Question:
You are given the full text of a scientific research paper.

    Summarize the paper's key **research findings** in the style of a policy brief. Your response should:
    - Focus on the major empirical or model-based results
    - Present findings that are policy-relevant and supported by the study
    - Use concise, non-technical language with essential technical terms as needed
    - Avoid quoting directly or summarizing minor details

    Write a short, clear paragraph appropriate for the “Scientific Research Findings” section of a policy brief.

    Scientific Research Paper:
    Nature Climate Change | Volume 14 | August 2024 | 876–882
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nature climate change
Analysis
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Opportunities to strengthen Africa’s efforts
to track national-level climate adaptation
Andreea C. Nowak
  1,2
, Lucy Njuguna
  3, Julian Ramirez-Villegas
  1,2,
Pytrik Reidsma2,6, Krystal Crumpler
  4 & Todd S. Rosenstock
  5
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.
Tracking adaptation implementation and effectiveness is needed
to enhance financial and technical support for climate action1–3 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 pro-
gress 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 reports8–11
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 adap-
tation tracking principles and frameworks3,12–17, 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 adapta-
tion 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 out-
lined in NDCs9,19,20. NAPs are increasingly accompanied by monitoring
and evaluation (M&E) systems to track implementation9. Adaptation
progress can then be intentionally reported through National Com-
munications, 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’ com-
mitments, against which progress can be measured13,14. 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
Received: 16 November 2023
Accepted: 3 June 2024
Published online: 19 July 2024
 Check for updates
1Climate Action, Bioversity International, Rome, Italy. 2Plant Production Systems Group, Wageningen University and Research, Wageningen,
the Netherlands. 3Climate Action, International Center for Tropical Agriculture (CIAT), Nairobi, Kenya. 4Food and Agriculture Organization of the
United Nations, Rome, Italy. 5Climate Action, Bioversity International, Montpellier, France. 6Deceased: Pytrik Reidsma.
 e-mail: a.nowak@cgiar.org

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We use a broad definition of adequacy to refer to the inclusion of a set
of elements and characteristics needed for enabling adaptation track-
ing. 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 aggrega-
tion 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.
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
‘the adaptation cycle’15,21–24. 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 GST2,25,26.
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 context8,21,27. Plan
quality is determined by the depth and breadth of its contents, which
should cover key adaptation cycle elements and ensure consistency
across them21,28–30, 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, fund-
ing), 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).
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 processes25,26 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 resources9,40. 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 vision15,29 and underlying the ‘planning’ component;
‘actions’, describing strategies and/or measures to respond to risks10,15
and referring to the ‘implementation’ component; and ‘indicators’,
representing tools to measure adaptation36,42 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+ criteria30,44–46: 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 efforts36,44,47; adaptation
processes46,47; outputs of actions36,44,46,47; or outcomes36,44,47,48.

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for adaptation. These include information on climate risk and impact
assessment, planning, implementation and MEL (Box 1). Despite vari-
ation 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—cover-
age, consistency and robustness—identified pervasive gaps and oppor-
tunities 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 sub-
missions (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).
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 Repub-
lic), adequacy scores remained low (0.4) across all policy documents of
these three countries. This potentially indicates limited complementa-
rities 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.
Consistency
Most documents provided evidence of only half of the linkages defining
consistency (mean = 0.53, s.d. = 0.28). NAPs registered higher consist-
ency 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 imple-
mentation and MEL (15%, 40%), as few documents included indicators
explicitly linked to planned goals, objectives or actions (Extended
Data Table 5).
Robustness
Twenty-two documents (about two-thirds of our sample) featured indi-
cators, that is, 10 NAPs and 12 NDCs (examples in Table 1). However, none
Criterion 1: Coverage
Are key elements of the adaptation
policy cycle covered, providing a
comprehensive baseline of adaptation
intentions at national level?
Component 1:
Climate risk
and impacts
assessment
Component 2:
Adaptation
planning
Component 3:
Adaptation
implementation
Component 4:
Monitoring,
evaluation and
learning
a
b
Criterion 2: Consistency
Are adaptation cycle components
consistently linked to one another,
providing the basis for a context-fit
adaptation tracking?
Criterion 3: Robustness
Are there sets of indicators that meet
SMART+ criteria and serve diverse
monitoring functions, providing a
robust basis for operational tracking?
SMART+
design
Diverse
functions
I
Component 4
Component 2
Component 3
Component 1
H
S
G
I
A
O
Component 3
Component 1
Component 4
Component 2
c
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.

