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Article

Factors Contributing to Effective Climate Change Adaptation Projects in Water Management: Implications from the Developing Countries

by
Yuki Shiga
* and
Rajib Shaw
Graduate School of Media and Governance, Keio University, 5322 Endo, Fujisawa 252-0882, Japan
*
Author to whom correspondence should be addressed.
Climate 2024, 12(12), 217; https://doi.org/10.3390/cli12120217
Submission received: 29 September 2024 / Revised: 7 November 2024 / Accepted: 26 November 2024 / Published: 10 December 2024
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Abstract

The adaptation finance gap is widening as the impact of climate change grows more disruptive around the globe. Although progress in adaptation planning and implementation has been observed across all sectors and regions, this trend of a widening resource gap calls for more ‘effective’ climate adaptation projects. Therefore, the purpose of this paper is to provide a comprehensive analysis to explore potential factors contributing to the effectiveness of climate change projects in developing countries with a particular focus on water management financed under multilateral funds that have been implemented on the ground, completed and documented. Thirty-five projects from the multilateral funds were collected and analyzed for this purpose. Project evaluation documents have been studied, and the effectiveness rating at completion has been assessed against possible contributing factors through regression analysis. The results showed that the factors contributing to project effectiveness converge around several elements: (i) capacity building and education (|r| > 0.3); (ii) healthy and resilient livelihoods (|r| > 0.2); and (iii) climate data and a robust theory of change (stated by >30% of projects). The implications from this study can provide a useful quantitative ground for discussion around the effective adaptation projects in water management as well as inform relevant international processes such as the Global Goal on Adaptation and global stocktake.

1. Introduction

The adaptation finance gap is expanding at an unprecedented speed as the impact of climate change becomes more intense and widespread around the globe [1,2]. UNEP [2] estimates that the demands for adaptation financing are 10–18 times higher than the available international finance flows, a 50% increase over earlier estimates. While all sectors and regions have seen advancements in adaptation planning and implementation [1], the trend of a widening resource gap necessitates more ‘effective’ climate adaptation projects. Therefore, there is a vast and growing recognition of the need for more research in this area.
In fact, the literature on climate change adaptation is fast-growing. Nevertheless, relatively few studies in the literature discuss adaptation actions that have actually taken place on the ground due to reasons such as information and data availability [3,4]. While how effectiveness should be measured, is defined, or framed in climate change adaptation projects has been studied [5,6,7], few attempts have been made to explore factors contributing to effective climate change adaptation projects. Challenges in linking project activities with adaptation outcomes over an appropriate timescale and the lack of well-established methodologies, particularly to aggregate adaptation outcomes and compare progress beyond the context, are just some of the reasons for this research gap [4,5,8,9,10]. General principles, such as local community engagement and investment in capacity building, have been proposed to ensure adaptation actions are relevant, sustainable, equitable, and effective, although these are rather based on consultations with relevant stakeholders or a limited number of case studies and are not always backed up by actual projects on the ground at the cross-portfolio level [7,10,11,12,13]. Shiga and Shaw [14] have identified four potential elements for effective climate adaptation projects based on a comprehensive and quantitative analysis, although the research was limited to adaptation projects in the agriculture sector.
Therefore, the purpose of this paper is to provide a comprehensive analysis to explore potential factors contributing to the effectiveness of climate change projects financed under multilateral funds, the main source of climate change adaptation projects in developing countries [4], that have been implemented on the ground, completed, and documented with a particular focus on water management, an important area in climate adaptation [2]. Around the globe, water security is being affected by the intensified hydrological cycle due to the changing climate [15]. The majority of these impacts are negative and felt disproportionally by vulnerable communities especially in the Global South—with every degree of global warming, more people are projected to face greater water-related risk [15,16,17]. In fact, water is the second largest sector only after agriculture in terms of the number of projects supported by the Least Developed Countries Fund (LDCF) and Special Climate Change Fund (SCCF), two funds dedicated to addressing adaptation priorities of the countries managed by the Global Environment Facility (GEF) [18].
The study will build on and is expected to complement the findings from the earlier research on adaptation projects in agriculture [14].

2. Materials

Project implementation documents from the multilateral funds for climate change adaptation projects with a particular focus on water management in developing countries, implemented on the ground and completed, were studied in this paper. Project implementation documents for such projects were available for a total of 41 projects from the Adaptation Fund (AF), LDCF, and SCCF. Three funds serve the United Nations Framework Convention on Climate Change and the Paris Agreement as financial instruments and support Parties in implementing their strategies and priorities on climate adaptation. The documents were collected from the respective publicly accessible online databases in April 2024 (Appendix A).
Three funds mandate terminal evaluation at project completion [19,20]. In these terminal evaluations, the effectiveness of a project is assessed and rated per the actual outcomes of the project, or by the extent to which the project’s actual outcomes are commensurate with the project objectives and targets [19,20]. The rating is on a six-point scale: six or ‘highly satisfactory’ being the highest and one or ‘highly unsatisfactory’ being the lowest. Ratings of four or above were considered as a satisfactory range, while those of three or lower were regarded as an unsatisfactory range. The ratings and related information were available in English for most of the projects except for one that was excluded from the sample cohort. These ratings are subject to the evaluator’s discretion to an extent, but the documents and the ratings underwent a validation process that involves being reviewed by a quality assurance entity for the LDCF and SCCF projects [20], while AF projects were evaluated by an independent evaluator [19]. Therefore, while the studied effectiveness evaluations were from indirect sources, information provided in the terminal evaluations was considered to be of good quality and credible [21] for the purpose of this study, while two projects with low monitoring and evaluation design quality were further excluded from the cohort. In addition, a comparison was made between the evaluator’s rating and that of the quality assurance entity to check the consistency between the two ratings, where possible. The ratings of the two overall agreed well. Where a discrepancy in rating by one point scale between the two was found, a lower rating was used for this study to be on the conservative side. Three projects that had a discrepancy by two points or more were excluded from the sample cohort.
The final sample cohort consisted of 35 projects in total. A complete list of projects can be found in Appendix A. Among these, LDCF had the most projects, totaling 17. SCCF and AF had 12 and six projects, respectively. Approximately half of the projects, or 17 projects, were implemented in Africa. This was followed by Asia and Latin America, where 11 and seven projects, respectively, were implemented. These projects had a primary focus on water management, such as water supply systems and flood management to adapt to climate change.
Figure 1 summarizes the sample cohort’s project effectiveness rating distribution. Eighty-three percent of the projects were in the satisfactory range or had an effectiveness rating of four or above. Six percent of the projects, i.e., two projects, received the highest rating of six. On the other hand, 17% of the projects, six projects in total, were in the unsatisfactory range, and all such projects had an effectiveness rating of three.

