorities, needs and committements¹⁸. National Adaptation Plans (NAPs) provide details on implementation including goals, objectives and actions, and support the operationalization of the adaptation components outlined in NDCs^9,19,20. NAPs are increasingly accompanied by monitoring and evaluation (M&E) systems to track implementation⁹. Adaptation progress can then be intentionally reported through National Communications, Adaptation Communications (Adcoms) and/or Biennial Transparency Reports (BTRs)¹⁸. While the first BTRs are due at the end of 2024, stand-alone Adcoms deliver partial information on countries’ adaptation actions and programmes but less on their results⁸.

Tracking adaptation requires setting a baseline of countries’ commitments, against which progress can be measured^13,14. However, it is unclear to what extent NDCs and NAPs can deliver this¹. Existing approaches for assessing adaptation plans offer insights into their adequacy for providing meaningful baselines of intentions. Plan quality and adaptation tracking studies highlight the importance of ‘the adaptation cycle’^{15,21,22,23,24}. The adaptation cycle sets out four ideal stages of adaptation including: (1) climate risk and impact assessment, (2) planning, (3) implementation and (4) monitoring, evaluation and learning (MEL). The cycle underpins adaptation planning and tracking support under the UNFCCC, particularly in the context of NAPs, the GGA and the GST^2,25,26.

Effective plans follow the steps of the adaptation cycle, iterate as new information becomes available and facilitate implementation by ensuring meaningfulness of adaptation efforts to context^8,21,27. Plan quality is determined by the depth and breadth of its contents, which should cover key adaptation cycle elements and ensure consistency across them^21,28,29,30, including providing a robust basis for tracking its implementation⁹. Key factors found to affect plan quality include policy and economic aspects (for example, ability to prioritize action, funding), technical capacity (for example, risk assessments, existence of M&E systems) and legitimacy (for example, stakeholder integration)²⁷. With few exceptions, plan quality characteristics described above have been used in qualitative case studies within developed or urban contexts, with limited proof of scalability for large-scale assessments to identify broad trends.

Here we evaluate African NDC and NAPs to determine their adequacy for informing adaptation tracking (Supplementary Table 1). We use a broad definition of adequacy to refer to the inclusion of a set of elements and characteristics needed for enabling adaptation tracking. This is distinct from the strict IPCC definition, which refers to the sufficiency of adaptation solutions to avoid or manage risks³¹, or the GST definition of adequacy, which refers to policies’ ability to capture national needs, considering the nature and severity of risks¹⁷. We focus on Africa because of its general high levels of vulnerability to climate change and the urgent need to adapt³². We apply three criteria derived from the plan quality and adaptation tracking literature: coverage, consistency and robustness, organized around the adaptation cycle (Box 1, Fig. 1 and Methods). We apply a pragmatic 0–1 scoring system to assess performance of the documents against each criterion and compute an adequacy index on the basis of equally weighted aggregation of criteria scores. The criteria allow us to: (1) inventory countries’ intentions, establishing a baseline for future assessments of progress and (2) identify opportunities for enhancing second-generation NDCs and improving NAPs to enable future adaptation tracking efforts.

Fig. 1: Conceptual framework for assessing adequacy of NDCs and NAPs.

a–c, The iterative adaptation policy cycle (a) applied to NDCs and NAPs, which describes key foundational components of adaptation. This informs (b) criteria and questions for assessing policy adequacy, visualized in c.

Box 1 Concepts and criteria for assessing policy adequacy for enabling adaptation tracking

Adaptation cycle. Describes an iterative adaptation process used in policies and projects. The framework used by the UNFCCC to support national adaptation processes^25,26 and the GGA dimensional targets agreed upon at COP28 (ref. ⁶) includes four key components: climate risk and impact assessment; adaptation planning; implementation; and monitoring, evaluation and learning (MEL).

Application of the cycle. Policy documents describing intentions, such as NDCs and NAPs, do not provide information on implemented adaptation, but rather on planned goals and objectives and priority actions for implementation. Hence, we adjust the second, third and fourth components of the cycle to the context of NDCs and NAPs, as described below. Specifically, planning (Component 2) refers to desired effects of adaptation; actions (Component 3) refer to planned rather than implemented actions, which cover measures, projects and programmes (hereafter ‘actions’) and MEL (Component 4) refers to indicators for tracking adaptation.

Component 1: Climate risk and impact assessment. We refer to the assessment of ‘hazards and systems at risk’ or impacted by climate change, which provides the rationale for adaptation.

Component 2: Planning. We refer to the adaptation ‘goals’ and ‘objectives’ that countries plan for. These describe the country’s adaptation vision and desired outcomes.

Component 3: Implementation. We refer to the ‘actions’ or measures to be implemented, including projects, programmes and initiatives aimed to respond to risks and to achieve objectives and goals.

