Global Assessment of Climate Change-Attributed Loss and Damage

Global Assessment of Climate Change-Attributed Loss and Damage | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Global Assessment of Climate Change-Attributed Loss and Damage Nandan Mukherjee, John Rowan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7149291/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Climate-induced disasters are rapidly escalating, yet the global Loss and Damage (L&D) mechanism remains constrained by the absence of robust, evidence-based frameworks for assessing and allocating support. This study introduces the first global map of realised, climate-attributed loss and damage, integrating disaster event data (EM-DAT), attribution science (Fraction of Attributable Risk, FAR), and equity-sensitive indicators to develop a standardised Expected Annual Loss and Damage (EALD) framework. We assess six key impact indicators—deaths, injuries, homelessness, affected population, national economic loss, and per capita economic loss—across 11,721 climate-related disaster events from 2000 to 2023. Our findings estimate that anthropogenic climate change accounts annually for 89 million people affected, 338,000 rendered homeless, 122,000 injured, 26,700 deaths, and $ 82.3 billion in PPP-adjusted economic losses. Contrary to prevailing assumptions, high-income countries also experience significant climate-attributed impacts, while 32 nations emerge as high-risk only under attribution-based metrics. We critique the reductive ranking of “particularly vulnerable” countries as a geopolitical beauty contest and instead propose a dual typology based on compound, absolute, and relative risk. We offer this assessment as a more appropriate entry point for investigating how structural vulnerability and emerging hazards, not historical exposure alone, drive climate injustice. Our results highlight a vast gap between pledged and required L&D finance and call for a justice-centred, attribution-informed framework that reflects the lived realities of affected populations across political boundaries. Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Governance Earth and environmental sciences/Environmental social sciences/Climate-change policy Figures Figure 1 Figure 2 Figure 3 1 Introduction Climate change is no longer a distant threat; it is actively reshaping global patterns of loss and damage (L&D). A recent attribution study 1 found that climate change tripled heat-related deaths during the 2025 European heatwave. According to EM-DAT 2 , the frequency of climate-related hazards has increased by 70% since 2000. Over the past two decades, these events have resulted in 785,000 deaths, affected 4.3 billion people, and caused over US $ 3.6 trillion in losses. The World Meteorological Organisation 3 confirms a sevenfold rise in disaster damages since the 1970s. The Intergovernmental Panel on Climate Change (IPCC) now states it is “unequivocal” that human activity is the primary driver of both rapid- and slow-onset climate impacts 4 . In response, L&D has become a key, albeit contested, pillar of global climate governance under the United Nations Framework Convention on Climate Change (UNFCCC). The UN Environment Programme defines L&D as the residual impacts of climate change that mitigation and adaptation cannot prevent. Though formally recognised under the Cancun Agreements 5 , the agenda dates to early 1990s proposals by small island states 6 . It gained traction with the establishment of the L&D Fund at COP27 7 and its operationalisation at COP28 8 . While UNFCCC frameworks prioritise “particularly vulnerable” nations, events like the Texas flash floods and the 2025 European heatwave (with thousands of deaths) highlight that L&D spans geopolitical boundaries. Traditional assessments such as Post-Disaster Needs Assessments (PDNAs) use deterministic, event-specific data. While helpful for immediate relief, they often overlook systemic vulnerabilities. In contrast, probabilistic risk models analyse multiple events over time using frequency and severity metrics. The IPCC’s Sixth Assessment Report (AR6) reframes risk as “the potential for adverse consequences” 9 , distinguishing between “potential” future risks and “realised” risks based on observed data. Realised risk bridges science and justice by revealing where adaptation has failed and governance gaps persist 10 . A growing body of literature reveals critical gaps in the assessment and governance of L&D. Empirical studies from Nigeria, India, and Bangladesh show how climate-induced flooding disrupts livelihoods through intersecting social, economic, and health vulnerabilities 11 , 12 . Yet these insights rarely inform national reporting or global finance mechanisms. Scholars stress the systemic and compounding nature of L&D 13 , 14 , but prevailing models remain fragmented, overly reliant on static indicators, and inattentive to relational vulnerability 15 , 16 . Global disaster databases like EM-DAT and DesInventar prioritise monetised infrastructure losses while underreporting non-economic and slow-onset L&D such as trauma, displacement, and biodiversity decline 17 – 19 . Remote sensing lacks participatory validation 20 , 21 , and standardised global protocols for intangible losses remain absent 13 , 22 . Risk governance remains centralised and exclusionary 23 , and finance tools like parametric insurance neglect informal and slow onset losses 15 , 22 . Attribution science—primarily via the Fraction of Attributable Risk (FAR)—shows promise but faces integration barriers in data-poor contexts, particularly in the Global South 21 , 24 . The global estimate of climate-attributed L&D by 25 , combining FAR with the Value of a Statistical Life (VSL), represents a pioneering step—but also invites critique. While the authors defend their use of U.S./U.K. benchmarks as “convenient,” critics argue that VSL undermines ethical fairness 26 , embeds political biases 27 , and omits non-economic harms 28 , 29 . Even early proponents acknowledged its normative limits 30 . Scholars caution against using attribution as the sole basis for L&D finance. Calls for plural lines of evidence 31 , critiques of scientific reductionism 32 , and warnings about exclusion risks due to evidentiary gaps 33 all converge to suggest that attribution must be integrated with justice considerations. In fact, attribution has often been used as “tactical opposition” to delegitimise claims from developing nations 34 . Addressing these key empirical and conceptual gaps, this study presents the first global, indicator-based assessment of climate-attributed realised loss and damage (L&D). We integrate disaster time-series data from EM-DAT (with appropriate caution), attribution science (Fraction of Attributable Risk, FAR), and equity-sensitive L&D indicators to construct a spatially resolved map of Expected Annual Loss and Damage (EALD). We employed six indicators across economic and non-economic typologies—mortality, injury, affected population, homelessness, national economic loss, and personal loss—each expressed in both absolute and relative terms, following the approach of 35 . Absolute indicators capture total impact (e.g., deaths, economic loss), while relative formulations (e.g., per 100,000 population, percentage of GNI) standardise risk, enabling equity-sensitive comparisons across contexts and aligning with Sendai and SDG targets. Absolute values reflect national burdens, whereas relative indicators—adjusted for population and Gross National Income (GNI)—highlight disproportionately affected smaller countries, such as small island developing states. Reliance on a single metric can distort risk perception; hence, we propose a composite risk index to integrate both. For the relative assessment, we utilised population and economic data (GDP and GNI) from the World Bank. We pre-processed these datasets using a machine learning algorithm for missing value imputation, as detailed in the Methods section and supplementary Annex A. The resulting “World Map of Climate-Attributed L&D” offers a transparent, reproducible tool for informing the Loss and Damage Fund and the Santiago Network. Aligned with international frameworks such as the Sendai Framework and the Paris Agreement, our approach grounds risk assessment in empirically observed harm, supporting ethically grounded, evidence-based policymaking. This study reframes L&D not merely as a reparative mechanism, but as a forward-looking decision-support framework rooted in the principles of common but differentiated responsibilities and respective capabilities. 2 Results This section presents a global assessment of realised loss and damage (L&D) from climate-induced hazards, focusing exclusively on hydrological, meteorological, and climatological extremes—namely floods, storms, and droughts. Observed risks are expressed using Expected Annual Loss and Damage (EALD), defined as the mean annual exceedance of loss and damage. Between 2000 and 2023, disasters linked to climate-related hazards have resulted in an average of 625 million people affected, 2.39 million rendered homeless, 160,000 injuries, 70,000 deaths, and $564 billion in economic losses annually. The Fraction of Attributable Risk (FAR) method is applied to isolate the share of these losses attributable to anthropogenic climate change. A FAR of 0.4 implies that 40% of the observed losses would not have occurred without human-induced climate forcing. A summary of climate-attributed absolute and relative loss and damage account is shown in Box 1. Box 1: Climate-Attributed Loss and Damage Summary Affected population • 89 million people affected annually (range: 13.7–308.6 million; low confidence; as likely as not ) • Equivalent to 796 per 100,000 population ( medium confidence; unlikely ) Homelessness • 338,000 homeless annually (range: 110,000–1.17 million; low confidence; as likely as not ) • Equivalent to 11.3 per 100,000 population ( low confidence; virtually certain ) Injuries • 122,000 injured annually (range: 118,000–135,000; high confidence; virtually certain ) • Equivalent to 1.94 per 100,000 population ( high confidence; virtually certain ) Deaths • 26,700 deaths annually (range: 17,000–45,000; low confidence; likely ) • Equivalent to 0.30 per 100,000 population ( medium confidence; virtually certain ) Economic losses • $82.3 billion PPP-adjusted loss annually (range: $17.5–271.4 billion; low confidence; as likely as not ) • Equivalent to $49 per person ( low confidence; virtually certain ) • 0.064% of national GNI lost annually ( medium confidence; virtually certain ) To determine this climate attribution, we analyse a total of 11,721 disaster events across three natural disaster subgroups—Climatological (1,088 events), Hydrological (6,052), and Meteorological (4,581)—representing approximately 89% of all recorded disasters, excluding biological events. We reveal that disaster frequency is highly concentrated within dominant subtypes across natural hazard groups: droughts lead climatological events (58.9%), riverine floods dominate hydrological disasters (43.1%), and tropical cyclones account for the largest share of meteorological hazards (43.2%). As shown in the following plot, some other subtypes occur with significantly lower relative frequencies, indicating strongly skewed risk typologies within each subgroup. Assigning climate attribution to disaster-related loss and damage draws on shifts in Annual Exceedance Probability (AEP), with consistent increases observed across key hazards and indicators between 1980 and 2023. This global analysis, as shown in Figure 1, indicates that the return periods for events such as floods, droughts, and extreme temperatures have shortened dramatically, from multi-decadal to near-decadal timescales, reflecting a marked increase in hazard frequency. The estimated Fraction of Attributable Risk (FAR) ranges from 0.5 to 1.0, indicating that more than half of this increased frequency is likely due to anthropogenic climate change. While we investigated the pattern of hazard typologies attributing to loss and damage in Figure 1 , we reveal floods show consistent and substantial increases in AEP across all five indicators—affected population, deaths, economic damage, homelessness, and injuries—confirming their status as the most frequently recurring hazard. Droughts demonstrate notable increases in AEP for economic damage and affected populations but no meaningful change for deaths, homelessness, or injuries. Extreme temperature events show a marked increase in AEP for deaths, with negligible or declining AEP trends for other indicators. Storms display moderate increases in AEP for economic damage and injuries but slight decreases or no change in affected populations, homelessness, and deaths. Wildfires show rising AEP for homeless and affected populations, with no apparent change in deaths or injuries. Mass movement (wet) hazards show minimal change or slight decreases across all indicators, with no significant rise in AEP. Uncertainty bands in Table 1 reflect the confidence and likelihood of climate attribution based on observed impacts and causal robustness. While confidence remains low across most hazard–indicator combinations due to data and attribution gaps, injury-related losses exhibit high agreement and evidence, rendering them virtually certain to be climate-influenced. This global hazard- and indicator-wise analysis reveals important differences across economic and non-economic dimensions of loss and damage. However, aggregate global figures obscure the disproportionate burden faced by vulnerable countries and communities, often masked by data from large, high-income nations. To expose the uneven geography of climate-attributed loss and damage, the following section presents a disaggregated assessment by region, hazard, and impact type. Table 1: Summary of hazard-wise changes in baseline vs. present-day return periods, and estimated Fraction of Attributable Risk (FAR). Hazard Baseline Return Period (years) Present Return Period (years) Attributable Risk (FAR) Drought 29.41 (21–48.97) 8.99 (6.47–14.7) 0.53 (0.38–0.78) Extreme temperature 57.17 (41.42–91.87) 8.43 (6.18–13.25) 0.83 (0.78–0.91) Flood 11.24 (8.34–17.31) 4.67 (3.56–6.75) 0.53 (0.35–0.76) Glacial lake outburst ∞ (∞–∞) 15.33 (11.5–23.0) 1.00 (1–1) Mass movement (wet) 28.33 (20.55–45.45) 