The purpose of this research was to explore the link between Environmental, Social, and Governance (ESG) performance and corporate financial performance in the Pacific Alliance countries (Mexico, Colombia, Peru, Chile). The study used regression models to examine the correlation between ESG scores, environmental pillar scores, and financial performance metrics like return on assets (ROA) and EBITDA for 86 companies over 2016-2022. Control variables like firm size and leverage were included. Data was obtained from Refinitiv and Bloomberg databases. The regression models showed no significant positive correlations between overall ESG or environmental pillar scores and the financial valuation measures.The inconclusive results on ESG-firm value connections underscore the need for continued research using larger samples, localized models, and exploring which ESG aspects drive financial performance Pacific Alliance.

This study investigates the complex interrelationship between democracy, corruption, and economic growth in Greece over the period 2012–2022. Using data from Transparency International, the Economist Intelligence Unit, and Eurostat, appropriate methods such as Ordinary Least Squares (OLS) regression, Generalized Method of Moments(GMM) estimation, and Granger causality tests were applied. The findings reveal that increased democracy correlates positively with reported corruption, likely reflecting heightened transparency and exposure. Conversely, economic growth shows a negative association with corruption, underlining the role of structural reforms and institutional improvements. These insights emphasize the need for strengthening democratic institutions, promoting digital governance, and implementing targeted economic reforms to reduce corruption and foster sustainable development.

This paper investigates the transformative role of Artificial Intelligence (AI) in enhancing infrastructure governance and economic outcomes. Through a bibliometric analysis spanning more than two decades of research from 2000 to 2024, the study examines global trends in AI applications within infrastructure projects. The analysis reveals significant research themes across diverse sectors, including urban development, healthcare, and environmental management, highlighting the broad relevance of AI technologies. In urban development, the integration of AI and Internet of Things (IoT) technologies is advancing smart city initiatives by improving infrastructure systems through enhanced data-driven decision-making. In healthcare, AI is revolutionizing patient care, improving diagnostic accuracy, and optimizing treatment strategies. Environmental management is benefiting from AI’s potential to monitor and conserve natural resources, contributing to sustainability and crisis management efforts. The study also explores the synergy between AI and blockchain technology, emphasizing its role in ensuring data security, transparency, and efficiency in various applications. The findings underscore the importance of a multidisciplinary approach in AI research and implementation, advocating for ethical considerations and strong governance frameworks to harness AI’s full potential responsibly.

New Institutional Economics (NIE) uses solutions from law, economics and organization. The purpose of this article is to link in a single analytical approach the institutional environment, its change in the organizations uniting in one, what is happening in contracts with agricultural lands. The explanation of this type of governance means to integrate: theoretical definitions; formal rules (laws, court decisions and other legal acts); economic institutions—means and mechanisms of exchange; legal and economic forms in which, through governance of transactions property rights are transferred and protected. In order to achieve this goal, it is necessary to present the elements of the institutional matrix that are the cause of changes in subordination and coordination. Following the process of implementing an approach for reconciling the legal and economic nature of the contract forms and integrating the states, contract organizations and transaction costs in a common model. In order to solve the research problems tasks are adapted methods from law, economics, statistics. Such are: (a) positive legal analysis of legislation; (b) historical (retrospective) method of analysis of changes; (c) discrete-structural analysis to explain the process; (d) comparative-institutional analysis to clarify alternatives and an explanation of any of the effects; (е) regression analysis to model the relationships and present possible one’s scenarios to show the direction in which changes are needed. Changes in legislation, legal forms, mechanisms and the amount of payments create new behavioral patterns that change the contract. Therefore, in retrospect, we are witnessing how the number of changes in legal acts, the amount of fees; the number of participants-administrators of the processes; the number and registers - change the number of transactions; the duration of the actions in the contracts, which ultimately predetermines the different amounts of transaction costs for agricultural lands. This interdependence was established by constructing an econometric model. The analysis presents opportunities for change that would lead to scenarios with a reduced level of transaction costs, that is, improving governance and showing the way to improve the institutional environment related to agricultural lands in Bulgaria.

The proposed research work encompasses implications for infrastructure particularly the cybersecurity as an essential in soft infrastructure, and policy making particularly on secure access management of infrastructure governance. In this study, we introduce a novel parameter focusing on the timestamp duration of password entry, enhancing the algorithm titled EPSBalgorithmv01 with seven parameters. The proposed parameter incorporates an analysis of the historical time spent by users entering their passwords, employing ARIMA for processing. To assess the efficacy of the updated algorithm, we developed a simulator and employed a multi-experimental approach. The evaluation utilized a test dataset comprising 617 authentic records from 111 individuals within a selected company spanning from 2017 to 2022. Our findings reveal significant advancements in EPSBalgorithmv01 compared to its predecessor namely EPSBalgorithmv00. While EPSBalgorithmv00 struggled with a recognition rate of 28.00% and a precision of 71.171, EPSBalgorithmv01 exhibited a recognition rate of 17% with a precision of 82.882%. Despite a decrease in recognition rate, EPSBalgorithmv01 demonstrates a notable improvement of approximately 14% over EPSBalgorithmv00.