Component 4: MEL. We refer to ‘indicators’ embedded in existing or forthcoming (that is, in development) adaptation MEL systems, as tools to support assessment of implementation and effectiveness of actions. We limit the scope to indicators to inform the UAE-Belém Work Program and technical resources of M&E systems, rather than focusing on broader organizational structures, legal frameworks, governance, and human, financial and technical resources^9,40. Hence, we do not address the aspect of ‘learning’ in MEL.

Adequacy. In our assessment, adequacy describes the extent to which NDCs and NAPs can enable a basis for adaptation tracking. Adequacy is determined by aggregating insights (or scores) on the coverage, consistency and robustness of documents, as described below and in Methods.

Criterion 1: Coverage. Refers to the inclusion of a set of elements linked to the adaptation cycle components¹⁵. These elements refer to: ‘climate hazards’ and ‘systems at risk’⁴¹ underlying the ‘climate risk and impact assessment’ component; ‘goals’ and ‘objectives’, describing the adaptation vision^15,29 and underlying the ‘planning’ component; ‘actions’, describing strategies and/or measures to respond to risks^10,15 and referring to the ‘implementation’ component; and ‘indicators’, representing tools to measure adaptation^36,42 and underlying the MEL component.

Criterion 2: Consistency. Describes the internal coherence of policies, manifested through intentional linkages established between the key components of the adaptation cycle, namely ‘climate risk and impact assessment, planning, implementation and MEL’^21,28,30,43. Consistency enables context-fit tracking and meaningful insights on progress and effectiveness.

Criterion 3: Robustness. Describes the quality of indicator sets included. Robustness is determined by the inclusion of indicator sets that meet SMART+ criteria^30,44,45,46: specific, measurable, with an assigned data source, relevant, time-bound and with a measurable target, and that measure distinct aspects of adaptation across the cycle, such as climate parameters³⁰, which determine the nature and extent of adaptation actions required; inputs needed for adaptation efforts^36,44,47; adaptation processes^46,47; outputs of actions^36,44,46,47; or outcomes^36,44,47,48.

Results

We developed a protocol (Supplementary Tables 2–4) and reviewed 53 NDCs and 15 NAPs available as of 30 September 2022 from which we extracted more than 7,000 data points on countries’ intentions for adaptation. These include information on climate risk and impact assessment, planning, implementation and MEL (Box 1). Despite variation among countries and documents, our results show that most NAPs and NDCs provide only a fraction of the information required to enable adaptation tracking. Examination of the three criteria—coverage, consistency and robustness—identified pervasive gaps and opportunities for informing ongoing discussions on the GGA framework and opportunities to enhance depth and breadth of future NDCs and NAPs.

Adequacy

The adequacy of NDCs and NAPs for informing adaptation tracking varied greatly among countries (Fig. 2a–c and Extended Data Table 1). Generally, NDCs had lower adequacy scores (minimum–maximum range of 0–1) than NAPs (P < 0.001), except for the Democratic Republic of the Congo (NDC = 0.8, NAP = 0.7) and Sierra Leone (0.9, 0.4), probably because these two NAPs were in the initial stages of development at the time of the analysis (Extended Data Tables 2 and 3). NDC adequacy scores ranged between 0.2 and 0.9 (median = 0.39, s.d. = 0.19). Only 11% (6) of NDCs had scores in the upper quartile (>0.75), including NDCs from Angola, the Democratic Republic of the Congo, Ethiopia, Sierra Leone, Burundi (2021) and Uganda (2022). Lower quartile scores (<0.25) were first submissions (between 2016–2018) (n = 4), updated first submissions (2016–2021) (n = 4) and second submissions (2021–2022) (n = 2). Low-income countries (Methods), representing 38% of the countries in the dataset, had higher scores for their NDCs compared with countries in middle- and high-income groups together (P < 0.05, N = 53) (Extended Data Tables 1 and 3). Neither adaptation funding nor governance influenced NDC adequacy (P > 0.05).

Fig. 2: Adequacy of African NDCs and NAPs for informing adaptation tracking.

a,b, Adequacy scores for NDCs (N = 53) (a) and NAPs (N = 15) (b). c–f, Summary of scores for adequacy (c) and by criteria of coverage (d), consistency (e) and robustness (f). Adequacy scores represent aggregated weighted scores across coverage, consistency and robustness criteria. Each violin represents the density of scores (y axis) within a criteria group and document type (x axis), with wider areas indicating higher density (that is, higher number of documents with that score). The violin plot is overlaid with a swarm plot, where individual data points represent a document and are separated randomly (jittered) to reduce overplotting and enhance visualization of data distribution. Scores for each criterion are scaled to 0–1 for comparison (Methods). Results from two-sided Wilcoxon rank-sum tests with continuity correction show significant differences in adequacy, coverage (P < 0.001), consistency and robustness (P < 0.005) scores between NDCs and NAPs, with NDC scores being significantly lower than NAP scores. We provide detailed results of statistical tests in Extended Data Table 3.