7.86 (5.73–12.52) 0.60 (0.51–0.80) Storm 15.88 (11.84–24.04) 6.37 (4.78–9.57) 0.52 (0.41–0.75) Wildfire 48.28 (35.05–76.44) 7.81 (5.76–12.17) 0.71 (0.60–0.86) 2.1.1 Regional Profile of Hazard Typology and Climate Attribution of Loss and Damage Table 2 presents a disaggregated regional summary of climate-attributed loss and damage across six impact indicators and seven major hazards. Floods emerge as the most widespread and damaging hazard across nearly all regions, with particularly severe impacts in South, Southeast, and East Asia; Sub-Saharan Africa; Central America; and parts of Europe. Storms follow, especially in tropical and subtropical belts. Extreme temperatures drive elevated mortality in Europe and Asia, while droughts result in extensive affected populations and economic loss in Africa and South Asia. Although hazards like wildfires, landslides, and glacial lake outburst floods (GLOFs) have more localised profiles, their region-specific impacts remain significant. These patterns highlight the dual nature of climate risk: it is both globally widespread and regionally differentiated, affecting both low- and high-income settings. Table 2: Summary of regional climate-attributed hazard impacts across human and economic loss dimensions Hazard Hotspot Description Floods Very high confidence is the most widespread and impactful hazard. Significant affected, homeless, injured, and economic losses across nearly all regions. Especially severe in South, Southeast, and East Asia; Northern, Western, and Eastern Africa; South and Central America; Caribbean; Southern and Western Europe; and Melanesia. Storms High confidence in tropical and subtropical regions. Severe homelessness, economic loss, and affected indicators in South, Southeast, and East Asia, the Caribbean and Central America. Moderate confidence in Western and Eastern Africa. Western Europe and Micronesia show economic and non-economic damages. Extreme Temperature Very high confidence in heat-attributed deaths in Southern and Western Europe. Medium to high confidence in South and Western Asia. Moderate confidence in North America. Low confidence in Africa for affected indicators. Injuries were observed in several mid- and high-latitude regions. Drought High confidence in Africa and Asia, especially Eastern, Western, and Northern Africa. Widespread affected populations, resulting in deaths and economic loss. Medium confidence in Latin America and parts of Asia. Low confidence in Europe and Oceania due to localised events and adaptive capacity. Wildfire Medium confidence overall. Moderate to high confidence in Southern and Eastern Europe. North and South America exhibit indicators of affected and injured populations. Northern and Southern Africa present weak signals. Minimal or low-confidence data in Asia and Oceania. Glacial Lake Outburst Floods (GLOFs) Geographically limited. Observed only in South and Southeast Asia with low to medium confidence, affecting homeless, injured, and economic loss. No significant data from other regions. Landslides Low to medium confidence. Localised impact in South and Southeast Asia and Eastern Africa (homeless, injured). Minor losses in Southern Europe and Oceania. Negligible impacts elsewhere. 2.2 Compound and Attributed Risk Typologies at the National Level To characterise the global distribution of climate risk, we construct a dual-axis typology based on principal component analysis (PCA) of both absolute and relative disaster outcomes, as well as FAR-weighted climate-attributed losses. Based on composite scores of observed and climate-attributed loss and damage, countries are clustered via k-means into four distinctive emergent L&D risk zones: · Compound High Risk (High–High): Countries experiencing both high aggregate losses and high relative per capita losses and damages (e.g., Peru, Afghanistan, Italy for climate-attributed risk; Peru, Nepal, United States for disaster-observed risk). · High Absolute Risk (High–Low): Economies with substantial expected annual economic losses but lower population-scaled burdens (e.g., Virgin Islands, Liechtenstein, Bhutan for climate-attributed risk; Brazil, China for disaster-observed risk). · High Relative Risk (Low–High): Countries facing disproportionately high losses relative to population or economic size, despite modest aggregate Expected annuals (e.g., Lesotho, Uruguay, Bulgaria for climate-attributed risk; Virgin Islands, Bahamas, Greece for disaster-observed risk). · Low Risk (Low–Low): Countries with minimal observed or attributed climate-related losses (e.g., Costa Rica, Jordan, Cyprus for climate-attributed risk; Yemen, Algeria, Slovakia for disaster-observed risk). These clusters are visualised in Figure 2, where the upper panel depicts disaster-observed risk, and the lower panel shows climate-attributed risk. While certain countries—such as Bangladesh, Haiti, and Italy—consistently appear in the compound high-risk cluster, others, like the Marshall Islands and Guatemala, emerge as high-risk only under climate attribution analysis. Finally, two global maps in Figure 3 provide a comparative perspective: the first captures the ‘Expected’ annual disaster-attributed L&D from historical records, while the second isolates the subset that is scientifically attributable to anthropogenic climate change. Global spatial patterns reveal a notable gradient in risk exposure. Tropical and low-latitude countries particularly in South Asia, Central America, and the Pacific are prominent in the compound high-risk group. At the same time, high-income temperate nations, including several European states and the United States, exhibit high absolute loss profiles, indicating significant asset exposure even where individual-level vulnerability is buffered by institutional capacity. Across both disaster and attribution datasets, countries classified as compound high risk or high absolute risk are predominantly located in tropical and high-latitude zones. In the attribution analysis, this includes countries such as Peru, Italy, and Afghanistan (compound risk), as well as the Virgin Islands, Liechtenstein, and Bhutan (absolute risk). Relative risk clusters (e.g., Lesotho, Uruguay, Bulgaria) exhibit a similar latitudinal skew, whereas low-risk groups are distributed across all geographic zones. Observed disaster data reflect analogous patterns. Compound risk is again concentrated in tropical and high-latitude countries (e.g., Peru, Nepal, the USA), while high absolute risk remains concentrated mainly in tropical regions (e.g., Brazil, China). High relative risk is also disproportionately represented in the tropics and higher latitudes (e.g., Virgin Islands, Bahamas, Greece). In contrast, low-risk countries are geographically diffuse, appearing across all zones (e.g., Yemen, Algeria, Slovakia). Out of the 199 countries analysed in this study, 57 countries are classified as compound high-risk in both disaster-observed and climate-attributed assessments, highlighting persistent and compound exposure to loss and damage (detailed results are available in Table in Annex B). This group spans a broad income spectrum—from low-income countries (e.g., Myanmar, Afghanistan, Somalia) to lower-middle-income economies (e.g., Pakistan, Nigeria, Vietnam) to high-income nations (e.g., United States, Japan, Germany, Italy). Critically, it cuts across major UNFCCC constituencies, including the Least Developed Countries (LDCs), Small Island Developing States (SIDS), the African Group, the Arab States, and European Union (EU) member states, emphasising the structural pervasiveness of climate risk. Although classified by dual exposure, the reported figures represent only the climate-attributed component, providing insight into the escalating impacts of anthropogenic climate change superimposed on existing vulnerabilities. Among this group, Myanmar (MMR) and Afghanistan (AFG) are especially illustrative of climate-amplified humanitarian stress. Myanmar reports over 458,000 people affected and 2,280 homeless, alongside 931 injuries and 3,895 deaths, underscoring its vulnerability as an LDC. Afghanistan faces over 2.28 million affected, more than 10,000 homeless, and 828 deaths, yet reports zero formal economic loss—highlighting the disjunction between human toll and economic accounting in fragile economies. Countries such as Germany (DEU), France (FRA), and Italy (ITA), despite being high-income EU states, continue to register climate-attributed economic losses exceeding US$ 1.5 billion each, alongside high per capita burdens (e.g., Italy: US$ 16.4). These values underscore the persistent exposure even in contexts of institutional strength and advanced infrastructure. Meanwhile, Somalia (SOM) and Madagascar (MDG) show millions affected yet minimal reported losses, reflecting not resilience but the invisibility of damages in monetised terms. Somalia alone reports over 2.4 million affected and almost no economic loss—indicating chronic underreporting in contexts of limited data infrastructure. Small Island Developing States face a different profile of existential risk. Puerto Rico (PRI) and Bahamas (BHS) report climate-attributed per capita economic losses of US$ 207.9 and US$ 410.3, respectively—among the highest globally. Despite their small size, the impact per person is disproportionately high, revealing acute vulnerabilities driven by geographic isolation and concentrated exposure to risk. Taken together, this compound high-risk group exemplifies the multi-scalar, cross-regional nature of climate-attributed loss and damage. Manifestations range from large-scale displacement in low-income fragile states to financial disruption in industrialised economies. This underscores the urgency of differentiated, equity-centred interventions that account for exposure, capability, and justice in addressing the growing burden of climate change. Conversely, a total of 32 countries is classified as compound high-risk only under the climate-attribution analysis despite not being flagged in historical disaster-based risk assessments. This divergence highlights the emergence of new geographies of climate vulnerability regions where anthropogenic climate change is now producing measurable impacts that extend beyond traditional disaster trends. This emergent group spans diverse income levels and UNFCCC groupings. High-income nations, such as Japan (JPN), Switzerland (CHE), and Czechia (CZE), exhibit climate-attributed mortality and economic losses, indicating that a strong institutional capacity does not confer immunity. Japan, for instance, reports over 27,000 injuries, 218 deaths, and a national economic loss of US$ 4 billion (upper bound: US$ 7.6 billion), with a per capita loss exceeding US$ 30. Such figures mark a notable shift, positioning industrialised nations within the frontier of climate-attributed impacts. Major emerging economies, such as Brazil (BRA) and Thailand (THA), also feature prominently. Brazil reports over 800,000 affected, 81 deaths, and national damages nearing US$ 1.66 billion, with per capita burdens over US$ 8. These statistics underscore how rising global temperatures are driving impacts in countries not traditionally considered high-risk. Several Least Developed Countries (LDCs), including Lesotho (LSO), Mali (MLI), and South Sudan (SSD), now exhibit significant human impacts. Lesotho, for example, reports over 420,000 affected despite no previous high-risk designation—revealing climate stress in under-monitored, structurally fragile contexts. Mali adds nearly 700,000 affected, hundreds homeless, and measurable mortality, indicating deepening exposure. Small Island Developing States (SIDS) such as Guyana (GUY) and the Marshall Islands (MHL) also appear. Although their absolute economic losses remain modest, per capita losses are disproportionately high. Guyana, for instance, shows a per capita loss exceeding US$ 27, demonstrating the outsized risk faced by small island nations from sea-level rise and intensified extremes. In sub-Saharan Africa, countries such as Ethiopia (ETH), Kenya (KEN), the Democratic Republic of the Congo (COD), and Namibia (NAM) reflect a reconfiguration of climate risk. These nations, previously underrepresented in disaster risk datasets, now face climate-attributed displacement, mortality, and economic burdens, indicating the expansion of zones of vulnerability in development-constrained regions. Together, this emerging risk cohort illustrates a geospatial and structural shift in climate vulnerability—extending to new regions, economies, and social groups. These findings necessitate an urgent recalibration of global climate finance, monitoring, and adaptation efforts to include these newly exposed populations before the impacts become irreversible. 