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PUB2024/085/ELWho Are Climate Migrants? A Global Analysis of the Profiles of Communities Affected by Weather-related Internal DisplacementsWho Are Climate Migrants? iii CONTENTS List of figures .............................................................. iv Abbreviations and acronyms .................................................. v Executive summary ........................................................ vii Introduction ............................................................... 1 Methodology .............................................................. 3

I. Analysis ............................................................... 5 1.1. Demography ........................................................ 5 1.2. Socioeconomic profile ............................................... 10 1.3. Land use ........................................................... 13 1.4. Coastal populations ................................................. 17

2. The policy ............................................................ 19

3. Recommendations ...................................................... 21

References ............................................................... 23List of figuresiv LIST OF FIGURES Figure 1. Internal Displacement Monitoring Centre records of weather-related internal displacements between 2018 and 2024 used in the analysis ........ 3 Figure 2. Average age of populations in areas affected by weather-related internal displacement .............................................. 6 Figure 3. Average percentage of children in areas affected by weather-related internal displacement .............................................. 7

internal displacements between 2018 and 2024 used in the analysis ........ 3 Figure 2. Average age of populations in areas affected by weather-related internal displacement .............................................. 6 Figure 3. Average percentage of children in areas affected by weather-related internal displacement .............................................. 7 Figure 4. Average percentage of females in areas affected by weather-related internal displacement .............................................. 8 Figure 5. Average income of populations in areas affected by weather-related internal displacement ............................................. 11 Figure 6. Average number of schooling years of populations in areas affected by weather-related internal displacement ............................. 13 Figure 7. Average percentage of cropland in areas affected by weather-related internal displacement ............................................. 13 Figure 8. Average percentage of grazing land in areas affected by weather-related internal displacement ............................................. 15 Figure 9. Average percentage of urban land in areas affected by weather-related internal displacement ............................................. 16 Figure 10. Average percentage of coastal land in areas affected by weather-related internal displacement ............................................. 18Who Are Climate Migrants? v

ABBREVIATIONS AND ACRONYMS

DTM Displacement Tracking Matrix EIB European Investment Bank FAO Food and Agriculture Organization of the United Nations GADM Database of Global Administrative Areas GBV gender-based violence GDP gross domestic product GNI gross national income IDMC Internal Displacement Monitoring Centre IDP internally displaced person IOM International Organization for Migration IPCC Intergovernmental Panel on Climate Change km. kilometre TDP temporary displaced person UNDP United Nations Development Programme UNFCCC United Nations Framework Convention on Climate Change UNFPA United Nations Population Fund UNICEF United Nations Children’s Fund USD United States dollarsWho Are Climate Migrants? vii EXECUTIVE SUMMARY The ramifications of climate shocks on human mobility remain a critical concern for practitioners, policymakers and citizens. There are clear geographic differences observed in global displacement caseloads and in the types of hazards that drive displacement in different regions. The intersection of hazards with populations made vulnerable by factors

vii EXECUTIVE SUMMARY The ramifications of climate shocks on human mobility remain a critical concern for practitioners, policymakers and citizens. There are clear geographic differences observed in global displacement caseloads and in the types of hazards that drive displacement in different regions. The intersection of hazards with populations made vulnerable by factors like poverty, social inequality and insufficient infrastructure disproportionately expose certain communities to risk. Vulnerable groups, particularly those in hazard-prone or impoverished areas, often lack the resources to prepare for, respond to or recover from these events. As a result, socioeconomic conditions can magnify the impact of hazards for some, creating a cycle of reduced resilience and increased exposure to risk. Global estimates of disaster-induced internal displacement caseloads provide critical insights into affected populations and the impacts of climate shocks and extreme weather events. T o build further on these overarching estimates, it is vital to gain more granular insights into the demographic and socioeconomic characteristics of populations affected by disaster displacement. Analysis from this report reveals marked differences in the profiles of populations affected by different hazards and in different parts of the world. For example, it is estimated that populations that reside in areas affected by drought displacements are typically young (18.1  years on average), have a high proportion of children (43%), are male skewed (51.8%  male), have limited education (average 3.0 years of schooling), and are largely pastoral (51% of affected areas is grazing land). In contrast, populations in areas affected by wildfire displacements are typically older (37.1 years on average), have a low proportion of children (19%), are female skewed (50.5% female), have advanced education (12.6 years of schooling) and are largely urban (71% of affected areas are built-up land). The data shows that storm and flood displacements disproportionately affect farming communities (42% and 38% of affected areas are used for crop cultivation), and that the majority of populations affected by wildfire and storm displacements are coastal (74% and 65%, respectively). These findings reflect the geographic, sociopolitical and economic profiles of areas where