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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 inter-
national 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 func-
tion 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).
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 rela-
tive adequacy of NAPs is anticipated as they tend to be comprehensive
and operational9, often the result of multiyear, multistakeholder pro-
cesses embedded in domestic policies33, and are backed by substantial
technical and financial support9,34. 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 progress9,19. However, only a fraction of African countries
have formulated NAPs so far. The target towards ensuring that all coun-
tries 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 criti-
cal opportunity in the coming years. Enabling cross- and intra-country
learning would be one approach to catalyse enhancement. While net-
works 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 poten-
tial of the former to advance NAP planning processes, in addition
to their observed political and commitment functions11,18. While the
potential for NDCs to provide adequate reference points for tracking
0
0.2
0.4
0.6
0.8
1.0
a
0
0.2
0.4
0.6
0.8
1.0
b
c
d
e
f
0
0.2
0.4
0.6
0.8
1.0
Score
NDC
NAP
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.

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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 addi-
tional 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 effec-
tive 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 indi-
cators, which represent 84% of all indicators mapped, are needed for
understanding progress in implementation; however, complementary
outcome indicators are needed to track effectiveness36,37 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 mean-
ingful 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 frag-
mented cases usually investigated. Intercountry comparative studies
can help identify broader trends and new opportunities for accelerat-
ing support, which is typically not possible with deeper case studies on
individual countries, cities or landscapes. Furthermore, our approach
0
20
40
60
80
100
Hazards
Systems at risk
Goals
Objectives
Actions
Indicators
a
Hazards
Systems at risk
Goals
Objectives
Actions
Indicators
Documents (%)
b
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.
a
Assess
Plan
Implement
MEL
Assess
72
87
23
Plan
72
68
17
Implement
87
68
15
M&E
23
17
15
b
Assess
Plan
Implement
MEL
Assess
87
100
60
Plan
87
87
67
Implement
100
87
40
M&E
60
67
40
15
100
Documents (%)
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.

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only looks at adaptation intentions as baselines for future tracking.
Intentions and ‘good’ plans are insufficient; they are only a precondi-
tion but not a guarantee for effectiveness9,21,27,39. 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 struc-
tures 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 gov-
ernments’ engagement with adaptation tracking; they initiate the
conversation on what could be tracked and how. Our assessment dem-
onstrates the opportunities to build on African NDCs and NAPs, with
clear steps forward to enable future tracking and reporting processes.
Online content
Any methods, additional references, Nature Portfolio reporting sum-
maries, source data, extended data, supplementary information,
acknowledgements, peer review information; details of author contri-
butions and competing interests; and statements of data and code avail-
ability are available at https://doi.org/10.1038/s41558-024-02054-7.
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Table 1 | Robustness of adaptation indicators
Indicator examples by main function
Criteria
S
M
A
R
T
+
Climate parameter
 Change in annual temperaturea
x
x
x
 Extreme precipitation eventsa
x
x
x
 Number of hot daysa
x
x
x
Input
 Number of gender-sensitive
institutional analyses carried outb
x
x
x
x
 Number of value-chain climate risk
assessments completedc
x
x
x
 Accurate data on exposure to climate in
high-risk areas by 2030d
x
x
x
x
Process
 Detailed spatial plans for all districts by
2025 and 2030d
x
x
x
x
 Number of SNRM plans integrating
adaptation (…)c
x
x
x
 Number of development plans
integrating adaptatione
x
x
x
x
x
Output
 Area (ha) under irrigation by 2030f
x
x
x
x
x
 Number of climate-resilient crop
varieties developedd
x
x
x
x
 Rate of women farmers benefiting from
technical (…) support (%)g
x
x
x
x
Outcome
 Rate of recovery and restoration of the
fertility of degraded soilsh
x
x
x
 Percentage reduction of crop and
animal disease cases (30% reduction
by 2030)i
x
x
x
x
x
 Increase in yield per hectare (%)c
x
x
x
Examples extracted from a subset of agriculture indicators, organized by their monitoring
and evaluation function (that is, climate parameter, input, process, output and outcome)
and assessed by the SMART+ characteristics: specific (S), measurable (M), assigned Data
(A), relevant (R), and time-bound (T) and target (+). One indicator can simultaneously meet
several SMART+ characteristics but only one function. Climate parameter indicators are
sourced from the entire set of indicators, as none of the agriculture sector ones included
climate parameters. ‘x’ indicates that the indicator meets the respective characteristic. A full
assessment of each indicator in the dataset is available in Supplementary Table 5. aAngola
NDC. bDemocratic Republic of Congo NAP. cEthiopia NAP. dRwanda NDC. eDemocratic
Republic of Congo NDC. fUganda NDC. gBenin NDC. hBurkina Faso NAP. iEthiopia NDC. SNRM,
sustainable management of natural resources (the acronym was not spelled out in the
formulation of the indicator, but in the body of the document).

