Urbani izziv, volume 36, no. 2, 2025 81 UDC: 316.334.55:332.14(574) doi: 10.5379/urbani-izziv-en-2025-36-02-01 Received: 17 August 2025 Accepted: 2 November 2025 Gaukhar AIDARKHANOVA Gaukhar AUBAKIROVA Laura KENESPAYEVA Damira TAZHIYEVA Gaukhar KAIRANBAYEVA Farabi YERMEKOV A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan With increasing territorial disparities in Kazakhstan, there is a growing need for a systematic assessment of social sustainability at the subregional level. This study develops and pilots an integrated social sustainability index for the districts of the North Kazakhstan Region based on fifteen indicators grouped into five key compo- nents: demography, healthcare, education, quality of life, and social integration. The methodology includes data normalization, indicator aggregation, and spatial analysis using GIS tools. The analysis reveals a stable yet markedly asymmetric territorial structure of social sustainability: although the regional centre (Petropavl) has relatively high values across most components, the majority of rural districts show persistent signs of demographic de- cline, social vulnerability, and insufficient access to basic services and infrastructure. A sensitivity analysis confirms the model’s applicability under conditions of incomplete statistical reporting. Based on the results, a typology of districts is developed, and directions for territorially ori- ented social policy are proposed. The proposed method- ology can be applied for interregional monitoring, social risk assessment, and justification of sustainable develop- ment priorities in regions with transitional demographic structures. Keywords: social sustainability, demographic structure, spatial differentiation, North Kazakhstan Region, GIS, sustainability index, sustainable development goals Urbani izziv, volume 36, no. 2, 2025 82 1 Introduction Regional social sustainability is gaining relevance amid the implementation of the UN sustainable development goals (SDGs) and current socioeconomic challenges. Research high- lights that social sustainability extends beyond basic needs, encompassing equity, inclusiveness, adaptability, and spatial justice. Accordingly, regional-level analysis is crucial because subnational territories often reveal the most acute disparities in access to services and development opportunities. The North Kazakhstan Region illustrates these challenges, marked by population decline, infrastructure gaps, and migration de- pendence. Despite national efforts to promote sustainable development, spatial differences in social sustainability across Kazakhstan’s regions remain underexplored. This study quantitatively assesses social sustainability across the North Kazakhstan Region’s districts using a composite index. Emphasis is placed on selecting relevant indicators aligned with the SDGs and regional conditions, applying normaliza- tion techniques and using cartographic analysis. Spatial dispar- ities are highlighted, and an evidence base is provided for a differentiated regional policy aimed at promoting sustainable territorial development. The concept of social sustainability has undergone significant evolution in academic discourse, gaining increasing analytical significance within the context of regional sustainable devel- opment strategies. Studies emphasize that social sustainability goes beyond the simple provision of basic needs, encompassing aspects of inclusivity, equality, participation, and the capacity of communities to adapt to transformations. Eizenberg and Jabareen (2017) proposed a conceptual framework for social sustainability as a hybrid category combining the values of justice, safety, recognition, and participation, with a strong emphasis on spatial and social structures. Their work laid the foundation for integrating social sustainability into urban and regional planning, particularly in peripheral territories. A significant theoretical contribution was made by Vallance et al. (2011), who distinguished three dimensions of social sus- tainability: development, maintenance, and adaptation. They argue that underestimating cultural and symbolic aspects leads to simplified models incapable of explaining sustainability in complex local contexts. Shirazi and Keivani (2020) analyse so- cial sustainability in the context of urbanization, emphasizing social inclusion, housing justice, and access to opportunities. Their approach is applicable for assessing spatial inequality in urban environments and can be used to analyse the dispar- ities between Petropavl and the rural districts of the North Kazakhstan Region. At the international level, significant progress has been made in formalizing composite index systems. Geniaux et al. (2009) classify sustainable development indicator systems based on degree of aggregation, spatial coverage, and analytical purpose. Their classification is useful for comparing approaches used in Kazakhstan. Lacmanović and Tijanić (2025) explore the progress of social policy in the EU by applying the Social Pro- gress Index (SPI), and Wang and Chen (2022) apply principal component analysis to assess social sustainability, demonstrat- ing its effectiveness in reducing multidimensional data. The annual report of the Social Progress Imperative (2025) offers updated data and methodologies for assessing social progress, focusing on rights, health, and opportunities. The World So- cial Report by the United Nations (2025) provides a frame- work for assessing social sustainability through the lenses of equity and solidarity. The Sustainable Development Report by SDSN (2024) justifies the metrics for achieving the SDGs and provides a foundation for constructing comparable sub- national indices. Examples of regional differentiation in social sustainability highlight the potential for using GIS and remote sensing technologies. Kazakh researchers have made a substantial contribution to the advancement of sustainable development assessment method- ologies. Nyussupova et al. (2021) demonstrated the potential of using GIS to monitor SDG indicators, which is particularly relevant for spatial assessments of social infrastructure in the North Kazakhstan Region. Kuanova et al. (2023) developed a regional sustainability index for Kazakhstan based on national statistical data and normalization methods. Aidarkhanova et al. (2025) constructed a predictive model of sociodemographic processes using business intelligence systems. Studies by Bek- temyssova et al. (2025) and Satybaldin et al. (2025) proposed methodological approaches to clustering and adaptive