This study is devoted to a comprehensive analysis of the determinants of youth unemployment in Kazakhstan and the specifics of the transition from education to sustainable employment for young people. The aim of the work is to identify key structural, institutional and socio-economic factors that affect youth employment, as well as to assess the dynamics of Not in Employment, Education or Training (hereinafter – NEET) and employment indicators from 2020 to 2044. The methodological basis includes descriptive statistics, comparative analysis and correlation analysis. Initial data were obtained from official sources such as the Bureau of National Statistics of Kazakhstan, International Labor Organization, and World Bank, disaggregated by gender, region, and level of education. Results showed that between 2019 and 2039, the youth unemployment rate decreased from 7% to 6%, NEET decreased from 6% to 4%, and the proportion of informal employment fell from 18% to 9%.. Young women have consistently higher NEET rates (6.7% in 2024) than men (4.9%). The regions with the highest unemployment rates are Turkestan Oblast and Shymkent, at 7.8% and 7.2% respectively. Educational differences remain significant: the employment rate for young people with a higher education is 78%, compared to only 38.9% for those with basic secondary education. These results confirm the structural nature of youth unemployment, resulting from a mismatch between graduates' skills and job market demand, as well as regional imbalances and limited entry-level positions. Future research paths involve the development of more sophisticated quantitative models to evaluate government programs and their impact on job creation.  

The rapid development of digital technologies and the growth of cross-border online platforms significantly change the competitive environment and the functioning mechanisms of small and medium-sized businesses (hereinafter – SMEs). The aim of this study is to assess the impact of digital infrastructure on SMEs' economic activity and competitiveness in Kazakhstan, focusing on the benefits of participating in cross-border e-commerce. The methodology is based on bibliographic analysis, graph methods, and a structured survey of respondents. Empirical data was collected from the National Bureau of Statistics of the Republic of Kazakhstan, the United Nations Global E-Government Development Database, and analytical materials from Euromonitor International.. The results of the study demonstrate that SMEs actively involved in cross-border e-commerce have a significantly higher level of awareness of market trends and consumer preferences. Thus, 79.5% of respondents in the experimental group indicated that the fuel and energy complex allows them to systematically meet the needs of customers, compared to 33.3% in the base group. The statement that the fuel-energy sector helps to assess the trajectory of industry development was supported by 77.2% of participants in the experimental group, and only 19.1% in the control group. Further research should focus on developing integrated interdisciplinary models that combine infrastructure, regulatory, behavioral and logistical aspects, and expand empirical research into developing regions, such as Central Asia. 

In the context of accelerated urbanization and the transition to a low-carbon economy, the integration of the gender dimension into models of sustainable development of urban ecosystems is particularly important. The purpose of this study is to develop and substantiate a gender-inclusive model for sustainable economic development in cities in Kazakhstan, considering economic, social, and environmental factors in the framework of a “green” transition. The methodological basis for this study was an interdisciplinary and multi-level approach, including the creation of a system of 35 quantitative indicators, compositional indexing, spatially differentiated analysis, and elements of institutional and predictive analysis. The empirical base includes official statistical data, materials from national and regional development programs, as well as the results of specialized analytical and sociological studies, with mandatory gender disaggregation of employment and social infrastructure indicators. The results show that the implementation of the proposed model makes it possible to reduce the carbon intensity of the urban economy by 25-30% by 2030, while maintaining economic growth rates, and reduce gender gaps in access to employment, infrastructure, and decision-making mechanisms. The developed model can be used as a practical tool for strategic management of sustainable, low-carbon, and inclusive development of cities in Kazakhstan and other transforming economies. 

In the context of accelerated digitalization and the expansion of the use of artificial intelligence (hereinafter – AI) in critical sectors, the importance of forming effective AI management models focused on ensuring the sustainability of national security is increasing. The purpose of this study is to analyze the risk-based approach to artificial intelligence management in Singapore and assess its contribution to strengthening national security resilience in the period 2020-2025. The methodological basis of the study was a qualitative analysis of regulatory and strategic documents, comparative institutional analysis, as well as thematic coding of AI management tools from the perspective of riskbased regulation theory. The results of the study show that Singapore's AI management model is based on a combination of "soft" regulation, technical verification, and intersectoral collaboration, which minimizes the risks associated with cyber threats, vulnerability of critical infrastructure, and reduced public trust, without limiting innovation activity. In 2023-2024, the level of AI adoption among small and medium—sized enterprises increased by more than three times, and among large companies - by more than 18 percentage points. The share of employees using AI tools in their professional activities has reached almost 74%, which indicates the deep integration of AI into socio-economic processes. The practical significance of the work lies in the possibility of adapting the Singapore model in the development of national AI management systems in countries with a high degree of digitalization, including Kazakhstan.

In the context of structural and demographic changes, accelerated digitalization, and increasing demands on the quality of the workforce, the analysis of labor resources is of key importance for the sustainable socio-economic development of Kazakhstan. The purpose of this article is to analyze the dynamics and structural transformation of the labor force in Kazakhstan with an emphasis on the development of human capital, changes in age, gender, educational, and sectoral employment structure. The methodological basis of the research consists of methods of comparative, structural, and economic-statistical analysis, as well as trend analysis, used to assess the dynamics of employment, unemployment, and qualitative characteristics of the workforce. The empirical base of the study is based on official data from the Bureau of National Statistics (2024), materials from sample surveys of the labor force, as well as regulatory documents in the field of employment and qualifications. The results of the study showed that in 2019-2024. The employment rate in the country remained at a high level (about 65%), while the unemployment rate remained stable in the range of 4.7-4.9%. The number of employees increased by almost 5%, while the share of workers with higher and technical education increased, which together account for more than 90% of employment. The prospects for further research are related to the development of models for forecasting the demand for competencies, evaluating the effectiveness of the National Qualifications System, and analyzing the impact of digital transformation on employment quality and labor productivity.