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Analysis
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dependent variable problem in comparing and evaluating climate
change adaptation policies. Glob. Environ. Change 23, 1476–1487
(2013).
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implementation guidance in US local adaptation plans. Nat. Clim.
Change 6, 796–802 (2016).
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and metrics: state of local policy practice. Ecol. Indic. 145, 109657
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31.	 Begum, A. et al. in Climate Change 2022: Impacts, Adaptation and
Vulnerability (eds Pörtner, H.-O. et al.) 121–196 (Cambridge Univ.
Press, 2023).
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and Vulnerability (eds Pörtner, H.-O. et al.) 1285–1455 (Cambridge
Univ. Press, 2023).
33.	 Leiter, T. Too little, too slow? Climate adaptation at the United
Nations climate change negotiations since the adoption of the
Paris Agreement. Carbon Clim. Law Rev. 16, 243–258 (2022).
34.	 In-country NAP Support Programs (NAP Global Network, 2024).
35.	 Adaptation Committee. Draft Supplementary Guidance for
Voluntary Use by Parties in Communicating Information in
Accordance with the Possible Elements of an Adaptation
Communication (UNFCCC, 2022).
36.	 Leiter, T. et al. Adaptation Metrics. Current Landscape and Evolving
Practices (Global Commission on Adaptation, 2019).
37.	 Craft, B. & Fisher, S. Measuring the adaptation goal in the global
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(2018).
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Analysis
https://doi.org/10.1038/s41558-024-02054-7
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 adapta-
tion. 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 adapta-
tion2. 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 link-
ages 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 support2,25,26 as well as GGA and GST processes.
Because NAPs and NDCs outline plans for adaptation, we adjusted defi-
nitions 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 comprehen-
sive understanding of adaptation progress rests on an assessment
of the vulnerability context, goals, actions and results. In our assess-
ment, 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 pro-
vide 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 progress21,30. Intentional linkages maximize
effectiveness of plans in reducing risks and vulnerabilities28. Our assess-
ment 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 link-
ages across all four components of the adaptation cycle. For instance,
actions to switch to drought-resistant crops or strengthen early warn-
ing 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 tracking9,30,46. First, robustness refers to the SMART design
characteristics45,46, which allow determination of whether indicators
are specific (S), measurable (M), with an assigned data source (A), rele
vant (R) for the adaptation context, time-bound (T) and with a target
(+). The target characteristic is not typically included in SMART assess-
ments, yet it has been acknowledged as critical for gauging benchmarks
for progress in implementation and effectiveness30,44. 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 indica-
tors 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 risks30,47;
inputs, such as human, financial or technical resources required for
designing and implementing adaptation actions36,44,47; process, which
indicate progress in designing adaptation policy processes and insti-
tutions46,47; outputs, referring to products or immediate results from
activities36,44,46,47; and outcomes, describing effects of adaptation,
including changes in behaviour, environmental, social and/or eco-
nomic conditions36,44,47,48. 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 adapta-
tion 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 com-
parative insights into adaptation priorities, needs and commitments
across the continent, while acknowledging the strengths and opportu-
nities 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

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Analysis
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adaptation finance or loss and damage compensation11,18. In contrast,
NAPs are embedded in domestic policy processes, providing detailed
implementation plans that contribute to the Paris Agreement objec-
tives of adaptation33; they can help to operationalize adaptation com-
ponents 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 infor-
mation on implemented adaptation8. By 30 September 2022, only eight
African countries had submitted a stand-alone Adcom52. Their infre-
quent submission, especially by least developed countries, limits oppor-
tunities 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 provid-
ing 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 trans-
lated 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 pro-
cedures to help minimize interpretation differences across the extrac-
tors. 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, ‘Adapta-
tion 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 consist-
ency, we searched through text, figures and tables for evidence of link-
ages between adaptation cycle components: climate risk and impact
assessment, planning, implementation, MEL. We documented explicit
connections between sets of climate risk and impact assessment ele-
ments (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 high-
lighted 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 docu-
ment 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 regula-
tory 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 addi-
tion, 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 adaptation9,11,18.
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 consist-
ent 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 sum-
marized 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 docu-
ment was determined, with min-max range of 0–6. The consistency
score (min-max 0–1) was obtained by dividing the total number of link-
ages by the maximum value; higher coverage scores indicate a higher
number of linkages. The summarized data were also used to generate

