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Data-driven complementary indices and metrics for assessing national progress on climate risk and adaptation [1]

['Fidel Serrano-Candela', 'Instituto De Ecología', 'Universidad Nacional Autónoma De México', 'Mexico City', 'Francisco Estrada', 'Instituto De Ciencias De La Atmósfera Y Cambio Climático', 'Institute For Environmental Studies', 'Ivm', 'Vrije Universiteit', 'Amsterdam']

Date: 2024-12

Climate change is a complex, multidimensional issue requiring decision-making and governance supported by extensive data from social and natural systems. Large cross-country datasets are available, and various methods are used to transform this data into information relevant for policy and decision-making. Summary indices provide insights into adaptation, mitigation, vulnerability, and risk, helping track countries’ climate-related ambitions and progress. However, many existing methods for constructing indices do not fully exploit the multivariate structures within the data, leading to potential redundancies and overlaps. We develop a set of complementary, non-overlapping indices using Principal Component Analysis to capture distinct dimensions of societal and climate interactions. These data-driven indices account for underlying data structures, ensuring each provides unique and independent insights. Our analysis includes harmonized country-level datasets, metrics relevant to loss and damage, public perceptions of climate change, and projections of economic damages. The application of these indices is illustrated with dissonance metrics that assess the alignment between a country’s adaptation capacities, societal concerns, and risks. The proposed approach for index construction can be valuable across various policy contexts and for informing climate-related strategies. An online tool is provided to visualize and access the results presented in this paper.

Data Availability: The data used in this paper is available at 10.6084/m9.figshare.26962678 . An interactive platform that allows visualizing the data and these indices is available at the following link: http://multidash.apps.lancis.ecologia.unam.mx/paper_cc/ .

Copyright: © 2024 Serrano-Candela et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

The remainder of this manuscript is structured as follows. Section 2 describes the methods, and the data used in this paper. The multivariate statistical methods are briefly described, and it is discussed how they are connected in the analysis and how they are used to derive the metrics and indices. Section 3 presents and discusses the indices and metrics that are proposed, as well as the ranking of countries that are derived from them. This section also includes a link to the online tool that accompanies this paper which allows the reader to explore, download and visualize the data, metrics, and indices in more depth. Section 4 summarizes the main results and concludes.

Combining indices, particularly when they are orthogonal [ 41 ], is an approach commonly used across various fields to uncover deeper insights [ 30 ]. In the context of climate change, integrating indices related to institutional capacity, public perception, and projected risks allows us to examine how these distinct dimensions interact or diverge. Far from being a simplistic or redundant exercise, this method synthesizes independent perspectives to provide a more comprehensive understanding of the complexities at play. The use of orthogonal indices ensures that each one conveys unique information, avoiding any overlap or dilution of insights. Because the indices are independent of one another (orthogonal), combining them is not about blending similar pieces into a single, indistinct whole; rather, it is about integrating complementary, non-redundant insights. This approach is particularly valuable in generating new knowledge that individual indices might miss. By maintaining the distinctiveness of each index while exploring their relationships, the combined analysis offers a richer, more actionable understanding of the challenges and opportunities in addressing climate change.

Frameworks for understanding the risks of climate change are continually evolving [ 35 – 37 ], with the IPCC’s Sixth Assessment Report offering an updated framework that includes hazard, exposure, vulnerability, and response as key drivers of risk [ 36 , 38 , 39 ]. This expanded framework acknowledges that the interaction of social and natural systems involves multiple stressors, leading to compounding, cascading, and non-linear effects that can produce surprises. In adopting this comprehensive framework, we emphasize the role of societal responses, which are shaped by public beliefs and attitudes, as critical factors in modulating risk determining the governmental actions that may be implemented [ 40 ]. To demonstrate the practical application of the proposed indices, we construct tailored composite indices to address specific research or policy questions. In particular, we develop dissonance metrics, which combine the proposed PCA-derived indices with additional data to assess the alignment between institutional adaptation capacities, societal perceptions, and projected risks. These dissonance metrics highlight potential misalignments that may amplify risks and characterize different capacities, challenges and risks that climate change implies for various countries worldwide. Moreover, these composite indices align with the IPCC’s risk framework by integrating multiple dimensions of risk and response, providing a richer, more nuanced understanding of the multifaceted challenges posed by climate change.

