Climate Change, Vulnerable Groups, and Data-Driven Policymaking
More than four in five people in Asia and the Pacific reportedly face multi‑hazard risks associated with slow or sudden onset climate events, according to the latest Asia‑Pacific Disaster Report published by the United Nations Economic and Social Commission for Asia and the Pacific (ESCAP). Many existing hotspots of climate‑related multi‑hazards are forecast to intensify. Individuals residing in these hotspots, often already low‑income and with limited access to basic services and infrastructure, will likely be exposed to more frequent and intense sudden‑ and slow‑onset natural disasters.
Noteworthy is the assessment that migrants, refugees, internally displaced persons (IDPs), and stateless persons residing in many parts of these vast geographic areas reportedly face even more daunting challenges in this category, due to their vulnerable legal status, limited coping capacity, and access to basic services and opportunities.
Migrants, refugees, IDPs, and stateless persons residing in many parts of Asia and the Pacific face daunting multi‑hazard risks associated with slow or sudden onset climate events.
Several countries in Northern and Central Asia are already classified as intensifying or emerging multi‑hazard risk hotspots under the 1.5‑ or 2.0‑degree Celsius global warming scenarios. For instance, close to 10 percent of the population in Turkmenistan and Uzbekistan are forecasted to be exposed to intensifying climate change‑related risks. The Aral Sea basin is a particular case in point. Over 50 million people residing around that basin in Uzbekistan, Tajikistan, Kyrgyzstan, Turkmenistan, and Kazakhstan are said to be increasingly vulnerable.
At the same time, new disaster hotspots are emerging, as well. For instance, over 15 percent of the population in Azerbaijan and Kazakhstan is expected to live in emerging hotspots. High water stress is one of the major channels through which climate change is projected to impact people in the subregion. According to the latest Assessment Report issued by the United Nations Intergovernmental Panel on Climate Change (2021), Central Asia is warming faster than other regions, with the implication of melting glaciers and a loss of snow and ice sheets. Water scarcity and increased frequency of drought are expected. The exposure of energy infrastructure to climate hazards is another important impact channel.
This analysis aims to widen the knowledge base in the context of climate change and the “leaving no one behind” premise of the UN 2030 Agenda for Sustainable Development. It analyzes several household surveys collected recently in the Northern and Central Asia region, using an innovative empirical methodology. In the context of vulnerability and capacity to cope with climate change, the results shed light on the “who” and the “where” of what are called “furthest behind” individuals and households in that part of the world. The results presented here may inspire further academic research and support inclusive and evidence‑based sectoral policies as countries accelerate climate change mitigation and adaptation efforts.
Intricate Links
Various UN publications point to the fact that climate change and rising socioeconomic inequalities are inextricably linked, with severe adverse effects falling on the lives and livelihoods of people living in affected areas. Such effects are falling especially on marginalized people, like stateless persons, who live in vulnerable situations and lack the capacity and resources to cope with complex shocks.
What is more, there is a broad spectrum of exposure and vulnerability to climate‑related risks and how they affect people through multiple dimensions. These dimensions include, but are not limited to, health and food security, health systems and social services, economy, displacement, and human mobility and urbanization.
Climate change and rising socioeconomic inequalities are inextricably linked, with severe adverse effects falling on the lives and livelihoods of people living in affected areas.
Where and in what conditions people live and why can influence the degree to which they are exposed to climate change. Lower‑income households living in rural areas are particularly at risk in some countries, as shown in detail in ESCAP’s flagship report Social Outlook for Asia and the Pacific in 2024.
Take the example of Mongolia. On average, 3.3 percent of households experienced what is classified as a climate change‑induced disaster in 2022. Across the country’s five regions, the prevalence and variation of exposure is relatively low except in Western Mongolia, where 16 percent of households were exposed to disasters. Furthest behind are lower‑income households living in rural areas, where more than one in four were exposed to a disaster.
Lower‑income households living in remote areas are also affected in what are called in UN terminology “small island developing states,” including the Maldives and Vanuatu. Exposure to climate‑induced disasters was found to be twice as high in remote and rural areas as in urban areas in Vanuatu. The gaps were more striking in the Maldives, where households living in the capital were four times less likely to be affected by disasters than those living in other atolls. Without requisite data, such an analysis is not yet empirically possible to conduct in Northern and Central Asia countries.