Source data

Fifteen (15) countries had both an NDC and a NAP at the time of our analysis. Sixty-seven percent of the NAPs (8) had adequacy scores of more than 0.75. High-scoring NAPs were submitted in or after 2021, except for Burkina Faso and Cameroon NAPs which were submitted in 2015 and Ethiopia’s NAP submitted in 2019. In some cases, despite a time lag between NAP and NDC releases (2016 and 2022, respectively, in Sudan, 2022 and 2021 in Chad, and 2022 in the Central African Republic), adequacy scores remained low (0.4) across all policy documents of these three countries. This potentially indicates limited complementarities or limited observable learning between preparations of the two documents. Adaptation funding levels did not affect NAP adequacy scores (P > 0.05), yet governance readiness—an indicator of institutional preparedness—had a significant positive effect (β = 1.5182, s.e. = 0.4921, P = 0.0104), explaining a significant proportion of the variance in the NAP adequacy score (F_(1,11) = 9.52, adjusted R² = 0.415, P = 0.010).

Coverage

All NDCs and NAPs included at least half of the six elements used to assess coverage (Fig. 2d). However, coverage was more complete in NAPs than in NDCs (P < 0.001, N = 15 and N = 53, respectively), with mean scores of 0.7 and 0.9, respectively (Extended Data Table 1). Only four (4) or less than 10% of the NDCs, but more than half (8) of the NAPs, included information on all six elements: Angola, Burundi, Sierra Leone and Uganda (NDC); and Benin, Burkina Faso, Cameroon, Ethiopia, Liberia, Madagascar, South Africa and Togo (NAP). Elements most featured, in descending order, were climate hazards and systems at risk (all NDCs and NAPs), adaptation actions (95% and 100% of NDCs and NAPs, respectively), objectives (70%, 93%), goals (47%, 87%) and indicators (23%, 67%) (Fig. 3a,b). We provide detailed summaries of observations by element type, country and document in Extended Data Table 4.

Fig. 3: Extent of coverage of adaptation elements in policy documents.

a,b, NDCs (N = 53) (a) and NAPs (N = 15) (b). Percentages calculated on the basis of mentions of the six adaptation elements in each document.

Source data

Consistency

Most documents provided evidence of only half of the linkages defining consistency (mean = 0.53, s.d. = 0.28). NAPs registered higher consistency scores than NDCs (P < 0.001, N = 15 and N = 53, respectively) with a narrower spread (Fig. 2e). Eleven documents were fully consistent, indicated by maximum consistency scores. These included 5 NDCs (less than 10%), that is, Burundi, the Democratic Republic of the Congo, Guinea, Sierra Leone and Uganda, and 6 NAPs (less than 50%), including Benin, Burkina Faso, Cameroon, Liberia, Madagascar and South Africa. Most often, countries link climate risk and impact assessment and action implementation (87% and 100% of NDCs and NAPs, respectively) (Fig. 4a,b). Less often, climate risk and impact assessment intentionally link to planning (72%, 87%) or planning to implementation (68%, 87%). MEL is the least consistent component across the adaptation cycle, partly due to indicators being featured less overall (see ‘Coverage’ section). Fewer documents provided linkages between climate risk and impact assessment and MEL (23%, 60%), suggesting a potential disconnect between assessments of climate risks and measurements of the impacts of adaptation on risk reduction. Less frequent linkages were also observed between planning and MEL (17%, 67%) and implementation and MEL (15%, 40%), as few documents included indicators explicitly linked to planned goals, objectives or actions (Extended Data Table 5).

Fig. 4: Consistency across adaptation cycle components.

a,b, NDCs (N = 53) (a) and NAPs (N = 15) (b). Values represent percentages of documents establishing a linkage between the adaptation cycle components (abbreviated for figure formatting): assessment (referring to climate risk and impact assessment), planning, implementation and MEL.

Source data

Robustness

Twenty-two documents (about two-thirds of our sample) featured indicators, that is, 10 NAPs and 12 NDCs (examples in Table 1). However, none met all characteristics of robustness (Fig. 2f). Overall, NDCs had lower robustness scores than NAPs (P < 0.001, N = 12 and N = 10, respectively). On average, NDCs met 2 out of 11 characteristics (s.d. = 2.9, N = 12) and NAPs met 5 (s.d. = 3.5, N = 10) (Extended Data Table 1). Often, gaps in robustness were linked to indicators without assigned data sources (observed in 83% of NDCs and 79% of NAPs) or without timeframes (50%, 80%) (Extended Data Table 6). Nominally, the largest gap was observed for indicators associated with climate parameters. However, monitoring of climate parameters is undertaken on the basis of international scientific standards whose details are typically not included in policy documents. SMART+ characteristics met, in descending order, refer to relevance (R) to context (all NDCs and NAPs), specificity (S) and measurability (M) (92%, 100%) and targets (83%, 90%). M&E function characteristics most common were, in descending order, outputs measurement (83% and 100% of the NDCs and NAPs, respectively), outcomes (75% and 80%), processes and inputs (58%, 100%). NDCs of Ethiopia and Rwanda and Madagascar’s NAP had highest robustness scores (0.8). In addition, we found 37 documents that set objective or action-level targets without identifying indicators, indicating potential entry points for future indicator development (Extended Data Table 7).