In addition, the similarities between countries are analyzed using hierarchical cluster analysis. The combination of these statistical multivariate methods allows us to extract further insight from the data, improving the interpretation of indices and the ranking of countries accordingly. By not replacing but rather building upon traditional methods, the proposed approach aims to enrich the climate change literature with a more detailed, data-driven perspective that can enhance and refine existing frameworks.

However, as discussed in Scown et al. [ 26 ], evaluating the capacities, challenges and risks of countries to climate change should ideally be as holistic as possible, accounting for a wide range of contributing factors. The traditional approach for constructing climate change indices includes selecting fixed weights (like uniform) for the variables that integrate it [ 30 ]. These indices have been instrumental in providing broad overviews and assisting policy [ 31 – 33 ]. To complement these established methods, we propose an alternative approach using Rotated Principal Component/Factor Analysis to estimate from the data the appropriate weights for combining different variables [ 23 , 25 , 34 ]. This allows us to derive a set of complementary indices suggested by the data itself, rather than relying on predetermined structures. Through this data-driven approach, we can capture relationships between variables and countries that might otherwise be overlooked, providing additional robustness to the overall analysis. Moreover, the orthogonality of the indices derived from PCA ensures that each index captures a distinct aspect of climate resilience without overlapping with others. This clarity complements traditional indices, where dimensions might overlap, by adding specificity and focus to the analysis. In contrast with other indices that have been previously reported in the literature [ 18 , 27 – 29 ], we provide a non-overlapping (orthogonal) set of indices that are complementary and they jointly summarize the information contained in recently available harmonized country-level datasets about different dimensions of climate change.

Assessments like the GST require large amounts of data from a broad variety of sources. To be helpful for policy makers, such data needs to be transformed into relevant information. In practice, this means summarizing heterogeneous sources into meaningful and insightful metrics or indices, which is challenging for multi-faceted problems such as climate change and sustainability [ 20 , 22 – 25 ]. For some sub-components of climate change, however, such indices do exist, creating harmonized country-level information on Loss and Damage [ 26 ], for example. In addition, there are several indices/metrics designed to assist decision-making regarding different aspects of climate change. Such metrics and indices are illustrated by the following examples. The Climate Action Tracker helps rate, track and classify the mitigation ambitions that different countries propose in their Nationally Determined Contributions (NDC) [ 18 ], providing a benchmark for countries self-assessment and cross-country comparison. The INFORM Risk Index, produced by the European Commission, focuses on supporting decision-making regarding prevention, preparedness and response to humanitarian crisis and disasters, particularly those that can overwhelm national response capacity [ 27 , 28 ]. It is composed of four main categories of information: hazards, human exposure, societal vulnerability, and capacity to cope. Similarly, the Notre Dame Global Adaptation Initiative’s (ND-GAIN) index provides a country-level assessment of the national current vulnerability to climate disruptions [ 29 ], combining about 40 core indicators across vulnerability and readiness.

During the latest Conference of the Parties (COP28), the first Global Stocktake (GST) took place with mixed results [ 17 , 18 ]. GST is a mechanism to assess the global collective progress made to meet the goals of the Paris Agreement, focusing on long term mitigation goals, adaptation, and means for implementation. It also considers the socioeconomic consequences of response measure and address loss and damage produced by climate change [ 19 ]. This type of assessment heavily relies on the availability and quality of data, as well as on how this data is processed to make it accessible and meaningful for decision-makers and stakeholders [ 20 , 21 ].