Furthermore, inequality in access to basic services and infrastructure amplifies the lack of resilience and thereby is found to perpetuate inequalities over time. Inequality of opportunity can be high in areas exposed to intensifying or emerging multi‑hazard risks under a 2‑degree global warming scenario.
Take the case of child malnutrition. There is a higher prevalence and wider variation in stunting among children under the age of five in selected disaster hotspots identified by ESCAP’s Risk and Resilience Platform. In disaster hotspots, children living in larger and lower‑income households are often furthest behind. There are also disruptions to education in disaster hotspots, leading to relatively weaker human capital accumulation. It is not only children who are affected, however. A similar picture emerges in access to basic sanitation and clean fuel. Inequality in access to such basic services is higher in disaster hotspots than in a country as a whole, in the cases of Bangladesh, Mongolia, and Vietnam.
At the same time, sudden‑ or slow‑onset climate change events are inevitably triggering the movement of affected people, leading to an increase in internal displacement numbers and international migration. While some displacements are short‑term and temporary, others may lead to permanent settlements. As noted above, migrants, refugees, IDPs, and stateless persons residing in such areas face even more daunting challenges due to their vulnerable legal status, limited coping capacity, and access to basic services and opportunities.
According to the Internal Displacement Monitoring Centre (IDMC), a non‑governmental organization established by the Norwegian Refugee Council in Geneva, Asia and the Pacific accounts for the majority of IDPs globally due to natural disasters. Specifically, the region accounts for over 50 percent of disaster‑induced internal displacement in 2022 and 80 percent of the world’s internal displacement from 2010 to 2021.
Latest IDMC data from 2023 shows that close to 22 million people in Asia and the Pacific were internally displaced due to disasters alone. More than half were located in the South and South‑West Asia subregion, including Afghanistan, Bangladesh, India, Pakistan, and Türkiye. However, the Northern and Central Asia region has so far been spared from large displacements due to natural disasters. In 2023, fewer than 65,000 people were internally displaced due to natural disasters, mainly in Georgia and the Russian Federation. In Azerbaijan, fewer than 1,692 were internally displaced in 2023.
A Deeper Dive
Understanding the nexus between climate change and inequalities inevitably calls for local empirical analysis, especially in and around affected cities and human settlements. Given a lack of microdata on exposure, my empirical analysis below focuses on resilience and coping capacity in the context of climate change.
SDG11, which pledges to make cities and human settlements inclusive, safe, resilient, and sustainable (it is popularly known under the shorthand moniker “sustainable cities and communities”), captures an important outcome to be monitored in the context of climate change. The status of housing conditions in which individuals live stands out as an important proxy for examining their vulnerability to climate change. Assessing the adequacy of housing conditions is not straightforward, however, as it has multiple dimensions with limited data availability. These dimensions include, but are not limited to, access to improved water and sanitation, sufficient living area, structural quality, durability and location, security of tenure, affordability, accessibility, and cultural adequacy.
The status of housing conditions in which individuals live stands out as an important proxy for examining their vulnerability to climate change.
In 2002, UN‑Habitat, the UN Statistics Division, and the Cities Alliance (an international membership organization that includes national governments, multilateral organizations, local authorities, philanthropies, and civil society groups) agreed on an empirical approach based on available data to take stock of and measure progress in SDG11. Their approach focused on fewer dimensions of housing adequacy, mostly due to the availability of publicly accessible data from nationally representative household surveys such as Demographic and Health Surveys (DHS) or Multiple Indicator Cluster Survey (MICS).
These surveys collect household‑level information on access to improved drinking water and sanitation, the type of materials used in the walls, floors, and roofs of dwellings, and the living area per household member. Households are considered “deprived of adequacy of housing” if they are said to be “deprived” in one or more of these conditions. In the case of housing materials, if natural or rudimentary materials are used in the walls, roofs, and floors, the dwelling is considered to be “inadequate.” In the case of sufficient living area, housing is deemed “inadequate” if more than three people share the same habitable room.
This empirical approach can be operationalized in the Northern and Central Asia region, as many countries undertake either DHS or MICS and make them publicly accessible. For instance, Azerbaijan and Kyrgyzstan have publicly available data from 2023, while Kazakhstan and Turkmenistan are expected to release their 2024 MICS data soon. This section focuses on Azerbaijan and assesses housing conditions primarily at the national level, with particular attention to inequality among socioeconomic and demographic groups.