3. Methods

First, the respective project documents of the highest rated (HR) and the lowest rated (LR) projects were carefully scanned to identify and extract key factors contributing to effectiveness (project document analysis). Types and credibility of the project documents studied are elaborated in Section 2. HR and LR projects in the cohort were projects with ratings of six and three, respectively (Figure 1). Next, possible country level factors were also examined against effectiveness ratings to cross-complement with the implications from the project document analysis (country level analysis). The following sub-sections discuss the two analyses in detail.

3.1. Project Document Analysis

Key factors contributing to the effectiveness of HR and LR projects were identified and extracted from the respective project documents to examine any trends. The highest and lowest rated projects were selected for the analysis to ensure that the factors mentioned in the documents had more likely and meaningfully contributed to the effectiveness of the projects on the ground. All of the projects reported one or more enablers or barriers to effectiveness. No project reported none. Enablers and barriers were extracted only if the project document explicitly stated that they had contributed to the outcome or the effectiveness. These enablers and barriers were recorded in a spreadsheet application and categorized into several groups, which are summarized in Figure 2. The number of projects in each category was counted and compared to determine the overall trend.

3.2. Country Level Analysis

Different country level factors were also examined to delineate the relationship between the effectiveness of the climate change adaptation projects and the background status of the countries where the projects had been implemented. Development projects are known to have a reciprocal association with country level indicators, meaning that projects are expected to improve the country level indicators, while country level indicators, such as the Human Development Index (HDI) and those related to governance, can also have an influence over the projects [22,23]. Therefore, different country level indicators were used for this purpose to determine if such indicators had an influence over the projects’ effectiveness. Country level indicators examined were (i) population; (ii) gross national income (GNI); (iii) GNI per capita; (iv) Corruption Perception Index (CPI) value; (v) Gender Inequality Index (GII) value; (vi) HDI value; and (vii) Inequality-adjusted HDI (IHDI) value. These indicators along with a brief description of key indicators are summarized in Table 1. In order to ensure comparability across projects that were implemented in different years, the 2018 values were referred to match the average and median completion year of the projects studied in this paper. Regression analysis was conducted, taking effectiveness ratings as a dependent variable and country level indicators as an independent variable to examine the correlation between the two. r values of the regression analysis were interpreted per the criteria commonly used in political science [24]. That is, |r| < 0.2, 0.3 > |r| > 0.2, 0.6 > |r| > 0.3, and |r| > 0.6 were interpreted as negligible/weak, moderate, strong, and very strong correlations, respectively.

4. Results

4.1. Project Document Analysis

Factors contributing to the effectiveness of the climate change adaptation projects with a particular focus on water management were identified and extracted from the project documents. Figure 2 is the summary of factors contributing to the effectiveness of HR and LR projects. As barriers identified in LR projects were often a lack of enablers mentioned in HR projects, enablers and barriers are summarized in an integrated manner. For instance, ‘institutional and technical capacity’ in Figure 2 includes both HR projects that mentioned ‘institutional and technical capacity’ as an enabler as well as LR projects that mentioned lack of ‘institutional and technical capacity’ as a factor contributing to an unsatisfactory project outcome.
Four factors namely, institutional and technical capacity, climate data and the theory of change, economic feasibility/improved income, and partnership and coordination, were mentioned by more than half of the projects (Figure 2). Among these, institutional and technical capacity was mentioned the most, or by 63% of the projects. Institutional and technical capacity includes both the quantity and quality of technical support from experts as well as the administrative capacity of the local partners. Quantity and quality of data and sound theory of change based on such data mentioned in the project documents were counted towards the climate data and the theory of change. Sound theory of change can be designed based on the evidence or robust data [28]. For example, an issue with data collected through the national hydromet monitoring network established during the Soviet era, mentioned by one project [29], is included in this category. Commitment/ownership/leadership, alignment with local policies, awareness raising, governance stability (due to situations such as conflict and unrest), and local community engagement and empowerment were mentioned by a quarter or more. While 38% of the projects mentioned commitment/ownership/leadership, the other four factors were mentioned by a quarter of the projects, respectively.
Table 2 summarizes the results of the project document analysis. The table also refers to the results from the adaptation projects in agriculture studied by Shiga and Shaw [14] for comparison.