3 Discussion The Loss and Damage (L&D) pillar under the UNFCCC centres on the needs of ‘particularly vulnerable countries’, recognising that they experience the most significant harm from climate change despite contributing the least to its causes. This harm is often compounded by limited capacity to cope with adverse impacts, underscoring the justice dimension of climate policy: those least responsible are often the most severely affected. While the Warsaw International Mechanism and Article 8 of the Paris Agreement appropriately emphasise the urgent needs of particularly vulnerable developing countries—such as Least Developed Countries (LDCs) and Small Island Developing States (SIDS)—our findings challenge the notion that L&D is confined to the Global South. Empirical evidence shows that high-income countries with advanced adaptive capacities are also experiencing significant, quantifiable, climate-attributed harm. Crucially, this does not dilute the historical responsibility of developed countries to finance adaptation and redress in under-resourced nations, as established in the 1992 UNFCCC and reaffirmed in successive COP decisions. Instead, it reinforces the need for a universal, capability-sensitive L&D governance framework—one that recognises differentiated but shared vulnerabilities across countries and contexts. Our classification of 38 countries as high-risk in both disaster-observed and climate-attributed loss and damage substantiates a key finding from IPCC AR6 WGII Chap. 16: anthropogenic climate change is intensifying pre-existing risk patterns, particularly in regions marked by persistent development deficits, institutional fragility, or socio-political instability 9 . This process, described as the “intensification of risk hotspots” 37 , results in compound and cascading risks arising from the interaction between climate hazards and structural vulnerabilities.Countries such as Afghanistan, Myanmar, and Somalia exemplify this dynamic: low adaptive capacity intersects with conflict, underdevelopment, and state fragility, producing high levels of human loss and damage—including displacement, injury, and affected populations—that are not matched by reported economic losses. This mismatch reflects what the IPCC terms “invisible losses”—non-economic harms such as psychosocial trauma, cultural dislocation, and identity loss 38 – 40 . Though rarely monetised, these losses are central to lived vulnerability and long-term recovery, particularly in fragile or marginalised settings where traditional coping systems have been eroded. Recent literature links these non-economic and residual losses to the notion of adaptation limits. According to AR6, residual losses arise when soft or hard adaptation limits are exceeded—not due to technical constraints alone but because of governance failures, social barriers, or political inaction 4 . In such cases, residual loss becomes less a function of climatic severity than of structural injustice. At the same time, high-income countries such as Germany, Italy, and France also fall within the compound risk category despite their advanced institutional capacity and disaster management infrastructure. This supports the IPCC’s assessment that “climate risks are increasingly visible in developed regions,” particularly in temperate zones experiencing more frequent and severe extremes 41 , 42 . Historical events such as the 2003 heatwave 43 further highlight enduring vulnerabilities in even the most developed states. Our results, which demonstrate substantial national and per capita economic losses in these countries, reinforce the IPCC’s recognition that climate vulnerability now extends to both affluent and capacity-constrained contexts 44 , 45 . Together, these profiles reflect the intersecting pressures of hazard intensification, development trajectories, and uneven resilience. Loss and damage can no longer be viewed solely through the lens of the Global South, nor can they be adequately captured by economic metrics or disaster frequency alone. A more granular, science-based, and justice-sensitive approach is needed—one that accounts for invisible, residual, and compounding harms across diverse geographies. These findings are echoed in recent critiques of vulnerability-based allocation frameworks in the climate finance literature. As 46 argue, the designation of “particularly vulnerable countries” under the UNFCCC remains politically contested and analytically unstable, often privileging Small Island Developing States (SIDS), Landlocked Developing Countries (LDCs), and African countries based on a negotiated consensus rather than dynamic or evidence-based criteria. Such a framing risks marginalising countries—like Afghanistan, Myanmar, and Somalia—that consistently experience high human impacts but fall outside formal prioritisation categories. Moreover, empirical evidence showing that countries like Germany and Italy rank high in climate-attributed economic losses challenges the assumption that high adaptive capacity insulates nations from harm. It underscores the structural reality that even well-resourced countries remain exposed to climate impacts driven by global emissions—often perpetuated by large corporate actors operating with limited accountability. This aligns with growing calls in the literature 4 , 40 , 44 for justice-based responses that are redistributive, anticipatory, and globally inclusive. Consequently, the political architecture of the Loss and Damage pillar under the UNFCCC must broaden to include all countries—across both the Global North and South—in discussions not only of potential risks but also of realised risks: the lived realities that are steadily eroding the resilience of communities facing climate loss and damage. While prioritising support for particularly vulnerable nations remains essential, a narrowly framed approach risks obscuring the growing exposure, systemic fragilities, and compound vulnerabilities emerging across diverse national contexts. A critical insight from this study is the identification of 32 countries that are classified as high-risk exclusively under climate-attributed metrics but not within conventional historical disaster datasets. This divergence underscores the IPCC’s framing of “non-analogue futures” and “emerging systemic risks,” where attribution science reveals latent and overlooked patterns in global risk geographies. Emerging climate-related hazards display distinct regional signatures: extreme temperatures are newly appearing across Western and Southern Europe; floods and glacial hazards are intensifying in Central Asia; and compound flood–storm risks are emerging in Eastern Africa and Small Island States. In Western Asia and parts of South Asia, the concurrent emergence of floods, droughts, and heat extremes is increasingly common—and often characterised by high recurrence. Out of the 199 countries analysed, 108 are now experiencing at least one newly emergent hazard not observed during the historical baseline period. Notably, countries such as Japan, Iraq, and Serbia are exhibiting sub-annual return periods for such hazards—including extreme heat, floods, and landslides—indicating that events once considered rare are now recurring multiple times per year. When it comes to the emergence of climate-attributed loss and damage, China represents one of the most striking cases. While not traditionally ranked among the most disaster-prone nations, our analysis identifies China as incurring one of the highest climate-attributed economic burdens globally, exceeding US $ 17.9 billion and affecting more than 33 million people. This aligns with IPCC findings that East Asia is facing escalating compound risks from urban flooding, heatwaves, and riverine surges 47 . Similarly, Least Developed Countries (LDCs) such as Mozambique, Burundi, and South Sudan emerge as hotspots of climate-attributed human impacts, despite being historically underrepresented in disaster-based risk classifications. The IPCC has noted that data scarcity and institutional capacity gaps frequently obscure the true extent of risk in such contexts 37 . The inclusion of high-income countries like Switzerland, Czechia, and Denmark within this emerging high-risk group further reflects the growing vulnerability of temperate regions to climate-related mortality and economic loss, particularly from heatwaves and hydrological extremes 43 , 48 . Together, these findings reinforce the IPCC’s call for a paradigm shift toward anticipatory risk frameworks rather than reactive models and align with the emerging literature that emphasises the role of attribution science in identifying latent vulnerabilities and future systemic risks 44 , 45 . 4 Limitations and Way Forward While this study presents the first global quantification of climate-attributed loss and damage using observed disaster data and attribution science, it is essential to acknowledge the scope and limitations of this approach. The analysis is grounded in the EM-DAT disaster database, which captures sudden-onset, reported disaster events; however, it does not reflect the full spectrum of climate-induced loss and damage, particularly those arising from slow-onset processes, non-economic harms, or unrecorded and cumulative impacts. As 49 emphasise, climate-related losses extend beyond disasters to include intangible, irreversible, and systemic forms of harm—such as cultural erosion, biodiversity loss, forced displacement, and mental health deterioration. Similarly, 10 and 50 underscore how cascading risks, compound stressors, and socially differentiated vulnerabilities evolve beyond the visibility of event-based datasets. Therefore, while this study provides a robust, evidence-based map of realised disaster-attributed risk, it should be interpreted as only one dimension of the broader loss and damage landscape. The use of Fraction of Attributable Risk (FAR) introduces a degree of scientific uncertainty, particularly in regions or hazard types where observational data are sparse, or event attribution is methodologically complex. While some indicators—such as injuries and economic loss—yield high confidence in attribution, others exhibit wide confidence intervals or low signal-to-noise ratios. However, as 45 note, the presence of uncertainty in attribution science does not negate the urgency of policy response, especially when the magnitude and recurrence of observed losses are rising. Decisions regarding loss and damage finance and response should not be delayed by epistemic conservatism but instead informed by the best available evidence and the precautionary principle. Moreover, this study adopts a quantitative, risk-centric lens, focusing on physical impacts and attributable damage. It does not capture the lived experiences of affected communities, the social construction of vulnerability, or the political economy of loss and adaptation limits. As such, qualitative, ethnographic, and participatory methods are essential complements to the empirical framework presented here. Integrating local knowledge, structural analysis, and justice narratives would offer a more holistic understanding of how climate harms are experienced, contested, and negotiated. Looking forward, three key priorities emerge: Expand to Slow-Onset and Non-Economic Dimensions: Future assessments must incorporate sea-level rise, salinisation, desertification, and glacial retreat—hazards that unfold over decades but produce irreversible loss. Non-economic impacts should be systematically documented and valued through rights-based, culturally sensitive methodologies. Integrate Multidimensional and Participatory Approaches: Combining attribution-based disaster data with lived experiences, local knowledge, and bottom-up diagnostics would enable more context-sensitive and justice-oriented assessments. Community-led vulnerability and impact mapping should complement top-down metrics. Enhance Attribution Methods and Confidence Communication: Advancing regional attribution science, improving model ensembles, and developing standardised uncertainty reporting will strengthen the robustness and transparency of loss and damage quantification. Investment in climate services and data infrastructure in underrepresented regions is especially critical. This study provides a replicable, evidence-based method for prioritising loss and damage finance based on realised risk and attributable harm; however, it does not offer a comprehensive representation of the human toll of the climate crisis. It must be read as part of a broader epistemological and political shift—toward integrated, inclusive, and justice-centred approaches that recognise the multifaceted, evolving, and profoundly unequal nature of climate-induced loss and damage. 5 Conclusion Despite recent milestones, such as the establishment of the Loss and Damage Fund under the UNFCCC, the allocation of finance for loss and damage remains mired in ethical controversy, political contestation, and a lack of standardised methodology. This paper responds directly to that gap. It introduces a novel, evidence-based assessment framework rooted in the concept of realised risk, grounded in attribution science and supported by a new global mapping of loss and damage. By combining multiple hazard datasets with normalised economic and non-economic indicators, the study offers a replicable methodology to assess and prioritise funding needs, moving beyond static vulnerability framings and toward dynamic, accountable, and just climate finance allocation. The World Map of Loss and Damage developed in this research marks the first global visualisation of realised, climate-attributed risk, offering a new empirical foundation for financing decisions. It provides an integrated view of observed disaster impacts, attribution metrics (FAR), and regional hazard intensification. Between 2000 and 2023, climate-related disasters resulted in an average of 625 million people affected, 2.39 million homeless, 160,000 injuries, 70,000 deaths, and $ 564 billion in economic losses annually. Applying the Fraction of Attributable Risk (FAR), the study estimates that anthropogenic climate change accounts annually for 89 million affected, 338,000 homeless, 122,000 injured, 26,700 deaths, and $ 82.3 billion in PPP-adjusted losses, equivalent to 0.064% of national GNI in disaster-exposed countries. The most impactful hazards include floods, tropical cyclones, and droughts, with floods showing consistent increases across all five impact indicators. Event frequency has intensified, with return periods shortening from decades to near-decadal scales and FAR values often exceeding 0.5. Among all impacts, injury-related losses are most confidently attributed to climate change. These results underscore the systemic and accelerating nature of climate-induced risk, as well as the urgent need for targeted, evidence-based loss and damage finance. Regionally disaggregated analysis confirms that floods are the most pervasive and damaging climate-attributed hazard worldwide, followed by storms in tropical zones, heat-driven mortality in Europe and Asia, and drought-induced non-economic losses in Africa and Asia. While hazards like wildfires, landslides, and GLOFs show localised but significant impacts, the results underscore that climate-attributed loss and damage is globally widespread yet regionally differentiated, affecting both low- and high-income regions with varying intensity and confidence. At the national level, a dual-axis typology reveals four distinct climate risk clusters: countries with compound high risk, high absolute risk, high relative risk, and low risk. A total of 57 countries is classified as compound high-risk under both observed and climate-attributed data—spanning low- to high-income nations across all UNFCCC constituencies. Additionally, 32 countries emerge as high-risk only in the attribution analysis, including under-recognised cases like Japan, Brazil, and Lesotho, signalling a shift in the