The world has already warmed about 1.2°C with respect to preindustrial times, with 2023 being the first year to temporarily exceed the 1.5°C warming target established by the Paris Agreement [ 1 – 4 ]. A world exceeding 1.5°C warming can potentially trigger climate tipping points [ 5 ] with serious implications for natural and human systems and severe socioeconomic consequences worldwide [ 6 – 10 ]. Current warming has already induced large changes across the climate system like more intense and frequent extreme events [ 11 , 12 ], changes in global atmospheric and oceanic circulation patterns [ 13 , 14 ], changes in storm tracks [ 13 , 15 ]. Climate-related policies need to be significantly increased in the near future to meet the goals of the Paris Agreement. In particular, adaptation is urgent and must involve a variety of societal actors including government and individuals, and major shifts in perceptions are needed for transformational adaptation [ 16 ].

The measures are defined in such a way that values close to zero denote low dissonance/risk, while values close to 1 indicate high dissonance/risk. A composite index called Social and Institutional Challenge Index is defined as the average score of the individual dissonance measures and provides a summary metric of the present and future institutional and social challenges related to climate change, for each country.

The variable Y = {y 1 , y 2 ,…,y n } can be mapped onto R = {r 1 , r 2 ,…,r n } in such way that if y i is the i-th largest/smallest observation, r i is its rank, if ranked in descending/ascending order. After calculating the variables ranks, the differences d between pairs of a selection of them are calculated and the results are normalized using min-max normalization d*:

We consider that the distances between both adaptation capacities and people’s perceptions with respect to current and projected damages are central to understand the social and institutional challenges a country will face under current and future warming levels. Here we propose a procedure to assess the dissonance between current social and institutional scores related to climate adaptation, and the expected damages from climate change. The procedure consists in calculating the differences in ranking for the same country in different adaptation capacity and risk/damage indices and normalizing these differences. These individual metrics can then be combined into a composite index that conveys which countries are best prepared for climate change and which are characterized by higher dissonances between their current capacities/awareness and present and future risk. The procedure is based on rank statistics, and it is implemented as follows.

To illustrate the usefulness of the derived set of indices we combine a selection of them with data about the projected economic impacts related to different levels of warming. Social risk perceptions about climate change are highly heterogeneous among countries [ 40 , 48 , 49 ], and so are institutional strength and governance [ 50 ], as well as socioeconomic exposure, climate hazards and climate change impacts [ 11 , 42 , 51 , 52 ]. The literature on the impacts of climate change has documented over the past decades that less developed regions are likely to suffer the most [ 42 , 53 – 55 ], although these impacts can be reduced if adaptation strategies are implemented [ 56 – 58 ]. However, adaptation is expected to be harder for less developed countries because institutional strength and good governance are commonly lacking [ 21 , 59 , 60 ]. These factors have been identified as the main predictors of national adaptation capacity and precondition the existence of other requirements for adaptation [ 50 ]. Moreover, public perceptions and awareness about climate change risks have been identified as central for public engagement and climate action support [ 48 ].

Hierarchical cluster analysis is applied to the first two PCs for grouping countries in terms of the similarities expressed by these two main modes of variability. This clustering provides a space created by the two main modes of variability of the dataset to analyze the remaining factors, and to enrich their interpretation. Once the clusters are defined, the correlations between each factor and the most important variables that constitute it (i.e., those with factor loadings L≥0.6) are calculated for each cluster. These correlations constitute pseudo-factor loadings that allow to investigate the importance of the contribution of each original variable to the factor and compare them to the true factor loadings obtained for the full sample. By doing so, differences in the importance and dominance of some variables across clusters allow for a more detailed and specific interpretation of the factors.

Cluster analysis is an unsupervised learning technique that classifies similar cases or variables into groups based on the values of a data matrix X. Hierarchical clustering is the method used in this paper and it consist in initially considering every observation in the dataset as an individual cluster. Then, by means of a distance measure and a linkage rule, individual observations are progressively aggregated into clusters [ 47 ]. For the results presented here, Euclidean distances and Ward’s method are used as distance measure and linkage rule, respectively.