evalua- tion of regional sustainability, emphasizing multidimensional indices and comparative spatial assessment. ESCAP reports (United Nations, 2023) emphasize the importance of spatial analytics in sustainable governance and advance the integra- tion of geoinformation platforms into regional sustainable de- velopment policies. Thus, a review of existing approaches to assessing social sustainability underscores the need to develop a regionally adapted model that takes into account the specif- ic demographic, infrastructure, and socioeconomic features of the North Kazakhstan Region. This research makes a new contribution because, for the first time, a long-term district-level assessment of social sustainabili- ty has been conducted for a region of Kazakhstan. Unlike pre- vious studies limited to interregional comparisons, the meth- odology allows for the identification of hidden intra-regional disparities and their correlation with the SDGs. The findings complement international research on transitional economies G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV Urbani izziv, volume 36, no. 2, 2025 83A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan and offer a reproducible platform for regular monitoring at the level of local governance. The hypothesis of the study is that regional differentiation in social sustainability in the North Kazakhstan Region necessitates implementing target- ed sustainable development programmes aimed at reducing territorial disparities and improving the quality of life of the population. To test this hypothesis, a composite social sustain- ability index was calculated, followed by a spatial analysis of its distribution across the region. 2 Materials and methods This study is based on a quantitative assessment of social sustainability across the administrative districts of the North Kazakhstan Region, using a composite index specifically devel- oped for this purpose. The empirical foundation of the analysis is official statistical data from the Bureau of National Statistics of the Republic of Kazakhstan (BNS). Regional-level data were collected for thirteen districts and the city of Petropavl over a thirteen-year period (2011–2023) from the Taldau infor- mation and analytical system (BNS Taldau, no date) and the Sustainable Development Goals National Reporting Platform of Kazakhstan (no date). To construct the composite social sustainability index, fifteen indicators were carefully selected and grouped into five the- matic domains: demography, healthcare, education, quality of life and basic infrastructure, and social integration and safety. This structure was designed to capture the multidimension- al nature of social sustainability by encompassing key factors that affect the quality of life in the region. Among the se- lected fifteen indicators, four fully correspond to official SDG target indicators: the unemployment rate (SDG 8.5.2), the availability of medical doctors (SDG 3.c.1), the proportion of households with access to water (SDG 6.1.1), and the crime rate per 100,000 population (approximating SDG 16.1.4). An additional nine indicators were classified as alternative or proxy indicators. These include, for example, the crude birth rate, which corresponds to reproductive health metrics, and the digital literacy rate, which reflects adult ICT competence. Two further indicators – overall mortality and age structure – while not directly included in the SDG framework, are widely used in international assessments of demographic resilience, including those by UNDP and OECD (Table 1). The princi- pal criteria for indicator selection were 1) data availability for all districts of the North Kazakhstan Region and 2) official validation of the indicators in Kazakhstan’s national statistical systems. In the set “social integration and safety”, objective indicators were used: unemployment rate, ratio of average wage to the subsistence minimum, and crime rate. These indicators reflect the formal conditions of economic inclusion and public safety, available in official district-level statistics for 2011 to 2023. Admittedly, subjective dimensions such as life satisfaction, per- ceived social connectedness, trust, and sense of security are important components of social sustainability. However, due to the lack of representative data at the district level, they were not included in the social sustainability index calculations. Due to the lack of regional data on enrolment in higher and vocational education, the digital literacy rate was used as a proxy, reflecting basic ICT skills and serving as an approxi- mate indicator of educational capital. To fill temporal gaps, an average was calculated for 2018, 2020, and 2021. The se- lected indicators reflect both global SDG principles and the practical limitations of regional statistics, balancing analytical rigor with data availability and ensuring the index’s relevance for monitoring and spatial comparison. Normalization was conducted via linear scaling (0 to 1), al- lowing comparability across indicators and ensuring that high- er values indicate stronger social sustainability. Aggregation occurred in two steps: first, sub-indices were calculated for each thematic block; second, the social sustainability index was computed as the mean of these sub-indices, yielding a comprehensive score for each district and the regional seat. To assess the robustness of the final results, a sensitivity analysis of sub-index weights was conducted to examine how the ranking of districts would change under alternative weighting schemes. In addition to the baseline model with equal weights (20% assigned to each sub-index), two alternative configurations were tested: a) weights proportional to the factor loadings of the first principal component, reflecting statistical variance explained by each thematic dimension, and b) expert-assigned weights, reflecting the perceived priority of each sub-index in light of regional development challenges: 30% for healthcare, 25% for education, 20% for quality of life, 15% for demogra- phy, and 10% for social integration and safety. The expert weights were determined through a consensus-based consultation with five academic specialists in demography, re- gional development, and social geography from Al-Farabi Ka- zakh National University. All experts have extensive experience in human capital assessment and regional sustainability studies in Kazakhstan, and their input was used to balance statistical and contextual relevance when assigning thematic priorities. This weighting scheme is consistent with approaches