that storm and flood displacements disproportionately affect farming communities (42% and 38% of affected areas are used for crop cultivation), and that the majority of populations affected by wildfire and storm displacements are coastal (74% and 65%, respectively). These findings reflect the geographic, sociopolitical and economic profiles of areas where the different hazard types are most prevalent. Further to this point, the analysis highlights regional differences, quantifying how displacement-affected populations are overall younger, more male and have lower income and education levels in Africa, Asia and Oceania than in Europe and the Americas. This reflects the demographic profiles of the general populations in these regions, with some variation between the general population and population in displacement-affected areas.Executive summaryviii These demographic and socioeconomic factors have evident implications for policymakers and first responders in crises, as well as for the long-term recovery of these communities. Disaggregating the data by geographic area, hazard type and other factors highlights important differences between affected populations. Understanding these differences is critical for effective policy and practice that addresses the specific challenges and needs of different groups. While the macrolevel data cannot capture sociocultural differences at the household or individual levels, it offers an important statistical baseline for how different hazards impact communities across regions, contributing to a more comprehensive understanding of disaster displacement and pathways to sustainable solutions.Who Are Climate Migrants? 1 INTRODUCTION Weather-related hazards, including floods, storms, wildfires and droughts, have become a major driver of human mobility worldwide, leading to an estimated 218 million internal displacements over the past decade (IDMC, 2024), with substantial regional variation across both geographical areas and types of hazards. In recent years, the collection of displacement data has grown increasingly sophisticated and systematic, offering valuable insights for policymaking, humanitarian efforts and improving support for affected communities. While global estimates of internal displacement numbers by hazard type and region are well-established, a significant gap remains in the availability of disaggregated data on key variables – such as age, sex, education and income – for the populations impacted by these

policymaking, humanitarian efforts and improving support for affected communities. While global estimates of internal displacement numbers by hazard type and region are well-established, a significant gap remains in the availability of disaggregated data on key variables – such as age, sex, education and income – for the populations impacted by these events. Outside of specific case studies, this lack of granular data complicates the efforts of policy and operational actors, as the vulnerabilities and needs of different groups – such as children and older persons; persons with disabilities; people of different genders; wealthy people and people whose incomes are below the poverty threshold; and rural, urban and indigenous communities – often vary drastically. Addressing these varying needs requires targeted, context-sensitive interventions, which are challenging to adequately design without a deeper understanding of the specific profiles of those affected. This paper analyses data on populations in areas affected by disaster-induced internal displacement. It aims to bridge the knowledge gap on profiles of populations affected by extreme weather events that cause internal displacement. The analysis provides estimates of the demographic, socioeconomic and geographical profiles of populations in locations with weather-related internal displacements. The data analysis in this paper focuses on three key dimensions: • Demographic structure: Examining the age and sex distribution of affected populations. • Socioeconomic background: Exploring income and education levels. • Geography and resident population land use: Investigating agricultural, pastoral, urban and coastal populations. The estimates are based on a data set of approximately 14,000 geolocated displacement records compiled by IDMC (n.d.), which are combined with high-resolution global maps of demographic, socioeconomic and land-use variables (see Methodology section ). Crucially, the approach used here assesses the profiles of the general populations in regions that have experienced weather-related displacement (that is, including people residing in these areas that were not displaced), rather than directly analysing the specific individuals who were displaced.Introduction2 This approach is necessary because on-the-ground surveys conducted during displacement events often lack the global coverage and methodological consistency needed for a comprehensive assessment, despite providing valuable direct insights into those who move. Data collected during or after displacement events focuses on specific geographic