Table 1 Robustness of adaptation indicators

Discussion

Our analysis of African NDCs and NAPs indicates that they generally lack sufficient information to enable adaptation tracking. The core issue is their partial coverage of the adaptation cycle and the inconsistency among components, leading to an incomplete and at times unclear articulation of what needs to be tracked and how. Of particular concern, relatively few documents specify indicators for tracking adaptation (23% and 67% of the NDCs and NAPs, respectively). Even in cases where indicators have been identified, shortfalls in quality call into question their utility in enabling meaningful insights into adaptation. These results underscore the challenges that African governments face in assessing and reporting national progress on adaptation, and reveal specific opportunities for African governments to target in the near term, as they develop adaptation plans, revise NDCs and define the specificities of the GGA framework.

We found that NAPs provide a more adequate basis for adaptation tracking than NDCs (Fig. 2, and Extended Data Tables 1 and 3). The relative adequacy of NAPs is anticipated as they tend to be comprehensive and operational⁹, often the result of multiyear, multistakeholder processes embedded in domestic policies³³, and are backed by substantial technical and financial support^9,34. The gap in adequacy between NAPs and NDCs underscores the crucial role of NAPs in operationalizing adaptation components of NDCs, including articulating detailed plans for assessing progress^9,19. However, only a fraction of African countries have formulated NAPs so far. The target towards ensuring that all countries have NAPs by 2030, established in the GGA framework⁶, provides a welcome political imperative for focusing on NAPs development and implementation as a pathway towards improving adaptation tracking infrastructure.

Accelerating this process for African countries represents a critical opportunity in the coming years. Enabling cross- and intra-country learning would be one approach to catalyse enhancement. While networks such as the Adaptation Forum can provide a space for knowledge exchange, concrete learning modalities remain to be determined. Our dataset indicates countries that have more adequate NAPs and countries with high scores on individual criteria. These can serve as noteworthy exemplars. We were also able to identify countries with high-scoring NDCs and no published NAPs, which illustrate the potential of the former to advance NAP planning processes, in addition to their observed political and commitment functions^11,18. While the potential for NDCs to provide adequate reference points for tracking is limited to a few African countries, this positive deviance presents a compelling opportunity for further exploration and exploitation, as countries progress towards developing second-generation NDCs by 2025. Moreover, existing voluntary guidelines on communicating adaptation information under the UNFCCC³⁵ can inform NAP and NDC alignment efforts, deemed pivotal for consistent national planning.

The first GST² and recent research suggest that climate plans improve over time⁹. Updated NDCs have been enhanced to include additional adaptation targets and indicators compared with their original versions. Similarly, NAPs have increasingly become more consistent and operational. In our continental analysis, however, the evidence was varied. Updated NDCs tended to have higher adequacy scores than the first submissions, which were mainly rebranded Intended Nationally Determined Contributions (INDCs) developed before the enactment of the Paris Agreement¹¹; yet the improvements, with scores ranging between 0.2 and 0.4, still miss many of the critical aspects for effective tracking. Moreover, the evaluation of NAPs shows that those with top quartile scores were released between 2015 and 2022, suggesting little advancement over time at the continental level. It is critical to note that these results do not reflect an individual country’s progress over time. An important limitation of our approach was the inability to track document content over time, as earlier versions of NDCs were not available in the UNFCCC Registry. This underscores an opportunity for enhanced transparency and systematic archiving of NDCs and NAPs to effectively monitor and assess the evolution of adaptation ambitions and baselines for adaptation tracking. Moreover, given the stark variation in the level of detail captured in the NDC updates, the second-generation NDCs due in 2025 provide an opportune juncture for taking stock and drawing lessons from NDC evolution.

The development of more robust indicators represents another opportunity to strengthen the basis of tracking. Activity-based indicators, which represent 84% of all indicators mapped, are needed for understanding progress in implementation; however, complementary outcome indicators are needed to track effectiveness^36,37 and facilitate enhanced result reporting in future Adcom⁸. Further development of effectiveness indicators can be inspired by existing examples in NDCs and NAPs (Table 1) but also by existing objective-level targets (Extended Data Table 7), which can facilitate the identification of outcome-based indicators that align with established national priorities. Similarly, the adaptation cycle framework highlights the importance of identifying and tracking climate risk indicators to determine whether actions effectively reduce climatic risks. While such indicators are probably embedded in specialized institutional structures for weather and climate observation, they need to be acknowledged and integrated in planning processes to ensure consistency with national priorities and enable effective adaptation tracking. Lastly, improvements in indicator quality along the SMART+ criteria, particularly specificity, data sources, targets and timeframes, are warranted to ensure meaningful and practical indicators. Our analysis indicates that countries seeking to operationalize tracking are typically already moving in the direction of contextually relevant and measurable indicators. This sets a valuable precedent. The recent decision of the IPCC on the Seventh Assessment Cycle³⁸ and the UAE-Belém Work Program offer momentum for enhanced guidance on how governments can increase coherence between nationally relevant indicators and the GGA.