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Analysis
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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 link-
ages, 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 meth-
ods. 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 data17,59,60. 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+ char-
acteristics 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 func-
tion, we averaged the number of characteristics met by all indicators
in a document (M&Ed). The robustness score was then computed by
summing SMART+d and M&Ed 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 construct-
ing an index that aggregated the weighted scores for coverage, consist-
ency 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 govern-
ance (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:
A-NAP = β0 + β1 × Governance + ϵ
(1)
A-NAP = β0 + β1 × Finance + ϵ
(2)
A-NDC = β0 + β1 × Governance + ϵ
(3)
A-NDC = β0 + β1 × Finance + ϵ
(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 coeffi-
cient 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.
Data availability
Data are available on Harvard Dataverse at https://doi.org/10.7910/
DVN/VK3CP9 (ref. 54). Source data are provided with this paper.
Code availability
The supporting code for the results is available on GitHub at
https://doi.org/10.5281/zenodo.11237938 ref. 61.
References
49.	 Nowak, A. C., Njuguna, L. & Crumpler, K. How can governments
engage in adaptation tracking? A protocol for assessing national
adaptation policies. protocolexchange https://doi.org/10.21203/
rs.3.pex-2399/v1 (2023).
50.	 NAP Central https://napcentral.org/ (UNFCCC, 2023).
51.	 Nationally Determined Contributions Registry https://unfccc.int/
NDCREG (UNFCCC, 2023).
52.	 Adaptation Communications Registry https://unfccc.int/ACR
(UNFCCC, 2024).
53.	 Lesnikowski, A., Ford, J. D., Berrang-Ford, L., Barrera, M. &
Heymann, J. How are we adapting to climate change? A global
assessment. Mitig. Adapt. Strateg. Glob. Change 20, 277–293
(2015).
54.	 Nowak, A., Njuguna, L. & Crumpler, K. Adaptation elements
in African Nationally Determined Contributions (NDCs) and
National Adaptation Plans (NAPs). Harvard Dataverse https://doi.
org/10.7910/DVN/VK3CP9 (2023).
55.	 World Bank Country and Lending Groups – World Bank Data Help
Desk (The World Bank Group, 2024).
56.	 Country Index Technical Report (Univ. of Notre Dame Global
Adaptation Initiative, 2023).
57.	 Meattle, C. et al. Landscape of Climate Finance in Africa (Climate
Policy Initiative, 2022).
58.	 R Core Team. R: A Language and Environment for Statistical
Computing http://www.R-project.org/ (R Foundation for Statistical
Computing, 2023).
59.	 Erlingsson, C. & Brysiewicz, P. A hands-on guide to doing content
analysis. Afr. J. Emerg. Med. 7, 93–99 (2017).
60.	 Krippendorff, K. Content Analysis: An Introduction to Its
Methodology (Sage, 2004).
61.	 Nowak, A. Adaptation-tracking-africa. GitHub https://doi.org/
10.5281/zenodo.11237938 (2024).
Acknowledgements
The World Bank funded ‘Accelerating Impacts of CGIAR Climate
Research’ (AICCRA) Project supported this work (A.C.N., T.S.R.) and it
was implemented by the Alliance of Bioversity and CIAT with partners.
We thank G. Wamukoya for direction and consultation.

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Author contributions
A.C.N., L.N., T.S.R., J.R.-V. and P.R. designed the study. A.C.N., L.N.
and K.C. collected and curated the data. A.C.N. analysed the data,
prepared and finalized the paper. L.N., T.S.R., J.R.-V., P.R. and K.C.
edited the paper.
Competing interests
The authors declare no competing interests.
Additional information
Extended data is available for this paper at
https://doi.org/10.1038/s41558-024-02054-7.
Supplementary information The online version contains supplementary
material available at https://doi.org/10.1038/s41558-024-02054-7.
Correspondence and requests for materials should be addressed to
Andreea C. Nowak.
Peer review information Nature Climate Change thanks Timo Leiter,
Alexandre Magnan, Diana Reckien and the other, anonymous,
reviewer(s) for their contribution to the peer review of this work.
Reprints and permissions information is available at
www.nature.com/reprints.