The PCA analysis was performed on a dataset that combines a selection of metrics relevant for losses and damages analysis and information about people’s perceptions on climate change. A sequential selection process was conducted in which PCs were calculated starting with variables in Table 1 and the existence of numerical problems due to high correlations was evaluated and variables removed. This process also helps in obtaining a more parsimonious model. The variables that were retained are shown in Table 2 .

Principal component analysis (PCA) is a statistical technique commonly used for dimension reduction by finding a limited number of linear combinations of the original variables that retain the maximum fraction possible of the variance contained in the original dataset [ 43 , 44 ]. These new variables, called principal components (PC), can also provide easier interpretation of the information contained in the original dataset, as well as to give insights about the relationships between variables, and patterns across observations [ 45 ]. which makes them particularly useful for constructing indices that may be related to latent variables. Rotated principal component analysis (rPCA) can help increase the interpretability of PCs [ 23 , 44 , 45 ]. The rotated PCs (factors or factor scores) are calculated as F = BZ, where Z are the normalized values of X, B = L(L′L) −1 is the matrix factor score coefficients, and L are the retained factor loadings [ 46 ]. Although there are several rotation methods, in this paper normalized varimax rotation is used. The reader can find a thorough description of these technique in authoritative textbooks [ 44 , 47 ]. A property of PCA and varimax rotation is that the resulting components are orthogonal to each other. This implies that the identified indices have no-overlap, no redundancy and each provide an independent fraction of the information contained in the original dataset.

Part of the data was harmonized and summarized at the country level in a recent paper [ 26 ] to provide a global overview of loss and damage in the context of the global stocktake. Projected data about economic damages from climate change comes from numerical simulations using a large dimensional intertemporal computable general equilibrium trade model that accounts for various effects of global warming [ 42 ]. This dataset contains estimates of GDP losses by country for 1°C, 2°C, 3°C and 4°C global warming scenarios. The dataset on social perceptions about different aspects of climate change was obtained from an international survey that covered public climate change knowledge, beliefs, attitudes, policy preferences, and behavior among Facebook users [ 40 ]. The resulting database contains 103 countries, and 159 numerical fields plus the ISO code and the name of each country.

3. Results and discussion

Description of estimated factors The scree plot of the eigenvalues shows a relatively smooth decrease in explained variance with a possible shelf occurring between components 4 and 5, which could be used as a cut-off point for rotation (S1 Fig). However, up to the first eight components the eigenvalues exceed unity and discarding PCs 6–8 could lead to ignoring potentially important information [44, 61]. For this reason, we decided to retain the first eight PCs as suggested by the Kaiser truncation rule [47]. The retained PCs account for 85.27% of the variance of the original dataset (Table 1). As discussed below, these PCs have clear and insightful interpretations regarding institutional capacities, risk and vulnerability to, and the people’s perceptions of, climate change. Table 3 provides the acronyms, long names and summary descriptions of all PCs for quick reference for the reader. PPT PowerPoint slide

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TIFF original image Download: Table 3. List of acronyms, long names and summary description of the proposed indices. https://doi.org/10.1371/journal.pclm.0000365.t003 The first PC explains 25% of the total variance of the dataset. As shown in S1 Table, the loadings of the first factor (shown in parenthesis) indicate that the variables that contribute more to PC1 can be divided in: Metrics of institutional strength, development, and societal responsibility: Rule of law (0.954), Governance effectiveness (0.947); Regulatory quality (0.941); Control of corruption (0.928); Political stability and absence of violence (0.835); Human Development Index (0.819); Voice and accountability (0.805);

Measures of how much people are informed about climate change: how often do you hear about climate change in your daily life (0.811); Climate awareness (0.796); Climate beliefs (0.738), and;