geography of climate vulnerability. These findings expose the limitations of past disaster records and underscore the need for equity-centred climate finance that reflects the growing and diversifying global footprint of climate-attributed loss and damage. This study challenges the prevailing notion that climate-induced loss and damage are confined to traditionally designated “particularly vulnerable” countries. It reveals that significant climate-attributed harm is occurring across both low- and high-income settings, highlighting the need for a more inclusive, evidence-based framework that recognises shared but differentiated vulnerabilities. Addressing invisible and residual losses, especially in fragile states, requires integrating non-economic impacts and acknowledging the limits of adaptation shaped by structural injustice. As climate risks intensify and spread, the political architecture of loss and damage finance must evolve toward justice-centred, capability-sensitive, and attribution-informed approaches that reflect the lived realities of affected communities worldwide. The identification of countries newly classified as high-risk under climate attribution—yet absent from historical disaster records—signals a critical shift in global risk geographies. These emerging risks, from heatwaves in Europe to flood–storm clusters in Africa and Asia, underscore the limitations of retrospective models and highlight the urgency of adopting forward-looking, attribution-informed frameworks. As climate extremes become more frequent and geographically diffuse, even in high-income and historically low-risk nations, adaptation and finance strategies must shift from reactive to anticipatory governance, grounded in real-time evidence and inclusive of data-scarce contexts. One of the major takeaways from this study is the empirical evidence base on climate injustice, highlighting that climate-induced loss and damage are unequally distributed, with Small Island States and low-income countries bearing disproportionate per capita and non-economic burdens despite contributing the least to global emissions. Traditional GDP-based loss metrics obscure this injustice, overlooking profound human, cultural, and psychological impacts. By introducing a global climate justice typology grounded in risk, responsibility, and capacity, the study reveals that structural vulnerability—not adaptive readiness—is the dominant driver of climate injustice. As such, future loss and damage finance must shift from cost-based approaches to justice-centred allocation frameworks that prioritise per capita impact, human vulnerability, and historical responsibility. However, this study cautions against turning loss and damage finance into a geopolitical “beauty contest” that ranks countries by vulnerability using static or technocratic metrics. Vulnerability is a contextual, dynamic, and multidimensional concept that affects both low- and high-income nations in distinct ways. Rather than focusing narrowly on “who is most deserving,” L&D governance must shift toward differentiated, justice-informed support that reflects both absolute and relative risks. Adequate finance should deploy a tailored mix of instruments—grants, insurance, social protection—aligned with countries’ specific risk structures and adaptive capacities. The Loss and Damage architecture must evolve from a mechanism of reactive categorisation to a globally coordinated platform for addressing compound risks through layered, equitable, and adaptive responses. Our new framework for assessing loss and damage in place further guides the requisite level of funding necessary to avert, minimise, and address loss and damage effectively. At COP28, the UAE’s $ 100 million pledge initiated the loss and damage fund and inspired 15 additional countries to contribute, bringing the total pledges to around $ 700 million. However, our analysis reveals that the risk of economic loss alone exceeds $ 560 billion, excluding non-economic losses and damages such as human suffering and death, which far exceed the pledge of $ 100 billion annually. Nevertheless, the current pledges account for less than 1% of the expectations and only 0.1% of their actual needs. While the collective pledge reflects a growing recognition of the issue, it falls short of addressing the actual economic cost of realised risk, which exceeds five times the current pledge. This stark gap between funding pledges and the actual economic cost of loss and damage underscores the urgent need for a paradigm shift in the allocation of resources and a new international consensus regarding monitoring, reporting and evaluation to provide greater urgency to the decarbonisation challenge and deliver the just transition to net zero by 2050 at the latest. Vulnerability-based allocation strategies, while well-intentioned, may not be sufficient to address the complex and evolving nature of loss and damage. Instead, an evidence-based approach that considers the attributable annual economic loss should be adopted as the yardstick for defining the scope of demand for loss and damage finance. As such, future loss and damage finance must abandon one-size-fits-all models and embrace a justice-centred, evidence-based allocation framework. This means shifting: From cost-based approaches to impact- and justice-based criteria. From vulnerability contests to contextualised risk–responsibility–capacity assessments. From reactive funding pledges to anticipatory, layered, and needs-based support systems. The challenge of loss and damage is not merely technical or financial—it is profoundly political. It touches on questions of global solidarity, historical responsibility, and intergenerational equity. As climate risks accelerate and diversify across all income groups, the global architecture for loss and damage must evolve. It must become a platform not only for funding but also for remedying injustice, enhancing global accountability, and advancing a just transition toward a safer and more equitable future. 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Clim Policy 20:715–728 Vautard R et al (2023) Heat extremes in western europe increasing faster than simulated due to atmospheric circulation trends. Nat Commun 14:6803 Van Oldenborgh GJ et al (2024) Deadly mediterranean heatwave would not have occurred without human-induced climate change Robine J-M et al (2008) Death toll exceeded 70,000 in europe during the summer of 2003. Comptes Rendus Biologies 331:171–178 Boyd E, James RA, Jones RG, Young HR, Otto F (2017) E. L. A typology of loss and damage perspectives. Nat Clim Change 7:723–729 Huggel C, Stone D, Eicken H, Hansen G (2015) Potential and limitations of the attribution of climate change impacts for informing loss and damage discussions and policies. Clim Change 133:453–467 Robinson S, Roberts JT, Weikmans R, Falzon D (2023) Vulnerability-based allocations in loss and damage finance. Nat Clim Change 13:1055–1062 Wang Y et al (2020) Quantifying the response of potential flooding risk to urban growth in beijing. Sci Total Environ 705:135868 Russo S, Marchese AF, Sillmann J, Immé G (2016) When will unusual heat waves become normal in a warming africa? Environ Res Lett 11:054016 Boyd E et al (2021) Loss and damage from climate change: A new climate justice agenda. One Earth 4:1365–1370 Tschakert P, Ellis N, R., Anderson C, Kelly A, Obeng J (2019) One thousand ways to experience loss: A systematic analysis of climate-related intangible harm from around the world. Glob Environ Change 55:58–72 Additional Declarations There is NO Competing Interest. Supplementary Files AnnexAdemographicimputation.docx Annex A: Demographic Data Imputation Methodology AnnexBResults.docx Annex B: Results (Extended) Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7149291","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":496530704,"identity":"ba747efd-758c-4997-8b40-924857c39d63","order_by":0,"name":"Nandan Mukherjee","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIiWNgGAWjYHACZmYQwcDA2HAgoQLEToBK8BDUwnzwwYMzJGgBArZkw4dtRGgxFztjbFxQcY+dn73HTCJxnnXidvbkgw8YauwYDM4cwKrFcnaOcfKMM8XMkj1ngFq2pSfu7HmWbMBwLJnB4GwDVi0Gt3OMD/O2JTAb3MgBaTmcuAHEYGA7wGBwHrvDIFr+JTDb338D1DIHpCX/+w+Gf/i1JPM2AG2RYEs2SGwA28LGwNh2AI/D0oqNeY4lMEucAfo64Vi68YYzz4wlEvuSeSRxeN/gdvJmaZ6ahGT+9oMNB3/UWMtuOJ788MOHb3ZyfGcSsLsMCpJRuQn4IhIK7AgpGAWjYBSMghEMAFC6Y1OH4M2xAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-8397-3697","institution":"University of Dundee","correspondingAuthor":true,"prefix":"","firstName":"Nandan","middleName":"","lastName":"Mukherjee","suffix":""},{"id":496530705,"identity":"add89a11-16bc-4ae2-85f6-d013327c0a8b","order_by":1,"name":"John Rowan","email":"","orcid":"https://orcid.org/0000-0001-5693-9306","institution":"University of Dundee","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"","lastName":"Rowan","suffix":""}],"badges":[],"createdAt":"2025-07-17 12:56:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7149291/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7149291/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88560836,"identity":"1978e4e5-85a5-4083-825d-b11e238c5c92","added_by":"auto","created_at":"2025-08-07 17:59:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":285210,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eClimate Attribution in the Change of Frequency of Extreme Events\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage15.png","url":"https://assets-eu.researchsquare.com/files/rs-7149291/v1/631b93569b56852f16112e3a.png"},{"id":88561183,"identity":"9f72ca0e-b0c2-4de7-831d-216e487e4192","added_by":"auto","created_at":"2025-08-07 18:07:38","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":990452,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAbsolute and relative national clusters of disaster-observed and climate-attributed loss and damage [ISO code wise country names are listed in the Annex B].\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7149291/v1/57937fb2f2013ca72407961a.png"},{"id":88561185,"identity":"58db8cb7-f89d-4d73-a4eb-fc21718f5acb","added_by":"auto","created_at":"2025-08-07 18:07:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":979157,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eWorld Map of Loss and Damage from Extreme Events: (Top) Observed; (Bottom) Climate-attributed\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7149291/v1/8d13cdf73ab199390f68a476.png"},{"id":92483396,"identity":"fb003638-4561-4e59-89d5-2c27530a0d2f","added_by":"auto","created_at":"2025-09-30 08:08:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2848070,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7149291/v1/2ecdb4a0-e798-473e-8eb4-bad047574148.pdf"},{"id":88560839,"identity":"0f900fb6-39c1-4ec1-bbb9-99e811b8691f","added_by":"auto","created_at":"2025-08-07 17:59:38","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":142656,"visible":true,"origin":"","legend":"Annex A: Demographic Data Imputation Methodology","description":"","filename":"AnnexAdemographicimputation.docx","url":"https://assets-eu.researchsquare.com/files/rs-7149291/v1/8e375a5d7d4d0f7ba459b91e.docx"},{"id":88560835,"identity":"1db14746-7e1d-4034-ada5-e6f521867542","added_by":"auto","created_at":"2025-08-07 17:59:38","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":27920,"visible":true,"origin":"","legend":"Annex B: Results (Extended)","description":"","filename":"AnnexBResults.docx","url":"https://assets-eu.researchsquare.com/files/rs-7149291/v1/e3a463656e4f5f0e7e011d43.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Global Assessment of Climate Change-Attributed Loss and Damage","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eClimate change is no longer a distant threat; it is actively reshaping global patterns of loss and damage (L\u0026amp;D). A recent attribution study\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e found that climate change tripled heat-related deaths during the 2025 European heatwave. According to EM-DAT\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, the frequency of climate-related hazards has increased by 70% since 2000. Over the past two decades, these events have resulted in 785,000 deaths, affected 4.3\u0026nbsp;billion people, and caused over US\u003cspan\u003e$\u003c/span\u003e3.6 trillion in losses. The World Meteorological Organisation\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e confirms a sevenfold rise in disaster damages since the 1970s. The Intergovernmental Panel on Climate Change (IPCC) now states it is \u0026ldquo;unequivocal\u0026rdquo; that human activity is the primary driver of both rapid- and slow-onset climate impacts\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn response, L\u0026amp;D has become a key, albeit contested, pillar of global climate governance under the United Nations Framework Convention on Climate Change (UNFCCC). The UN Environment Programme defines L\u0026amp;D as the residual impacts of climate change that mitigation and adaptation cannot prevent. Though formally recognised under the Cancun Agreements\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, the agenda dates to early 1990s proposals by small island states\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. It gained traction with the establishment of the L\u0026amp;D Fund at COP27\u003csup\u003e7\u003c/sup\u003e and its operationalisation at COP28\u003csup\u003e8\u003c/sup\u003e. While UNFCCC frameworks prioritise \u0026ldquo;particularly vulnerable\u0026rdquo; nations, events like the Texas flash floods and the 2025 European heatwave (with thousands of deaths) highlight that L\u0026amp;D spans geopolitical boundaries.