4.2. Country Level Analysis

Correlations between different country level indicators and effectiveness ratings were examined through regression analysis. Correlation values and statistical significance were calculated for seven country level indicators explained in Section 3.2. The results are summarized in Table 3. Similarly to Table 2, Table 3 also refers to the results for the agriculture projects from Shiga and Shaw [14] for comparison.
While a strong correlation was found between the effectiveness ratings and IHDI values of the countries in which projects took place (r: 0.310; p < 0.1), no statistically significant correlation was found against HDI. Figure 3a is a boxplot for the IHDI against the effectiveness rating. IHDI is considered the actual level of human development, while HDI is viewed as the potential level of human development if there were no inequality [30]. Therefore, three dimensions of IHDI (Table 1) were further examined to explore the contributing factors in more detail. These indicators were (vii-1) inequality-adjusted life expectancy index; (vii-2) inequality-adjusted education index; and, (vii-3) inequality-adjusted income index. Similarly to IHDI, these inequality-adjusted indexes are considered to be actual levels of life expectancy, education and income, respectively. The correlation values between effectiveness ratings and these country level indicator values, that is, inequality-adjusted life expectancy index, inequality-adjusted education index, and inequality-adjusted income index, were 0.284, 0.322, and 0.203, respectively (Table 3). Among these, the inequality-adjusted life expectancy index and inequality-adjusted education index were found to have statistical significance at p < 0.1. Figure 3b and Figure 3c are boxplots for the two indexes, respectively. The regression coefficient between the effectiveness rating and inequality-adjusted life expectancy index was 1.6, whereas that of the inequality-adjusted education index was 1.7.

5. Discussion

Factors contributing to the effectiveness of climate adaptation projects in water management were examined through project document as well as country level analyses for 35 project samples. These factors, while interlinked, appeared to converge around several elements. Some of these elements were similar to those implied in the climate adaptation projects in agriculture [14]. These were (i) capacity building and education and (ii) healthy and resilient livelihoods. On the other hand, the contribution from (iii) local engagement and social inclusion, which appeared to be a strong element for agriculture projects, seemed to be limited for the water management projects. In contrast, (iv) the availability of climate data and the robustness of the theory of change, was more significant for water management projects than for agriculture projects.

5.1. Capacity Building and Education

Several contributing factors appeared to converge around capacity building and education, which was also evident in the climate change adaptation projects in the agriculture sector (Table 2 and Table 3). Project document analysis found that institutional and technical capacity building was a factor contributing to the effectiveness of a majority of the projects studied (63%) (Figure 2). Institutional and technical capacity building was the only contributing factor mentioned by more than half of the projects. For example, the intended impact of one project in Africa, despite having delivered the planned water technologies such as water harvesting practices and irrigation pumps, was questionable due to insufficient technical support, both in terms of quantity and quality, provided to ensure the new technologies could be properly adopted [31]. Considering the complexity and integrated nature of different technologies involved in water management, a significant amount and diversity of technical support had to be provided for the recipients to fully adopt the technologies. On the other hand, another project that paid close attention to the capacity of the beneficiaries not only succeeded in developing good capacity at the municipality and village levels that could be used beyond the project period, but was further able to incorporate local level knowledge and expertise in creating a sustainable structure for protecting the river bank (boulder dumping), which led to effective project outcomes [32].
This trend was further confirmed by a country level analysis that examined the correlations between different country level indicators and effectiveness ratings. A positive and strong correlation (r: 0.322) was found between the effectiveness rating and the inequality-adjusted education index or the actual level of education, when inequality was considered, in the countries where the projects had taken place. This implied that effective projects can be expected more in countries with a higher educational level. The regression coefficient was 1.7, meaning for every increase in the inequality-adjusted education index by 0.1 (scale: 0–1; a higher value indicates a higher actual education level of the country), the effectiveness rating can be expected to increase by around 0.2 (scale: 0–6; a higher value indicates more effective project outcome). This result, which suggested a positive correlation between project effectiveness and the indicator, which had been adjusted per the inequality of the country, also implied the importance of building capacity at all levels, but particularly for those groups in the most disadvantageous positions, to close the inequality gap. Although investing in capacity building, especially for the most vulnerable, is not a new idea in climate adaptation [7,12] (Table 1), previous studies were based mostly on dialogue with stakeholders or a limited number of case studies. Therefore, the finding could provide a quantitative basis for such an argument.

5.2. Healthy and Resilient Livelihoods

Several other factors appeared to also converge around ‘healthy and resilient livelihoods’. Though the correlation was not as strong as that with the inequality-adjusted education index, projects in countries with a higher inequality-adjusted life expectancy appeared to have better chances for higher effectiveness ratings than projects in countries with a lower inequality-adjusted life expectancy (r: 0.284). The regression coefficient was 1.6, meaning for every increase in the inequality-adjusted life expectancy index (scale: 0–1; a higher value indicates a higher actual life-expectancy level of the country) by 0.1, the effectiveness rating can be expected to increase by around 0.2. Life expectancy is considered a proxy for the overall health and well-being of a population [33]. While health was not explicitly mentioned as a factor contributing to project effectiveness in the project documents, several projects stated a contribution to the health of the population. For example, a project in the Pacific is helping to reduce or eliminate the use of contaminated water, which, in turn, is further expected to reduce the incidence of water-borne diseases, therefore lowering health treatment costs [34]. While health is quickly gaining more attention in the climate adaptation arena [1], compared to other areas such as agriculture and water, it still remains a relatively small area in terms of financing and the number of projects [18,35,36]. The results are aligned with a study by Alcayna et al. [37], which found that health is largely a co-benefit and not a principal objective of the majority of climate adaptation projects. Considering the significant adverse impacts of climate change on human health, both physically and mentally [1], this implication may warrant further study to enhance actions in this area.
In addition, economic feasibility or improved income was mentioned as a factor contributing to effectiveness by more than 30% of the projects (Table 2). This was at the same level as agriculture projects. One project in Asia achieved and demonstrated actual water savings and water productivity increases by nearly 60% and 50%, respectively, with minimal investments by improving and making use of the existing infrastructure without investing in new and more costly infrastructure, thereby contributing to overall project success [38]. In contrast, another project lacked a tariff collection system for the water supply system to cover its operating and maintenance costs, which resulted in unsustainable and ineffective project outcomes [31]. However, the correlations between project effectiveness and GNI, GNI per capita (PPP) and the inequality-adjusted income index were not statistically significant (Table 3). These results implied that while the economic feasibility of, or income improved through, the projects could yield a certain level of positive contribution towards the effective outcomes, better economic conditions or a higher income level of the population itself does not necessarily contribute to that effectiveness.