were displaced.Introduction2 This approach is necessary because on-the-ground surveys conducted during displacement events often lack the global coverage and methodological consistency needed for a comprehensive assessment, despite providing valuable direct insights into those who move. Data collected during or after displacement events focuses on specific geographic areas that can be defined based on the priorities and capacities of responders, rather than comprehensive coverage of the affected population. Additionally, this data is by nature highly context-specific, which makes it difficult to compare across contexts. This means that data on IDPs, while highly useful for actors within the context, is difficult to use for comparative global-level analysis such as what is provided in this report. In the absence of such data, the estimates serve as a statistical baseline, offering a clearer picture of the populations most affected by weather-related displacement events. The added value of this analysis, even while it is not specific to IDP populations, is fourfold. Firstly, it provides a baseline from which profiles for displaced populations can be inferred. This is a valuable starting point, as data quality and comparability for IDPs improves through global initiatives to drive standardization, including the International Recommendations on IDP Statistics (European Union and the United Nations, 2020). Secondly, it provides a useful baseline for comparison at the country or regional level where comparable IDP statistics do exist, for future study. Thirdly, it provides much needed baseline data to investigate immobile and stranded populations, who are often invisible. Finally, the analysis juxtaposes the macrolevel estimates with case studies using operational data sources such as the DTM of IOM to highlight the challenges faced by internally displaced populations. T ogether, the analysis reveals crucial nuances in the profiles of diverse communities and their specific challenges and needs in humanitarian contexts.Who Are Climate Migrants? 3 METHODOLOGY The analysis is based on 13,987 records of displacement events caused by floods, storms, wildfires and droughts between 2018 and 2024 (IDMC, n.d.) (Figure 1). Each record includes the year, geographical coordinates (usually at Admin 1 resolution), type of weather hazard

METHODOLOGY The analysis is based on 13,987 records of displacement events caused by floods, storms, wildfires and droughts between 2018 and 2024 (IDMC, n.d.) (Figure 1). Each record includes the year, geographical coordinates (usually at Admin 1 resolution), type of weather hazard and number of displacements. These data are combined with gridded (~10 km. resolution) global maps of the following demographic and socioeconomic variables: local age and sex structure (World Population Hub, n.d.); per-capita GNI, average life expectancy and mean number of schooling years (Kummu et al., 2018); local land area covered by cropland, grazing land and built-up/urban land ( Goldewijk et al., 2017); and distance to the nearest coastline (Pacific Islands Ocean Observing System, n.d. ). Based on the age and sex data, the percentages of women and of children (0–14 years) are computed. T o make these data compatible with the displacement records, all maps are aggregated to Admin 1 level, using the population-weighted average (weighted median in the case of per-capita GNI, weighted mean otherwise) of the grid cells in each Admin 1 unit ( GADM, 2022). An Admin 1 unit is considered as coastal if the population-weighted average distance to the nearest coastline is within 100 km. After assigning values for all variables to each individual displacement event in this way, displacement-weighted averages for each hazard (aggregating across all world regions) and each world region (aggregating across all hazards) are computed for each variable. For each region and hazard, the average age of displacement-affected populations is computed as the median of the relevant age distribution. An analogous methodology is used to compute variable averages for the general (that is, not only displacement-affected) population worldwide and in each region as the population-weighted average of each variable across the grid cells composing a given region. Figure 1. Internal Displacement Monitoring Centre records of weather-related internal displacements between 2018 and 2024 used in the analysis Displacements

population worldwide and in each region as the population-weighted average of each variable across the grid cells composing a given region. Figure 1. Internal Displacement Monitoring Centre records of weather-related internal displacements between 2018 and 2024 used in the analysis Displacements 10K 1M 7M Floods Storms Wildfires Droughts

Source: IDMC, n.d.

Note: This map is for illustration purposes only. The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the International Organization for Migration.Who Are Climate Migrants?