used in previous sustainability studies that apply expert-based calibra- tion (Gan et al., 2017; Mikulić et al., 2015; Abreu et al., 2022; OECD, 2008). A comparison of district rankings calculated under the three weighting schemes demonstrated high robust- Urbani izziv, volume 36, no. 2, 2025 84 Table 1: Key indicators of the social sustainability index for the North Kazakhstan Region. Indicator SDG Alignment with global SDG framework Function within sustainability framework A. Demography a1: Crude birth rate 3.1 Alternative to SDG 3.7.2 (adolescent birth rate) Reflects level of population reproduction; aligned with international demographic practice a2: Crude death rate 3.2 — Fundamental indicator for assessing health risks and population ageing a3: Net international migration balance 10.7 — Indicates scale of population “outflow” due to labour and educational migration a4: Share of children 0–14 and elderly 65+ 3.c — Characterizes age structure and dependency burden on working-age population B. Healthcare b1: Infant mortality rate (< 1 year) 3.2.1 Alternative to SDG 3.2.2 (neonatal mortality rate) Key indicator of child health, sensitive to quality of healthcare and living conditions b2: Availability of medical doctors 3.c.1 Full correspondence with SDG 3.c.1 (health workforce density) Reflects access to healthcare services and capacity of health system C. Education c1: Number of preschool institutions 4.2 Alternative to SDG 4.2.2 (participation in organized learning) Early childhood development indicator that provides the foundation for further learning c2: Digital literacy rate of population 4.4 Alternative to SDG 4.4 (ICT skills among adult population) Reflects level of core competencies and cognitive capital D. Quality of life and basic infrastructure d1: Housing provision 11.1 — Reflecting comfort of housing conditions and degree of overcrowding d2: Households with access to piped water 6.1.1 Alternative to SDG 6.1.1 (proportion of population using safely managed drinking water services) Infrastructure-related indicator, but does not guarantee water quality d3: Households with access to sanitation 6.2.1 Alternative to SDG 6.2.1 (access to adequate sanitation and hygiene) Reflects basic sanitary and hygiene conditi- ons aligned with decent standard of living and health E. Social integration and safety e1: Unemployment rate 8.5.2 Full correspondence with SDG 8.5.2 (unemployment rate) Indicator of economic integration of population e2: Ratio of average wage to subsistence minimum 1.2.1 Alternative to SDG 1.2.1 (national poverty rate) Reflects level of relative income adequacy and risk of poverty e3: Crime rate 16.1.4 Alternative to SDG 16.1 (reduction of violence) Indicator of public safety and social cohesion ness of the results. The Spearman rank correlation coefficients between the baseline and alternative models were 0.89 (with PCA-based weights) and 0.77 (with expert-assigned weights), confirming the reliability and reproducibility of the index. For interpretative purposes, three simple diagnostics were used to flag districts with potentially unstable year-to-year social sustainability index dynamics from 2011 to 2023: the coeffi- cient of variation (CV) of annual social sustainability index values; the count of inter-class switches across the five social sustainability index tiers from year to year; and a mean-rever- sion share, defined as the proportion of years in which the absolute deviation from the district’s long-run mean declined relative to the previous year. As a heuristic threshold, districts with CV > 0.12 and/or ≥ 4 tier switches over the period were treated as high-volatility cases for discussion. Short-term var- G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV Urbani izziv, volume 36, no. 2, 2025 85 iations were interpreted as oscillations that are not sustained over time and tend to revert toward the district’s long-run average. These diagnostics are intended to guide interpreta- tion and avoid over-generalization from small-number effects in sensitive indicators (e.g., infant mortality or crime), rather than to provide formal statistical testing. The number of social sustainability levels was determined using Sturges’ formula, which is commonly applied for categorizing small samples into statistically meaningful classes. The final range of the composite index values was evenly divided into five intervals. Based on this classification, districts were catego- rized as having low, below average, average, above average, or high levels of social sustainability. Using the distribution of the final social sustainability index, the following interval thresh- olds were determined for the North Kazakhstan Region: low (0.000–0.380), below average (0.381–0.490), average (0.491– 0.600), above average (0.601–0.710), and high (above 0.711). This approach ensures consistency and comparability of classi- fication over time, allowing for stable gradation in future mon- itoring cycles. Spatial analysis was performed using methods of cartographic visualization and geoinformation modelling in ArcGIS 10.8. Spatial data were integrated into the system to produce thematic maps showing the distribution of the com- posite index and its component sub-indices across administra- tive-territorial units. The cartographic outputs made it possible to identify geographical patterns that reveal both sustainable and vulnerable district-level positions within the region. 3 Results 3.1 Long-term trends (2011–2023) Between 2011 and 2023, the composite social sustainability index of the North Kazakhstan Region had a complex and uneven trajectory. The initial years of the study period were characterized by relatively low index values across most dis- tricts, primarily due to a combination of demographic decline, infrastructure deficits, and economic constraints. However, from the mid-2010s onward, a gradual upward trend in so- cial sustainability scores became evident. This improvement is associated with the implementation of social sector moderniza- tion programmes, enhanced access to healthcare and education services, and a degree of stabilization in infrastructure devel- opment. The pace of this growth, however, was tempered by ongoing demographic pressure: population loss driven by both natural decline and out-migration remained a persistent struc- tural constraint (UNDP Kazakhstan, 2020). By 2017–2019, index values across districts began to converge, indicating that several territories had reached a relative plateau of sustainabil- ity. During this period, further positive changes slowed, and