Kazakhstan's agro-industrial complex is a key sector that ensures food security and the country's sustainable economic development. The study aims to assess the impact of investments on agricultural productivity and their share in gross domestic product, accounting for lag effects. The initial data includes annual statistical indicators of the Bureau of National Statistics of the Republic of Kazakhstan for the period 2015-2024. The methodological basis consists of correlation analysis, calculation of Pearson correlation coefficients, and multidimensional analysis of variance (MANCOVA), followed by a univariate decomposition of effects. The results showed that investments in the non-resource sector demonstrate the strongest positive relationship with labor productivity in agriculture (r = 0.959), while private investments also have a high correlation (r = 0.882). On the contrary, the agricultural investment index has no significant impact on production indicators (r = -0.116) and output dynamics (r = 0.120). At the same time, the share of agriculture in GDP decreased from 4.8% to 3.8% over the study period, despite total investments increasing by more than 2 times. MANCOVA's results confirmed a statistically significant impact of investments in the non-resource sector on productivity alone (p < 0.001), whereas the impact of investments directly in agriculture and of private investment was statistically insignificant. Thus, a gap has been identified between investment volume and economic returns, indicating a need to improve the effectiveness of investment policy and to focus on structural and technological factors in agricultural sector development.

In the context of global decarbonization, the digitalization of the financial sector is becoming a key factor in the development of green finance and ensuring socially inclusive economic growth. The aim of the study is to assess the impact of digitalization of the financial sector on the development of green finance and its social effects in Kazakhstan and Central Asian countries. The methodological basis consists of system and comparative analysis, correlation and regression analysis, as well as scenario modeling. The empirical basis of the study is formed by data for the period 2015-2025, as well as a sample from 12 countries for cross-country analysis. The results of the study show a very strong positive relationship between the level of digitalization of the financial sector and the volume of green investments per capita (r = 0.988; R2 = 0.977; p < 0.001). Multiple regression revealed that the greatest contribution to the development of green finance is provided by the coverage of ESG regulation, followed by digitalization and financial inclusion. Scenario modeling has shown that upon reaching a high level of digital maturity, the volume of green bonds in Kazakhstan may increase from $2.8 billion to $14.2 billion by 2030, accompanied by the creation of up to 67,000 new “green” jobs. The results obtained confirm that digitalization is a necessary but insufficient condition for the development of green finance without appropriate institutional and regulatory support for ESG.

Financial literacy is becoming an important factor in household sustainability and the quality of economic behavior among the population, but in Kazakhstan, its level varies significantly across regions and social groups. The aim of the study is to identify regional differences in financial literacy and to assess the social dimensions of gender differences in financial behavior, access to financial resources, and the use of financial instruments in Kazakhstan. The results of nationwide studies of the population of Kazakhstan's financial literacy, conducted by the National Bank of the Republic of Kazakhstan and the Agency for Regulation and Development of the Financial Market, as well as official regional statistical data, serve as the empirical basis. The results of the study showed stable interregional differences: the highest values of the financial literacy index were recorded in Almaty (48.5) and Astana (47.2), and the lowest in Turkestan (36.4) and Kyzylorda (35.7). It has been found that age and level of education have a statistically significant positive impact on financial literacy, whereas gender differences are primarily reflected in behavioral aspects. The gender gap is minimal in economically developed regions (1.2 points in Almaty and Astana) and increases significantly in regions with limited financial infrastructure (2.9 points in Turkestan and Kyzylorda regions). The results confirm that financial literacy is shaped by both individual characteristics and the regional institutional context, underscoring the need for region- and gender-oriented financial policy measures.

The growth of digitalization and the development of the smart city concept contribute to the active implementation of public-private partnership (hereinafter – PPP) projects using artificial intelligence technologies in the field of urban infrastructure and road safety. The purpose of this study is to assess the social risks of the Sergek intelligent video monitoring project in Kazakhstan, implemented within the framework of the smart city concept, using methods of system dynamics and sensitivity analysis of the discounted cash flow indicator. The research methodology is based on the study of project data, followed by the construction of a project model using system dynamics (hereinafter – SD), which allows us to demonstrate various scenarios of the impact of risk factors. The study uses data from open sources, including information from the PPP Portal of the Republic of Kazakhstan, data from the Akimat of Almaty and the National Bank of the Republic of Kazakhstan. The simulation results showed that the highest discounted cash flow (hereinafter – DCF) is observed in the baseline scenario without taking into account risks and reaches 7,668,340,000 tenge in 2024, decreasing to 6,781,390,000 tenge in 2025. The greatest negative impact on the financial stability of the project is the social risk associated with a shortage of revenue from fines, at which the DCF value is reduced to KZT 6,287,440,000. The results obtained indicate that social risk is the most significant factor in the financial stability of an intelligent video monitoring project.

The rapid development of artificial intelligence (hereinafter – AI), along with increasing the effectiveness of innovation activities, increases the relevance of ethical and legal issues of its regulation. The purpose of the study is to analyze the ethical and legal problems that arise in the context of the integration of AI into innovation activities, systematize international and national approaches to regulation, as well as assess the specifics of AI regulation in Kazakhstan. The methodological basis of the study was the dialectical method, comparative analysis, document analysis, literature review and content analysis. The study comparatively analyzed the approaches to AI regulation applied by UNESCO, OECD, G7/G20, IEEE, WIPO, the European Union and individual states. The results showed that the OECD identifies 10 key categories of AI risks, including cyber threats, information manipulation, increased social inequality, privacy violations, and a lack of governance mechanisms. The study showed that the European Union has developed one of the most comprehensive approaches to AI regulation by adopting the AI Act, which includes 85 articles and a system for classifying technologies by risk levels. It has been revealed that most countries maintain an approach according to which the author of the results of intellectual activity is recognized exclusively as a human being, despite the active use of AI. It is concluded that it is necessary to form an integrated system of ethical and legal regulation of AI, ensuring a balance between the development of innovations, the protection of human rights and the reduction of social and technological risks.