How much they think their own country should reduce emissions and fossil fuels consumption in the future: Country responsibility (0.717); Fossil fuel (-0.647). PC1 will be referred to as an institutional and societal development index (ISDI) in which positive values indicate countries that have strong institutions, high human development, and an informed and responsible society. Recent studies have shown how governance and institutions, education, development and financial/human resources are crucial for addressing sustainable development issues [62–64]. Moreover, another study identified institutional strength and good governance as the main determinants of national adaptation policy [50] which provides support for the importance such variables have on ISDI. This index is suggestive of the country’s capacities for vulnerability reduction, the availability of resources and capabilities for implementing adaptation strategies, as well as people’s willingness for GHG mitigation. This institutional and societal development index tracks closely other commonly used vulnerability and adaptation indices. For instance, despite the methodological and data differences, the correlation between ISDI and the ND-GAIN Country Index (https://gain.nd.edu/our-work/country-index/) is 89.5% and leads to similar country rankings. The countries with higher scores in this index are mainly in northern Europe (Finland, Norway, Denmark) and in Oceania (Australia and New Zealand), while the lowest scores are from countries in Africa and the Middle East (Congo, Yemen, Libya, Iraq). PC2 explains about 15% of the total variation of the dataset and constitutes a climate change social concern index (CCSCI). It is mainly composed of variables that represent the perceptions of population about how climate change could affect them: Climate worry (0.932): Reflects responses to "How worried are you about climate change?";

Climate change threat in the next 20 years (0.921): Based on responses to "Do you think climate change is a very serious threat, a somewhat serious threat, or not a threat at all to people in your country (or territory) over the next 20 years?";

Harm personally (0.912): Represents how much respondents believe climate change will harm them personally;

Government priority (0.841): Indicates people’s views on whether climate change should be a very high, high, medium, or low priority for their government;

Climate importance (0.827): Reflects how important the issue of climate change is to respondents personally;

Harm future generations (0.742): Reflects responses to "How much do you think climate change will harm future generations of people?";

Climate change happening (0.704): Captures the belief that climate change is indeed occurring. The CCSCI provides a comprehensive summary of the beliefs of people living in each country about the seriousness of climate change. It combines immediate and future concerns about the consequences of climate change, one’s own personal and future generation harm, and the level of priority they would like their government to assign to this threat. Latin American countries have the highest scores in the CCSCI, with Mexico being the most worried country, followed by Chile, Costa Rica, El Salvador, Brazil, Ecuador and Colombia. These results are broadly consistent with previous assessments of public perception of the seriousness of the climate change threat [48]. The ten least concerned countries are mainly from the Arab World (Yemen, Jordan, Egypt, Libya, Iraq, Lebanon, Kuwait) and a few European countries (Norway, Czech Republic and the Netherlands). The third most important component is PC5, which explains about 11.5% of the total variance of the dataset. It is an index that associates economic losses from extreme events, historical responsibility for current climate change and GDP size (ELCCG). This PC is composed by: Cumulative economic losses during 1990–2019 due to extreme events (0.953);

Historical cumulative CO2 emissions (0.951);

GDP size in 2010 (0.942);

Total number of droughts, extreme temperature, flood, storm, and wildfire events during 1990–2019 (0.786). Positive values on this index indicate countries that have experienced large economic losses from frequent extreme events and that tend to show high economic development historically based on fossil fuels. The countries with highest scores in PC5 can be divided into two types. The first include those with large economies and large historical emissions such as the US, Japan, the UK, Germany, France, Italy, and Australia. The second group includes developing economies, showing significant vulnerability to climate and weather extremes and large populations, such as Mexico, Philippines and Vietnam. PC3 can be interpreted as an index of population size and expected exposure to extremes (PSEI) and explains 10% of the total variance of the dataset. The largest factor loadings of PC3 indicate that the variables that contribute the most to it are: Population counts in 2010 (0.954);

Expected average annual population exposed to droughts (0.924);

Total number of affected persons by droughts, extreme temperature, flood, storm, and wildfire events (0.920);