inter-district differentiation stabilized. The COVID-19 pan- demic, which peaked in 2020–2021, introduced short-term disruptions to the established dynamics. A decline in index values was observed, primarily due to deteriorating health indi- cators (increased mortality and a greater burden on healthcare systems) and a temporary reduction in quality of life (World Bank, 2021). Nevertheless, by 2022, conditions had partially stabilized, and key sub-indexes returned to pre-pandemic lev- els. Despite this recovery, the overall demographic situation continued to worsen, thereby constraining the full rebound of the composite index. As a result, over the entire study period, the social sustainabil- ity index for the North Kazakhstan Region had a moderately positive dynamic, without evidence of a pronounced break- through. Improvements in education, healthcare, and quality of life have largely offset the negative effects of ongoing de- mographic trends. According to official sources, the region’s population declined by nearly 9% over the past decade (BNS, no date), which has constrained the potential for sustained growth in the composite index. Thus, the observed trajecto- ry reflects the emergence of a stable yet unevenly distributed positive trend, marked by stagnation in recent years. A signif- icant gap persists between districts with high and low levels of social sustainability, indicating the need for targeted policy and investment efforts aimed at strengthening resilience in the region’s most vulnerable territories. 3.2 Regional clustering of sustainability By the end of 2023, all thirteen districts and the city of Pet- ropavl were grouped into five categories based on their social sustainability levels, derived from the composite index. These categories reveal clusters of territories with similar profiles across key sub-indices: demography, healthcare, education, quality of life, and social integration. A high level of social sustainability was recorded only in Petropavl, which outper- formed all districts across major components. This is linked to the city’s concentration of socioeconomic resources, strong institutional capacity, and developed infrastructure. High ac- cess to healthcare and education, reliable gas and water systems, and comparatively low poverty and unemployment contribute to its comprehensive and resilient sustainability profile. The above-average sustainability group includes the Qyzyljar district and Ğabit Müsırepov district. The former benefits from its geographic proximity to the regional seat, and the latter stands out due to its agro-industrial base and recent im- provements in social infrastructure. Both districts consistently demonstrate strong performance in the sub-indices of health- care, education, and quality of life (Figure 1). A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan Urbani izziv, volume 36, no. 2, 2025 86 Figure 1: Spatial distribution of social sustainability sub-index values across the districts of the North Kazakhstan Region, 2023: a) demograph- ic index; b) health index; c) education index; d) index of quality of life and infrastructure; e) index of social integration and environmental safety (maps: authors, based on official statistical data). G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV a b Urbani izziv, volume 36, no. 2, 2025 87A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan c d Urbani izziv, volume 36, no. 2, 2025 88 A medium level of social sustainability was recorded in the Taiynşa, Aiyrtau, Jambyl, and Mamliut districts. These ter- ritories are characterized by balanced performance across all components, without notable achievements or severe de- ficiencies. Their sub-index values fluctuate near the regional average, reflecting a relatively stable, though unspectacular, sociodemographic profile. The below-average group includes the Aqqaiyñ district and Mağjan Jūmabaev district. These areas are constrained by relatively weak demographic indi- cators, limited access to quality healthcare, and insufficient basic infrastructure. The accumulation of social challenges in these districts has resulted in lower composite index values (Institute of Economic Research, 2021). The low sustainabil- ity group comprises the districts of Aqjar, Uälihanov, and Şal Aqyn. These remote and sparsely populated rural areas exhib- it critically low values across all components. Their profile is shaped by infrastructure isolation, demographic decline, and limited employment opportunities – factors that collectively contribute to chronic social vulnerability. Access to education, healthcare, and essential living conditions in these districts re- mains significantly below the regional average. This spatial distribution of social sustainability confirms the city-centric nature of regional development: Petropavl forms a distinct core of social resilience, and the more remote districts remain in a zone of persistent vulnerability. Most rural territo- ries occupy intermediate positions, underscoring the need for a territorially differentiated regional policy aimed at addressing spatial disparities and enhancing local capacities. 3.3 Structural decomposition of the index An analysis of the structure of the composite index reveals the territorial specificity in the formation of social sustainability. The five sub-indices collectively determine the overall assess- ment; however, their relative contributions vary significantly between leading and lagging districts. Petropavl has a balanced and uniformly high profile, with all five sub-indices exceeding the regional average (Figure 2). Three of them – education, healthcare, and quality of life – reach the highest values across the entire region. Petropavl holds a leading position in the education component due to its high rates of preschool and higher education coverage, sup- ported by the presence of universities and vocational colleges. The city also ranks first in the healthcare sub-index, benefit- ing from a high density of medical personnel, the presence of specialized facilities, and favourable demographic health in- dicators, including infant mortality and life expectancy (Sus- G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV e Urbani izziv, volume 36, no. 2, 2025 89 tainable, no date). The quality of life sub-index in Petropavl is driven by its extensive coverage with utility services: more than 95% of households are connected to centralized water supply and heating, and a significant portion have access to gas. Active residential construction and the availability of apartment hous- ing further