\u003c/p\u003e\u003cp\u003eTraditional assessments such as Post-Disaster Needs Assessments (PDNAs) use deterministic, event-specific data. While helpful for immediate relief, they often overlook systemic vulnerabilities. In contrast, probabilistic risk models analyse multiple events over time using frequency and severity metrics. The IPCC\u0026rsquo;s Sixth Assessment Report (AR6) reframes risk as \u0026ldquo;the potential for adverse consequences\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, distinguishing between \u0026ldquo;potential\u0026rdquo; future risks and \u0026ldquo;realised\u0026rdquo; risks based on observed data. Realised risk bridges science and justice by revealing where adaptation has failed and governance gaps persist\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA growing body of literature reveals critical gaps in the assessment and governance of L\u0026amp;D. Empirical studies from Nigeria, India, and Bangladesh show how climate-induced flooding disrupts livelihoods through intersecting social, economic, and health vulnerabilities\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Yet these insights rarely inform national reporting or global finance mechanisms. Scholars stress the systemic and compounding nature of L\u0026amp;D\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, but prevailing models remain fragmented, overly reliant on static indicators, and inattentive to relational vulnerability\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Global disaster databases like EM-DAT and DesInventar prioritise monetised infrastructure losses while underreporting non-economic and slow-onset L\u0026amp;D such as trauma, displacement, and biodiversity decline\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Remote sensing lacks participatory validation\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and standardised global protocols for intangible losses remain absent\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Risk governance remains centralised and exclusionary\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, and finance tools like parametric insurance neglect informal and slow onset losses\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAttribution science\u0026mdash;primarily via the Fraction of Attributable Risk (FAR)\u0026mdash;shows promise but faces integration barriers in data-poor contexts, particularly in the Global South\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. The global estimate of climate-attributed L\u0026amp;D by\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, combining FAR with the Value of a Statistical Life (VSL), represents a pioneering step\u0026mdash;but also invites critique. While the authors defend their use of U.S./U.K. benchmarks as \u0026ldquo;convenient,\u0026rdquo; critics argue that VSL undermines ethical fairness\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, embeds political biases\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, and omits non-economic harms\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. Even early proponents acknowledged its normative limits\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Scholars caution against using attribution as the sole basis for L\u0026amp;D finance. Calls for plural lines of evidence\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, critiques of scientific reductionism\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, and warnings about exclusion risks due to evidentiary gaps\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e all converge to suggest that attribution must be integrated with justice considerations. In fact, attribution has often been used as \u0026ldquo;tactical opposition\u0026rdquo; to delegitimise claims from developing nations\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAddressing these key empirical and conceptual gaps, this study presents the first global, indicator-based assessment of climate-attributed realised loss and damage (L\u0026amp;D). We integrate disaster time-series data from EM-DAT (with appropriate caution), attribution science (Fraction of Attributable Risk, FAR), and equity-sensitive L\u0026amp;D indicators to construct a spatially resolved map of Expected Annual Loss and Damage (EALD). We employed six indicators across economic and non-economic typologies\u0026mdash;mortality, injury, affected population, homelessness, national economic loss, and personal loss\u0026mdash;each expressed in both absolute and relative terms, following the approach of\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Absolute indicators capture total impact (e.g., deaths, economic loss), while relative formulations (e.g., per 100,000 population, percentage of GNI) standardise risk, enabling equity-sensitive comparisons across contexts and aligning with Sendai and SDG targets. Absolute values reflect national burdens, whereas relative indicators\u0026mdash;adjusted for population and Gross National Income (GNI)\u0026mdash;highlight disproportionately affected smaller countries, such as small island developing states. Reliance on a single metric can distort risk perception; hence, we propose a composite risk index to integrate both. For the relative assessment, we utilised population and economic data (GDP and GNI) from the World Bank. We pre-processed these datasets using a machine learning algorithm for missing value imputation, as detailed in the Methods section and supplementary Annex A. The resulting \u0026ldquo;World Map of Climate-Attributed L\u0026amp;D\u0026rdquo; offers a transparent, reproducible tool for informing the Loss and Damage Fund and the Santiago Network. Aligned with international frameworks such as the Sendai Framework and the Paris Agreement, our approach grounds risk assessment in empirically observed harm, supporting ethically grounded, evidence-based policymaking. This study reframes L\u0026amp;D not merely as a reparative mechanism, but as a forward-looking decision-support framework rooted in the principles of common but differentiated responsibilities and respective capabilities.\u003c/p\u003e"},{"header":"2 Results","content":"\u003cp\u003eThis section presents a global assessment of realised loss and damage (L\u0026amp;D) from climate-induced hazards, focusing exclusively on hydrological, meteorological, and climatological extremes\u0026mdash;namely floods, storms, and droughts. Observed risks are expressed using Expected Annual Loss and Damage (EALD), defined as the mean annual exceedance of loss and damage. Between 2000 and 2023, disasters linked to climate-related hazards have resulted in an average of 625 million people affected, 2.39 million rendered homeless, 160,000 injuries, 70,000 deaths, and $564 billion in economic losses annually. The Fraction of Attributable Risk (FAR) method is applied to isolate the share of these losses attributable to anthropogenic climate change. A FAR of 0.4 implies that 40% of the observed losses would not have occurred without human-induced climate forcing. A summary of climate-attributed absolute and relative loss and damage account is shown in Box 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBox 1: Climate-Attributed Loss and Damage Summary\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffected population\u003c/strong\u003e\u003cbr\u003e\u0026bull; 89 million people affected annually (range: 13.7\u0026ndash;308.6 million;\u0026nbsp;\u003cem\u003elow confidence; as likely as not\u003c/em\u003e)\u003cbr\u003e\u0026bull; Equivalent to 796 per 100,000 population (\u003cem\u003emedium confidence; unlikely\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHomelessness\u003c/strong\u003e\u003cbr\u003e\u0026bull; 338,000 homeless annually (range: 110,000\u0026ndash;1.17 million;\u0026nbsp;\u003cem\u003elow confidence; as likely as not\u003c/em\u003e)\u003cbr\u003e\u0026bull; Equivalent to 11.3 per 100,000 population (\u003cem\u003elow confidence; virtually certain\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInjuries\u003c/strong\u003e\u003cbr\u003e\u0026bull; 122,000 injured annually (range: 118,000\u0026ndash;135,000;\u0026nbsp;\u003cem\u003ehigh confidence; virtually certain\u003c/em\u003e)\u003cbr\u003e\u0026bull; Equivalent to 1.94 per 100,000 population (\u003cem\u003ehigh confidence; virtually certain\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeaths\u003c/strong\u003e\u003cbr\u003e\u0026bull; 26,700 deaths annually (range: 17,000\u0026ndash;45,000;\u0026nbsp;\u003cem\u003elow confidence; likely\u003c/em\u003e)\u003cbr\u003e\u0026bull; Equivalent to 0.30 per 100,000 population (\u003cem\u003emedium confidence; virtually certain\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEconomic losses\u003c/strong\u003e\u003cbr\u003e\u0026bull; $82.3 billion PPP-adjusted loss annually (range: $17.5\u0026ndash;271.4 billion;\u0026nbsp;\u003cem\u003elow confidence; as likely as not\u003c/em\u003e)\u003cbr\u003e\u0026bull; Equivalent to $49 per person (\u003cem\u003elow confidence; virtually certain\u003c/em\u003e)\u003cbr\u003e\u0026bull; 0.064% of national GNI lost annually (\u003cem\u003emedium confidence; virtually certain\u003c/em\u003e)\u003c/p\u003e\n\u003cp\u003eTo determine this climate attribution, we analyse a total of 11,721 disaster events across three natural disaster subgroups\u0026mdash;Climatological (1,088 events), Hydrological (6,052), and Meteorological (4,581)\u0026mdash;representing approximately 89% of all recorded disasters, excluding biological events. We reveal that disaster frequency is highly concentrated within dominant subtypes across natural hazard groups: droughts lead climatological events (58.9%), riverine floods dominate hydrological disasters (43.1%), and tropical cyclones account for the largest share of meteorological hazards (43.2%). As shown in the following plot, some other subtypes occur with significantly lower relative frequencies, indicating strongly skewed risk typologies within each subgroup.\u003c/p\u003e\n\u003cp\u003eAssigning climate attribution to disaster-related loss and damage draws on shifts in Annual Exceedance Probability (AEP), with consistent increases observed across key hazards and indicators between 1980 and 2023. This global analysis, as shown in Figure 1, indicates that the return periods for events such as floods, droughts, and extreme temperatures have shortened dramatically, from multi-decadal to near-decadal timescales, reflecting a marked increase in hazard frequency. The estimated Fraction of Attributable Risk (FAR) ranges from 0.5 to 1.0, indicating that more than half of this increased frequency is likely due to anthropogenic climate change.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile we investigated the pattern of hazard typologies attributing to loss and damage in Figure\u0026nbsp;1 , we reveal floods show consistent and substantial increases in AEP across all five indicators\u0026mdash;affected population, deaths, economic damage, homelessness, and injuries\u0026mdash;confirming their status as the most frequently recurring hazard. Droughts demonstrate notable increases in AEP for economic damage and affected populations but no meaningful change for deaths, homelessness, or injuries. Extreme temperature events show a marked increase in AEP for deaths, with negligible or declining AEP trends for other indicators. Storms display moderate increases in AEP for economic damage and injuries but slight decreases or no change in affected populations, homelessness, and deaths. Wildfires show rising AEP for homeless and affected populations, with no apparent change in deaths or injuries. Mass movement (wet) hazards show minimal change or slight decreases across all indicators, with no significant rise in AEP.\u003c/p\u003e\n\u003cp\u003eUncertainty bands in Table 1 reflect the confidence and likelihood of climate attribution based on observed impacts and causal robustness. While confidence remains low across most hazard\u0026ndash;indicator combinations due to data and attribution gaps, injury-related losses exhibit high agreement and evidence, rendering them virtually certain to be climate-influenced. This global hazard- and indicator-wise analysis reveals important differences across economic and non-economic dimensions of loss and damage. However, aggregate global figures obscure the disproportionate burden faced by vulnerable countries and communities, often masked by data from large, high-income nations. To expose the uneven geography of climate-attributed loss and damage, the following section presents a disaggregated assessment by region, hazard, and impact type.\u003c/p\u003e\n\u003cp\u003eTable 1: \u003cstrong\u003eSummary of hazard-wise changes in baseline vs. present-day return periods, and estimated Fraction of Attributable Risk (FAR).\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBaseline Return Period (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePresent Return Period (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttributable Risk (FAR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDrought\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e29.41 (21\u0026ndash;48.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.99 (6.47\u0026ndash;14.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.53 (0.38\u0026ndash;0.78)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eExtreme temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e57.17 (41.42\u0026ndash;91.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8.43 (6.18\u0026ndash;13.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.83 (0.78\u0026ndash;0.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFlood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e11.24 (8.34\u0026ndash;17.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e4.67 (3.56\u0026ndash;6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.53 (0.35\u0026ndash;0.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eGlacial lake outburst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026infin; (\u0026infin;\u0026ndash;\u0026infin;)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15.33 (11.5\u0026ndash;23.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1.00 (1\u0026ndash;1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMass movement (wet)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e28.33 (20.55\u0026ndash;45.45)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.86 (5.73\u0026ndash;12.52)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.60 (0.51\u0026ndash;0.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eStorm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e15.88 (11.84\u0026ndash;24.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6.37 (4.78\u0026ndash;9.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.52 (0.41\u0026ndash;0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eWildfire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48.28 (35.05\u0026ndash;76.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e7.81 (5.76\u0026ndash;12.