Despite the growing interest in the problems of leadership, career development and social mobility of knowledge workers, the mechanisms of the influence of distributed (collective) leadership on professional advancement in underdeveloped regions remain insufficiently studied. The aim of the study is to assess the impact of distributed leadership on the career development and social mobility of knowledge workers in underdeveloped regions of China, as well as to determine the mediating role of self-efficacy and a collaborative climate. The study used quantitative analysis methods and partial least squares structural equation modeling (PLS-SEM) using SmartPLS 4.0 software. The empirical basis was the data from a questionnaire survey of 150 knowledge workers employed in the fields of management, education, technology and healthcare in the provinces of Guangxi and Guizhou (China). According to the results of the study, distributed leadership is a significant factor in the career development and social mobility of knowledge workers in underdeveloped regions of China. It has been revealed that distributed leadership has a direct positive impact on the career development of knowledge workers (β = 0.326; p < 0.001), as well as an indirect impact through the mechanisms of self-efficacy and collaborative climate. The high value of the coefficient of determination (R2 = 0.593) indicates the significant explanatory power of the proposed model and confirms the importance of leadership and organizational factors for professional development in conditions of resource constraints. The results obtained emphasize the need to implement distributed leadership practices, support professional independence of employees, and foster a culture of collaboration.

In conditions of highcapital intensity, technological complexity, and dependence on the stability of logistics chains, increasing the operational efficiency of oil refineries requires a comprehensive analysis of business processes to ensure social sustainability and reduce organizational and managerial losses. The purpose of the research is to develop an integrated approach to the management analysis of an oil refinery's business processes, ensuring the identification of factors affecting the operational stability and efficiency of production processes. The methodological basis of the research was strategic and process analysis methods, including PEST analysis, Porter's model, SWOT analysis, and the Business Model Canvas, providing multi-level diagnostics of the macro environment, industry environment, internal environment, and the enterprise's business model. The results of the study showed that economic factors (1.48) and technological factors (1.13) have the greatest impact on the sustainability of the company's activities, while the highest ratings were given to the seasonality of demand for bitumen (4.80), the criticality of unplanned equipment downtime (4,80), the reliability of equipment after modernization (4.60) and government road construction programs (4.60). The key limitations of operational efficiency are mainly organizational and managerial in nature and manifest as underutilization of capacity, logistical gaps, unplanned downtime, and insufficient consistency in cross-functional processes. Findings concluded that it is necessary to integrate strategic analysis tools and lean manufacturing principles to increase the transparency of business processes, the sustainability of the logistics circuit, the quality of management decisions, and to strengthen the socioeconomic sustainability of the enterprise.

In the context of increasing environmental pressures and waste volumes, there is a growing need to assess the relationship between economic growth and environmental performance indicators. The purpose of the study is to assess the relationship between Kazakhstan's economic growth and indicators of waste generation and recycling, energy consumption, energy intensity, and energy productivity. The study's information base consisted of official statistical data from the Republic of Kazakhstan for 2015-2024. The research employs normalization, gray relational analysis, and multiple regression analysis. According to the results of the gray relational analysis, the highest coefficients of correlation with GDP dynamics were established for the specific volume of municipal waste per unit of GDP (0.717), the volume of hazardous waste (0.714) and the volume of industrial waste (0.707), which indicates that the changes in these indicators most closely coincided with the direction and nature of the change in GDP in the studied period. The volume of hazardous waste has statistically significant negative relationship with GDP (B = -0.645; p = 0.012), while the association of industrial waste with GDP has not been statistically confirmed (B = -0.033; p = 0.827). The results show that Kazakhstan's economic growth was more closely related to an increase in waste volumes, especially hazardous waste, than to the development of recycling and increased energy efficiency. The findings substantiate the need to develop recycling infrastructure, strengthen control over hazardous waste, and transition to a more resourceefficient model of economic growth.

In the context of the global energy transition, the development of renewable energy sources is of particular importance for resourcedependent countries seeking to reduce their dependence on raw materials and ensure sustainable socio-economic development. The purpose of the study is to assess the impact of renewable energy development on the economic diversification of resource-dependent economies using the examples of Kazakhstan, Norway and the United Arab Emirates. The methodological basis of the study was made up of descriptive statistics, correlation analysis, and panel regression models with combined, fixed, and random effects. The initial data covers the period 2002-2021 and includes 60 panel observations. The results showed that the average value of the economic diversification index was 0.57, the share of renewable energy was 19.94%, oil rents were 13.88% of GDP, the human capital index was 3.25, and the quality of institutions was 66.43. Correlation analysis revealed a positive relationship between diversification and the quality of institutions (r=0.655) and GDP per capita (r=0.662). In the panel models, the development of renewable energy sources did not show a stable statistically significant effect on diversification, whereas oil rents had a negative effect in the fixed effects (β=-0.0046; p<0.01) and random effects (β=-0.0040; p<0.01) models. The results show that in resource-dependent countries, the development of renewable energy sources is important for economic diversification, but its effect is enhanced only by reducing oil dependence, developing human capital, and improving institutions.