The purpose of the study is to explore Artificial intelligence (AI) integration into sustainable marketing techniques highlights a transformational potential, combining modern technology with the urgent needs of sustainability. This article thoroughly examines how AI plays a crucial role in improving marketing intelligence by enabling more efficient and socially responsible marketing tactics that support sustainability goals.

Method: The study examines how AI-driven insights and analytics enhance decision-making processes, improve customer engagement, and increase the impact of marketing campaigns on environmental and social outcomes by reviewing existing literature and practices. The conversation delves into the difficulties and moral aspects involved in using AI in marketing, such as issues related to data privacy, algorithmic bias, and the importance of a strategic framework that focuses on sustainable development goals.

Results: The investigation shows a promising yet intricate marketing intelligence environment, where AI is seen as a crucial tool for balancing economic goals with the need for environmental sustainability and social responsibility. The research stresses the importance of continuous research, multidisciplinary teamwork, and policy creation to maximize the impact of AI on shaping sustainable practices in marketing intelligence.

This study provides valuable contributions to the scholarly discussion around sustainable marketing and artificial intelligence, while also offering practical guidance for professionals operating in this dynamic commercial sector.

This study explores the strategic management of metaverse ecosystems grounded in Web 3.0, emphasizing the theoretical foundations, conceptual framework, and practical tools required for their advancement.

The subject of this study is the development of decentralized, user-owned virtual worlds within metaverse ecosystems, bridging digital and physical realities.

The purpose of this study is to analyze the evolution from Web 1.0 to Web 3.0 and to highlight the transformative impact of these ecosystems within the context of Industry 5.0.

The relevance lies in the strategic significance of the metaverse as a driving force in future economic and technological development, reshaping industries, work environments, and digital economies.

The scientific novelty of the research lies in its introduction of a six-domain conceptual framework for managing the digital potential of complex systems in the metaverse, focusing on the blockchain-based democratization of digital assets and user-centric governance.

The findings reveal significant distinctions between the Web 2.0 and Web 3.0 metaverse ecosystems and demonstrate their transformative potential across various sectors.

The study concludes that metaverse ecosystems will play a pivotal role in shaping Industry 5.0, necessitating innovative management strategies to fully harness their digital and economic capabilities.

The global trend is mass employment of the population in the informal sector of the economy. At the same time, only in economically developed countries of the world such workers have relatively good working conditions. At the current stage of development, Russia is among the group of actively economically developing countries of the world. Therefore, the improvement of the mechanism of state social protection of those employed in the informal sector of the economy remains an urgent relevant issue for our country, which, in turn, implies monitoring of the situation.

The purpose of this study is to develop tools for such monitoring with the help of artificial intelligence (more precisely, modern machine learning methods). According to the results of cluster analysis carried out using the k-means method in the Python programming language, it was found that in modern Russia there is a high degree of differentiation of regions by the level of employment in the informal sector of the economy. At the same time, most of the subjects of the Russian Federation are characterised by the same situation as in economically developing countries of Eastern Europe (Bosnia and Herzegovina, Serbia, Czech Republic). Four regions of Russia (from the North Caucasus Federal District) have an abnormally high level of employment in the informal sector of the economy comparable only with economically developing countries of Asia, Africa, North and South America. In the course of solving the classification problem using a modern machine learning method (LightGBM), the key factors affecting the level of employment in the informal sector of the economy of Russian regions were identified.

According to the classification results, we can conclude that a cardinal change in the current situation is not expected in the future. Therefore, for modern Russia, it is necessary to improve the state social policy for a significant part of the regions.

The results of the empirical study can be applied to improve the effectiveness of the state social policy of the Russian Federation. Thus, in particular, it will be possible to specify the amount of financial resources required for additional social support of the employed population of certain regions of our country.

This study provides a theoretical analysis of the use and application of artificial intelligence (AI) in the energy sector as it relates to climate security.

The object of the study is energy and climate security as types of economic activity and social activity.

The subject of the research is artificial intelligence in relation to the object area of  research.

The purpose of the study is to create a sound scientific basis for the use of artificial intelligence in the energy sector, as well as to identify emerging problems in the formation of a science-based approach to climate policy development.

The authors’ research includes three interrelated research methodologies: topic modeling, text mining as part of qualitative analysis and object modeling as part of the systematization of results that are adequate to the subject area of  the study and correspond to their reality; in addition, the authors supplemented the quantitative results with a theoretical and heuristic analysis of the scientific results of other researchers. The concept of parametric optimization (PO) is used as an effective method for solving the applied problem of testing the hypothesis of managing energy costs and energy efficiency based on AI in order to achieve optimal performance of the technical system and compliance with the Sustainable Development Goals (SDGs) in the field of climate security.

The study’s findings suggest that AI is becoming fundamental to the development of a modern energy sector based on data and complex relationships and provides tools to improve technical system performance and efficiency in the face of sanctions restrictions.