This research applied a parsimonious approach to evaluating NDCs and NAPs adequacy for enabling adaptation tracking across all African countries. However, coverage, consistency and robustness represent only a fraction of the many facets of plan quality, and the data used to consider potential levers of change were coarse. Thus, we were only able to uncover some potential features that determine differences in plan adequacy, such as governance aspects and income status. However, the approach allowed us to investigate how 53 African countries intend to adapt and track efforts, in contrast to the many fragmented cases usually investigated. Intercountry comparative studies can help identify broader trends and new opportunities for accelerating support, which is typically not possible with deeper case studies on individual countries, cities or landscapes. Furthermore, our approach only looks at adaptation intentions as baselines for future tracking. Intentions and ‘good’ plans are insufficient; they are only a precondition but not a guarantee for effectiveness^9,21,27,39. This underscores the critical role of examining complementary documents to understand adaptation progress and effectiveness, such as Adcoms and BTRs, as well as collecting field data to assess whether and how intentions and plans translate into results on the ground.

Leveraging existing planning processes and policies would spare governments from the burden of establishing new institutional structures and data systems for the sole purpose of adaptation tracking. This would also allow them to track and report on adaptation aspects that are already prioritized in national policies, thus enhancing linkages between adaptation tracking at national and global scales. Existing NAPs and NDCs reflect a pragmatic, early-stage approach to governments’ engagement with adaptation tracking; they initiate the conversation on what could be tracked and how. Our assessment demonstrates the opportunities to build on African NDCs and NAPs, with clear steps forward to enable future tracking and reporting processes.

Methods

Our analysis of NDCs and NAPs focuses on the potential of national planning and international reporting documents to provide a basis for adaptation tracking. We explored three questions: (1) Do NDCs and NAPs provide information covering the adaptation cycle to enable a comprehensive understanding of adaptation intentions? (2) Do they provide consistent information across the adaptation cycle to facilitate context-meaningful tracking? (3) Do they provide robust indicators to ensure operational tracking? These questions guided data extraction and analysis. The resulting dataset represents the most comprehensive overview available so far on African countries’ intentions for adaptation. We developed a detailed protocol available at protocolexchange (https://doi.org/10.21203/rs.3.pex-2399/v1)⁴⁹, informed by the Global Adaptation Mapping Initiative (GAMI) protocols⁷ (see Data collection and Data analysis below).

Analytical framework

We used the adaptation cycle as the guiding framework to assess adequacy of NAPs and NDCs (Fig. 1 and Box 1). Each component of the cycle provides insights for understanding progress on adaptation². Interpretations of the cycle vary across the literature, involving between four and eight components covering different aspects of adaptation progress. For example, ref. ²³ used four cycle components: problem, adaptation vision, implementation and MEL, to assess drivers of incremental and transformative change in Australia’s wine sector. An assessment of European urban plans determined plan quality using five components: impact, risk and vulnerability assessments, goals, measures, implementation tools and processes, and M&E²¹. Others used a six-phase interpretation of the cycle: groundwork preparations, risk assessment, option identification, assessment, implementation and M&E, to identify opportunities for enhancing climate services in urban and peri-urban Europe²². An assessment of local adaptation plans used an eight-component cycle to describe local M&E systems: groundwork, assessments of current and future situation, objectives, strategies, option assessment, prioritization, implementation and M&E²⁴. All these approaches emphasize a logical sequence and linkages between the components and a cyclical sequence from climate risk assessments to M&E, and aim to provide an ideal framework for comprehensive policies²¹. We chose the four-component adaptation cycle for its practicality and alignment with the UNFCCC framework to guide adaptation support^2,25,26 as well as GGA and GST processes. Because NAPs and NDCs outline plans for adaptation, we adjusted definitions of the cycle components to match information types delivered by such documents (Box 1).

We established three criteria to assess adequacy, understood as the potential of NDCs and NAPs to enable the basis for adaptation tracking: coverage, consistency and robustness (Fig. 1). The criteria, drawn from the plan quality and adaptation tracking scholarship, are not exhaustive. Instead, they are designed to: (1) bridge theoretical discussions with practical application for a continental-scale analysis, (2) deliver insights into the breadth (that is, coverage) and quality (that is, consistency, robustness) of the policies’ content and (3) provide a baseline for how adaptation is planned, aimed at being implemented and tracked across countries.

