The discrepancy between economic growth and the population’s income level is becoming one of the key problems in the development of urban economies, as the expansion of production and business does not lead to an improvement in living standards.

   The purpose of the study is to identify the causes of uneven income growth across Kazakhstan’s cities and to determine the economic conditions that ensure their sustainable dynamics.

   The research methodology is based on a comprehensive analysis of indicator dynamics using an indicator system, calculation of relative changes, inter-city comparative analysis, and typologization based on thresholds. The empirical basis of the study was official statistical data for 17 cities of Kazakhstan for the period 2016–2024, including indicators of wages, employment, gross regional product, retail turnover, and population. The results showed significant differences in economic dynamics between cities. The highest values of the integral IED index were recorded in Astana (TotalMean = 44.98), Uralsk (23.67) and Shymkent (22.98), while negative values prevail in Karaganda (−11.71), Taraz (−9.77) and Kokshetau (−6.59). It has been established that steady income growth is driven mainly by cities with a developed labor market and an active consumer market, whereas output growth alone does not guarantee an increase in population well-being.

   The results confirm that the key factor in sustainable income growth is not the scale of economic activity, but the degree of its integration with employment and domestic demand.

   With the increasing role of cryptocurrencies in the global financial system, the analysis of the mechanisms by which volatility transfers between digital and traditional assets is becoming particularly relevant.

   The purpose of the study is to assess the degree, directions, and temporal variability of volatility spillovers between Bitcoin, leading cryptocurrencies, and traditional financial instruments.

   Using daily data and both static and rolling-window estimates, the analysis assesses how shocks to volatility are transmitted between cryptocurrency and macro-financial markets. The results of the study showed that the Total Spillover Index (TSI) in the static model is 28.37 %, while in the dynamic model it reaches an average of 35.9 %, with peak values up to 45.25 % in 2022. It has been established that Bitcoin acts as a net transmitter of volatility: the average level of transmitted effects is 60.31 %, received effects are 50.59 %, and the net spillover is +9.72 %. Moreover, Bitcoin’s place in the network is state-dependent to some extent: while it acts as a net transmitter in the average, speculative episodes in alternative cryptocurrencies can cause Bitcoin to act as a receiver of volatility shocks. The results indicate a high degree of internal connectivity in the cryptocurrency market, with limited integration with the traditional financial system, and a pronounced temporary variability in the structure of volatility interactions. These findings have implications for portfolio diversification, risk management, and the ongoing integration of digital assets into the global financial system.

   With the growing tourist load and increased anthropogenic pressure on coastal areas, the need to develop sustainable management models for tourist destinations based on the integration of natural and infrastructural solutions is becoming urgent.

   The aim of this study is to develop a scientifically grounded approach to assessing the capacity of green infrastructure and to propose mechanisms for managing tourist flows to reduce anthropogenic pressure and improve the quality of the destination’s tourism environment.

   The research methods include spatial analysis using geoinformation tools, and the calculation of recreational capacity based on normative, spatial, and temporal approaches, using the methodology for assessing the capacity of territories. The initial data are represented by spatial characteristics of the territory, standards of recreational load, infrastructure parameters (pedestrian and bicycle zones), and time indicators of attendance. The results of the study show that, based on normative spatial modeling, with an area of 40,000 m² and a standard of 50 m² per visitor, the simultaneous recreational capacity is 200 people. Considering the turnover rate, the daily throughput reaches 1,200 visits, and during the peak tourist period (60 days), 72,000 visits. The obtained values indicate the possibility of effectively redistributing tourist flows and reducing the burden on coastal ecosystems through the introduction of the AEEB model. The findings indicate the potential to regulate tourist flows and reduce anthropogenic pressure on coastal ecosystems.

   Regional differentiation of social protection remains one of the key problems of ensuring balanced socio-economic development and reducing inequality.

   The aim of the study is to assess inter-regional differences in the level of social protection in Kazakhstan and to determine the contribution of the level of payments and coverage of the population to the formation of these differences.

   The empirical base consists of official data from the Bureau of National Statistics of the Republic of Kazakhstan for the period 2014–2024, covering 17 regions. The research methodology includes the standardization of indicators (Z-score), their time averaging, the construction of an integral index of social protection, cluster analysis with the determination of the optimal number of clusters based on Gap Statistical, as well as multidimensional scaling using Euclidean metrics. The results showed the presence of stable regional differentiation: the values of the integral index range from –0,68 in the North Kazakhstan region to 0,95 in the Turkestan region. High values were also recorded in the East Kazakhstan region (0,72) and Almaty (0,69). Cluster analysis revealed five groups of regions that differ in the combination of payment levels and coverage, which confirms the heterogeneity of the functioning of the social protection system. The results show that an increase in payments has a more significant impact on the level of social protection than an expansion of coverage with a low level of funding, which should be taken into account when shaping regional social policy.

   The development of small and medium-sized businesses is considered one of the key factors for ensuring economic growth, employment, and sustainable regional development.

   The purpose of the study is to identify the structure of regional development among small and medium-sized businesses in Kazakhstan and to identify groups of regions with different levels of entrepreneurial activity.