5.3. Local Engagement and Social Inclusion

Contrarily to the abovementioned two elements, unlike agriculture projects, notable contributions towards project effectiveness from factors around local engagement and social inclusion were not identified in the water management projects through either project document or country level analyses (Table 2 and Table 3). While local community engagement was mentioned somewhat by >10% of the projects for water management projects, it was mentioned by a more moderate share, >30%, of agriculture projects. In addition, regression analysis showed that the correlation between project effectiveness ratings and GII was statistically insignificant (p > 0.1). For the agriculture projects, the correlation between effectiveness rating and GII was strong and statistically significant (r: 0.318; p < 0.1).
Local engagement and social inclusion are important also for water management projects, particularly in terms of understanding the needs and attaining social acceptance. For example, Leal Filho et al. [39] and Zvobgo et al. [40] claim that incorporating the local, traditional, and indigenous knowledge embedded in particular systems of cultural meaning or location into the planning and execution of adaptation interventions can strengthen local support for, and ownership of, these interventions, which can, in turn, boost long-lasting outcomes. However, the above finding of comparative insignificance of local engagement and social inclusion for water projects could possibly be explained by agriculture projects being relatively more reliant on soft technologies or knowledge of the wider population of the local community. Local farmers, both men and women, are the ones that need to be trained in the agriculture projects. Therefore, their involvement is critical. On the other hand, while capacity building is also important for water management projects, water management projects tend to rely relatively more on complex and larger-scale hard technologies (e.g., water distribution infrastructure and systems) compared to agriculture projects. Therefore, the capacity building involved in a water management project is not necessarily always relevant for the entire community, but more for a specific and targeted group of people, such as technicians for operation and maintenance, thereby lowering the relative importance of factors around local engagement and social inclusion.

5.4. Climate Data and Theory of Change

Another noticeable element identified for water management projects was the availability of climate data and the robustness of the theory of change, as this marked a stark difference with agriculture projects. More than 30% of water management projects mentioned climate data and theory of change as a contributing factor, while this was limited in agriculture projects (<10%) (Table 2). One possible explanation could be that the quality and quantity of data required for future climate change projections and modeling are more significant for water management projects. For example, one project reported an issue with the quality of data collected through the national hydromet monitoring network established during the Soviet era, which hindered the development of climate risk mapping [29]. It is also worth noting the significance of the systemic perspective, or robust theory of change, as water management is rather complex. For instance, despite having successfully increased the quantity of water, another project had an unsatisfactory rating because it failed to fully supply those in need due to a lack of attention to the distribution network, which was found to have water losses, and to the system to recover the cost (e.g., tariffs and water meters) for sustainable operation and maintenance [31].

6. Conclusions

The project documents were examined to identify factors contributing to project effectiveness. In addition, different country level factors were also analyzed against the effectiveness of 35 climate change adaptation projects in developing countries with a particular focus on water management, implemented on the ground, and completed. The results implied that the factors contributing to effectiveness, on a quantitative basis, converged around several elements.
First, capacity building and education was identified as a contributing element. A positive and strong correlation (r: 0.322) between the inequality-adjusted education index or the actual educational level of a country and the effectiveness rating was found. This implied that effective projects can be expected more in countries with a higher actual educational level. This finding was consistent with that from the project document analysis. Sixty-three percent of the projects mentioned that institutional and technical capacity building was a factor contributing to project effectiveness.
Second, healthy and resilient livelihoods were also implied to contribute to project effectiveness. Projects in countries with a higher inequality-adjusted life expectancy, or overall actual health and well-being of the population, have shown better potential for higher effectiveness ratings than projects in countries with a lower inequality-adjusted life expectancy (r: 0.284). In addition, economic feasibility or improved income was mentioned as a factor contributing to effectiveness by more than 30% of the project cohort. These results implied that the economic feasibility of or improved income through the projects could have a certain level of positive contribution toward effective outcomes. However, it should be noted that better economic conditions or a higher income level of the population as a background did not necessarily contribute to project effectiveness. The correlations between project effectiveness ratings and GNI, GNI per capita (PPP), and the inequality-adjusted income index were not statistically significant (Table 3).
On the other hand, in contrast to the agriculture projects, notable contributions to project effectiveness ratings from factors around local engagement and social inclusion were not found. This finding does not necessarily deny the importance of local engagement and social inclusion in water management projects, particularly in terms of understanding the needs and attaining social acceptance. However, it could possibly be explained by the nature of water management projects that tend to rely on complex and large-scale systems and infrastructure, thereby placing importance on targeted and focused groups of people such as operators and maintenance technicians rather than the entire community.
Another noticeable element identified for water management projects was the availability of climate data and the robustness of the theory of change, as this also marked a stark difference against the agriculture projects. More than 30% of water management projects mentioned climate data and theory of change as a contributing factor, while it was similarly cited by less than 10% of climate adaptation projects in agriculture (Table 2).
This study has identified several potential elements for effective climate change adaptation projects in water management; however, it should be noted that climate change adaptation projects are often local and context-specific [41,42,43]. Therefore, these elements should be regarded as elements that have worked on the ground, but not in any context. In fact, none of the elements found in this study were definite—for example, there were effective projects in countries with lower educational levels and vice versa (Figure 3). Therefore, further study may be warranted to examine those projects that could not be well explained by the elements identified in this study. In addition, although the study aimed to be as comprehensive as possible in terms of project coverage, analyses were based entirely on accessible documents. Nevertheless, the implications from this study can provide a useful quantitative basis for discussion around the effective adaptation projects in water management as well as inform relevant international processes such as the Global Goal on Adaptation and global stocktake.