Coverage

This criterion describes inclusion of key elements of the adaptation cycle, which describe the ‘why’, ‘what’, ‘how’ and ‘so what’ of adaptation. This approach builds on an existing framework to track adaptation among governments¹⁵, which suggests that a comprehensive understanding of adaptation progress rests on an assessment of the vulnerability context, goals, actions and results. In our assessment, policies with adequate coverage include information on six core elements mapped to the adaptation cycle (Supplementary Table 2). Hazards, systems at risk, goals, objectives and actions provide context to tracking and inform the development of adequate and meaningful indicators³⁰. Together, these six elements deliver a baseline understanding of intentions for adaptation.

Consistency

This criterion looks at alignment between adaptation cycle components. Policies need to establish clear linkages between climate risk and impact assessments, planning of goals and objectives, actions to implement, and MEL, to facilitate context-fit tracking and meaningful insights on progress^21,30. Intentional linkages maximize effectiveness of plans in reducing risks and vulnerabilities²⁸. Our assessment of consistency was pragmatic and did not examine the extent to which sets of actions sufficiently address risks. A document was considered fully consistent if it provided evidence of intentional linkages across all four components of the adaptation cycle. For instance, actions to switch to drought-resistant crops or strengthen early warning systems for hydroclimatic risks in coastal areas indicate linkages between assessment and planning (Supplementary Table 3). While such an approach is inherently subjective, it provides a practical, preliminary approximation of how policies can deliver meaningful information for tracking.

Robustness

This criterion focuses on the quality, rather than the sheer presence, of sets of adaptation indicators as an entry point for operational tracking^9,30,46. First, robustness refers to the SMART design characteristics^45,46, which allow determination of whether indicators are specific (S), measurable (M), with an assigned data source (A), relevant (R) for the adaptation context, time-bound (T) and with a target (+). The target characteristic is not typically included in SMART assessments, yet it has been acknowledged as critical for gauging benchmarks for progress in implementation and effectiveness^30,44. Considering the array of best practices for designing performance indicators⁴⁵, our choice of SMART+ characteristics is pragmatic; these are well known and widely used in the development and adaptation community.

Second, robustness refers to the ‘function’ of the set of indicators featured in the document as entry points for capturing progress across the adaptation cycle. We distinguished between indicators used for measuring: climate parameters, which define a baseline of climate conditions and inform the actions required to address risks^30,47; inputs, such as human, financial or technical resources required for designing and implementing adaptation actions^36,44,47; process, which indicate progress in designing adaptation policy processes and institutions^46,47; outputs, referring to products or immediate results from activities^36,44,46,47; and outcomes, describing effects of adaptation, including changes in behaviour, environmental, social and/or economic conditions^36,44,47,48. One indicator can only have one of these five functions; hence, documents cover this function diversity through the indicator sets they propose. This approach to robustness, while not exhaustive, allowed us to assess the quality (or depth) of information, which can enable effective tracking. This is particularly relevant in the context of the newly established UAE-Belém Work Program, which seeks to provide guidance on identifying and measuring indicators.

Data sources

We assessed countries’ adaptation intentions and therefore focused on NAPs and NDCs. This allowed us to explore whether and how existing plans and international reporting efforts can offer an adequate basis for tracking. We reviewed all NAPs (N = 15) and NDCs with an adaptation component (N = 53) available on NAP Central (https://napcentral.org/)⁵⁰ and NDC Registry (https://unfccc.int/NDCREG)⁵¹ and published before 30 September 2022 (Supplementary Table 1). They offer comparative insights into adaptation priorities, needs and commitments across the continent, while acknowledging the strengths and opportunities of national adaptation planning processes to contribute to future tracking efforts. NDCs and NAPs serve distinct, yet complementary roles within a country. NDCs are high-level strategic documents, also used to signal negotiation positions on key global agendas, such as adaptation finance or loss and damage compensation^11,18. In contrast, NAPs are embedded in domestic policy processes, providing detailed implementation plans that contribute to the Paris Agreement objectives of adaptation³³; they can help to operationalize adaptation components of NDCs and provide plans to track implementation, including detailed M&E systems that measure action progress in implementation and achievement of goals and objectives⁹.

Recent analyses found that stand-alone Adcom can provide information on implemented adaptation⁸. By 30 September 2022, only eight African countries had submitted a stand-alone Adcom⁵². Their infrequent submission, especially by least developed countries, limits opportunities for continental comparisons of adaptation progress. Seven countries including Angola, Burundi, Kenya, Mauritius, Mauritania, South Africa and Sudan had integrated Adcoms in their NDCs and they were considered in our analysis. Exclusion of stand-alone Adcoms from our analysis was a practical decision, yet it represents a limitation of our study. However, our findings remain highly relevant by providing comparable information on adaptation priorities at continental scale, representing starting points for countries’ future tracking and reporting efforts, including development of future BTRs and Adcoms.