   The methodological basis of the study includes data standardization using the Z-score method, principal component analysis (hereinafter – PCA) and cluster analysis using the Ward method. The study uses data from the Bureau of National Statistics for the period 2018-2024. The results of the study revealed significant interregional differences in the development of small and medium-sized businesses. The highest values of the indicators were recorded in the cities of Almaty and Astana. Thus, in Almaty, the standardized values for gross value added, output, employment, and the number of enterprises were 3.22, 3.11, 3.59, and 2.92, respectively. In Astana, the corresponding figures were 1.76, 1.92, 1.53, and 1.70. Cluster analysis identified two stable clusters of regions, with an intercluster sum of squares of 62.94 and a total sum of squares of 80.71, indicating high interregional differentiation. The results of the study showed that growth in the number of enterprises is not accompanied by proportional increases in output, employment, and value added, indicating structural heterogeneity in the development of small and medium-sized businesses across regions of Kazakhstan.

   Economic cooperation between Russia and Kazakhstan has been undergoing significant changes in recent years under the influence of external economic constraints and the transformation of regional economic ties.

   The aim of this article is to assess how Russia–Kazakhstan economic cooperation has evolved under sanctions by examining changes in bilateral trade, organizational embeddedness, and relative structural position between 2018 and 2025.

   The analysis uses Kazakhstan as a focused empirical setting and traces developments from 2018 to 2025, with the earlier years serving as a pre-shock benchmark. The results indicate that bilateral trade rose sharply after 2022, then remained elevated without further expansion. The results showed that a structural asymmetry of trade flows was revealed: Kazakhstan’s exports to Russia decreased from 9.55–9.56 billion US dollars in 2024 to 8.14 billion US dollars in 2025, while imports from Russia increased from 18.24 billion US dollars to 19.26 billion US dollars. Russian-linked firms also retained the largest foreign business presence in Kazakhstan. At the same time, the growing weight of China altered Russia’s relative standing rather than removing its importance. The study shows that cooperation under sanctions is better understood as differentiated restructuring than as simple expansion and contraction. Thus, the economic cooperation between Russia and Kazakhstan does not show signs of either steady expansion or consistent reduction, but is developing in the form of structural adjustment at a high level of interaction.

   In the context of digital transformation, climate risks and geo-economic instability, ESG approaches are becoming an important direction for the modernization of logistics, distribution systems and supply chain management.

   The purpose of the study is to conduct a bibliometric analysis of scientific publications to identify the dynamics, thematic structure, and role of Central Asian countries in the development of ESG-oriented research in logistics and supply chain management.

   The methodological basis of the study was made up of bibliometric analysis, the PRISMA approach, co-authorship analysis, co-word analysis and network visualization using the VOSviewer program. The initial empirical base was formed from publications indexed in the Web of Science Core Collection for 2021–2025. The results demonstrate a more than threefold increase in publication activity, from 70 publications in 2021 to 226 publications in 2025, reflecting the growing scientific interest in sustainable logistics systems and ESG-oriented supply chain management. The results of the network analysis revealed the key thematic areas of ESG research: digital ESG ecosystems, artificial intelligence, machine learning, blockchain technologies, supply chain sustainability, circular economy, green logistics, and ESG-oriented risk management. Kazakhstan and Uzbekistan are increasingly serving as connecting nodes between European and Asian research clusters, reflecting the growing importance of Central Asia in Eurasian transport corridors and in the transformation of sustainable logistics within an ESG-oriented economy. The results indicate that ESG is increasingly functioning as an integrated governance framework for adaptive, digitally connected, and sustainable supply chain systems amid climate risks, geo-economic instability, and global disruptions.

   The acceleration of digitalization of financial services and the expansion of non-cash and online transactions is transforming the economic behavior of households, changing the structure of consumption, borrowing and saving, as well as strengthening the relationship between retail business activity and banking financial instruments.

   The study aims to assess the impact of digital financial transactions on household behaviour in Kazakhstan, focusing on consumption, borrowing, savings, retail activity, household lending, and deposit accumulation within the banking system.

   The study’s methodological basis is a quantitative approach using a system of indicators reflecting households’ financial behaviour, including consumption, credit activity, savings, and digital transactions. The study covers the period 2014–2024 and is based on macroeconomic and financial indicators characterising the dynamics of consumption, lending, savings, and digital payments. The results showed that in 2014-2024, household deposits increased from 4.3 to 23.0 trillion tenge, household loans from 3.7 to 20.3 trillion tenge, the number of online transactions increased almost 190-fold, and the volume of digital payments almost 500-fold. Regression models confirmed a significant association of digital transactions with consumption, lending, deposits, and retail turnover; R2 values were 0.973, 0.958, 0.979, and 0.940, respectively. The findings show that the digitalization of financial transactions enhances not only household financial activity but also the development of the retail business environment, as online payments and digital services increase consumption intensity and support the growth of Kazakhstan’s consumer market.

   Cross-border agri-food supply chains are increasingly facing institutional uncertainty, logistical constraints, and geopolitical disruptions, which increases the importance of supply chain sustainability for the stable development of international trade.