According to this criterion, 61 percent of households in Azerbaijan live in “inadequate” housing, as they are deprived in one or more of the housing conditions elaborated above. In other words, only 39 percent of households live in “adequate” housing. This surprising result is largely driven by the widespread use of wood planks in dwellings across Azerbaijan, which are classified as rudimentary flooring materials. In the other five dimensions, 80 percent of households are not “deprived” at all. In other words, this surprising result comes down to the preference for wooden flooring.
Despite being relatively high, this national average can mask differences across Azerbaijan’s demographic and socioeconomic groups. The ESCAP Leaving No One Behind (LNOB) algorithm can help disaggregate the national average. This approach leverages innovative methodologies in machine learning, such as Classification and Regression Tree (CART) analysis, and uses DHS and MICS in 33 countries across Asia and the Pacific.
The LNOB methodology follows objective criteria to identify the shared circumstances of mutually exclusive groups who live in significantly different housing conditions. The circumstances can be individual, such as age, gender, level of education, disability, and migration status, among others. They can also be household characteristics, such as the income level of the households based on assets they own, the location of residence, and the number of children under the age of 5, among others.
Since the analysis in this section focuses on household‑level outcome, the circumstances used in the disaggregation model are also kept at the household level. The implication is that age, gender, and level of education of the head of household are included in the model.
Following this approach, a broad range of demographic and socioeconomic groups are identified, with two extreme cases. On one extreme, the group with the highest prevalence of “inadequate” housing is the furthest left behind. On average, the furthest behind group will have the highest prevalence for a negative outcome like “inadequate” housing. On the other extreme, the group with the lowest prevalence for “inadequate” housing is the furthest ahead. On average, the furthest ahead group will have the lowest prevalence for a negative outcome. There can be many other groups with different prevalence between the two, based on their circumstances.
The most important consideration in disaggregating the prevalence of “inadequate” housing is the selection of demographic and socioeconomic factors that can explain who does and does not live in “inadequate” housing. For this analysis, five circumstances are selected, namely household wealth, location, age, gender, and the educational background of heads of households. These factors may drive the differential prevalence of living in “adequate” housing conditions.
The LNOB analysis captures interesting intersections of circumstances that leave people behind. At the national level, the furthest behind consists of lower‑income households living in rural areas with relatively older heads of households (i.e., aged 46 and above). Among the furthest behind, 86 percent live in “inadequate” housing. In other words, only 14 percent of the population in the farthest behind group live in “adequate” housing (note that this group represents 22 percent of all households in the country). On the other hand, higher‑income and urban households where household heads have tertiary education are furthest ahead. Still, 34 percent of the furthest ahead live in “inadequate” housing. Conversely, 66 percent are living in “adequate” housing.
The analysis can also be repeated at the sub‑national level, but doing so in detail is beyond the scope of this essay. Suffice it to say that this indicator also has wide variation across Azerbaijan, with Baku being one of a handful of exceptions. Notably, “inadequate” housing is much less prevalent there, where only one‑third of the households live in “inadequate” housing.
While prevalence is low, the gap between the furthest behind and furthest ahead is notably wide. In Baku, over half of the furthest behind group live in “inadequate” housing, while 18 percent of the furthest ahead group live in “inadequate” housing. Unlike in most other parts of the country, where the furthest behind groups are composed of lower‑income households, this is not necessarily the case in Baku.
At the individual level, while the gender and educational levels of household heads rarely matter, the age of the household head is an important factor, with households headed by older persons standing out with a disadvantage.
Inequalities and Coping Capacity
Moving beyond exposure and vulnerability, there are also gaps in capacity to cope with climate change‑related events. The survey data referenced above indicates that more than two‑thirds of the population in Asia and the Pacific report that it is difficult or very difficult to raise emergency funds within 30 days. This is a grave concern due to various crises faced by the region, be it induced by climate change, pandemics, or economic shocks. The fact that close to half of the population in Asia and the Pacific do not have access to at least one social protection scheme escalates this concern, as per the International Labour Organization’s flagship World Social Protection Report (2024‑2026).
The LNOB methodology is again operationalized to measure gaps in coping capacity (Figure 1). An alternative data source is used this time, and results are shared at the national level only. This section focuses on eight Northern and Central Asian countries (i.e., Armenia, Azerbaijan, Georgia, Kazakhstan, Kyrgyzstan, the Russian Federation, Tajikistan, and Uzbekistan). Overall, 22 percent of the adult population in the region can raise emergency funds without difficulty on average.
