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Regional Vulnerabilities to Climate Change in Europe: insights from the SPARCCLE project
Understanding the vulnerability of the EU population to both slow- and fast-onset climate impacts is crucial. By examining various vulnerability indicators, including age, health, economic status, and housing situation at the territorial level, several areas with higher concentrations of vulnerable populations were identified.The analysis shows that vulnerability across EU regions varies significantly by degree of urbanisation. Urban and rural areas provide different levels of protection depending on the country and the type of vulnerability considered. Overall, older individuals living in predominantly rural areas are more at risk of experiencing health issues and facing economic and social disadvantages. This is especially true in Eastern and Southern Europe, where these populations are more likely to be adversely affected.The findings underscore the need for targeted policies that address the specific vulnerabilities of different demographic groups and regions, ensuring that protective measures are appropriately tailored to the unique needs of each community.
The impacts of climate change vary across regions of Europe. Levels of vulnerability to climate impacts are influenced by local conditions related to economic, social, and demographic factors. For example, high levels of unemployment, poverty, and emigration, as well as ageing populations, will hamper the ability of regions to adapt to climatic changes. At the same time, heat waves or extreme rainfall will have a particularly severe impact on densely populated urban regions, which are often characterised by soil sealing or limited green spaces.
In light of the challenges posed by climate change, the Socioeconomic Pathways, Adaptation and Resilience to Changing CLimate in Europe (SPARCCLE) project, funded by the European Commission, aims at assessing climate impacts and extremes across Europe, and the population's vulnerability and adaptive capacities at the regional level. This in turn will help in developing mitigation and adaptation strategies that are climate resilient. The project will support people, organisations, and governments to make better decisions to reduce risks and build resilience to climate change.
The objectives of the SPARCCLE project are to (i) develop quantitative methods for assessing climate hazards, damages, and risks; (ii) deliver detailed projections of how society develops, improving understanding of vulnerability to climate change; (iii) understand how climate mitigation and adaptation measures can work together or against each other; (iv) co-create with public and private stakeholders so that project outputs can inform decision-making; (v) explore stress-test scenarios to help Europe prepare for plausible, high-impact socio-economic climate risks.
This story contributes to these objectives by presenting a comprehensive multidimensional analysis of vulnerability to climate extremes at the regional level for Europe. The regions are described in terms of their demographic, health, socioeconomic, and housing characteristics. The following three dimensions of vulnerability are analysed:
To maintain coherence and accuracy in the data set disaggregation process, all visualizations are based on the reporting year 2019.
The demographic dimension of vulnerability
Older people are more vulnerable. They face a higher risk of illness and chronic diseases. Their immune system is weaker as became sadly visible during the COVID-10 pandemic.While younger populations are more exposed to a loss of quality life years than older populations, older adults are especially vulnerable to climate change impacts because age reduces the body's ability to handle environmental hazards, like air pollution. Existing health conditions make them more sensitive to climate hazards, worsening illnesses. Limited mobility and dependence on others for care increases their vulnerability during extreme weather events.In most EU regions, the share of the population aged over 65 is higher in rural and intermediate areas, but there are many exceptions, such as Italy or Hungary.The map and the bar chart show the distance from the country median of the share of people aged 65 years and over in the total population. Data is shown at the NUTS-3 level for the EU and each Member State.
The map and the bar chart show the distance from the country median of the share of people aged 65 years and over in the total population. Data is shown at the NUTS-3 level for the EU and each Member State.
Source
Eurostat demo_r_pjangrp3 “Population on 1 January by age group, sex and NUTS 3 region”
(url: https://doi.org/10.2908/DEMO_R_PJANGRP3)
A key dimension of wellbeing
Life expectancy is a summary measure of mortality across all ages. A high life expectancy at the national or territorial level means the survival to older ages of most individuals, avoiding many of the risks to human life throughout the years, including during the first years of life when infants and children are most fragile, as well as later in life when people are confronted with non-communicable diseases. It is indicative of an efficient health system and healthy lifestyles among individuals.
Overall, men have systematically lower life expectancies than women, which is mostly the result of differences in health behaviour, especially among older cohorts. This is particularly the case in Eastern Europe, where the difference can be above 10 years in some territories, mostly due to heavy smoking and alcohol consumption.
In most European regions, life expectancy is slightly higher in urban and intermediate areas compared to rural areas, partly due to easier access to healthcare facilities and the selectivity of urban citizens who are usually wealthier and more educated.
* NoteLife expectancy at age 0 is the average number of years a new-born is expected to live, considering mortality rates over the entire first year of life and beyond. Life expectancy at birth also reflects this but is often used more broadly to describe the expected lifespan from the moment of birth.
