In Kazakhstan, the transition to a green economy remains insufficiently empirically researched, despite the availability of strategic documents on innovative development. The aim of the study is to assess the readiness of Kazakhstan’s workforce for the transition to a “green” economy based on an analysis of factors affecting the number of people employed in “green” jobs.The methodological base of the study includes correlation analysis and the method of statistical equations of dependencies, which identify the stability of relationships in conditions of multicollinearity of factors and a limited length of time series. The empirical study is based on official statistical data from the Bureau of National Statistics for the period 2020-2024. The results of the correlation analysis showed a high degree of correlation between the dynamics of “green” employment and a number of factors (10 out of 19), including GDP per capita, investment in fixed assets, R&D expenditures, and financing of “green” economy projects. However, the application of the method of statistical equations of dependencies revealed the absence of stable functional relationships between the number of people employed in “green” jobs and most of the analyzed factors. The only variable for which a stable statistical relationship was recorded (stability coefficient K > 0.7) was the unemployment rate. The prospects for further research include analyzing institutional mechanisms that shape demand for “green” competencies, expanding the time series of data, and conducting a comparative analysis with practices in EU countries.

The transition to carbon neutrality represents a strategic imperative for large industrial companies in Kazakhstan in the context of global climate challenges and increasingly stringent environmental requirements. The purpose of this study is to develop an integrated risk and opportunity management framework for industrial decarbonization in Kazakhstan based on scenario modelling and expert assessment. The study uses a mixed methodological approach combining quantitative and qualitative methods of analysis. The empirical basis of the study was made up of sectoral data on greenhouse gas emissions in Kazakhstan’s industry for 2015-2024, official statistics from government agencies, corporate non-financial reports, as well as the results of 32 semi-structured expert interviews with representatives of industrial companies, government regulators and international organizations. The results of the study show that Kazakhstan’s industrial CO2 emissions increased 18.1% from 224.7 million tonnes (2015) to 265.4 million tons (2024), with metallurgy accounting for 35.9%, energy 28.1%, oil and gas 22.8%, and chemicals 13.4%. Scenario analysis shows that under the business-as-usual scenario, emissions could increase to 322.5 million tons of CO2 by 2050, while the moderate scenario reduces emissions to 102.3 million tons (55% compared to 2024), and the ambitious scenario to 35 million tons (-84%). This research extends sustainability theory by demonstrating how to transition economy characteristics-legacy infrastructure, resource dependence, institutional capacity-shape decarbonization pathways, providing actionable insights for climate policy formulation and corporate strategy in resource-rich developing nations.

The development of capital markets in emerging economies is considered a key factor in ensuring sustainable economic growth and enhancing financial stability. Given Mongolia’s high dependence on commodity exports and external shocks, the study of the stock market’s role in economic growth dynamics is increasingly relevant. The objective of this study is to empirically test the role of Mongolia’s capital market in economic growth. The methodological basis of the study comprises stationarity tests (ADF, KPSS), the ARDL model with the bounds-testing procedure for detecting cointegration, the vector error-correction model (VECM) for causality analysis, and the FMOLS and DOLS methods for testing the stability of long-run coefficients. The empirical base comprises quarterly data for 2000-2024, sourced from the National Statistical Office of Mongolia, the Central Bank of Mongolia, and the Mongolian Stock Exchange. The results of the ARDL model confirm the existence of a long-term cointegration relationship between GDP and capital market indicators (F-statistic = 4.538, which exceeds the upper critical limit of 3.99 at the significance level of 1%). In the long term, market capitalisation has a statistically significant positive impact on economic growth (coefficient 0.956; p <0.01), while the TOP-20 index shows a negative relationship. The results from FMOLS and DOLS confirm the stability of the estimates. The findings indicate the structural importance of capital market development for Mongolia’s long-term economic growth, however, the identified market concentration and limited liquidity may constrain its potential contribution to the economy.

