We aim to map the ESG patterns of emerging-market companies from 2018 to 2021 in order to determine whether the COVID-19 pandemic exerted any influence on sustainable corporate behavior. Thus, the ESG performances were assessed by employing the Kohonen Self-Organizing Map (also known as the Kohonen neural network) for clustering purposes at three levels: (i) ESG overall, including country and sectoral perspectives; (ii) ESG thematic; and (iii) ESG four-folded (stakeholder, perspective, management, and focus strategic views). Our results show that emerging-market companies focus their ESG efforts on social and governance issues rather than on environmental. However, environmental and social behavior differ more acutely than governance behavior across clusters. The analyses of country-level ESG performance and that of eleven market-based economic sectors corroborate the geographic and sector dependence of ESG performance. The thematic-level analysis indicates that operational activities and community issues received more attention, which suggests that emerging-market companies address distinct ESG topics according to their particularities and competitiveness. Furthermore, our empirical findings provide evidence that the ESG behavior of companies has changed over the course of the COVID-19 pandemic. Thus, our findings are relevant to policy makers involved in regulating ESG disclosure practices, investors focused on enhancing their sustainable investment strategies, and firms engaged in improving their ESG involvement.

In the contemporary era of accelerated artificial intelligence (AI) development, governments are progressively embedding AI technologies within administrative structures and public service delivery systems, thereby influencing green growth through a variety of direct and indirect mechanisms. This study examines the impact of governmental artificial intelligence, captured by readiness of governments for the adoption of artificial intelligence, alongside key macroeconomic, social, and institutional governance factors, on green growth across European Union countries between 2019 and 2023. Employing a combination of static and dynamic panel econometric techniques (OLS, FGLS, PCSE, LIML-IV, and system GMM), the analysis reveals that governmental deployment of AI exerts a positive effect on green growth within the sample. The empirical results demonstrate that governments’ readiness to systematically deploy AI exerts a positive influence on green growth, thereby supporting the hypothesis that governmental AI capabilities foster sustainable development. The positive coefficient on the lagged green growth, together with the stable significance levels across all model specifications, provides quantitative evidence that governmental AI readiness has a positive and statistically significant effect on green growth. The synergistic interaction among macroeconomic, social, and institutional governance factors provides strong empirical support for the design of robust policies that promote the implementation of AI at the governmental level, coupled with mechanisms to stimulate economic expansion, public investment, and foreign capital inflows.

This research aims to measure the financial performance of companies in the water and sewerage sector by creating a sustainable econometric model for making long-term strategic decisions for managers and stakeholders. The research methodology consisted of the use and statistical processing of the data included in the summary financial statements of 40 regional operators in the field from 2014 to 2020. Multiple linear regression has been created with which stakeholders and water and sewerage specialists can shape changes in value-added variation, the average cost per employee, labor productivity, and energy expenditure on the net profit of water operators and sewerage. The results indicated that the independent variables used, such as value-added, labor productivity, or intangible assets have a direct influence on increasing the net profit of water and sewerage companies. Other independent variables such as the average cost per employee or the expenditure on electricity and water negatively influence the increase in the net profit of companies in the water and sewerage sector. The conclusions indicated that the average net profit is influenced by independent variables and the model created, and it can be successfully applied to other international companies in the field.