Initial document screening

We first reviewed a random sample of 20 documents for their structure and content type. This allowed us to assess the applicability of the framework criteria to the source documents, to fine-tune definitions and establish clear rules in the Protocol. For instance, we broadened our initial definition of actions to include on-the-ground initiatives and relevant policies and programmes, recognizing that NDCs and NAPs focus mainly on planned adaptation⁵³. We also refined our approach to recording information on targets, which are not mapped as stand-alone elements but linked to actions, objectives or indicators.

Data collection

Data were collected manually by a team of three over the course of 5 months. Documents in French (n = 23) and Spanish (n = 1) were translated into English using open-source software (Google Translate). Extraction was guided by the Protocol informed by GAMI codebooks⁷, which outlined criteria for information inclusion, definitions and procedures to help minimize interpretation differences across the extractors. Data extracted were cross-checked by the team lead member (A.N.), who randomly revisited 75% of source documents to validate and enhance the accuracy of the information extracted. In addition, the team conducted regular reconciliation meetings to discuss and resolve any discrepancies in extraction, ensuring consistent adherence to Protocol guidelines and criteria⁴⁹. Data were recorded in two separate databases⁵⁴. The main database, ‘Adaptation Elements’, captures data on ‘coverage’ and ‘robustness’, where each row represents a unique reference to an adaptation element. The secondary database, ‘Adaptation Elements Linkages’, captures data on document ‘consistency’, with each row representing a document evaluated for consistency.

Data types

For coverage, we recorded explicit mentions of hazards, systems at risk, adaptation goals, objectives, actions and indicators, as individual observations in the database⁵⁴. For each of these elements, we collected additional information on targets, understood as desired benchmark values for future reference⁴⁴, whenever available in the documents. Variable definitions are available in Supplementary Table 2. For consistency, we searched through text, figures and tables for evidence of linkages between adaptation cycle components: climate risk and impact assessment, planning, implementation, MEL. We documented explicit connections between sets of climate risk and impact assessment elements (that is, hazards, systems at risk), sets of planning elements (that is, goals, objectives), sets of implementation elements (that is, actions) and MEL elements (that is, indicators), rather than mapping linkages between unique observations. We only included connections that were clear in the source documents (rather than assumed), because they provide a clear indication of intentional and deliberate articulation of adaptation elements (Supplementary Table 3). For robustness, we relied on data collected to assess coverage, hence no additional data were required.

Other data

We used additional data to quickly investigate possible explainers of adequacy that could serve as foundations for future investigations. Hence, we looked at a selection of relevant variables that were highlighted in the literature and for which data were available to enable cross-country comparisons. Specifically, we used the World Bank ‘Country classification by income level’ (considering gross national income (GNI) per capita)⁵⁵, which categorizes countries into four income groups: low, lower-middle, upper-middle and high-income. We expected low-income countries to have fewer financial resources to invest in comprehensive planning processes and therefore lower document adequacy scores. In addition, we examined ‘governance’ scores that form the Notre Dame Global Adaptation Index (ND-GAIN) index⁵⁶; these measure institutional quality driving adaptation, such as regulatory quality, rule of law, political stability and control of corruption. We considered 5-year average governance scores, using the publication date of NDCs and NAPs as a benchmark to identify the relevant 5 years for each country. The hypothesis was that higher governance scores indicate higher institutional readiness to adapt and to make effective use of adaptation investment for developing adequate plans. In addition, we incorporated data on ‘adaptation finance’ compiled by the Climate Policy Initiative⁵⁷, which capture finance received by a country for climate adaptation activities from public, private and blended finance sources. As finance data were averages of 2019 and 2020, we used them under the assumption that countries that submitted NAPs and NDCs before that timeframe (2015–2019) or after it (2020–2022) received comparable levels of financial support for adaptation. We expected higher levels of climate finance to drive higher adequacy scores for NDCs and NAPs.

Data analysis

All analyses were performed in R⁵⁸ and focused on document type, given the distinct functions of NDCs and NAPs for adaptation^9,11,18.

Coverage scores were determined on the basis of documents’ inclusion of the six adaptation elements: hazards, systems at risk, goals, objectives, actions and indicators. First, we summarized the ‘Adaptation Elements’⁵⁴ dataset by country and document, using binary variables to indicate the presence (1) or absence (0) of an element in the document. This practical approach allowed us to ensure consistent evaluation across documents, reduce scoring ambiguity, improve inter-rater reliability and streamline data analysis. For each document–country combination, occurrences were summed, yielding a score with a min-max range of 0–6. The coverage score per document was then determined by dividing the total score by the maximum value (min-max range 0–1). Total counts of observations per element, document and country were also computed and are included in Extended Data Table 4.