In the context of the transformation of post-socialist economies and increasing global competition for capital, the analysis of institutional factors in attracting foreign direct investment is of particular importance. The aim of the study is to assess the short– and long-term impact of economic freedom on the inflow of foreign direct investment in five Central Asian countries over the period 1998-2024. Using a panel autoregressive distributed lag (ARDL) approach with the Pooled Mean Group (PMG) estimator, the analysis distinguishes between short-run and long-run effects while accounting for cross-sectional dependence and heterogeneity across countries. Panel unit root tests indicate that the variables are integrated of mixed order, justifying the application of the panel ARDL framework. The results showed that there is a stable long-term relationship between economic freedom and investment inflows. The long-term coefficient of economic freedom is 0.095 (p < 0.01), which indicates a positive and statistically significant impact of institutional quality on the investment attractiveness of the region. In the extended model specification, this effect persists (0.114; p < 0.01). The error correction factor is -0.65 (p < 0.01), which indicates a correction of about 65% of short-term deviations from equilibrium during one period. The short-term effects turned out to be statistically insignificant, which confirms the inertial nature of investment decisions. The results have important policy implications, highlighting the need for sustained institutional reforms aimed at improving economic freedom to foster stable and long-term foreign investment inflows.

The development of the higher education system is considered one of the key factors in the formation of human capital, the increase in innovation activity, and long-term economic growth. The purpose of the article is to analyse the structural transformation of of Kazakhstan’s higher education system by identifying interrelations among educational, economic, and social indicators for 2004-2024. The methodological basis of the study consists of correlation analysis and principal component analysis (PCA) with Varimax orthogonal rotation, which enables the identification of hidden structural factors and the assessment of the consistency of indicator dynamics. The empirical basis of the study was made up of annual statistical data from the Bureau of National Statistics of the Republic of Kazakhstan for 2004-2024, including such indicators as the number of students and teachers, the number of universities, the unemployment rate, household incomes, spending on science, innovation activity, the Gini index, the share of education spending in GDP and average wages. The results reveal strong structural relationships between economic development and science financing (r = 0.986) and between income and wages (r = 0.985). Principal component analysis shows that the first component explains 73.6% of the variance. In comparison, the first two components explain 88.9% of the variance, indicating a structural transformation of higher education associated with the development of the knowledge economy. Future research may extend the analysis by incorporating regional data, panel econometric models, and additional institutional indicators to assess causal relationships between higher education development, labour market outcomes, and innovation dynamics.

Digital transformation of the agro-industrial complex is seen as a key factor in increasing the productivity and resilience of agri-food systems in the face of climate instability and increasingly complex global supply chains. Despite the rapid growth of scientific publications, research results remain fragmented, and the mechanisms by which digital technologies influence economic and sustainability indicators are poorly systematized.
The purpose of this study is to summarize and critically
analyze the scientific literature on the impact of digital technologies on efficiency of agricultural production, the sustainability of agri-food systems, and the role of data and platforms in shaping these effects. The paper addresses the following research questions: how do digital technologies influence agricultural productivity; to what extent do they contribute to environmental and economic sustainability; and what organizational and institutional conditions determine the effectiveness of digital transformation.
The study was conducted as a systematic review of scientific publications covering the period 2015-2025. Following a multi-stage selection process from an initial pool of over twenty-two thousand publications, fifty-six studies meeting criteria for topical relevance and methodological transparency were included in the final analysis. 
The analysis shows that digitalization is creating a new production infrastructure based on data and analytics, which contributes to increased resource efficiency, reduced uncertainty, and improved coordination in supply chains. However, the sustainability of these effects significantly depends on the institutional environment, the level of infrastructure development, and the quality of data management. It is concluded that the long-term effectiveness of digital transformation is determined not only by the implementation of technologies but also by the alignment of organizational, infrastructural, and managerial factors.