The health of older adults as a vulnerability dimension
The vulnerability of the population is assessed by focusing on the age group 55-74, examining two indicators: general health and the prevalence of chronic diseases at the regional level.
The differences between regions within EU countries are substantial, ranging from 21% in the Rieti region in Italy to 67% in the Pohjanmaa region in Finland. The difference is also important within regions. For example, in Spain, there is about a 27-percentage point (p.p.) difference in the share of people with chronic illnesses between the regions with the highest and lowest shares, while in France, the difference is 23 p.p., and in Greece, it is 31 p.p.
Across countries, there is no clear pattern in the distribution of general health status by degree of urbanization, with no inherent disadvantage to living in urban or rural areas.
Although women live longer than men on average across all EU regions, there is a higher share of women in poor health compared to men. This pattern is consistent across regions in most countries, such as the Netherlands and Portugal. In Sweden, some regions show a small difference, while in the southern regions of Greece, men are sometimes at a disadvantage. The literature indicates that men face a higher burden of disease throughout their lives, while women endure higher levels of non-fatal illness.
These differences are important to consider when developing the health system across European regions.
Healthy ageing reduces vulnerability
A key factor to assess vulnerability
The socio-economic dimension of vulnerability
Low-income households face greater challenges from climate change due to financial constraints that limit their ability to adapt. In many EU countries, these vulnerable households often reside in dense urban areas, exposing them to higher temperatures due to urban heat island effects. GDP per capita can be interpreted as a measure of economic resilience of regions in Europe as it takes into account individual incomes as well as the economic strength of a region.
A proxy for economic well-being in EU regions
Wealthier groups can more easily afford adaptation measures, such as air conditioning, which are often inaccessible to poorer households. Wealthier regions can also afford community preparedness adaptation measures, such as early-warning systems and flood defences. Rising food and water prices, driven by climate change, disproportionately affect low-income households as these costs typically represent a larger share of their budgets. In addition, workers in typically lower-paid occupations, such as agriculture, construction, and emergency services, are more directly exposed to climate hazards. Furthermore, access to healthcare, education, and social services tends to be more limited for poorer households, further exacerbating social inequalities. This hampers effective responses to climate-related health problems and limits knowledge about individual behaviours that could reduce vulnerability. To illustrate this dimension of regional socio-economic vulnerability, the figure shows the GDP per capita of the EU NUTS-3 regions divided by the national or the European average GDP per capita in 2019, expressed as a percentage. Values above 100 indicate above-average GDP per capita relative to the country or Europe.
Housing conditions: Amplifying the effects of climate change
Poor housing conditions significantly increase vulnerability to climate change by exacerbating exposure to extreme weather events and reducing the capacity to respond effectively.
Poorly constructed homes can suffer more severe damage from climate change-induced natural hazards, such as storms and floods, leading to higher housing repair costs and prolonged displacement. Inadequate insulation and ventilation make homes hotter in summer and colder in winter. In addition to limited access to cooling and heating, this can amplify health risks from heat strokes and dehydration or cold-related illnesses.
Moreover, low-income households are often located in areas more prone to flooding or heat waves, such as floodplains or areas of urban heat islands, compounding their vulnerability.
To illustrate this dimension of regional socio-economic vulnerability, the figure shows the share of people in each EU NUTS-3 region reporting poor structural conditions of their house in 2019.
Hover or click on a region in the maps or the chart to read detailed information.
Poverty significantly increases vulnerability to climate change
Poverty significantly increases vulnerability to climate change through various mechanisms. Poorer individuals have fewer resources to recover from climate shocks such as droughts and floods. Financial constraints also limit access to resources for investment in protective infrastructure. In addition, the livelihoods of the poor often depend on sectors that rely on natural resources and are vulnerable to climate impacts, such as agriculture or low-income outdoor jobs with minimal protection.
Poorer people tend to reside in high-risk areas with less insurance against climate events and have limited access to information about adaptation measures. High levels of illness, mental stress, and social burdens further diminish their ability to respond to climate shocks and plan for the future. The cognitive effects of poverty can lead to poorer financial decisions, reducing resilience. In urban areas, inadequate infrastructure and governance heighten vulnerability, while in rural areas, limited land ownership and market access also increase risks.
Poverty often intersects with other social exclusions, such as gender, race or disability, exacerbating vulnerabilities. Ultimately, poverty, combined with various social and economic factors, significantly amplifies the risks and impacts of climate change for already vulnerable populations.
Poverty and social exclusion significantly increase people's vulnerability to climate change, with the extent of this impact varying by age and sex.