Expected average annual population exposed to fire events (0.786);

Expected average annual population exposed to floods (0.624). Positive values of PSEI denote countries with large populations and high levels of population exposed each year to extreme events. The countries with highest PSEI values are those in developing regions, particularly southeast Asia, Latin America, and Africa. Examples of these countries are India, Brazil, Indonesia, Nigeria, Vietnam, Thailand, Bangladesh, Mexico, Pakistan, and Iraq. The fifth most important component (PC4) explains about 8% of the total variance and represents an expected severity index (ESI). It is composed by: The average (-0.925) and maximum (-0.894) annual economic damages (as a fraction of the country’s GDP);

The average annual deaths per capita (-0.836), all being caused by extreme events (droughts, extreme temperature, flood, storm, and wildfire). In contrast with PC5, this index contains information about the economic losses relative to the size of the economy, not the absolute expected and cumulative level of losses. Negative values of this index indicate countries where more severe weather/climate damages occur relative to the size of their GDP and population. The countries with high levels of vulnerability to extreme events have the lowest scores in this index and they are located mainly in Latin America, southeast Asia, and the Middle East. The countries with the ten highest scores are Honduras, Haiti, Bangladesh, Laos, Nicaragua, Vietnam, Cambodia, Thailand, Yemen and North Macedonia. PC6 accounts for 7% of the dataset’s total variance. This index combines: The maximum (0.719) and average (0.714) number of people per capita affected by extreme events (droughts, extreme temperature, flood, storm, and wildfire) during the period 1990–2019;

The people’s belief about the economic impact of addressing climate change (-0.701). This PC can be interpreted as an index of how experiencing extreme weather events modifies beliefs about how costly climate action is (EECB). It suggests that in countries where more people are affected by weather events, people tend to belief actions to mitigate climate change will not have a negative economic impact and will not reduce jobs. On the contrary, people in such countries belief these actions will benefit the economy. The countries with the highest values in this index are mainly in Africa such as Malawi, Kenia, Zimbabwe, Mozambique, Burkina Faso, and Ghana, as well as countries like Australia, Haiti and Philippines. The two remaining components (PC7 and PC8) explain about 4–5% each of the total variance. The main variable in PC7 represents the people’s belief about who is the most responsible entity in their country for reducing the pollution that causes climate change (0.736). Higher values on this responsibility index (RI) denote countries in which people believe the government to be the most responsible and lower values suggest progressively that business, individuals are responsible, and the lowest values indicates that nobody is responsible. Among the countries with lowest values in RI are those associated with fossil fuels’ production, such as Vietnam, Kuwait, Qatar, United Arab Emirates, Oman, Saudi Arabia, and Indonesia, as well as some with large shares of fossil fuels for power generation such as Japan, Hong Kong. PC8 mainly is composed by the total deaths (0.780) from drought, extreme temperature, flood, storm, wildfire during the 1990–2019 period, and the expected annual number of people affected by floods. This index suggests floods are associated with a higher number of deaths than other events. The highest values in this total death and flood index (TDFI) occur in Bangladesh, Philippines, and Japan. An interactive platform that allows visualizing the data and these indices is available at the following link: http://multidash.apps.lancis.ecologia.unam.mx/paper_cc/.