contribute to strong infrastructure performance. In the social integration sub-index, the city has moderately high values supported by low unemployment and a relatively small proportion of the population living below the subsist- ence minimum. The only relatively weaker dimension is safety because the level of registered crime in the city is traditionally higher than in rural areas, which slightly reduces the final value of the social integration sub-index (BNS, no date). In contrast, districts such as Uälihanov and Aqjar have what may be described as a “declining profile”: the values across all five sub-indices remain low, without any significant compensa- tory advantages (Figure 2). The demographic component pulls down the composite index due to persistently high mortality rates, which exceed birthrates, and active out-migration. The healthcare sub-index is negatively affected by personnel short- ages, remoteness from medical institutions, and less favourable health outcomes. The education component in these remote districts is largely limited to primary and secondary educa- tion, and the share of the population with higher education is significantly lower than in the regional seat. Infrastructure provision is chronically underdeveloped: many villages lack a reliable water supply and centralized heating, road quality remains poor, and gasification levels are minimal. Social inte- gration is further weakened by a high share of the population living below the poverty line, restricted access to labour mar- kets, and general economic stagnation. Under such conditions, even a relatively low crime rate has little impact on improving the overall index score. Consequently, leading districts benefit from multi-component reinforcement, in which each sub-index amplifies the effects of the others, producing a synergistic effect. In contrast, lagging districts experience a cumulative weakening, in which negative factors across multiple domains undermine the structure of sustainability as a whole. This imbalance calls for a territorially differentiated policy approach. In the case of Petropavl, the key task is to maintain the level of sustainability achieved and to enhance resilience to future shocks. In contrast, peripheral districts require targeted interventions in their most vulner- able components – primarily infrastructure, healthcare, and demographic stability – to overcome structural disadvantages and close the sustainability gap. Figure 2: Spatial distribution of the social sustainability index across districts of the North Kazakhstan Region, 2023 (map: authors, based on official statistical data). A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan Urbani izziv, volume 36, no. 2, 2025 90 3.4 Typology of districts in the North Kazakhstan Region by level of social stability An analysis of the dynamics of the social sustainability index across the administrative districts of the North Kazakhstan Region from 2011 to 2023 reveals both consistently stable and highly volatile territories. Stability is observed at both the high and low ends of the sustainability spectrum. For example, Petropavl consistently held a leading position in the compos- ite index throughout the entire study period. At the opposite end, chronically low-performing districts were also identified. The Uälihanov and Şal Aqyn districts remained in the bottom quintile of the regional ranking over the entire period. Minor year-to-year fluctuations did not affect their overall standing, indicating entrenched socioeconomic disparities and the ab- sence of effective mechanisms for adaptation to demographic and infrastructure challenges. In addition to persistently high- and low-performing districts, several areas in the region exhibited considerable variability in sustainability performance. For instance, the Taiynşa district was initially classified at a medium level of sustainability but, due to investments in the social sector, the modernization of educational and transport infrastructure, and the launch of new industrial facilities, it improved its standing in the 2020s and entered the above-average category (Prime Minister, 2020). A reverse trend was observed in the Mağjan Jūmabaev district. The district performed near the regional average during the first half of the period, but a marked decline was recorded after 2017. Increased out-migration, the downsizing of the social service network, and the deterioration of basic infrastructure led to falling sub-index values, placing the district in the be- low-average sustainability category (MTRK, 2021; Figure 3). Sparsely populated districts tend to exhibit higher year-to-year volatility of the social sustainability index. Using the screen- ing definitions introduced in Section 2 (CV and tier-switch counts, complemented by a simple mean-reversion share), several districts are flagged as high-volatility cases, in which small absolute changes in sensitive components (e.g., infant mortality or crime) can produce visible swings in normalized values. In most such cases, the direction of change is not per- sistent beyond one to two years, and the index reverts toward its long-run average, which is termed short-term variation here. With regard to policy, this pattern supports a distinction be- tween maintenance and incremental improvement in consist- ently high-sustainability districts, and stabilization measures in high-volatility districts – multiyear funding cycles, protected staffing for physicians and teachers, and territorially targeted governance instruments – to dampen acute swings and reduce exposure to shocks. Figure 3: Dynamics of the social sustainability index across the districts of the North Kazakhstan Region, 2011–2023 (illustration: authors, based on official statistical data). G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV Urbani izziv, volume 36, no. 2, 2025 91 4 Discussion 4.1 Interpretation of findings The integrated assessment of social sustainability in the dis- tricts of the North Kazakhstan Region (2011–2023) reveals persistent spatial polarization, with marked differences be- tween urban and rural areas. The highest index values are concentrated in Petropavl, confirming an urban-centric sus- tainability model, in which population density, resources, and institutional capacity support high levels of social provision. The balanced performance across all sub-indices indicates in- tegrated urban development, despite a slightly elevated crime rate. Rural districts display considerable variation. Although some central and southern districts (e.g., Qyzyljar and Ğabit Müsırepov) show positive trends, aided by proximity to the regional seat and investment, northeastern and border areas remain at consistently low levels. This reflects not only current socioeconomic conditions but also structural legacies: demo- graphic decline, weak infrastructure, and poor connectivity. Sub-index analysis reveals that underperformance in lagging districts stems from systemic deficits: demographic shrinkage, limited service access, infrastructure gaps, and persistent pov- erty. These factors reinforce each other, producing cumulative vulnerability. In terms of temporal dynamics, although urban centres and selected districts have improved, others – such as Aqjar, Uälihanov, and Şal Aqyn – remain chronically low-per- forming. This inertia suggests structural resistance to change and a lack of adaptive policy measures. The overall stability of rankings confirms both the model’s robustness and the en- trenched nature of territorial inequality. 