5

I. ANALYSIS The analysis is based on a data set of approximately 14,000 geolocated displacement records compiled by the IDMC (n.d.), combined with high-resolution global maps of demographic, socioeconomic and land-use variables (see Methodology section ). Importantly, the approach assesses the profiles of the general populations in regions that have experienced weather-related displacement, rather than directly analysing the specific individuals who were displaced due to a lack of suitable data available for a global comparative study of IDP populations. T o provide insight into IDP populations themselves, the analysis includes short case studies using IDP data collected by country-level actors, such as the DTM of IOM.

1.1. Demography 1.1.1. Age Demographic impacts are context specific; different groups react in very different ways to the same events, making factors like age and sex relevant considerations for policymakers and practitioners. Globally, the average age of populations in places affected by weatherrelated internal displacements is slightly younger than that of the general population, with an average age of 27.6 years compared to 30.8 years (Figure 2). This can be explained by the fact that the regions – predominantly Africa, the Caribbean, Asia and Oceania, whose populations are more vulnerable to whether-related internal displacement due to poor infrastructure, and low investment in climate adaptation, early warning systems and programmes that support the recovery and resilience of these populations – tend to have younger populations vis-à-vis the global average. This means the data reflects regional

whose populations are more vulnerable to whether-related internal displacement due to poor infrastructure, and low investment in climate adaptation, early warning systems and programmes that support the recovery and resilience of these populations – tend to have younger populations vis-à-vis the global average. This means the data reflects regional demographic idiosyncrasies that are relevant when situating the displacement data. In Europe, populations in areas affected by weather-related internal displacements are older, with an average age of 43.5 years, reflecting the continent’s ageing population. The Americas have the second oldest average age in affected areas (34.1 years). In Asia and Oceania, the average age of people in displacement-affected areas is 30.2. In contrast, the population in Africa living in areas that experienced weather-related displacements is the youngest, with an average age of 17.5 years. While these observations reflect the demographics of the regions in question, they are nevertheless relevant for policymakers and responders. Both younger and older populations experience specific impacts during disasters, with long-term implications on recovery if these are not considered by actors. Disaggregating the available data by drivers of displacement reveals important variations across hazard types. 1 Drought tends to affect the youngest populations, with an average age of 18.1 years, while areas affected by wildfire displacements have the oldest average age (37.1 years), a reflection of the fact that droughts are particularly prevalent in Africa, while wildfires are more commonly recorded in North America. The average ages in areas affected by flood and storm displacements, 25.1 and 30.3 years respectively, suggest possible implications for the workforce across affected regions. Damage to livelihoods for this demographic can also have lasting impacts, inducing economic hardship, which is compounded by widespread infrastructure damage and damage to housing in floods 1 Droughts, floods, storms and wildfires.I. Analysis6 and storms (IOM, 2024a ). Additionally, populations that lack resources to move may be trapped in at-risk areas (IOM and IDMC, 2023), further exacerbating their vulnerabilities.

1 Droughts, floods, storms and wildfires.I. Analysis6 and storms (IOM, 2024a ). Additionally, populations that lack resources to move may be trapped in at-risk areas (IOM and IDMC, 2023), further exacerbating their vulnerabilities. Figure 2. Average age of populations in areas affected by weather-related internal displacement General population 20 30 40 Droughts Floods Storms Wildfires Africa 20 30 40 Droughts Floods Storms Wildfires Americas 20 30 40 Droughts Floods Storms Wildfires Asia and Oceania 20 30 40 Droughts Floods Storms Wildfires Europe 20 30 40 Droughts Floods Storms

Wildfires World Source: Unless otherwise indicated, infographics are authors’ own elaboration based on results of the study, using the sources specified in the Methodology section.

Note: Markers represent the estimated average age for each weather hazard and region, including globally. Dotted lines represent the average age of the general population in each region.

This map is for illustration purposes only. The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the International Organization for Migration.