Lower‑income individuals living in rural areas are furthest left behind. Among them, 10 percent can raise emergency funds without difficulty. The furthest ahead group consists of higher‑income men with tertiary education. In this group, 41 percent can raise emergency funds without difficulty. Such gender gaps are evident only among relatively higher‑income individuals. Educational attainment is not helping to close the gap, as the gaps are similarly wide among tertiary‑educated higher‑income individuals and lower‑ or secondary‑educated higher‑income individuals.
Moreover, the location of residence is an important driver of inequality only among lower‑income individuals. Individuals with lower incomes living in urban areas are 50 percent more likely to raise emergency funds than lower‑income individuals living in rural areas.
Across the eight countries, there are some notable differences. While 13 percent of the adult population can raise emergency funds without difficulty in Azerbaijan, Georgia, and Kazakhstan, over 25 percent of adult populations can do so in Uzbekistan, Kyrgyzstan, and the Russian Federation. Notably, as the share of the population that can raise emergency funds without difficulty increases, so does the gap between the furthest behind and the furthest ahead groups.
Among the furthest behind individuals, two circumstances intersect most frequently, namely income and education. Overall, having a lower income and less than secondary education are key factors that limit the capacity to raise emergency funds. Among individuals with a lower income and a lower education, women are particularly at risk and left behind, especially in Kyrgyzstan and the Russian Federation. The location of residence is a shared characteristic of the furthest behind groups only in Azerbaijan, where the rural population is at a disadvantage.
Figure 1: Gaps in the Ability to Raise Emergency Funds Across North and Central Asia (2021)
Note: The author’s calculations are based on the ESCAP LNOB algorithm applied to the World Bank Global Financial Inclusion Database (2021).
Both digital and financial inclusion matter broadly for coping capacity. Information and communications technologies can effectively reach affected communities and ensure their access to critical information, including early warning messages. Access to financial services, including bank accounts and insurance, enhances disaster resilience and facilitates recovery from shocks.
When the preceding LNOB model is extended with digital and financial inclusion, the latter’s importance stands out. At the regional level, access to the internet can improve the odds of raising emergency funds without difficulty, especially among women with lower or secondary education. Owning a bank account is equally important, especially among lower‑income individuals living in urban areas.
Given their importance, Figures 2 and 3 focus on proxy indicators for financial and digital inclusion, focusing on countries with available data in the Northern and Central Asia region.
Figure 2: Overview of Gaps in Bank Account Ownership (2021)
Note: The author’s elaborations are based on the ESCAP LNOB algorithm applied to the World Bank Global Financial Inclusion Database (2021).
Bank account ownership is nearly universal in the Russian Federation, where, on average, 90 percent of the population owns a bank account. Among the furthest behind individuals, who are composed of lower‑income individuals with primary or secondary education, 85 percent own a bank account. Kazakhstan and Georgia follow suit, with over two‑thirds of their populations owning bank accounts. Notably, inequalities start widening as average ownership decreases. In Azerbaijan, gaps are wider, with only 25 percent of the furthest behind owning a bank account. Comprising 20 percent of the country’s adult population, lower‑income women with less than a tertiary education are furthest behind.
Education is a far more important indicator than income, age, gender, or location in driving inequalities. People with no or primary education are often left behind. Youth are left behind in Tajikistan. In Kazakhstan, youth with less than a tertiary education are left behind. An important policy implication here is that financial literacy programs may be able to help increase bank account ownership rates across the region, since demand‑side factors (i.e., not having enough money) may not be the leading driver of inequality.
Education is a far more important indicator than income, age, gender, or location in driving inequalities.
Figure 3 offers a far more positive picture. At the national level, average internet access rates are relatively higher in many countries and with narrower gaps. As noted earlier, access to the internet today is a main source of timely information, including early warnings for climate change‑related events.
Therefore, leaving no one behind in this outcome is a strategic objective. Between 80 and 90 percent of the population had access to the internet in Armenia, Georgia, Kazakhstan, Kyrgyzstan, and the Russian Federation. In Azerbaijan, two‑thirds of the population had access to the internet in 2021 (the last year of available data, and this figure is likely to have gone up in the interim), notably with widest gaps between the furthest behind and the furthest ahead groups. Rural and lower‑educated individuals are furthest behind. Note that this is a tiny group, representing 7 percent of the population.
Across the subregion, again, education matters most. Women, especially those who have a lower income and a lower education, are left behind in Tajikistan and Uzbekistan.