Consistency scores were determined by the number of linkages identified across the adaptation cycle in each document. We summarized the ‘Adaptation Linkages’⁵⁴ dataset into a table indicating the presence (1) or absence (0) of linkages between: climate risk and impact assessment and planning, climate risk and impact assessment and implementation, climate risk and impact assessment and MEL, planning and implementation, planning and MEL, and implementation and MEL. On the basis of this, the total number of linkages per document was determined, with min-max range of 0–6. The consistency score (min-max 0–1) was obtained by dividing the total number of linkages by the maximum value; higher coverage scores indicate a higher number of linkages. The summarized data were also used to generate a 4 × 4 adjacency matrix and heat maps summarizing how often (that is, number of countries) each pair of components co-occurred in the dataset. We did not assess the quality of the linkages, such as whether the assumptions underlying the connections were scientifically sound. This represents a limitation of the current analysis but can be explored in future works that seek to test the validity and reliability of these linkages, providing evidence of results and outcomes.

Robustness scores were determined on the basis of documents’ inclusion of sets of indicators that satisfied a combination of SMART+ design and M&E function characteristics. First, this required an expert-based analysis of the indicators using deductive content methods. Content analyses based on expert assessments are time consuming and inherently subjective, driven by value judgments, yet they provide important complementary sources of information, allowing distillation of nuanced insights in the data^17,59,60. To mitigate bias in categorization and ensure reproducibility, coding definitions and instructions were grounded in established literature and expert knowledge and are detailed in the Protocol (Supplementary Tables 4 and 5). In addition, the Protocol includes examples to ensure a uniform understanding of the definitions and instructions. Each indicator was assessed by two other individuals to ensure coding reliability and consistency. Reconciliation meetings were organized to address coding discrepancies.

We used binary values to classify indicators that met (1) or not (0) the 11 quality traits defining robustness: specific (S), measurable (M), assigned data source (A), relevant (R), time-bound (T), with target (+) and used to measure climate parameters, inputs, processes, outputs or outcomes. One indicator can satisfy multiple or all 6 SMART+ characteristics but can only meet 1 of 5 M&E functions. As our analysis of robustness focused on the document level, we first counted the number of SMART+ characteristics met by each indicator (SMART+_i) and then averaged it across all indicators to calculate a mean number of SMART+ characteristics per document (SMART+_d). Similarly, for the M&E function, we averaged the number of characteristics met by all indicators in a document (M&E_d). The robustness score was then computed by summing SMART+_d and M&E_d and then dividing it by the maximum value possible (11). This yielded a score with min-max range of 0–1, with highest values indicating higher number of robustness characteristics met by that document.

Adequacy scores for each document were calculated by constructing an index that aggregated the weighted scores for coverage, consistency and robustness. In the absence of substantive evidence in the literature regarding the relative importance of one criterion over the other, we assigned equal weights of 1/3. This meant that each criterion contributed equally to the adequacy score. The resulting adequacy score had values with min-max range of 0–1.

To determine the statistical significance of differences in scores between groups (that is, NDCs vs NAPs and low-income countries vs others) (Extended Data Table 3), we conducted Wilcoxon rank-sum tests, which allowed us to assess whether there were significant disparities in adequacy scores between groups, without relying on assumptions of normality. A continuity correction was applied to the test to adjust for ties. For country income ranking, we grouped low- and lower-middle-income countries into the ‘lower’ group, while upper-middle- and high-income-countries were grouped in the ‘other’ category.

Lastly, we built linear regression models using the ‘lm’ function in R to investigate the relationship between adequacy scores for each document type (dependent variable), adaptation finance and governance (independent variables). The Democratic Republic of the Congo and South Sudan did not have data on governance, hence the models were based on 51 NDCs and 13 NAPs. The following equations were used in the linear models:

$${\rm{A}}{\hbox{-}}{\rm{NAP}}={\beta }_{0}+{\beta }_{1}\times {\rm{Governance}}+\epsilon$$

(1)

$${\rm{A}}{\hbox{-}}{\rm{NAP}}={\beta }_{0}+{\beta }_{1}\times {\rm{Finance}}+\epsilon$$

(2)

$${\rm{A}}{\hbox{-}}{\rm{NDC}}={\beta }_{0}+{\beta }_{1}\times {\rm{Governance}}+\epsilon$$

(3)

$${\rm{A}}{\hbox{-}}{\rm{NDC}}={\beta }_{0}+{\beta }_{1}\times {\rm{Finance}}+\epsilon$$

(4)

where A-NAP is the NAP adequacy score; A-NDC is the NDC adequacy score; β₀ is the baseline adequacy score for NAP/NDC; β₁ is the coefficient for the independent variable, indicating the change in adequacy score for a one-unit increase in governance/finance; governance and finance are independent variables, representing the values for the governance readiness and finance variable, respectively; and ϵ is the error term, representing the random variation in adequacy score that is not explained by the independent variables.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.