   This study aims to assess how perceived institutional and spatial frictions influence supply chain resilience in the Sino-Kazakhstan agri-food corridor, with particular attention to the mediating roles of relational governance and trust, and flexible strategies.

   The study uses a quantitative approach based on partial least squares (PLS-SEM) structural equation modeling. The empirical basis was based on data from a survey of 100 specialists involved in cross-border agri-food trade between China and Kazakhstan. The results showed that perceived institutional and spatial barriers have a strong positive impact on relational governance and trust (β = 0.502; t = 6.223; p < 0.001), while their direct impact on flexible strategies is not statistically significant at the 5 % level (β = 0.197; t = 1.782; p = 0.075). Mediation analysis confirmed that relational governance and trust significantly mediate the relationship between institutional barriers and supply chain sustainability (β = 0.207; t = 3.978; p < 0.001), whereas the mediating effect of flexible strategies was not confirmed (β = 0.111; t = 1.705; p = 0.088). The findings show that, in the context of cross-border institutional barriers, relational governance is a key mechanism for ensuring sustainability, especially for resource-constrained companies operating in transition economies.

   The agro-industrial complex is one of the key sectors of Kazakhstan’s economy, ensuring food security, rural employment and the development of regional investment potential.

   The purpose of this study is to assess the effectiveness of financial instruments in stimulating investment activity in Kazakhstan’s agro-industrial complex and to develop recommendations for improving regionally differentiated investment policy.

   The study is based on regional panel data for 17 regions and cities of national importance in Kazakhstan for 2015-2025. Descriptive statistics, comparative regional analysis, fixed-effect panel regression model, Haussmann test, diagnostics of multicollinearity, heteroscedasticity and autocorrelation are used as methods. The dependent variable is investment in agriculture, while the key explanatory variables include government subsidies, concessional lending, financing through development institutions, infrastructure development, and digitalization. Regression analysis confirmed a statistically significant positive impact of subsidies (β = 0.32; p < 0.01), concessional lending (β = 0.27; p < 0.01), infrastructure (β = 0.21; p < 0.01), financing through development institutions (β = 0.19; p < 0.01), and digitalization (β = 0.18; p < 0.01) on investment activity. The results show that subsidies and credit resources have the strongest positive effect on investment activity. At the same time, the negative coefficient on the quadratic subsidy term indicates an inverted U-shaped relationship, suggesting that excessive government support may reduce investment efficiency. The findings support a transition from subsidy-dominated support toward a mixed financing model based on targeted subsidies, concessional lending, guarantees, blended finance, infrastructure modernization, and digital platforms.

   The increasing uncertainty in the business environment has intensified the need for reliable financial distress prediction models, particularly within the manufacturing sector, which plays a strategic role in economic development.

   The study aims to compare the effectiveness of logistic regression and multivariate adaptive regression splines (hereinafter – MARS) in predicting financial distress among manufacturing companies listed on the Indonesian stock exchange.

   The study employs a quantitative research design with purposive sampling, using data from 70 manufacturing firms and 210 firm-year observations over the 2022–2025 period. Financial distress is examined using four key financial indicators, namely current ratio, total liabilities to total assets, return on assets, and sales to total assets. The findings reveal that both models are statistically valid; however, MARS outperforms logistic regression in terms of predictive accuracy, achieving an overall classification rate of 82.4 % compared to 65.7 %. Logistic regression revealed a statistically significant effect of return on assets only on financial distress (p = 0.003; Exp(B) = 0.006), whereas MARS showed that all financial indicators under consideration contributed to the predictive model. These findings highlight the importance of profitability as a primary determinant of financial distress and suggest that MARS provides a more robust framework for developing early warning systems and supporting financial decision-making. The practical significance of the study lies in the potential for businesses, investors, creditors, and regulatory authorities to use the results to identify financial risks early, which is important for the economy’s stable development.

   The National Fund of the Republic of Kazakhstan is essential to stabilizing public finances and ensuring oil revenues for future generations of Kazakhstan.

   The aim of this study is to identify the key macroeconomic determinants of transfers from the National Fund of the Republic of Kazakhstan and to assess their sensitivity to external and domestic macroeconomic shocks using econometric modeling and scenario analysis.

   The empirical base covers 2001-2024 and includes annual data from the Ministry of Finance of the Republic of Kazakhstan, the National Bank of Kazakhstan, the Bureau of National Statistics, and the World Bank. The study uses regression analysis with lagged explanatory variables, diagnostic tests (Breusch–Godfrey, Zharko, and Breusch–Pagan), and scenario modeling. The results of the extended model showed that the price of Brent crude oil is negatively related to the volume of transfers: the coefficient was -9.152 at p = 0.002. Receipts to the National Fund, the cost of oil production, and inflation have a positive, statistically significant relationship with transfers: the corresponding coefficients were 1,047, 8,306, and 21,651. Scenario calculations showed that a 10 % decrease in the Brent price increases the forecast value of the transfer logarithm from 14.465 to 15.430, while a 10 % increase in the price reduces it to 13.593. The findings emphasize that better fiscal rules should be implemented to address procyclical withdrawals, transparency should be increased, and Kazakhstan’s sovereign wealth management system should be maintained over time.