4.2 Comparison with previous studies The findings of this study are consistent with the conclusions drawn in earlier research on territorial resilience and region- al differentiation in post-Soviet and developing countries. As noted by Gan et al. (2017) and Abreu et al. (2022), the resil- ience of territories is shaped not only by formal macroeconom- ic indicators, but also by structural balance in access to basic social services, demographic potential, and the quality of the environment. From this perspective, the observed dominance of the regional seat and the vulnerability of remote districts align with global trends in asymmetric regional development, as documented in Central Asia, eastern Europe, and Latin America. In the case of Kazakhstan, these results corroborate and expand upon findings by UNDP Kazakhstan (2020), the Institute of Economic Research (2021), and Zhanibayeva (2022), which emphasize the structural inequality between urban and rural areas. In particular, issues such as limited access to basic servic- es, a shortage of labour resources, and low income levels in rural districts of the northern regions have previously been identified as key barriers to achieving sustainable regional development (OECD, 2008). Unlike more aggregated approaches as part of which analysis is conducted at the oblast or macroregional level, this study offers a micro-level perspective by mapping social sustainability at the district level. This approach makes it possible to identify not only general development patterns but also intra-group disparities that are often obscured by aggregat- ed indicators. Furthermore, the use of an integrated index with sensitivity analysis of weights helps mitigate methodological bias, enhancing the reliability of inter-territorial comparisons. Similar models have been applied in studies in India, Turkey, and Latin America, where district- or municipal-level assess- ments have proven highly effective in identifying local hotspots of social vulnerability (CEEW, 2020; DST-GoI, 2024; Tanır et al., 2022; IPEA, 2015; Menezes et al., 2018; León-Cruz et al., 2024). Thus, the results obtained are in line with and confirm international trends while making a significant contribution to the understanding of Kazakhstan by providing high spatial resolution and a comprehensive calculation model. This en- hances their relevance for integration into policy-making and regional governance practices. 4.3 Strengths and limitations of the approach The proposed approach to assessing social sustainability has several important methodological and practical strengths. By integrating five interrelated sub-indices it provides a compre- hensive assessment of the social sphere at the district level, moving beyond fragmented analyses toward a holistic under- standing of sustainability. A major advantage lies in transparent data normalization and the use of multiple weighting schemes. Sensitivity analysis confirmed the consistency of district rank- ings across these models, enhancing the robustness of results and reducing potential bias, in line with international guide- lines (Gan et al., 2017; OECD, 2008). The use of GIS tools, particularly ArcGIS, allowed the visualization of index values and dynamics, revealing spatial patterns and areas of elevated risk. These visual outputs improve the accessibility of results for both experts and decision-makers. However, several limitations should be noted. The reliance on official district-level statistics constrains the ability to cap- ture intra-district disparities, especially between urban centres and remote villages, potentially leading to aggregation bias. In addition, some dimensions – such as informal institutions, perceptions of justice, or governance quality – remain beyond the scope of available data and would benefit from comple- mentary qualitative research. The timeframe (2011–2023) is A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan Urbani izziv, volume 36, no. 2, 2025 92 adequate for short- and medium-term analysis, but it limits insight into the long-term impacts of structural reforms. Fu- ture studies should consider extending the temporal horizon and integrating scenario-based forecasts. In sum, despite these constraints, the methodology proves effective in data-limited contexts and offers a sound basis for monitoring social sus- tainability and informing regional policy. The absence of comparable subjective indicators at the district level – such as life satisfaction, social cohesion, and trust – limits the completeness of the “social integration” dimension. For future monitoring rounds, the following is proposed 1) introducing a survey module of eight to ten questions with an annual stratified sample of three hundred to four hundred respondents; 2) hybridizing the index through a latent variable “social engagement” (confirmatory factor analysis) calibrated against unemployment, income, and crime indicators; and 3) regularly incorporating municipal administrative data (partic- ipation in volunteering) as indirect proxies. 