Author Contributions

The manuscript was prepared by Y.S. with inputs from R.S. Analysis was conducted by Y.S. Overall supervision was provided by R.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The original contributions presented in the study are included in the article; further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. A list of projects analyzed.
Table A1. A list of projects analyzed.
Project TitleRegion *Fund †
Adaptation of Nicaragua’s Water Supplies to Climate ChangeLACSCCF
Adaptation to Climate Impacts in Water Regulation and Supply for the Area of Chingaza–Sumapaz–GuerreroLACSCCF
Adapting Water Resource Management in Comoros to Increase Capacity to Cope with Climate ChangeAFRLDCF
Addressing Climate Change Risks on Water Resources in Honduras: Increased Systemic Resilience and Reduced Vulnerability of the Urban PoorLACAF
Addressing climate change risks to farming systems in Turkmenistan at national and community levelECAAF
Building Adaptive Capacity and Resilience to Climate Change in the Water Sector in Cape VerdeAFRLDCF
Building Adaptive Capacity to Catalyze Active Public and Private Sector Participation to Manage the Exposure and Sensitivity of Water Supply Services to Climate Change in Sierra LeoneAFRLDCF
Climate Change Adaptation Project in the Areas of Watershed Management and Water Retention (PAFA)AFRLDCF
Climate Change Adaptation to Reduce Land Degradation in Fragile Micro-Watersheds Located in the Municipalities of Texistepeque and Candelaria de la FronteraLACSCCF
Climate Proofing Local Development Gains in Rural and Urban Areas of Machinga and Mangochi DistrictsAFRLDCF
Developing Climate Resilient Flood and Flash Flood Management Practices to Protect Vulnerable Communities of GeorgiaECAAF
Ecosystem Based Adaptation to Climate Change in SeychellesAFRAF
GGW: Agriculture Production Support Project (with Sustainable Land and Water Management)AFRLDCF
Implementing NAPA Priority Interventions to Build Resilience in the Agriculture and Water Sectors to the Adverse Impacts of Climate ChangeAFRLDCF
Increased Resilience to Climate Change in Northern Ghana through the Management of Water Resources and Diversification of LivelihoodsAFRAF
Increasing Resilience of Ecosystems and Vulnerable Communities to CC and Anthropic Threats Through a Ridge to Reef Approach to BD Conservation and Watershed ManagementLACLDCF
Increasing Resilience to Climate Variability and HazardsAPLDCF
Integrating climate change risks into water and flood management by vulnerable mountainous communities in the Greater Caucasus region of AzerbaijanECASCCF
Mainstreaming Adaptation to Climate Change into Water Resources Management and Rural DevelopmentAsiaSCCF
Mainstreaming Climate Change in Integrated Water Resources Management in Pangani River BasinAFRSCCF
Promoting Climate-resilient Development and Enhanced Adaptive Capacity to Withstand Disaster Risks in Angola’s Cuvelai River BasinAFRLDCF
Promoting Climate-Resilient Water Management and Agricultural PracticesAPLDCF
Reducing the Vulnerability of Cambodian Rural Livelihoods through Enhanced sub-national Climate Change Planning and Execution of Priority ActionsAPLDCF
Reducing Vulnerability from Climate Change in the Foothills, Lowlands and the Lower Senqu River Basin AFRLDCF
Reduction of Risks and Vulnerability Based on Flooding and Droughts in the Estero Real WatershedLACAF
Scaling Up Community Resilience to Climate Variability and Climate Change in Northern Namibia, with a Special Focus on Women and ChildrenAFRSCCF
Strategic Planning and Action to Strengthen Climate Resilience of Rural Communities in Nusa Tenggara Timor Province (SPARC)APSCCF
Strengthening Capacities of Rural Aqueduct Associations’ (ASADAS) to Address Climate Change Risks in Water Stressed Communities of Northern Costa RicaLACSCCF
Strengthening Resilience and Adaptive Capacity to Climate Change in Guinea-Bissau’s Agrarian and Water SectorsAFRLDCF
Strengthening the Resilience of Rural Livelihood Options for Afghan Communities in Panjshir, Balkh, Uruzgan and Herat Provinces to Manage Climate Change-induced Disaster RisksAPLDCF
Strengthening the Resilience of Women Producer Group’s and Vulnerable Communities in MaliAFRLDCF
Supporting Climate Resilient Livelihoods in Agricultural Communities in Drought-prone AreasECASCCF
Supporting Rural Community Adaptation to Climate Change in Mountain Regions of DjiboutiAFRLDCF
To Promote the Implementation of National and Transboundary Integrated Water Resource Management that is Sustainable and Equitable Given Expected Climate ChangeAFRSCCF
TT-Pilot (GEF-4) DHRS: Irrigation Technology Pilot Project to Face Climate Change ImpactAPSCCF
*: AFR—Africa; AP—Asia and the Pacific; ECA—East and Central Asia; LAC—Latin America and Caribbean. †: Source: LDCF/SCCF projects—https://www.thegef.org/projects-operations/database (accessed on 25 November 2024). AF projects—https://www.adaptation-fund.org/projects-programmes/ (accessed on 25 November 2024).