Using bivariate spaces to enhance interpretation of the proposed indices The proposed indices are orthogonal by design, each capturing distinct aspects of the information within the original datasets. These indices can be combined to construct n-dimensional spaces that are free from overlapping information yet complement each other, together representing a new, comprehensive latent variable. This n-dimensional space can be valuable on its own or serve as a basis for exploring another index or independent variable within the context of the selected dimensions. To demonstrate this approach, we combine the Institutional and Societal Development Index (ISDI, PC1) and the Climate Change Social Concern Index (CCSCI, PC2) to create a bidimensional space (plane), which is then used to further investigate the Economic Losses from Extreme Events, Fossil Dependence and GDP Size Index (ELCCG, PC5) and the Total Death and Flood Index (TDFI, PC8), providing deeper insights into their interpretation. As an initial step, we generate clusters of countries using hierarchical clustering. These clusters aim to represent the information in the ISDI/CCSCI bidimensional space. ISDI and CCSCI were selected to construct the bidimensional space because together they account for approximately 40% of the total variance in the original dataset and offer meaningful interpretability. This space can be understood as representing governance and societal adaptation potential and response capacity, as it reflects both the structural readiness of institutions (captured by ISDI) and the potential societal willingness to engage in climate adaptation (captured by CCSCI) [65]. Specifically, this space approximates adaptive capacity by capturing the ability of institutions and society to adjust to potential impacts, seize opportunities, and respond effectively to climate challenges [66]. Additionally, it can serve as a proxy indicator for resilience, given that strong institutions and high societal concern are relevant drivers of a society’s ability to maintain or return to a stable state following a climate shock [67]. In the following paragraphs we describe the space defined by ISDI and CCSCI, how countries can be clustered in it, and then we illustrate how projecting the ISDI/CCSCI space onto ELCCG and ESI.

Description of the ISDI/CCSCI space and clusters A hierarchical cluster analysis was applied to group countries according to their scores in the ISDI and CCSCI indices. A linkage distance of 10 was chosen, which lead to defining six clusters of countries (Fig 1A, S2 Fig). S2 Table shows for each cluster its corresponding median, as well as the upper and lower quartiles, while S3 Table lists the countries that belong to each of the defined clusters. PPT PowerPoint slide

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TIFF original image Download: Fig 1. Cluster map and scatterplot of ISDI and CCSCI. Panel a) shows a map of the hierarchical clustering of countries based on ISDI and CCSCI. Panel b) presents the scatter plot of ISDI and CCSCI in which observations are colored according to the cluster they belong to. Cluster1 is shown in red, while Cluster2 is in gray, Cluster3 in yellow, Cluster4 in green, Cluster5 in blue, and Cluster6 in brown. The base layer of the map is available at: https://datacatalogfiles.worldbank.org/ddh-published/0038272/DR0046667/wb_boundaries_geojson_lowres.zip?versionId=2023-01-19T09:29:19.8668282Z, and the license information for the base layer can be found at https://datacatalog.worldbank.org/public-licenses?fragment = cc. https://doi.org/10.1371/journal.pclm.0000365.g001 Fig 1B shows a scatter plot of these two indices, divided into its four quadrants. Table 4 relates clusters and quadrants and provides a summary of the dominant characteristics and regions associated to each cluster. The first quadrant (QI) of the ISDI/CCSCI space represents countries with high adaptive capacity due to favorable conditions in both institutional and societal dimensions. Strong institutions are likely to act on the social concerns expressed by their citizens, and countries in QI very likely have the technical, economic, and political capacities to implement the required adaptation strategies. QI is mainly composed of two clusters. The dominant cluster in QI is represented by red circles (Cluster1 in Fig 1) and, for values of ISDI>0.5, includes European countries such as Spain, France, Hungary, Slovenia, Croatia, Poland and Cyprus, and Japan, South Korea, and Uruguay. For scores of ISDI<0.5 countries like Italy, Greece, Botswana, India, and Jamaica are included. For values of CCSCI>1 another cluster of countries is defined (Cluster3, yellow circles) in which the level of climate change concern is high. Only three countries in this cluster belong to Q1 (Portugal, Chile, and Costa Rica), while the rest of Cluster3 extends over QII. PPT PowerPoint slide