Operational data spotlight: The impacts of extreme weather events for working‑aged internally displaced persons in Uganda Uganda has been affected by frequent extreme weather events, including floods that have caused significant human and economic losses, resulting in over 200,000 deaths and at least USD 80 million in economic damages between 1900 and 2018 ( World Bank, n.d.a). DTM assessments covering the month of May 2024 reveal that weather-related hazards affected 46,457 individuals (10,191 households), with 2,341 homes completely destroyed and a further 519 damaged. Approximately 58 per cent of those affected are in the working-age group of 18–64, highlighting the impact of weather-related disruptions for the working age population on the country’s economic backbone. The extensive destruction of homes

further 519 damaged. Approximately 58 per cent of those affected are in the working-age group of 18–64, highlighting the impact of weather-related disruptions for the working age population on the country’s economic backbone. The extensive destruction of homes severely hampers the ability of internally displaced individuals to return to their places of origin, disrupting their livelihoods and exacerbating economic instability with implications for long-term recovery. 1.1.2. Children Displacement significantly heightens children’s vulnerability, leading to increased exposure to risks of exploitation and abuse, separation from families and caregivers, child trafficking, child marriage and forced labour (UNICEF, 2023a ). Inadequate shelter conditions as a result of displacement also intensify risks to children and other vulnerable populations

(UNFPA, 2020).

The analysis highlights that children make up 29.2 per cent of populations in areas affected by weather-related internal displacements compared to 25.5 per cent in the general world population (Figure 3), reflecting context-specific demographic differences. For example, in Africa, 44.1 per cent of the population in weather-related displacement locations areWho Are Climate Migrants? 7 children, reflecting the region’s younger demographics (UNICEF, 2023b ). In contrast, data from the Americas (21.5%), Asia and Oceania (25.7%) and Europe show that children make up a lower proportion of populations in affected areas, with Europe having the lowest percentage at 15.8 per cent. Across all regions, the percentage of children in areas affected by weather-related internal displacements is largely consistent with the general population. When broken down by drivers of displacement, the geographic distribution of hazards once again reflects the demographic profile of populations in affected areas. Droughts have the highest percentage of children in affected areas at 43.1 per cent, followed by floods (32.5%), storms (25.7%) and wildfires (19.3%). Given the distinct challenges and needs faced by children in disaster displacement context, and the large proportion of children in many populations exposed to displacement-inducing extreme weather events, it is still vital that policymakers and responders consider children

(32.5%), storms (25.7%) and wildfires (19.3%). Given the distinct challenges and needs faced by children in disaster displacement context, and the large proportion of children in many populations exposed to displacement-inducing extreme weather events, it is still vital that policymakers and responders consider children as a distinct demographic category. Figure 3. Average percentage of children in areas affected by weather-related internal displacement General population 10 20 30 40 50 Droughts Floods Storms Wildfires Africa 10 20 30 40 50 Droughts Floods Storms Wildfires Americas 10 20 30 40 50 Droughts Floods Storms Wildfires Asia and Oceania 10 20 30 40 50 Droughts Floods Storms Wildfires Europe 10 20 30 40 50 Droughts Floods Storms

Wildfires World Note: Markers represent the estimated percentage for each weather hazard and region, including the world. Dotted lines represent the average percentage of children in the general population in each region.

This map is for illustration purposes only. The boundaries and names shown and the designations used on this map do not imply official endorsement or acceptance by the International Organization for Migration.

Operational data spotlight: Internally displaced children and extreme drought in Kenya In September 2021, the Government of Kenya declared a drought emergency, highlighting that the environmental and human security conditions across Kenya are deteriorating due to the extreme effects of prolonged drought (IOM, 2024b ). In February 2023, the lack of rainfall impacted the availability of vegetation for food and fodder, as well as water availability for people and livestock. This resulted in a decline in food sources and an increase in water-based diseases, both leading to a critical global acute malnutrition rate of 26.4 per cent among children in Turkana County (Turkana County Government, 2024).

The Key Informant-based Multisector Location Assessment by DTM was deployed in

increase in water-based diseases, both leading to a critical global acute malnutrition rate of 26.4 per cent among children in Turkana County (Turkana County Government, 2024). The K