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e0.71 (0.60\u0026ndash;0.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch3\u003e\u003cbr\u003e\u003c/h3\u003e\n\u003ch3\u003e2.1.1 \u0026nbsp; \u0026nbsp;Regional Profile of Hazard Typology and Climate Attribution of Loss and Damage\u003c/h3\u003e\n\u003cp\u003eTable 2 presents a disaggregated regional summary of climate-attributed loss and damage across six impact indicators and seven major hazards. Floods emerge as the most widespread and damaging hazard across nearly all regions, with particularly severe impacts in South, Southeast, and East Asia; Sub-Saharan Africa; Central America; and parts of Europe. Storms follow, especially in tropical and subtropical belts. Extreme temperatures drive elevated mortality in Europe and Asia, while droughts result in extensive affected populations and economic loss in Africa and South Asia. Although hazards like wildfires, landslides, and glacial lake outburst floods (GLOFs) have more localised profiles, their region-specific impacts remain significant. These patterns highlight the dual nature of climate risk: it is both globally widespread and regionally differentiated, affecting both low- and high-income settings.\u003c/p\u003e\n\u003cp\u003eTable 2: \u003cstrong\u003eSummary of regional climate-attributed hazard impacts across human and economic loss dimensions\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHazard\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHotspot Description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eFloods\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eVery high confidence is the most widespread and impactful hazard. Significant affected, homeless, injured, and economic losses across nearly all regions. Especially severe in South, Southeast, and East Asia; Northern, Western, and Eastern Africa; South and Central America; Caribbean; Southern and Western Europe; and Melanesia.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eStorms\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eHigh confidence in tropical and subtropical regions. Severe homelessness, economic loss, and affected indicators in South, Southeast, and East Asia, the Caribbean and Central America. Moderate confidence in Western and Eastern Africa. Western Europe and Micronesia show economic and non-economic damages.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eExtreme Temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eVery high confidence in heat-attributed deaths in Southern and Western Europe. Medium to high confidence in South and Western Asia. Moderate confidence in North America. Low confidence in Africa for affected indicators. Injuries were observed in several mid- and high-latitude regions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eDrought\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eHigh confidence in Africa and Asia, especially Eastern, Western, and Northern Africa. Widespread affected populations, resulting in deaths and economic loss. Medium confidence in Latin America and parts of Asia. Low confidence in Europe and Oceania due to localised events and adaptive capacity.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eWildfire\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eMedium confidence overall. Moderate to high confidence in Southern and Eastern Europe. North and South America exhibit indicators of affected and injured populations. Northern and Southern Africa present weak signals. Minimal or low-confidence data in Asia and Oceania.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eGlacial Lake Outburst Floods (GLOFs)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eGeographically limited. Observed only in South and Southeast Asia with low to medium confidence, affecting homeless, injured, and economic loss. No significant data from other regions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eLandslides\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 264px;\"\u003e\n \u003cp\u003eLow to medium confidence. Localised impact in South and Southeast Asia and Eastern Africa (homeless, injured). Minor losses in Southern Europe and Oceania. Negligible impacts elsewhere.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003ch2\u003e\u003cbr\u003e\u003c/h2\u003e\n\u003ch2\u003e2.2 \u0026nbsp; \u0026nbsp; Compound and Attributed Risk Typologies at the National Level\u003c/h2\u003e\n\u003cp\u003eTo characterise the global distribution of climate risk, we construct a dual-axis typology based on principal component analysis (PCA) of both absolute and relative disaster outcomes, as well as FAR-weighted climate-attributed losses. Based on composite scores of observed and climate-attributed loss and damage, countries are clustered via k-means into four distinctive emergent L\u0026amp;D risk zones:\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eCompound High Risk (High\u0026ndash;High):\u003c/strong\u003e Countries experiencing both high aggregate losses and high relative per capita losses and damages (e.g., Peru, Afghanistan, Italy for climate-attributed risk; Peru, Nepal, United States for disaster-observed risk).\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eHigh Absolute Risk (High\u0026ndash;Low):\u003c/strong\u003e Economies with substantial expected annual economic losses but lower population-scaled burdens (e.g., Virgin Islands, Liechtenstein, Bhutan for climate-attributed risk; Brazil, China for disaster-observed risk).\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eHigh Relative Risk (Low\u0026ndash;High):\u003c/strong\u003e Countries facing disproportionately high losses relative to population or economic size, despite modest aggregate Expected annuals (e.g., Lesotho, Uruguay, Bulgaria for climate-attributed risk; Virgin Islands, Bahamas, Greece for disaster-observed risk).\u003c/p\u003e\n\u003cp\u003e\u0026middot; \u003cstrong\u003eLow Risk (Low\u0026ndash;Low):\u003c/strong\u003e Countries with minimal observed or attributed climate-related losses (e.g., Costa Rica, Jordan, Cyprus for climate-attributed risk; Yemen, Algeria, Slovakia for disaster-observed risk).\u003c/p\u003e\n\u003cp\u003eThese clusters are visualised in Figure 2, where the upper panel depicts disaster-observed risk, and the lower panel shows climate-attributed risk. While certain countries\u0026mdash;such as Bangladesh, Haiti, and Italy\u0026mdash;consistently appear in the compound high-risk cluster, others, like the Marshall Islands and Guatemala, emerge as high-risk only under climate attribution analysis. Finally, two global maps in Figure 3 provide a comparative perspective: the first captures the \u0026lsquo;Expected\u0026rsquo; annual disaster-attributed L\u0026amp;D from historical records, while the second isolates the subset that is scientifically attributable to anthropogenic climate change.\u003c/p\u003e\n\u003cp\u003eGlobal spatial patterns reveal a notable gradient in risk exposure. Tropical and low-latitude countries particularly in South Asia, Central America, and the Pacific are prominent in the compound high-risk group. At the same time, high-income temperate nations, including several European states and the United States, exhibit high absolute loss profiles, indicating significant asset exposure even where individual-level vulnerability is buffered by institutional capacity. Across both disaster and attribution datasets, countries classified as compound high risk or high absolute risk are predominantly located in tropical and high-latitude zones. In the attribution analysis, this includes countries such as Peru, Italy, and Afghanistan (compound risk), as well as the Virgin Islands, Liechtenstein, and Bhutan (absolute risk). Relative risk clusters (e.g., Lesotho, Uruguay, Bulgaria) exhibit a similar latitudinal skew, whereas low-risk groups are distributed across all geographic zones. Observed disaster data reflect analogous patterns. Compound risk is again concentrated in tropical and high-latitude countries (e.g., Peru, Nepal, the USA), while high absolute risk remains concentrated mainly in tropical regions (e.g., Brazil, China). High relative risk is also disproportionately represented in the tropics and higher latitudes (e.g., Virgin Islands, Bahamas, Greece). In contrast, low-risk countries are geographically diffuse, appearing across all zones (e.g., Yemen, Algeria, Slovakia).\u003c/p\u003e\n\u003cp\u003eOut of the 199 countries analysed in this study, 57 countries are classified as compound high-risk in both disaster-observed and climate-attributed assessments, highlighting persistent and compound exposure to loss and damage (detailed results are available in Table in Annex B). This group spans a broad income spectrum\u0026mdash;from low-income countries (e.g., Myanmar, Afghanistan, Somalia) to lower-middle-income economies (e.g., Pakistan, Nigeria, Vietnam) to high-income nations (e.g., United States, Japan, Germany, Italy). Critically, it cuts across major UNFCCC constituencies, including the Least Developed Countries (LDCs), Small Island Developing States (SIDS), the African Group, the Arab States, and European Union (EU) member states, emphasising the structural pervasiveness of climate risk. Although classified by dual exposure, the reported figures represent only the climate-attributed component, providing insight into the escalating impacts of anthropogenic climate change superimposed on existing vulnerabilities. Among this group, Myanmar (MMR) and Afghanistan (AFG) are especially illustrative of climate-amplified humanitarian stress. Myanmar reports over 458,000 people affected and 2,280 homeless, alongside 931 injuries and 3,895 deaths, underscoring its vulnerability as an LDC. Afghanistan faces over 2.28 million affected, more than 10,000 homeless, and 828 deaths, yet reports zero formal economic loss\u0026mdash;highlighting the disjunction between human toll and economic accounting in fragile economies.\u003c/p\u003e\n\u003cp\u003eCountries such as Germany (DEU), France (FRA), and Italy (ITA), despite being high-income EU states, continue to register climate-attributed economic losses exceeding US$ 1.5 billion each, alongside high per capita burdens (e.g., Italy: US$ 16.4). These values underscore the persistent exposure even in contexts of institutional strength and advanced infrastructure. Meanwhile, Somalia (SOM) and Madagascar (MDG) show millions affected yet minimal reported losses, reflecting not resilience but the invisibility of damages in monetised terms. Somalia alone reports over 2.4 million affected and almost no economic loss\u0026mdash;indicating chronic underreporting in contexts of limited data infrastructure. Small Island Developing States face a different profile of existential risk. Puerto Rico (PRI) and Bahamas (BHS) report climate-attributed per capita economic losses of US$ 207.9 and US$ 410.3, respectively\u0026mdash;among the highest globally. Despite their small size, the impact per person is disproportionately high, revealing acute vulnerabilities driven by geographic isolation and concentrated exposure to risk. Taken together, this compound high-risk group exemplifies the multi-scalar, cross-regional nature of climate-attributed loss and damage. Manifestations range from large-scale displacement in low-income fragile states to financial disruption in industrialised economies. This underscores the urgency of differentiated, equity-centred interventions that account for exposure, capability, and justice in addressing the growing burden of climate change.\u003c/p\u003e\n\u003cp\u003eConversely, a total of 32 countries is classified as compound high-risk only under the climate-attribution analysis despite not being flagged in historical disaster-based risk assessments. This divergence highlights the emergence of new geographies of climate vulnerability regions where anthropogenic climate change is now producing measurable impacts that extend beyond traditional disaster trends. This emergent group spans diverse income levels and UNFCCC groupings. High-income nations, such as Japan (JPN), Switzerland (CHE), and Czechia (CZE), exhibit climate-attributed mortality and economic losses, indicating that a strong institutional capacity does not confer immunity. Japan, for instance, reports over 27,000 injuries, 218 deaths, and a national economic loss of US$ 4 billion (upper bound: US$ 7.6 billion), with a per capita loss exceeding US$ 30. Such figures mark a notable shift, positioning industrialised nations within the frontier of climate-attributed impacts. Major emerging economies, such as Brazil (BRA) and Thailand (THA), also feature prominently. Brazil reports over 800,000 affected, 81 deaths, and national damages nearing US$ 1.66 billion, with per capita burdens over US$ 8. These statistics underscore how rising global temperatures are driving impacts in countries not traditionally considered high-risk.\u003c/p\u003e\n\u003cp\u003eSeveral Least Developed Countries (LDCs), including Lesotho (LSO), Mali (MLI), and South Sudan (SSD), now exhibit significant human impacts. Lesotho, for example, reports over 420,000 affected despite no previous high-risk designation\u0026mdash;revealing climate stress in under-monitored, structurally fragile contexts. Mali adds nearly 700,000 affected, hundreds homeless, and measurable mortality, indicating deepening exposure. Small Island Developing States (SIDS) such as Guyana (GUY) and the Marshall Islands (MHL) also appear. Although their absolute economic losses remain modest, per capita losses are disproportionately high. Guyana, for instance, shows a per capita loss exceeding US$ 27, demonstrating the outsized risk faced by small island nations from sea-level rise and intensified extremes. In sub-Saharan Africa, countries such as Ethiopia (ETH), Kenya (KEN), the Democratic Republic of the Congo (COD), and Namibia (NAM) reflect a reconfiguration of climate risk. These nations, previously underrepresented in disaster risk datasets, now face climate-attributed displacement, mortality, and economic burdens, indicating the expansion of zones of vulnerability in development-constrained regions. Together, this emerging risk cohort illustrates a geospatial and structural shift in climate vulnerability\u0026mdash;extending to new regions, economies, and social groups. These findings necessitate an urgent recalibration of global climate finance, monitoring, and adaptation efforts to include these newly exposed populations before the impacts become irreversible.