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TIFF original image Download: Table 4. List of cluster numbers, their dominant characteristics and regions. https://doi.org/10.1371/journal.pclm.0000365.t004 Quadrant II (QII) includes countries which adaptive capacity is potentially constrained by institutional weaknesses, despite societal strengths. The populations in these countries show concern about climate change (positive CCSCI) but institutions and development are lagging in comparison with QI. This combination of factors can translate into a mismatch between implemented climate policy and citizens’ assessment of risk. Moreover, lower levels of development and institutional strength can imply that policies are not guided by the best available knowledge [68, 69] and that support for science and technology is likely not among government priorities [33, 70, 71]. These are mainly Latin American countries and a few African and southeast Asian countries. The countries in this cluster with moderately low levels of ISDI (ISDI>-0.5) are Sri Lanka, Malawi, Panama, Brazil, Philippines, Peru, and Colombia. With a considerably lower level of ISDI El Salvador, México, Nicaragua and Bolivia are found. QII is dominated by Cluster5 with countries that show substantially lower scores of CCSCI and tend to have lower IDSI scores (blue circles). The countries in this cluster are mainly in Africa (e.g., Zambia, Angola, Côte d’Ivoire, Burkina Faso, Kenya, Mozambique, Cameroon, Ghana), but also Latin America (Honduras, Guatemala. Paraguay, Dominican Republic), and Cambodia, Turkey, and Nepal. Part of this cluster continues into QIII, where lower scores of CCSCI and even more negative ISDI are found (Nigeria, Pakistan, Tanzania, Benin, Bangladesh, and Senegal). The third quadrant (QIII) contains the countries in which adaptive capacity is expected to be severely limited by weaknesses in both institutional and societal dimensions as both ISDI and CCSCI are negative. Countries in QIII are likely those with highest levels of vulnerability as they may fall short of institutional, economic, technical, and political resources to design and implement adaptation strategies to address climate change’s challenges and their citizens are likely not to press their governments on this issue. Moreover, the low level of concern shown by populations in these countries is likely associated with lack of information about climate change science (also supported by their low scores in ISDI), and likely ignore this phenomenon’s current and projected impacts. QIII is mainly composed of two clusters. Cluster2 combines the lowest scores of ISDI and CCSCI (gray circles) and, apart from Haiti, it exclusively composed of Arab countries. Yemen, Libya and Iraq show the most extreme combination of scores, followed by Haiti, Algeria, Egypt and Lebanon, Kuwait, and Jordan with more moderate combination of values. Cluster6 (brown circles) shows slightly lower scores in the societal dimension that the previous cluster but more moderate scores in the institutional dimension, with countries such as (ISDI<-0.3) Indonesia, Azerbaijan, Tunisia, Armenia, Saudia Arabia, Morocco, and (ISDI>-0.3) Bosnia and Herzegovina, Laos, Albania, Serbia, and Thailand. Part of this cluster is located in QIV and is characterized by more favorable conditions for adaptive capacity potential both in the societal and institutional dimensions. It includes countries such as the United Arab Emirates, the US, North Macedonia, Bulgaria, Malaysia, Qatar, Rumania, and Oman. For countries in Quadrant IV (QIV) their adaptive capacity is potentially constrained by societal weaknesses, despite institutional strengths. These countries likely possess the institutional, technical, economic, and political capacities to respond to climate change. However, they risk overconfidence regarding their vulnerabilities and impacts regarding this phenomenon. Cluster4 (green circles) is composed of countries with the highest scores in ISDI and, with the exception of the Arab countries cluster, the lowest scores of concerns regarding climate change. Northern European countries show the most extreme combinations of scores (Norway, Netherlands, Finland, Sweden, and Denmark). The clustering of countries based on the ISDI and CCSCI indices provides a more nuanced interpretation of how institutional/adaptation capacities, development and the social concern about climate change are related. The analysis shows that the simplistic interpretation that lower levels of development and institutional capacities are associated with less concern about climate change is not supported by the data. The lowest levels of CCSCI occur for both countries with the highest and lowest levels of ISDI, such as northern European (Cluster 4) and Arab world (Cluster 2) countries, respectively. Similarly, the highest levels of concern are shown by countries with relatively lower levels of ISDI (Cluster 3). The clustering suggests that many other factors are at play like those related to cultural and religious aspects of societies around the world.

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