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 1 | Descriptive statistics
Descriptive statistics from analysis of African NDCs (N = 53) and NAPs (N = 15).

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 2 | Scores by country and document
Scores for adequacy (index), coverage, consistency and robustness for African NDCs (N = 53) and NAPs (N = 15). Countries’ names are presented in descending ordered by overall adequacy
scores. “Date” refers to the publication date of the document on the UNFCCC web page at the time of the analysis. “Publication” version refers to the label of the document on the UNFCCC
web page. Calculation of adequacy index, coverage, consistency, and robustness criteria are provided in the Methods section of the article.

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 3 | Comparisons between NDCs and NAPs: statistical tests
We used two-sided Wilcoxon rank-sum tests with continuity correction to compare adequacy, consistency, coverage, and robustness scores between NDCs and NAPs. The data were not
normally distributed; therefore, non-parametric tests were chosen. The p-values indicate the significance of the observed differences between NDCs and NAPs, with asterisks denoting levels
of significance: ***p < 0.001: highly significant, **p < 0.01: very significant; *p < 0.05: significant. Sample size for NDCs is N = 53 and for NAPs is N = 15 for all comparisons in Set 1. In Set 3, we only
use sample size for NDCs (N = 53).

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 4 | Coverage of adaptation elements (total counts, %)
Total counts refers to the frequency of mentions for each adaptation element in NDCs (N = 53) and NAPs (N = 15). For hazards and systems at risk, the absolute number represents the number of
unique hazard and systems at risk categories identified in a document (15 and 15, respectively).

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 5 | Consistency between adaptation cycle components
Consistency is indicated by the number of linkages between adaptation cycle components for each NDC (N = 53) and NAP (N = 15). Total linkages represents the sum across linkages between
all six combinations of components. Values of 1 indicate presence of a linkage, empty cells indicate absence. Adaptation cycle components abbreviated for table format.

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 6 | Robustness of documents
NDCs (N = 53) and NAPs (N = 15) that fulfill criteria of robustness, by robustness characteristics. “Rob.” refers to the number of robustness characteristics fulfilled by the document, by averaging
information for each country-document combination. “Cl”-“Oc” indicate number of indicators for each country-document combination fulfilling function characteristics, namely climate,
input, processes, outputs and outcomes, respectively; “S”-“T + ” indicate number of indicators for each country-document combination fulfilling SMART design characteristics, namely
specific, measurable, with assigned data sources, relevant, time-bound and with a target, respectively.

Nature Climate Change
Analysis
https://doi.org/10.1038/s41558-024-02054-7
Extended Data Table 7 | Adaptation targets in NDCs and NAPs with no indicators
Subset of NDCs (N = 34) and NAPs (N = 3) that do not identify indicators, but include targets linked to goals, objectives, and actions. Target n refers to the total number of targets that link to
a goal, objective or an action identified in the document. Target % indicates the proportion of targets from all observations extracted from the document. “Goal”-“Action” indicate numbers
of targets linked to a goal, objective, or action, respectively. “Measurable”-“Measurable & Timebound” indicates the proportion of targets that are measurable, timebound, or measurable &
timebound, respectively calculated from Targets n. Empty cells indicate no target meeting the specific characteristic.

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    Scientific Research Findings:

Answer: Despite variation among countries and documents, most African NAPs and NDCs provide only a fraction of the information fundamental for adaptation tracking. Only eight NAPs and four NDCs covered information on all key elements of the adaptation cycle: climate hazards, systems at risk, goals, objectives, actions and indicators. Six NAPs and five NDCs presented fully consistent narratives, articulating linkages between risk assessments, adaptation planning, implementation and tracking. Ten NAPs and five NDCs included adaptation indicators, yet no indicator set was fully robust. This calls into question the utility of existing indicators in tracking adaptation progress. NAPs provide a more adequate basis for tracking compared with NDCs, underscoring the importance of accelerating NAP development and implementation as a pathway towards improving tracking infrastructure. Examining the coverage, consistency and robustness of these documents provides a benchmark for policy adequacy and reveals opportunities for advancing adaptation tracking.