References

  1. IPCC (Intergovernmental Panel on Climate Change). Summary for Policymakers. In Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Pörtner, H.-O., Roberts, D.C., Poloczanska, E.S., Mintenbeck, K., Tignor, M., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; pp. 3–33. [Google Scholar] [CrossRef]
  2. UNEP (United Nations Environment Programme). Emissions Gap Report 2023: Broken Record—Temperatures Hit New Highs, Yet World Fails to Cut Emissions (Again); UNEP: Nairobi, Kenya, 2023. Available online: https://www.unep.org/resources/emissions-gap-report-2023 (accessed on 16 December 2023).
  3. Berrang-Ford, L.; Siders, A.R.; Lesnikowski, A.; Fischer, A.P.; Callaghan, M.W.; Haddaway, N.R.; Mach, K.J.; Araos, M.; Shah, M.A.R.; Wannewitz, M.; et al. A systematic global stocktake of evidence on human adaptation to climate change. Nat. Clim. Chang. 2021, 11, 989–1000. [Google Scholar] [CrossRef]
  4. Canales, N.; Klein, R.J.T.; Bakhtaoui, I.; Macura, B. Assessing adaptation progress for the global stocktake. Nat. Clim. Chang. 2023, 13, 413–414. [Google Scholar] [CrossRef]
  5. Leiter, T. Assessing results of climate change adaptation projects in practice: Learning from German Technical Development Cooperation. In Adaptation Metrics: Perspectives on Measuring, Aggregating and Comparing Adaptation Results; Christiansen, L., Martinez, G., Naswa, P., Eds.; UNEP DTU: Partnership, Copenhagen, 2018; pp. 139–155. [Google Scholar]
  6. Owen, G. What makes climate change adaptation effective? A systematic review of the literature. Glob. Environ. Chang. 2020, 62, 102071. [Google Scholar] [CrossRef]
  7. Singh, C.; Iyer, S.; New, M.G.; Few, R.; Kuchimanchi, B.; Segnon, A.C.; Morchain, D. Interrogating ‘effectiveness’ in climate change adaptation: 11 guiding principles for adaptation research and practice. Clim. Dev. 2021, 14, 650–664. [Google Scholar] [CrossRef]
  8. Michaelowa, A.; Stadelmann, M. Development of universal metrics for adaptation effectiveness. In Adaptation Metrics: Perspectives on Measuring, Aggregating and Comparing Adaptation Results; Christiansen, L., Martinez, G., Naswa, P., Eds.; UNEP DTU Partnership: Copenhagen, Denmark, 2018; pp. 63–72. [Google Scholar]
  9. Leiter, T.; Olhoff, A.; Al Azar, R.; Barmby, V.; Bours, D.; Clement, V.W.C.; Dale, T.W.; Davies, C.; Jacobs, H. Adaptation Metrics: Current Landscape and Evolving Practices; UNEP DTU Partnership: Rotterdam, The Netherlands; Washington, DC, USA, 2019. [Google Scholar]
  10. UNEP. Adaptation Gap Report 2022: Too Little, Too Slow—Climate Adaptation Failure Puts World at Risk; UNEP: Nairobi, Kenya, 2022. Available online: https://www.unep.org/adaptation-gap-report-2022 (accessed on 30 December 2023).
  11. Brooks, N.; Anderson, S.; Aragon, I.; Smith, B.; Kajumba, T.; Beauchamp, E.; d’Errico, S.; Rai, N. Framing and Tracking 21st Century Climate Adaptation: Monitoring, Evaluation and Learning for Paris, the SDGs and Beyond; IIED Working Paper; IIED: London, UK, 2019. [Google Scholar]
  12. Soanes, M.; Bahadur, A.V.; Shakya, C.; Smith, B.; Patel, S.; Rumbaitis del Rio, C.; Coger, T.; Dinshaw, A.; Patel, S.; Huq, S.; et al. Principles for Locally Led Adaptation—A Call to Action; IIED: London, UK, 2021. [Google Scholar]
  13. Vij, S.; Biesbroek, R.; Stock, R.; Gardezi, M.; Ishtiaque, A.; Groot, A.; Termeer, K. Power-sensitive Design Principles’ for Climate Change Adaptation Policy-making in South Asia. Earth Syst. Gov. 2021, 9, 100109. [Google Scholar] [CrossRef]
  14. Shiga, Y.; Shaw, R.; (Graduate School of Media and Governance, Keio University, Fujisawa, Japan). Unpublished manuscript in preparation, 2024.
  15. Caretta, M.A.; Mukherji, A.; Arfanuzzaman, M.; Betts, R.A.; Gelfan, A.; Hirabayashi, Y.; Lissner, T.K.; Liu, J.; Lopez Gunn, E.; Morgan, R.; et al. Water. In Climate Change 2022: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change; Pörtner, H.-O., Roberts, D.C., Tignor, M., Poloczanska, E.S., Mintenbeck, K., Alegría, A., Craig, M., Langsdorf, S., Löschke, S., Möller, V., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2022; pp. 551–712. [Google Scholar] [CrossRef]
  16. Jiménez Cisneros, B.E.; Oki, T.; Arnell, N.W.; Benito, G.; Cogley, J.G.; Döll, P.; Jiang, T.; Mwakalila, S.S. Freshwater Resources in Climate Change 2014: Impacts, Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel of Climate Change; Field, C.B., Barros, V.R., Dokken, D.J., Mach, K.J., Mastrandrea, M.D., Bilir, T.E., Chatterjee, M., Ebi, K.L., Estrada, Y.O., Genova, R.C., et al., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014; pp. 229–269. [Google Scholar]
  17. WRI (World Resources Institute). Unaffordable and Undrinkable: Rethinking Urban Water Access in the Global South; World Resources Institute: Washington, DC, USA, 2019. Available online: https://www.wri.org/research/unaffordable-and-undrinkable-rethinking-urban-water-access-global-south (accessed on 25 October 2024).
  18. GEF (Global Environment Facility). Progress Report on the Least Developed Countries Fund and the Special Climate Change Fund. 2024. Available online: https://www.thegef.org/council-meeting-documents/gef-ldcf-sccf-36-05 (accessed on 13 August 2024).
  19. AF (Adaptation Fund). Guidelines For Adaptation Fund Project/Programme Final Evaluations; Adaptation Fund: Washington, DC, USA, 2015. Available online: https://www.adaptation-fund.org/document/guidelines-for-projectprogramme-final-evaluations/ (accessed on 18 December 2023).