\u003c/p\u003e"},{"header":"3 Discussion","content":"\u003cp\u003eThe Loss and Damage (L\u0026amp;D) pillar under the UNFCCC centres on the needs of \u0026lsquo;particularly vulnerable countries\u0026rsquo;, recognising that they experience the most significant harm from climate change despite contributing the least to its causes. This harm is often compounded by limited capacity to cope with adverse impacts, underscoring the justice dimension of climate policy: those least responsible are often the most severely affected. While the Warsaw International Mechanism and Article 8 of the Paris Agreement appropriately emphasise the urgent needs of particularly vulnerable developing countries\u0026mdash;such as Least Developed Countries (LDCs) and Small Island Developing States (SIDS)\u0026mdash;our findings challenge the notion that L\u0026amp;D is confined to the Global South. Empirical evidence shows that high-income countries with advanced adaptive capacities are also experiencing significant, quantifiable, climate-attributed harm. Crucially, this does not dilute the historical responsibility of developed countries to finance adaptation and redress in under-resourced nations, as established in the 1992 UNFCCC and reaffirmed in successive COP decisions. Instead, it reinforces the need for a universal, capability-sensitive L\u0026amp;D governance framework\u0026mdash;one that recognises differentiated but shared vulnerabilities across countries and contexts.\u003c/p\u003e\u003cp\u003eOur classification of 38 countries as high-risk in both disaster-observed and climate-attributed loss and damage substantiates a key finding from IPCC AR6 WGII Chap.\u0026nbsp;16: anthropogenic climate change is intensifying pre-existing risk patterns, particularly in regions marked by persistent development deficits, institutional fragility, or socio-political instability\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This process, described as the \u0026ldquo;intensification of risk hotspots\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, results in compound and cascading risks arising from the interaction between climate hazards and structural vulnerabilities.Countries such as Afghanistan, Myanmar, and Somalia exemplify this dynamic: low adaptive capacity intersects with conflict, underdevelopment, and state fragility, producing high levels of human loss and damage\u0026mdash;including displacement, injury, and affected populations\u0026mdash;that are not matched by reported economic losses. This mismatch reflects what the IPCC terms \u0026ldquo;invisible losses\u0026rdquo;\u0026mdash;non-economic harms such as psychosocial trauma, cultural dislocation, and identity loss\u003csup\u003e\u003cspan additionalcitationids=\"CR39\" citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Though rarely monetised, these losses are central to lived vulnerability and long-term recovery, particularly in fragile or marginalised settings where traditional coping systems have been eroded. Recent literature links these non-economic and residual losses to the notion of adaptation limits. According to AR6, residual losses arise when soft or hard adaptation limits are exceeded\u0026mdash;not due to technical constraints alone but because of governance failures, social barriers, or political inaction\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. In such cases, residual loss becomes less a function of climatic severity than of structural injustice.\u003c/p\u003e\u003cp\u003eAt the same time, high-income countries such as Germany, Italy, and France also fall within the compound risk category despite their advanced institutional capacity and disaster management infrastructure. This supports the IPCC\u0026rsquo;s assessment that \u0026ldquo;climate risks are increasingly visible in developed regions,\u0026rdquo; particularly in temperate zones experiencing more frequent and severe extremes\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Historical events such as the 2003 heatwave\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e further highlight enduring vulnerabilities in even the most developed states. Our results, which demonstrate substantial national and per capita economic losses in these countries, reinforce the IPCC\u0026rsquo;s recognition that climate vulnerability now extends to both affluent and capacity-constrained contexts\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Together, these profiles reflect the intersecting pressures of hazard intensification, development trajectories, and uneven resilience. Loss and damage can no longer be viewed solely through the lens of the Global South, nor can they be adequately captured by economic metrics or disaster frequency alone. A more granular, science-based, and justice-sensitive approach is needed\u0026mdash;one that accounts for invisible, residual, and compounding harms across diverse geographies.\u003c/p\u003e\u003cp\u003eThese findings are echoed in recent critiques of vulnerability-based allocation frameworks in the climate finance literature. As\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e argue, the designation of \u0026ldquo;particularly vulnerable countries\u0026rdquo; under the UNFCCC remains politically contested and analytically unstable, often privileging Small Island Developing States (SIDS), Landlocked Developing Countries (LDCs), and African countries based on a negotiated consensus rather than dynamic or evidence-based criteria. Such a framing risks marginalising countries\u0026mdash;like Afghanistan, Myanmar, and Somalia\u0026mdash;that consistently experience high human impacts but fall outside formal prioritisation categories.\u003c/p\u003e\u003cp\u003eMoreover, empirical evidence showing that countries like Germany and Italy rank high in climate-attributed economic losses challenges the assumption that high adaptive capacity insulates nations from harm. It underscores the structural reality that even well-resourced countries remain exposed to climate impacts driven by global emissions\u0026mdash;often perpetuated by large corporate actors operating with limited accountability. This aligns with growing calls in the literature\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e for justice-based responses that are redistributive, anticipatory, and globally inclusive. Consequently, the political architecture of the Loss and Damage pillar under the UNFCCC must broaden to include all countries\u0026mdash;across both the Global North and South\u0026mdash;in discussions not only of potential risks but also of realised risks: the lived realities that are steadily eroding the resilience of communities facing climate loss and damage. While prioritising support for particularly vulnerable nations remains essential, a narrowly framed approach risks obscuring the growing exposure, systemic fragilities, and compound vulnerabilities emerging across diverse national contexts.\u003c/p\u003e\u003cp\u003eA critical insight from this study is the identification of 32 countries that are classified as high-risk exclusively under climate-attributed metrics but not within conventional historical disaster datasets. This divergence underscores the IPCC\u0026rsquo;s framing of \u0026ldquo;non-analogue futures\u0026rdquo; and \u0026ldquo;emerging systemic risks,\u0026rdquo; where attribution science reveals latent and overlooked patterns in global risk geographies. Emerging climate-related hazards display distinct regional signatures: extreme temperatures are newly appearing across Western and Southern Europe; floods and glacial hazards are intensifying in Central Asia; and compound flood\u0026ndash;storm risks are emerging in Eastern Africa and Small Island States. In Western Asia and parts of South Asia, the concurrent emergence of floods, droughts, and heat extremes is increasingly common\u0026mdash;and often characterised by high recurrence. Out of the 199 countries analysed, 108 are now experiencing at least one newly emergent hazard not observed during the historical baseline period. Notably, countries such as Japan, Iraq, and Serbia are exhibiting sub-annual return periods for such hazards\u0026mdash;including extreme heat, floods, and landslides\u0026mdash;indicating that events once considered rare are now recurring multiple times per year.\u003c/p\u003e\u003cp\u003eWhen it comes to the emergence of climate-attributed loss and damage, China represents one of the most striking cases. While not traditionally ranked among the most disaster-prone nations, our analysis identifies China as incurring one of the highest climate-attributed economic burdens globally, exceeding US\u003cspan\u003e$\u003c/span\u003e 17.9\u0026nbsp;billion and affecting more than 33\u0026nbsp;million people. This aligns with IPCC findings that East Asia is facing escalating compound risks from urban flooding, heatwaves, and riverine surges\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Similarly, Least Developed Countries (LDCs) such as Mozambique, Burundi, and South Sudan emerge as hotspots of climate-attributed human impacts, despite being historically underrepresented in disaster-based risk classifications. The IPCC has noted that data scarcity and institutional capacity gaps frequently obscure the true extent of risk in such contexts\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe inclusion of high-income countries like Switzerland, Czechia, and Denmark within this emerging high-risk group further reflects the growing vulnerability of temperate regions to climate-related mortality and economic loss, particularly from heatwaves and hydrological extremes\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Together, these findings reinforce the IPCC\u0026rsquo;s call for a paradigm shift toward anticipatory risk frameworks rather than reactive models and align with the emerging literature that emphasises the role of attribution science in identifying latent vulnerabilities and future systemic risks\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"4 Limitations and Way Forward","content":"\u003cp\u003eWhile this study presents the first global quantification of climate-attributed loss and damage using observed disaster data and attribution science, it is essential to acknowledge the scope and limitations of this approach. The analysis is grounded in the EM-DAT disaster database, which captures sudden-onset, reported disaster events; however, it does not reflect the full spectrum of climate-induced loss and damage, particularly those arising from slow-onset processes, non-economic harms, or unrecorded and cumulative impacts. As\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e emphasise, climate-related losses extend beyond disasters to include intangible, irreversible, and systemic forms of harm\u0026mdash;such as cultural erosion, biodiversity loss, forced displacement, and mental health deterioration. Similarly,\u003csup\u003e10\u003c/sup\u003e and\u003csup\u003e50\u003c/sup\u003e underscore how cascading risks, compound stressors, and socially differentiated vulnerabilities evolve beyond the visibility of event-based datasets. Therefore, while this study provides a robust, evidence-based map of realised disaster-attributed risk, it should be interpreted as only one dimension of the broader loss and damage landscape.\u003c/p\u003e\u003cp\u003eThe use of Fraction of Attributable Risk (FAR) introduces a degree of scientific uncertainty, particularly in regions or hazard types where observational data are sparse, or event attribution is methodologically complex. While some indicators\u0026mdash;such as injuries and economic loss\u0026mdash;yield high confidence in attribution, others exhibit wide confidence intervals or low signal-to-noise ratios. However, as\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e note, the presence of uncertainty in attribution science does not negate the urgency of policy response, especially when the magnitude and recurrence of observed losses are rising. Decisions regarding loss and damage finance and response should not be delayed by epistemic conservatism but instead informed by the best available evidence and the precautionary principle. Moreover, this study adopts a quantitative, risk-centric lens, focusing on physical impacts and attributable damage. It does not capture the lived experiences of affected communities, the social construction of vulnerability, or the political economy of loss and adaptation limits. As such, qualitative, ethnographic, and participatory methods are essential complements to the empirical framework presented here.\u003c/p\u003e\u003cp\u003eIntegrating local knowledge, structural analysis, and justice narratives would offer a more holistic understanding of how climate harms are experienced, contested, and negotiated. Looking forward, three key priorities emerge:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eExpand to Slow-Onset and Non-Economic Dimensions: Future assessments must incorporate sea-level rise, salinisation, desertification, and glacial retreat\u0026mdash;hazards that unfold over decades but produce irreversible loss. Non-economic impacts should be systematically documented and valued through rights-based, culturally sensitive methodologies.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eIntegrate Multidimensional and Participatory Approaches: Combining attribution-based disaster data with lived experiences, local knowledge, and bottom-up diagnostics would enable more context-sensitive and justice-oriented assessments. Community-led vulnerability and impact mapping should complement top-down metrics.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eEnhance Attribution Methods and Confidence Communication: Advancing regional attribution science, improving model ensembles, and developing standardised uncertainty reporting will strengthen the robustness and transparency of loss and damage quantification. Investment in climate services and data infrastructure in underrepresented regions is especially critical.