This study explores the impact of corporate governance on firms’ environmental, social, and governance (ESG) performance, with a focus on board characteristics and ownership structures. Using a panel dataset of 6 financial and 16 non-financial firms listed on the Borsa Istanbul (BIST) from 2013 to 2021, the study investigates how ownership (blockholder, foreign, or institutional) and board composition (size, gender diversity, and foreign directors) influence ESG disclosures. The analysis distinguishes between financial and non-financial firms, revealing that corporate governance mechanisms affect ESG performance differently across sectors. Foreign ownership and the presence of foreign and female board members are positively associated with higher ESG disclosures, while ownership concentration is negatively correlated with ESG performance. These findings suggest caution when comparing firms across sectors based solely on ESG disclosures, as governance factors influence outcomes differently in financial and non-financial contexts. This study provides a detailed analysis of effective corporate governance mechanisms in Türkiye, emphasizing the crucial roles of ownership structure and board composition in enhancing ESG transparency. The results offer valuable insights for regulators and investors, contributing to a nuanced understanding of how governance structures shape ESG performance in both financial and non-financial firms in Türkiye.

Understanding and examining energy markets correctly is crucial for stakeholders to attain maximum benefit and avoid risks. As a matter of fact, the volatility that occurred in energy markets and recent crises had major impacts on national economies. Dynamic connectedness relationships (DCRs) can make quite powerful predictions for both low-frequency data and limited time-series data. The objective of this study is to explicate the dynamic connectedness relationships among the BIST sustainability index, BIST 100 index, S&P Global Clean Energy index (S&P GCEI), and S&P GSCI carbon emission allowances (EUA). The daily data obtained over the period 11 April 2014–11 November 2022 were used for the research study. The DCRs among the variables used in the study were investigated by employing the time-varying parameter vector autoregressive (TVP-VAR) model. As a result of the study, the volatility from carbon emission allowances was determined to spill over to S&P GCEI, BIST 100, and BIST sustainability indexes. During the COVID-19 pandemic, significant reductions were detected in the volatility spillover (VS) from carbon emission allowances to S&P GCEI, BIST 100, and BIST sustainability indexes. Moreover, it was revealed that a weak VS existed from S&P GCEI to BIST sustainability and BIST 100 indexes. The findings reveal the importance of policymakers taking some incentive measures in EUA prices and also its role in portfolio diversification.

Despite the challenges posed by the consequences of the global pandemic, achieving the country's national development goals remains a strategic direction of public health policy. The article aims to study the dynamics of healthcare financing in Kazakhstan, provide forecasts of upcoming development, and compare it with healthcare systems of developed countries. Based on the analysis of the current state and potential problems, recommendations were developed to improve the healthcare financing system in Kazakhstan. This article also analyzes the main parameters of health financing in the Republic of Kazakhstan for the period 2012–2022 and, in comparison with developed countries, provides the main reasons for maintaining low parameters of public health financing, which becomes a real obstacle to the implementation of measures aimed at achieving universal health coverage. During the research, a wide range of methodological approaches was applied, including system analysis (the study of an object as an integral system, interrelated elements), comparative analysis (comparison of the studied object with others in a number of characteristics), functional analysis (identification of the functions of the elements of the object and their interrelations), economic and statistical analysis and synthesis (collection, processing, interpretation of economic and statistical data), mathematical methods (modeling of studied processes and phenomena using mathematical models), dialectical methods (the study of an object in its development, change and contradictions), analysis of the burden of the disease (assessment of the economic and social consequences of the disease).

The state debt of the Republic of Kazakhstan is a key indicator of the country’s financial stability, significantly influencing macroeconomic stability, investment attractiveness, and economic development. This article presents a comprehensive analysis of Kazakhstan's national debt, including its structure, dynamics, and growth factors. Special attention is given to both external and internal borrowing, exchange rate risks, and debt servicing costs. The research includes an international comparison of national debt indicators, allowing for the assessment of Kazakhstan’s debt sustainability within a global context. Key challenges have been identified, such as the economy's dependence on raw materials, geopolitical instability, and the rising social obligations that require borrowing. The study offers recommendations for optimizing debt policy, including measures to reduce the budget deficit, diversify the economy, strengthen the domestic debt market, and improve transparency in debt management. The research employs desk research methods, including the analysis of strategic documents from Kazakhstan and international sources. The application of statistical and comparative research methods allowed for an analysis of national debt, with international comparisons made to developed countries. Content analysis techniques were used to examine publications and reports from international rating agencies. The use of inductive and deductive approaches helped to identify the cause-and-effect relationships in the dynamics of the state debt.

The emergence and development of the concept of artificial intelligence is a logical consequence of the logic of scientific and technological progress in the context of the emergence of the digital economy. Artificial intelligence (AI) is currently becoming a key trend in the improvement of management technologies in many countries around the world. Kazakhstan is currently making certain efforts to join this community of countries with a developed AI concept. In this regard, the purpose of this scientific work is to study the issues of adapting the AI concept in the context of the modern development of the country, taking into account the existing experience in applying AI technologies to solving the problems of effective development of the digital economy. At the same time, an important task, in the author’s opinion, is to explore the potential for improving the quality of public management, bearing in mind the strengthening of consistency in justifying decisions at all levels and in all levels of public administration through the use of digital and AI technologies. In the course of the research, the author paid special attention to the establishment of relationships between IT technologies and AI, on the one hand, and the quality of human capital in public management, on the other hand. In the author’s opinion, this is especially important at the lower levels of the public administration system, where the economy in practice is directly and deeply tied to the tasks of social development.