4.4 Policy and planning implications The results obtained directly link the initial research hy- pothesis with the need for targeted sustainable development programmes at the subregional level. The social sustainability index mapping revealed a stable core (Petropavl and adjacent municipalities) and a perimeter of chronic vulnerability (re- mote, sparsely populated districts). This indicates that univer- sal equalization measures will be insufficient: differentiated intervention packages are required, combining infrastructure investments (water supply, gasification, roads, and digital con- nectivity), strengthening of human capital in healthcare and education, and economic incentives to retain young people and qualified specialists. In practical terms, this implies a transition toward district-level “sustainability roadmaps”, in which goals and funding are aligned with subindex profiles, and moni- toring is conducted based on the annual updating of social sustainability index indicators. To align the findings with local policy implementation, it is advisable to cluster policy measures as follows: • A high-sustainability core (Petropavl): maintaining achieved levels through preventive health programmes, urban safety initiatives, fine-tuning of education and ICT competencies, and effective management of migration and housing. • Medium and above-average sustainability (the Qyzyljar and Ğabit Müsırepov districts): implementing “growth accelerators” such as upgrading infrastructure to urban standards, expanding access to specialized medical ser- vices, vocational colleges and IT courses, and developing local employment programmes. • Below-average and low sustainability (Uälihanov, Aqjar, Şal Aqyn, and others): ensuring stabilization through ba- sic infrastructure improvements (water, sanitation, roads, communications), targeted medical staffing and incentive contracts for teachers and doctors, creation of local eco- nomic activity hubs (small-scale processing and logistics), and prioritized targeted support for households. The findings have strong practical relevance for regional poli- cy, particularly regarding the sustainable development goals – reducing inequality (SDG 10) and promoting health (SDG 3), education (SDG 4), infrastructure access (SDG 11), and institutional capacity (SDG 16). The concentration of high social sustainability in Petropavl and nearby suburbs under- scores the need to revise uniform infrastructure policies. Struc- tural disparities in rural areas require adaptive planning and targeted support for districts with persistently low sub-index values – especially in healthcare, infrastructure, and demogra- phy. The clustering of districts by sustainability levels supports a differentiated policy approach. Lagging areas such as Aqjar and Uälihanov require integrated strategies: upgrading medi- cal and educational facilities, improving transport and digital connectivity, and fostering employment through local enter- prise development. Social support must be closely linked to economic and infrastructure interventions. Another priority is the institutionalization of monitoring mechanisms. The composite index developed can be incorpo- rated into regional development tools, allowing regular data updates, dashboard visualizations, and integration with fund- ing programmes. This would facilitate more effective needs- based investment planning. Promising districts such as Taiynşa and Qyzyljar, which show upward trends, can serve as pilots for innovation in social services and the digital economy. Scal- ing successful models while adapting them to local contexts may foster a more balanced and resilient regional system. In conclusion, this study offers not only a snapshot of current territorial disparities but also a policy framework to reduce spatial inequality and enhance the adaptive capacity of vul- nerable districts. 5 Conclusion Using an integrated social sustainability index, persistent terri- torial disparities and long-term spatial inequality trends were identified in the North Kazakhstan Region. Urbanized and resource-concentrated areas, such as the city of Petropavl and its surrounding suburbs, are the most sustainable areas in this region. In contrast, remote and sparsely populated districts exhibit structural vulnerability, characterized by demograph- ic depopulation, limited access to basic services, and a weak economic base. This confirms the existence of a spatial divide G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV Urbani izziv, volume 36, no. 2, 2025 93 that cannot be addressed through universal policy measures and instead requires territorially sensitive strategies. Priority ar- eas should include the stabilization of demographic processes, improvement in the accessibility and quality of healthcare and education, infrastructure modernization, and human capital support in peripheral districts. Moreover, the integration of spatial planning tools and digital technologies is essential to improve the targeting and effectiveness of decision-making. Thus, the hypothesis that territorial differentiation of social sustainability in the North Kazakhstan Region necessitates tar- geted programs to reduce disparities and improve quality of life is confirmed. The results presented in this study can serve as a foundation for monitoring regional disparities, guiding territorial development planning, and shaping equitable social policy within Kazakhstan’s sustainable development agenda. The composite index developed can be adapted for application in other regions of the country, integrated into the monitor- ing system of territorial development programmes, and used to justify the priority allocation of resources. The proposed typology of districts and the operationalized subindices make it possible to directly translate the results into the design of dis- trict-level sustainable development programmes, results-based budgeting, and annual monitoring of target indicators. Gaukhar Aidarkhanova, Al-Farabi Kazakh National University, Almaty, Kazakhstan E-mail: gaukhar.aidarkhanova@gmail.com Gaukhar Aubakirova, Al-Farabi Kazakh National University, Almaty, Kazakhstan E-mail: gauhara_91@mail.ru Laura Kenespayeva, Al-Farabi Kazakh National University, Almaty, Kazakhstan E-mail: laura.kenespaeva81@gmail.com Damira Tazhiyeva, Al-Farabi Kazakh National University, Almaty, Kazakhstan E-mail: damira.tazhiyeva@gmail.com Gaukhar Kairanbayeva, Al-Farabi Kazakh National University, Almaty, Kazakhstan E-mail: gaukhara_new@mail.ru Farabi Yermekov, Kazakh National Agrarian Research University, Almaty, Kazakhstan E-mail: f.yermekov@gmail.com Acknowledgements This research was funded by the Science Committee of the Ministry of Science and Higher Education of Kazakhstan (grant no. BR24993222). References Abreu, I., Mesías, F. J. & Ramajo, J. (2022) Design and validation of an index to measure development in rural areas through stakeholder participation. Journal of Rural Studies, 95, 232–240. doi:10.1016/j.jrurstud.2022.09.022 Aidarkhanova, G., Zhumagulov, C., Nyussupova, G. & Kholina, V. (2025) Assessing the impact of demographic growth on the educational