The authors conclude that the truth of the hypothesis has been proven: the use of AI as a control feedback loop at a technical facility for purification and energy generation is a more cost-effective and technically optimal alternative to a “live” operator, which will eliminate the human error factor. In this regard, the energy industry, utilities, grid operators and independent power producers must pay special attention to the introduction of AI technologies into existing technical systems.

Goal: This paper examines the effect of digital transformation on corporate risk-taking in Japanese firms and, more importantly, identifies links between digital technology integration and risk appetite. This study inspects how digital transformation impacts internal control quality, investment efficiency, and general financial soundness, with special emphasis on the differences between state-owned versus non-state-owned enterprises.

Methods: The empirical analysis uses the data of Nikkei Index firms from 2010 through 2023. Out of the total, excluding the financial and insurance sectors as well as aberrant statuses in trading, 225 firms resulted in 14,567 observations. The regression models controlled for a number of different factors, such as enterprise size, profitability, and industry type of firm.

Results: The empirical evidence based on the pooled sample implies that enhanced digital transformation significantly boosts the capability of corporate risktaking. Specifically, a comparison of the estimated coefficients obtained across the state-owned enterprises versus their non-state-owned counterparts shows a large difference in the magnitude for the latter. The increasing adoption of digital technologies heightens the propensity of those firms to invest in high-risk investments, hence improving their value at large.

Conclusions: The study contributes to an understanding of how digital transformation affects corporate behavior in terms of risk-taking. It underlines the need to develop digital initiatives that contribute to investment efficiency and financial stability. The findings imply that policymakers and business leaders should encourage strategies of digital transformation, especially for non-state-owned enterprises, to achieve economic growth through increased risk-taking ability.

This article is aimed at presenting a wholesome approach to the management of a syndicated loan portfolio.

Methods utilized include the following: (i) portfolio analysis —  calculating the parameters of a syndicated loan portfolio (main, liquidity, diversification, and commercial parameters); (ii) measuring completion of the Key Performance Indicators (KPIs) —  comparing the actual values of the parameters of the syndicated loan portfolio to the target values of the KPIs and making the required managerial decisions; (iii) portfolio management — using the various syndicated loan market techniques with which a portfolio can be managed to achieve the completion of the KPIs (actively, passively, and via restructurings). Active syndicated loan portfolio management includes the execution of transactions in both primary and secondary syndicated loan markets. Cases of passive syndicated loans management relate to repayments of the syndicated loans in the portfolio: voluntary full or partial repayments based on the decisions of the borrowers; mandatory repayments when the borrowers have to fully or partially repay the syndicated loans based on the decisions of the lenders; scheduled repayment in accordance with the repayment schedules of the syndicated loans. The portfolio can also be affected by restructurings, when the lenders agree to change a number of major terms and conditions of the syndicated loans due to the circumstances of the borrowers.

In order to assess the results of the syndicated loan portfolio management, a managerial dashboard is built, an important accounting tool allowing for decision-making based on the comparison of the actual values of the parameters of the syndicated loan portfolio to the target values of the KPIs. An important issue in syndicated loan portfolio management is the monitoring of compliance of the borrowers with financial covenants: (i) the ratio of the Net Debt to EBITDA (earnings before interest, taxes, depreciation and amortization); (ii) the ratio of Net Interest Payments to EBITDA. In cases when the financial covenants or other terms and conditions of syndicated loans are violated, the borrowers can request the lenders with either waiver requests (for “one-off” issues) or amendment requests (for permanent changes). The process of handling waiver and amendment requests, including the involved parties, documents and timelines is reviewed.

The main conclusion involves combining of syndicated loan portfolio management results, financial covenant monitoring, and working on waiver and amendment requests in order to create the executive report, as well as the formalization of the general scheme for managing a portfolio of syndicated loans.

As economies become increasingly interconnected, individual economies are at risk of shocks from external uncertainties ranging from fluctuations in climate regulations to geopolitical conflicts and international economic policies.

The purpose of the study is to investigate the time-varying correlation between global uncertainties (e. g., global economic policy uncertainty, climate policy uncertainty and geopolitical risk) and economic activity in a developing economy using a dynamic conditional correlation generalized autoregressive conditional heteroskedasticity (GARCH) model.

The relevance of the research lies in the increasing interconnectedness of global economies and the subsequent exposure of individual economies to external shocks.

The scientific novelty is hinged on the study being among the first to study the relationship in Ghana. Using monthly data for the 2002–2022 period for Ghana, we estimate a multivariate GARCH model.

The results of the study indicate that climate policy uncertainty and global economic policy uncertainty are mean reverting, implying that the volatility of the variables decay slowly and persists for a longer time such that the conditional variance will eventually return to its long-term average level after being disturbed by shocks. Global uncertainties over time are strongly negatively correlated with economic activity and produce significant spikes, especially during periods of major world events.

The study recommends that policymakers need to consider the prolonged impact of global uncertainties on economic performance when designing economic policies and interventions. The significant spikes during major global events highlight the importance of crisis management and preparedness in maintaining economic stability during periods of heightened uncertainty.