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThis study provides a replicable, evidence-based method for prioritising loss and damage finance based on realised risk and attributable harm; however, it does not offer a comprehensive representation of the human toll of the climate crisis. It must be read as part of a broader epistemological and political shift\u0026mdash;toward integrated, inclusive, and justice-centred approaches that recognise the multifaceted, evolving, and profoundly unequal nature of climate-induced loss and damage.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eDespite recent milestones, such as the establishment of the Loss and Damage Fund under the UNFCCC, the allocation of finance for loss and damage remains mired in ethical controversy, political contestation, and a lack of standardised methodology. This paper responds directly to that gap. It introduces a novel, evidence-based assessment framework rooted in the concept of realised risk, grounded in attribution science and supported by a new global mapping of loss and damage. By combining multiple hazard datasets with normalised economic and non-economic indicators, the study offers a replicable methodology to assess and prioritise funding needs, moving beyond static vulnerability framings and toward dynamic, accountable, and just climate finance allocation.\u003c/p\u003e\u003cp\u003eThe World Map of Loss and Damage developed in this research marks the first global visualisation of realised, climate-attributed risk, offering a new empirical foundation for financing decisions. It provides an integrated view of observed disaster impacts, attribution metrics (FAR), and regional hazard intensification. Between 2000 and 2023, climate-related disasters resulted in an average of 625\u0026nbsp;million people affected, 2.39\u0026nbsp;million homeless, 160,000 injuries, 70,000 deaths, and \u003cspan\u003e$\u003c/span\u003e564\u0026nbsp;billion in economic losses annually. Applying the Fraction of Attributable Risk (FAR), the study estimates that anthropogenic climate change accounts annually for 89\u0026nbsp;million affected, 338,000 homeless, 122,000 injured, 26,700 deaths, and \u003cspan\u003e$\u003c/span\u003e82.3\u0026nbsp;billion in PPP-adjusted losses, equivalent to 0.064% of national GNI in disaster-exposed countries.\u003c/p\u003e\u003cp\u003eThe most impactful hazards include floods, tropical cyclones, and droughts, with floods showing consistent increases across all five impact indicators. Event frequency has intensified, with return periods shortening from decades to near-decadal scales and FAR values often exceeding 0.5. Among all impacts, injury-related losses are most confidently attributed to climate change. These results underscore the systemic and accelerating nature of climate-induced risk, as well as the urgent need for targeted, evidence-based loss and damage finance. Regionally disaggregated analysis confirms that floods are the most pervasive and damaging climate-attributed hazard worldwide, followed by storms in tropical zones, heat-driven mortality in Europe and Asia, and drought-induced non-economic losses in Africa and Asia. While hazards like wildfires, landslides, and GLOFs show localised but significant impacts, the results underscore that climate-attributed loss and damage is globally widespread yet regionally differentiated, affecting both low- and high-income regions with varying intensity and confidence.\u003c/p\u003e\u003cp\u003eAt the national level, a dual-axis typology reveals four distinct climate risk clusters: countries with compound high risk, high absolute risk, high relative risk, and low risk. A total of 57 countries is classified as compound high-risk under both observed and climate-attributed data\u0026mdash;spanning low- to high-income nations across all UNFCCC constituencies. Additionally, 32 countries emerge as high-risk only in the attribution analysis, including under-recognised cases like Japan, Brazil, and Lesotho, signalling a shift in the geography of climate vulnerability. These findings expose the limitations of past disaster records and underscore the need for equity-centred climate finance that reflects the growing and diversifying global footprint of climate-attributed loss and damage.\u003c/p\u003e\u003cp\u003eThis study challenges the prevailing notion that climate-induced loss and damage are confined to traditionally designated \u0026ldquo;particularly vulnerable\u0026rdquo; countries. It reveals that significant climate-attributed harm is occurring across both low- and high-income settings, highlighting the need for a more inclusive, evidence-based framework that recognises shared but differentiated vulnerabilities. Addressing invisible and residual losses, especially in fragile states, requires integrating non-economic impacts and acknowledging the limits of adaptation shaped by structural injustice. As climate risks intensify and spread, the political architecture of loss and damage finance must evolve toward justice-centred, capability-sensitive, and attribution-informed approaches that reflect the lived realities of affected communities worldwide.\u003c/p\u003e\u003cp\u003eThe identification of countries newly classified as high-risk under climate attribution\u0026mdash;yet absent from historical disaster records\u0026mdash;signals a critical shift in global risk geographies. These emerging risks, from heatwaves in Europe to flood\u0026ndash;storm clusters in Africa and Asia, underscore the limitations of retrospective models and highlight the urgency of adopting forward-looking, attribution-informed frameworks. As climate extremes become more frequent and geographically diffuse, even in high-income and historically low-risk nations, adaptation and finance strategies must shift from reactive to anticipatory governance, grounded in real-time evidence and inclusive of data-scarce contexts.\u003c/p\u003e\u003cp\u003eOne of the major takeaways from this study is the empirical evidence base on climate injustice, highlighting that climate-induced loss and damage are unequally distributed, with Small Island States and low-income countries bearing disproportionate per capita and non-economic burdens despite contributing the least to global emissions. Traditional GDP-based loss metrics obscure this injustice, overlooking profound human, cultural, and psychological impacts. By introducing a global climate justice typology grounded in risk, responsibility, and capacity, the study reveals that structural vulnerability\u0026mdash;not adaptive readiness\u0026mdash;is the dominant driver of climate injustice. As such, future loss and damage finance must shift from cost-based approaches to justice-centred allocation frameworks that prioritise per capita impact, human vulnerability, and historical responsibility.\u003c/p\u003e\u003cp\u003eHowever, this study cautions against turning loss and damage finance into a geopolitical \u0026ldquo;beauty contest\u0026rdquo; that ranks countries by vulnerability using static or technocratic metrics. Vulnerability is a contextual, dynamic, and multidimensional concept that affects both low- and high-income nations in distinct ways. Rather than focusing narrowly on \u0026ldquo;who is most deserving,\u0026rdquo; L\u0026amp;D governance must shift toward differentiated, justice-informed support that reflects both absolute and relative risks. Adequate finance should deploy a tailored mix of instruments\u0026mdash;grants, insurance, social protection\u0026mdash;aligned with countries\u0026rsquo; specific risk structures and adaptive capacities. The Loss and Damage architecture must evolve from a mechanism of reactive categorisation to a globally coordinated platform for addressing compound risks through layered, equitable, and adaptive responses.\u003c/p\u003e\u003cp\u003eOur new framework for assessing loss and damage in place further guides the requisite level of funding necessary to avert, minimise, and address loss and damage effectively. At COP28, the UAE\u0026rsquo;s \u003cspan\u003e$\u003c/span\u003e100\u0026nbsp;million pledge initiated the loss and damage fund and inspired 15 additional countries to contribute, bringing the total pledges to around \u003cspan\u003e$\u003c/span\u003e700\u0026nbsp;million. However, our analysis reveals that the risk of economic loss alone exceeds \u003cspan\u003e$\u003c/span\u003e560\u0026nbsp;billion, excluding non-economic losses and damages such as human suffering and death, which far exceed the pledge of \u003cspan\u003e$\u003c/span\u003e100\u0026nbsp;billion annually. Nevertheless, the current pledges account for less than 1% of the expectations and only 0.1% of their actual needs. While the collective pledge reflects a growing recognition of the issue, it falls short of addressing the actual economic cost of realised risk, which exceeds five times the current pledge. This stark gap between funding pledges and the actual economic cost of loss and damage underscores the urgent need for a paradigm shift in the allocation of resources and a new international consensus regarding monitoring, reporting and evaluation to provide greater urgency to the decarbonisation challenge and deliver the just transition to net zero by 2050 at the latest. Vulnerability-based allocation strategies, while well-intentioned, may not be sufficient to address the complex and evolving nature of loss and damage. Instead, an evidence-based approach that considers the attributable annual economic loss should be adopted as the yardstick for defining the scope of demand for loss and damage finance. As such, future loss and damage finance must abandon one-size-fits-all models and embrace a justice-centred, evidence-based allocation framework. This means shifting:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eFrom cost-based approaches to impact- and justice-based criteria.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFrom vulnerability contests to contextualised risk\u0026ndash;responsibility\u0026ndash;capacity assessments.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eFrom reactive funding pledges to anticipatory, layered, and needs-based support systems.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003cp\u003eThe challenge of loss and damage is not merely technical or financial\u0026mdash;it is profoundly political. It touches on questions of global solidarity, historical responsibility, and intergenerational equity. As climate risks accelerate and diversify across all income groups, the global architecture for loss and damage must evolve. It must become a platform not only for funding but also for remedying injustice, enhancing global accountability, and advancing a just transition toward a safer and more equitable future.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eClarke B et al (2025) Climate Change Tripled Heat-Related Deaths in Early Summer European Heatwave. 23 \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.imperial.ac.uk/media/imperial-college/grantham-institute/public/publications/institute-reports-and-analytical-notes/Climate-change-tripled-heat-related-deaths-in-early-summer-European-heatwave.pdf\u003c/span\u003e\u003cspan address=\"https://www.imperial.ac.uk/media/imperial-college/grantham-institute/public/publications/institute-reports-and-analytical-notes/Climate-change-tripled-heat-related-deaths-in-early-summer-European-heatwave.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelforge D et al (2025) EM\u0026ndash;DAT: The emergency events database. 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Glob Environ Change 55:58\u0026ndash;72\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7149291/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7149291/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClimate-induced disasters are rapidly escalating, yet the global Loss and Damage (L\u0026amp;D) mechanism remains constrained by the absence of robust, evidence-based frameworks for assessing and allocating support. This study introduces the first global map of realised, climate-attributed loss and damage, integrating disaster event data (EM-DAT), attribution science (Fraction of Attributable Risk, FAR), and equity-sensitive indicators to develop a standardised Expected Annual Loss and Damage (EALD) framework. We assess six key impact indicators\u0026mdash;deaths, injuries, homelessness, affected population, national economic loss, and per capita economic loss\u0026mdash;across 11,721 climate-related disaster events from 2000 to 2023. Our findings estimate that anthropogenic climate change accounts annually for 89\u0026nbsp;million people affected, 338,000 rendered homeless, 122,000 injured, 26,700 deaths, and \u003cspan\u003e$\u003c/span\u003e82.3\u0026nbsp;billion in PPP-adjusted economic losses. Contrary to prevailing assumptions, high-income countries also experience significant climate-attributed impacts, while 32 nations emerge as high-risk only under attribution-based metrics. We critique the reductive ranking of \u0026ldquo;particularly vulnerable\u0026rdquo; countries as a geopolitical beauty contest and instead propose a dual typology based on compound, absolute, and relative risk. We offer this assessment as a more appropriate entry point for investigating how structural vulnerability and emerging hazards, not historical exposure alone, drive climate injustice. Our results highlight a vast gap between pledged and required L\u0026amp;D finance and call for a justice-centred, attribution-informed framework that reflects the lived realities of affected populations across political boundaries.\u003c/p\u003e","manuscriptTitle":"Global Assessment of Climate Change-Attributed Loss and Damage","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-07 17:59:33","doi":"10.21203/rs.3.rs-7149291/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ee9cf284-ce29-4cfa-8fa9-9ea0ef1fe6e0","owner":[],"postedDate":"August 7th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52741691,"name":"Earth and environmental sciences/Climate sciences/Climate change/Climate-change impacts/Governance"},{"id":52741692,"name":"Earth and environmental sciences/Environmental social sciences/Climate-change policy"}],"tags":[],"updatedAt":"2025-09-30T08:00:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-07 17:59:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7149291","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7149291","identity":"rs-7149291","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}