  20. GEF IEO (Global Environment Facility Independent Evaluation Office). Guidelines for GEF Agencies in Conducting Terminal Evaluation for Full-Sized Projects; Global Environment Facility Independent Evaluation Office: Washington, DC, USA, 2017. Available online: https://www.gefieo.org/sites/default/files/documents/evaluations/gef-guidelines-te-fsp-2017.pdf (accessed on 18 December 2023).
  21. GEF IEO. Evaluation of the Agency Self Evaluation Systems; GEF IEO: Washington, DC, USA, 2020. Available online: https://www.gefieo.org/sites/default/files/documents/agency-self-evaluation-approach-paper.pdf (accessed on 18 December 2023).
  22. Benmamoun, M.; Lehnert, K. Financing growth: Comparing the effects of FDI, ODA, and international remittances. J. Econ. Dev. 2013, 38, 43. [Google Scholar] [CrossRef]
  23. Andaish, Q.; Assadi, S. A study on the effectiveness of foreign aid on human development of Afghanistan. Sustain. Technol. Entrep. 2024, 3, 100056. [Google Scholar] [CrossRef]
  24. Akoglu, H. User’s Guide to Correlation Coefficients. Turk. J. Emerg. Med. 2018, 18, 91–93. [Google Scholar] [CrossRef]
  25. World Bank. World Development Indicators; World Bank: Washington, DC, USA, 2023. Available online: http://data.worldbank.org/data-catalog/world-development-indicators (accessed on 13 August 2024).
  26. TI (Transparency International). Corruption Perceptions Index 2020; Transparency International: Berlin, Germany, 2020. Available online: https://www.transparency.org/en/publications/corruption-perceptions-index-2020 (accessed on 19 December 2023).
  27. UNDP (United Nations Development Programme). Human Development Report 2023/2024—Breaking the Gridlock: Reimagining Cooperation in a Polarized World; UNDP: New York, NY, USA, 2024. Available online: https://hdr.undp.org/sites/default/files/2023-24_HDR/HDR23-24_Composite_indices_complete_time_series.csv (accessed on 2 July 2024).
  28. UNDG (United Nations Development Group). Theory of Change UNDAF Companion Guidance. 2017. Available online: https://unsdg.un.org/resources/theory-change-undaf-companion-guidance (accessed on 24 October 2024).
  29. UNDP. Integrating Climate Change Risks into Water and Flood Management by Vulnerable Mountainous Communities in the Greater Caucasus Region of Azerbaijan. 2017. Available online: https://www.gefieo.org/sites/default/files/documents/projects/tes/4261-terminal-evaluation.pdf (accessed on 13 August 2024).
  30. UNDP. Human Development Report 2016: Human Development for Everyone; UNDP: New York, NY, USA, 2016. Available online: https://hdr.undp.org/content/human-development-report-2016 (accessed on 5 August 2024).
  31. GEF IEO. Terminal Evaluation Review Form. 2016. Available online: https://www.gefieo.org/sites/default/files/documents/projects/ters/3430-ter-2016-final.pdf (accessed on 13 August 2024).
  32. Moreta, C.B.; Skhireli, K. Terminal Evaluation Report for UNDP-Supported AF-Financed Project “Developing Climate Resilient Flood and Flash Flood Management Practices to Protect Vulnerable Communities of Georgia” Project. 2017. Available online: https://fifspubprd.azureedge.net/afdocuments/project/52/52_4583_%20terminal%20evaluation%20report%20Georgia%20_final.docx (accessed on 13 August 2024).
  33. Gulis, G. Life expectancy as an indicator of environmental health. Eur. J. Epidemiol. 2000, 16, 161–165. [Google Scholar] [CrossRef] [PubMed]
  34. World Bank. Implementation Completion and Results Report on Grants in a Total Amount of US$11.57 million to the Republic of Kiribati for The Kiribati Adaptation Program—Phase III Project. 2019. Available online: https://www.gefieo.org/sites/default/files/documents/projects/tes/4068-terminal-evaluation.pdf (accessed on 23 October 2024).
  35. AF. Endorsement of Guiding Principles for Financing Climate and Health Solutions. 2023. Available online: https://www.adaptation-fund.org/wp-content/uploads/2023/11/Guiding-Principles-for-Financing-Climate-and-Health-Solutions.pdf (accessed on 8 August 2024).
  36. GEF. FY23 Annual Monitoring Review of the Least Developed Countries Fund and the Special Climate Change Fund. 2024. Available online: https://www.thegef.org/council-meeting-documents/gef-ldcf-sccf-36-04 (accessed on 8 August 2024).
  37. Alcayna, T.; O’Donnell, D.; Chandaria, S. How much bilateral and multilateral climate adaptation finance is targeting the health sector? A scoping review of official development assistance data between 2009–2019. PLoS Glob. Public Health 2023, 3, e0001493. [Google Scholar] [CrossRef] [PubMed]
  38. World Bank. Mainstreaming Adaptation to Climate Change into Water Resources Management and Rural Development. 2012. Available online: https://www.gefieo.org/sites/default/files/documents/projects/tes/3265-terminal-evaluation.pdf (accessed on 13 August 2024).
  39. Leal Filho, W.; Wolf, F.; Totin, E.; Zvobgo, L.; Simpson, N.P.; Musiyiwa, K.; Kalangu, J.W.; Sanni, M.; Adelekan, I.; Efitre, J.; et al. Is indigenous knowledge serving climate adaptation? Evidence from various African regions. Dev. Policy Rev. 2022, 41, e12664. [Google Scholar] [CrossRef]
  40. Zvobgo, L.; Johnston, P.; Williams, P.A.; Trisos, C.H.; Simpson, N.P.; Global Adaptation Mapping Initiative Team. The role of indigenous knowledge and local knowledge in water sector adaptation to climate change in Africa: A structured assessment. Sustain. Sci. 2022, 17, 2077–2092. [Google Scholar] [CrossRef]