Additionally, the age and sex distribution of those at risk of poverty or social exclusion varies across EU Member States.
The bar chart illustrates the proportion of individuals at risk of poverty or social exclusion across different sex and age groups for each EU Member State.
Comparing Regions' Vulnerability Profiles to National and EU Levels
In this final visualisation, several of the variables described above are integrated to construct a comprehensive multidimensional vulnerability profile. The variables included are:
To ensure comparability and clarity, these variables range on a scale from 0 to 100, with higher values indicating greater vulnerability to climate extremes. Specifically, GDP per capita is standardized within each country using min-max standardization, then scaled between 0 and 100 and inverted, so that each region is assigned the difference between 100 and its standardized and scaled GDP per capita (the relatively richest region in the country has a value of 0, while the poorest region has a value of 100).
The selected dimensions cover a broad range of aspects considered in the individual visualisations and are presented collectively without prioritising the potential severity of the impact of each dimension. The graphs help to identify potential sources of vulnerability at the regional level, allowing for the comparison of variables both within a single region and across different regions.
Country median and EU median bars provide references to compare regions with each other within the same country and within the EU respectively.
Some variables were disaggregated using information on the degree of urbanisation, following the steps described below.
Two datasets are used:
Combining these two pieces of information, a map of the population by age and sex and degree of urbanisation (DEGURBA) was defined at the NUTS-3 level. This means that we were able for each age and sex combination at the NUTS-3 level to allocate the three proportions of the population living the DEGURBA 1 (rural areas), 2 (towns and semi-dense areas) and 3 (cities). This cross-mapping is used to disaggregate at the subnational level information we have at the aggregate level but with the age, sex and DEGURBA specifications. For the EU-SILC, there is an intermediate step which is the construction of education distribution at the NUTS-3 level. This was done using the education composition of the DEGURBA which was extracted rom another data source (https://doi.org/10.2908/EDAT_LFS_9913)
To validate this approach, the 15-64 education composition at the NUTS-2 level (data available) was reconstructed using information about the DEGURBA composition of the NUTS-2 and the education composition of the DEGURBA at the national level. Steps:
All errors appear centred around 0 and do not display heavy or asymmetric tails.
At risk of poverty or social exclusion, abbreviated as AROPE, corresponds to the sum of persons who are either at risk of poverty, or severely materially and socially deprived or living in a household with a very low work intensity. People are included only once even if they are in more than one of the situations mentioned above. The AROPE rate is the share of the total population which is at risk of poverty or social exclusion. It is the main indicator to monitor the EU 2030 on poverty and social exclusion and was the headline indicator to monitor the EU 2020 Strategy poverty.
Eurostat applies urban-rural typology to NUTS-3 regional level, identifying three types of regions based on their share of rural population: - Predominantly rural regions; - Intermediate regions; - Predominantly urban regions. Please visit Eurostat - Territorial typologies manual – urban-rural typology for additional info.
The EU statistics on income and living conditions (EU-SILC) aim to collect timely and comparable cross-sectional and longitudinal data on income, poverty, social exclusion, and living conditions. Please visit Eurostat: EU statistics on income and living conditions - Microdata for additional info.
The GDP is an indicator for a nation´s economic situation. It reflects the total value of all goods and services produced less the value of goods and services used for intermediate consumption in their production. The GDP per capita is often used as a proxy of the economic well-being of population. Please refer to Eurostat glossary: Gross domestic product (GDP) for additional info.
To meet the demand for statistics at a local level, Eurostat maintains a system of local administrative units (LAUs) compatible with NUTS. These LAUs are the building blocks of NUTS and comprise the EU’s municipalities and communes. Please refer to Eurostat: Local administrative units (LAU) for additional information.
NUTS (Nomenclature of territorial units for statistics) are the reference countries’ regions for statistical purposes adopted by the EU. There are four levels of NUTS ranging from 0 (countries) to 3. This story builds on NUTS-3 region level. Please refer to Eurostat: NUTS - Nomenclature of territorial units for statistics - Overview for additional information.
Funded by the European Union under grant agreement No 101081369 (SPARCCLE). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or HORIZON-RIA – HORIZON Research and Innovation Actions. Neither the European Union nor the granting authority can be held responsible for them.
This Story was developed as a collaboration between researchers from the KCMD and the SPARCCLE project. SPARCCLE is a European Commission Horizon Europe research project investigating the socioeconomic risks of climate change in Europe.
Have a look at the following publications and websites on related subjects:
Data source Eurostat demo_r_pjangrp3 -
05 Nov 2025 | 14 May 2026