in- frastructure for sustainable regional development: Forecasting demand for preschool and primary school enrollment in Kazakhstan. Sustainabil- ity, 17(9), 4212. doi:10.3390/su17094212 Bektemyssova, G., Zhiyenbayev, M. & Utegenov, M. (2025) Spatial ag- gregation and urban population activity in Almaty. Sustainability, 17(7), 3243. doi:10.3390/su17073243 BNS = Bureau of National Statistics, Agency for Strategic Planning and Reforms of the Republic of Kazakhstan (no date). Home page. Available at: https://stat.gov.kz/ (accessed 22.05.2025). BNS Taldau (no date) Taldau information and analytical system. Available at: https://taldau.stat.gov.kz/en (accessed 22.05.2025). CEEW (2020) Mapping India’s climate vulnerability: A district level assessment. Available at: https://www.ceew.in/sites/default/files/ ceew-study-on-climate-change-vulnerability-index-and-district-lev- el-risk-assessment.pdf (accessed 18.05.2025). DST-GoI = Department of Science & Technology, Government of India (2024) District-level climate risk assessment for India: Mapping flood and drought risks using IPCC framework. Available at: https://dst.gov.in/sites/ default/files/Full%20Report_District-Level%20Climate%20Risk%20As- sessment%20for%20India_Mapping%20Flood%20and%20Drought%20 Risks%20Using%20IPCC%20Framework.pdf (accessed 18.05.2025). Eizenberg, E. & Jabareen, Y. (2017) Social sustainability: A new concep- tual framework. Sustainability, 9(1), 68. doi:10.3390/su9010068 Gan, X., Fernandez, I. C., Guo, J., Wilson, M. & Zhao, Y. (2017) When to use what: Methods for weighting and aggregating sustainability indica- tors. Ecological Indicators, 81, 491–502. doi:10.1016/j.ecolind.2017.05.068 Geniaux, G., Bellon, S., Deverre, C. & Powell, B. (2009) Sustainable de- velopment indicator frameworks and initiatives. Report 49, Wageningen University / SEAMLESS Integrated Project. Available at: https://agecon- search.umn.edu/bitstream/57937/2/Report_49_PD2.2.1.pdf (accesse 19.05.2025). Institute of Economic Research (2021) Integral indices and ratings of regions of Kazakhstan by the level of socio-economic development. Available at: https://ieconom.kz/en/integral-indices-and-ratings-of-re- gions-of-kazakhstan-by-the-level-of-socio-economic-development/ (accessed 19.05.2025). IPEA (2015) Atlas da vulnerabilidade social nos municípios brasileiros. Available at: https://repositorio.ipea.gov.br/bitstream/11058/4381/1/At- las_da_vulnerabilidade_social_nos_municipios_brasileiros.pdf (accessed 18.05.2025). Kuanova, L., Bekbossinova, A. & Abdykadyr, T. (2023) Assessment of the sustainable development of regions: The case of Kazakhstan. Eurasian Journal of Economics and Business Studies, 67(3), 122–135. doi:10.47703/ejebs.v3i67.310 Lacmanović, S. & Tijanić, L. (2025) Social progress in the European Union. Sustainability, 17(4), 1652. doi:10.3390/su17041652 León-Cruz, J. F., Romero, D. & Rodríguez-García, H. I. (2024) Spatial and temporal changes in social vulnerability to natural hazards in Mexico. ISPRS International Journal of Geo-Information, 13(3), 66. doi:10.3390/ijgi13030066 A comprehensive assessment of social sustainability in the North Kazakhstan Region of Kazakhstan Urbani izziv, volume 36, no. 2, 2025 94 Menezes, J. A., Confalonieri, U., Madureira, A. P., de Brito Duval, I., Santos, R. B. D. & Margonari, C. (2018) The construction of a municipal vulnerability index. PLOS One, 13(2), e0190808. doi:10.1371/journal.pone.0190808 Mikulić, J., Kožić, I. & Krešić, D. (2015) Weighting indicators of tour- ism sustainability: A critical note. Ecological Indicators, 48, 312–314. doi:10.1016/j.ecolind.2014.08.026 MTRK (2021) V SKO prokhodyat otchotnyye vstrechi akimov sel′skikh okrugov v onlayn formate. Available at: https://mtrk.kz/ru/2021/01/28/v- sko-prohodyat-otchyotnye-vstrechi-akimov-selskih-okrugov-v-onlajn- formate/ (accessed 19.05.2025). Nyussupova, G., Aidarkhanova, G., Kadylbekov, M., Kenespayeva, L., Kelinbayeva, R. & Kozhakhmetov, B. (2021) Nationalization of indica- tors for sustainable development goals in the Republic of Kazakh- stan through geoinformation technologies. GI_Forum, 1, 158–168. doi:10.1553/giscience2021_01_s158 OECD (2008) Handbook on constructing composite indicators: Methodolo- gy and user guide. Paris. doi:10.1787/9789264043466-en Prime Minister of Kazakhstan (2020) Rost investitsiy, proizvodstvo promyshlennoy produktsii i sozdaniye rabochikh mest — razvitiye Severo-Kazakhstanskoy oblasti v 2020 godu. Available at: https:// primeminister.kz/ru/news/reviews/rost-investiciy-proizvodstvo-promys- hlennoy-produkcii-i-sozdanie-rabochih-mest-razvitie-severo-kazahstan- skoy-oblasti-v-2020-godu-2002040 (accessed 18.05.2025). Satybaldin, A. A., Moldabekova, A., Alibekova, G. Z. & Azatbek, T. A. (2025) National adaptive social well-being index for measuring regional disparities in Kazakhstan. R-Economy, 10(4), 391–409. doi:10.15826/ recon.2024.10.4.024 SDSN (2024) Sustainable development report 2024: The SDG index and dashboards. Paris. Shirazi, M. R. & Keivani, R. (2020) Urban social sustainability: Theory, policy and practice. London: Routledge. doi:10.4324/9781315115740 Tanır, T., Fındık, S. B., Girayhan, T. F., in Yorulmaz, Ö. (2022) Flood social vulnerability assessment: A case study of Türkiye. Turkish Journal of Water Science and Management, 6(2), 237–259. doi:10.31807/tjwsm.1089403 UNDP Kazakhstan (2020) Human development report: Inequality in Ka- zakhstan. Available at: https://www.kz.undp.org (accessed 19.05.2025). United Nations (2025) World social report 2025: Building social cohesion through inclusive growth. New York, UN Department of Economic and Social Affairs. UNESCAP = United Nations Economic and Social Commission for Asia and the Pacific (2023) Geospatial solutions for sustainable development. Available at: https://www.unescap.org/blog/geospatial-solutions-sus- tainable-development (accessed 20.05.2025). Vallance, S., Perkins, H. C. & Dixon, J. E. (2011) What is social sustainabil- ity? A clarification of concepts. Geoforum, 42(3), 342–348. doi:10.1016/j.geoforum.2011.01.002 Wang, B. & Chen, T. (2022) Social progress beyond GDP: PCA of GDP and 12 alternative indicators. Sustainability, 14(11), 6430. doi:10.3390/su14116430 World Bank (2021) Kazakhstan systematic country diagnostic: Towards inclusive and sustainable growth. Washington, DC. Zhanibayeva, K. O. (2022) Analysis and evaluation of the territorial de- velopment program in Kazakhstan (case of North Kazakhstan Region). Economic Series of the APA Journal, 3, 87–94. G. AIDARKHANOVA, G. AUBAKIROVA, L. KENESPAYEVA, D. TAZHIYEVA, G. KAIRANBAYEVA, F. YERMEKOV