Despite the implementation of various government programs to support the agricultural sector, the institutional foundations for the formation of agrotechnological hubs remain insufficiently systematized and require a comprehensive analysis. The purpose of the study is to systematise the institutional conditions that influence the formation of agrohubs as key elements of the modernisation of Kazakhstan’s agro-industrial complex. The research methods are based on an institutional approach and qualitative content analysis of government programs, strategic documents, and analytical reports on the development of the agro-industrial complex. The initial data include state strategic documents, analytical reports, and statistical indicators on agricultural development in Kazakhstan. The results of the study showed deviations between the planned and actual indicators of industry development. Investments in agricultural fixed assets amounted to 1672.7 billion tenge, against the planned level of 1800.7 billion tenge (–128 billion tenge), and labour productivity grew by 112.8 p.p., against the target of 122 p.p. (–9.2 p.p.). At the same time, gross agricultural output reached 9.8 trillion tenge, grain production amounted to 19.3 million tons, and industry implemented 286 investment projects totalling 279.6 billion tenge, reflecting positive dynamics in the sector’s development. The findings suggest that Kazakhstan has developed a relatively comprehensive institutional framework for agricultural modernisation. The prospects for further research include a quantitative assessment of the effectiveness of agricultural policy instruments, spatial modelling of agrohub locations, and an analysis of the role of digital and innovative technologies in the development of agro-industrial clusters.

In the context of the transition to a knowledge economy and the increasing demands on the quality of human capital, the effectiveness of financing secondary education is becoming particularly relevant. The aim of the study is to assess the impact of financing mechanisms for secondary education on the quality of educational outcomes in Kazakhstan, considering infrastructural, human, and digital factors. The methodological basis of the research includes methods of descriptive statistics, correlation analysis, econometric modeling, and performance analysis. The empirical database is based on panel data for 17 regions of Kazakhstan for the period 2015–2025. The results of the study revealed a stable positive relationship between financing and the quality of education. Digitalization (β = 3.45) and infrastructure (β = 2.63) had the greatest impact, whereas the impact of financial expenses was more moderate (β = 1.92). The burden on teachers has a statistically significant negative impact (β = −1.08). The high coefficient of determination (R2 = 0.84) confirms the model’s significant explanatory power. The results of the DEA analysis demonstrate pronounced regional differentiation in efficiency, with Almaty (0.92) and Astana (0.90) occupying the leading positions, while the southern and western regions exhibit lower efficiency. The results show that financing is a necessary but insufficient condition for improving the quality of education. The research contributes to the development of the theory of educational financing by demonstrating the need to transition to an integrated resource management model focused on the effectiveness and quality of educational outcomes.

In the context of the transition to an innovation-driven economy and increasing demands for sustainable agricultural development, the effectiveness of innovation financing becomes particularly relevant. The aim of the study is to assess the impact of innovation financing on innovation activity in Kazakhstan’s agricultural sector, using econometric modeling and controlling for government support and labor productivity. The methodological framework includes descriptive statistics, correlation analysis, econometric modeling using the ordinary least squares (OLS) method, and scenario analysis. The empirical database is based on statistical data for the period 2015–2025. The econometric analysis shows that innovation financing has the strongest impact (β = 2.85), while government support demonstrates a statistically significant but weaker effect (β = 1.76). Labor productivity also has a positive influence (β = 0.09), confirming the interdependence between technological development and production efficiency. The results of the correlation analysis confirm a strong relationship between innovation activity and productivity (r = 0.91) and between financing and innovation activity (r = 0.88). The findings show that innovation financing is a necessary but not sufficient condition for the development of innovation in agriculture. Its effectiveness depends on the interaction with government support mechanisms, productivity growth, and institutional conditions. The study contributes to the development of the theory of innovation financing by demonstrating the nonlinear and cumulative nature of the relationship between investment and innovation activity and by emphasizing the need for an integrated approach to financial policy in the agricultural sector.