Evidence‑Based Public Policy
Leaving no one behind in sustainable development implies that inequalities in exposure, vulnerability, and capacity to cope with climate change events are tackled immediately. A country’s overall resilience against climate change depends critically on how resilient its furthest behind groups are. There are several policy areas where urgent action is needed to support climate change adaptation and mitigation. These include, among others, climate change policies, disaster reduction policies, social protection policies, housing policies, and active labor market policies.
A country’s overall resilience against climate change depends critically on how resilient its furthest behind groups are.
Data holds the key to ensuring that sectoral policies are based on evidence. From design phase to implementation, monitoring and evaluation, a broad range of data, including administrative, survey, and geospatial data must be leveraged and analyzed with innovative and rigorous methodologies. Since children, women, older persons and persons with disabilities, migrants, refugees, IDPs, and stateless persons are said by the UN to be disproportionately affected by climate change, policies must be inclusive and evidence‑based to reinforce the need for adaptation and mitigation measures that address underlying inequalities faced by people in vulnerable situations.
Figure 3: Overview of Gaps in Access to Internet (2021)
Note: The author’s elaborations are based on the ESCAP LNOB algorithm applied to the World Bank Global Financial Inclusion Database (2021).
Identifying the furthest behind households and tracking them over time should inform the design and implementation of policies. When expected results are not achieved, policies must correct their course. The results presented on furthest behind groups in terms of vulnerability to climate change (i.e. “inadequate” housing) and coping capacity (i.e. ability to raise emergency funds, ownership of bank accounts, and access to internet) in Northern and Central Asia can help governments as well as private sector and civil society to take requisite action in leaving no one behind.
Identifying the furthest behind households and tracking them over time should inform the design and implementation of policies. When expected results are not achieved, policies must correct their course.
Furthermore, it is important to highlight that the LNOB methodology employed in this paper does not lend itself to causal interpretation. As such, further research is needed in each country better to understand the relationship between climate change and inequality. Rigorous empirical methodologies that can establish causality should be used, especially when assessing the impact of sectoral policies.
The role of data collection and analysis is gaining more traction as digitalization unfolds as a global megatrend. For instance, social protection policies can be strengthened with more data analysis and digital technologies, as they can facilitate the identification and registration of individuals and link social protection databases to national identification systems and civil registries.
Analyzing administrative and census data can help identify individuals eligible to receive social protection but are missed in the due process. In the area of forecasting, data and emerging technologies play a crucial role as well.
Identifying and forecasting climate risks are far better today than in the past. This is thanks to significant advances in climate science and achievements in geospatial data analysis with digital tools. In the context of early warning systems, there is an urgent need to report on and improve the status of its multi‑hazard early warning system in the Northern and Central Asia region, given that some of the countries in the region (including Azerbaijan) are among the top 10 countries across Asia and the Pacific with populations most exposed to emerging climate risk hotspots. Considering the specific challenges faced by people in vulnerable situations, it is important to mainstream their needs and voices throughout social protection systems.
Notwithstanding the crucial role of data for inclusive and evidence‑based policies, there is an unmet need for solid investments in data collection and stronger national statistical systems. Without interoperability across data systems and platforms, analyzing policy‑relevant questions becomes nearly impossible.
Notwithstanding the crucial role of data for inclusive and evidence‑based policies, there is an unmet need for solid investments in data collection and stronger national statistical systems.
Equally important is the lack of data on certain groups of people living in vulnerable situations. Due to a lack of complete data on internally displaced persons and stateless persons, let alone migrants and refugees, the preliminary results elaborated earlier completely miss these important groups of people.
The recommendations put forward by the Expert Group on Refugee, IDP and Statelessness Statistics (EGRISS) are crucial in guiding the production, coordination, and dissemination of statistics on vulnerable groups. Standardizing definitions for statistical measurement and inclusion in census or nationally representative household surveys are highly needed. In this way, indicators related to many other SDGs, in addition to SDG11 and, implicitly, SDG8 (decent work and economic growth) and SDG17 (partnerships), can also be disaggregated to account for these vulnerable groups.
The Northern and Central Asia region stands out as having gained significantly from collecting and analyzing data to inform a wide array of policies in the context of climate change. In the area of household surveys, there is room for significant improvement. First, the frequency of household surveys needs to increase. Second, the modules of standard household surveys should be expanded to include more indicators relating to climate change. Finally, making such surveys accessible to all can help broaden the knowledge base for inclusive and evidence‑based policies.