  41. Ford, J.D.; Berrang-Ford, L. The 4Cs of Adaptation Tracking: Consistency, Comparability, Comprehensiveness, Coherency. Mitig. Adapt. Strat. Glob. Chang. 2016, 21, 839–859. [Google Scholar] [CrossRef] [PubMed]
  42. UNEP. The Adaptation Gap Report 2017: Towards Global Assessment; United Nations Environment Programme (UNEP): Nairobi, Kenya, 2017. Available online: https://www.unenvironment.org/resources/adaptation-gap-report-2017 (accessed on 30 December 2023).
  43. Pauw, W.P.; Grüning, C.; Menzel, C. Number of Beneficiaries as an Indicator for Adaptation: Do the Numbers Add Up? 2nd ed.; GCF Monitor: Incheon, Republic of Korea, 2020. [Google Scholar]
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Figure 1. Distribution of effectiveness rating.
Figure 1. Distribution of effectiveness rating.
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Figure 2. Factors contributing to effectiveness identified in the project documents (percentage of projects reported).
Figure 2. Factors contributing to effectiveness identified in the project documents (percentage of projects reported).
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Figure 3. Boxplots of effectiveness ratings (horizontal axis) against IHDI, inequality-adjusted education index, and inequality-adjusted life expectancy index (vertical axis). (a) (top left): IHDI. (b) (top right): inequality-adjusted education index. (c) (bottom left): inequality-adjusted life expectancy index.
Figure 3. Boxplots of effectiveness ratings (horizontal axis) against IHDI, inequality-adjusted education index, and inequality-adjusted life expectancy index (vertical axis). (a) (top left): IHDI. (b) (top right): inequality-adjusted education index. (c) (bottom left): inequality-adjusted life expectancy index.
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Table 1. Summary of country level indicators.
Table 1. Summary of country level indicators.
Country Level IndicatorSourceNote
Population[25]
GNI[25]
GNI per capita (PPP)[25]
CPI[26]Perceived level of public sector corruption. A higher value indicates that a country is perceived to have a lower level of corruption. Scale: 0–100.
GII[27]Level of inequality between women and men. A higher value indicates a higher level of gender inequality in a country. Scale: 0–1.
HDI[27]Measures key dimensions of human development (health, education and income); a higher HDI value indicates a higher level of human development. Scale: 0–1.
IHDI[27]HDI adjusted for inequality across the population. Three dimensions of HDI are adjusted per the respective level of inequality. Scale: 0–1.
Table 2. A summary of project document analysis. The results for the agriculture projects are also presented for comparison. More ‘+’ indicates that the factor is more widely mentioned by the projects as a contributing factor towards effective outcomes.
Table 2. A summary of project document analysis. The results for the agriculture projects are also presented for comparison. More ‘+’ indicates that the factor is more widely mentioned by the projects as a contributing factor towards effective outcomes.
FactorAgriculture *Water Mentioned by:
Economic feasibility/improved income+++++++>50% of projects
Institutional and technical capacity++++++++>30% of projects
Local community engagement/understanding/empowerment++++>10% of projects
Awareness raising++ Negligible/none
Governance stability++
Commitment/ownership/leadership+++++
Partnership and collaboration++++
Alignment with local policies +
Adaptive approach++
Climate data/theory of change ++
*: Ref. [14].
Table 3. A summary of country level analysis. The results for the agriculture projects are also presented for comparison. More ‘+’ indicates a stronger correlation between the factor and the project’s effectiveness.
Table 3. A summary of country level analysis. The results for the agriculture projects are also presented for comparison. More ‘+’ indicates a stronger correlation between the factor and the project’s effectiveness.
Factor TypeFactorAgriculture *Water
Development
Index		HDI +++Very strong correlation
(|r| > 0.6; p < 0.1)
IHDI++++++Strong correlation
(0.6 > |r| > 0.3; p < 0.1)
EconomicGNI +Moderate correlation
(0.3 > |r| > 0.2; p < 0.1)
GNI per capita+ Negligible/weak correlation
Inequality-adjusted income index
SocialInequality-adjusted education index++++
Inequality-adjusted Life expectancy index+++
GII++
Population
GovernanceCPI
*: Ref. [14].
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Shiga, Y.; Shaw, R. Factors Contributing to Effective Climate Change Adaptation Projects in Water Management: Implications from the Developing Countries. Climate 2024, 12, 217. https://doi.org/10.3390/cli12120217

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Shiga Y, Shaw R. Factors Contributing to Effective Climate Change Adaptation Projects in Water Management: Implications from the Developing Countries. Climate. 2024; 12(12):217. https://doi.org/10.3390/cli12120217

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Shiga, Yuki, and Rajib Shaw. 2024. "Factors Contributing to Effective Climate Change Adaptation Projects in Water Management: Implications from the Developing Countries" Climate 12, no. 12: 217. https://doi.org/10.3390/cli12120217

APA Style

Shiga, Y., & Shaw, R. (2024). Factors Contributing to Effective Climate Change Adaptation Projects in Water Management: Implications from the Developing Countries. Climate, 12(12), 217. https://doi.org/10.3390/cli12120217

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