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Addressing climate change requires a deeper understanding of all greenhouse gases, yet nitrous oxide (N2O)—despite its significant global warming potential—remains underrepresented in sustainability analysis and policy discourse. The paper examines N2O emissions from an environmental, social, and governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on emissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N2O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and country-specific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, this paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of this study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N2O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emission mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets.

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

This paper analyzes the relationship between urban waste recycling and innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013–2022 were used. To analyze the characteristics of European countries in terms of waste recycling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coefficient was used. The results show that the optimal number of clusters is three. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffusion of Information and Communication Technologies (ICTs), while it decreases with the infrastructure index, business sophistication index, and the average expenditure on research and development of large companies.

Climate change mitigation for the built environment has become a subject of greatest urgency, as buildings account for nearly 40% of total energy consumption and nearly one-third of total CO2 emissions. While environmental, social, and governance (ESG) indicators are increasingly used to monitor sustainability performance, their collective role in impacting building-related emissions is yet largely under-investigated. The current research closes that gap through an examination of the ESG dimension–CO2 emissions intersection of 180 nations from 2000 to 2022, in the hope of illuminating how environmental, social, and governance elements interact to facilitate decarbonization. The research is guided by a multi-method design, including econometric examination, cluster modeling, and machine learning techniques, which provide causal evidence and predictive analysis, respectively. The findings reveal that the deployment of renewable energy significantly reduces emissions, while per capita energy use and PM2.5 air pollution exacerbate this effect. The social indicators show mixed results: learning, women’s parliamentary representation, and women’s workforce representation reduce emissions, while food production and growth among the lowest-income individuals demonstrate higher emissions. Governance demonstrates mixed results as well, with good regulation reducing emissions under specific conditions yet primarily supporting high-income countries with superior infrastructure. The examination of clusters reveals that ESG-balanced performance is retained by countries in the low-emission clusters, whereas decentralized ESG pillars are associated with higher emissions. Machine learning confirms the existence of non-linear effects and identifies PM2.5 exposure and renewable energy deployment as the strongest predictors of the relationship. In summary, the findings suggest that successful policies for decarbonizing the built environment are constructed upon the consistency of environmental, social, and governance plans, rather than single steps.

This study investigates the complex relationship between the performance of logistics and Environmental, Social, and Governance (ESG) performance, drawing upon the multi-methodological framework of combining econometrics with state-of-the-art machine learning approaches. Employing Instrumental Variable (IV) Panel data regressions, viz., 2SLS and G2SLS, with data from a balanced panel of 163 countries covering the period from 2007 to 2023, the research thoroughly investigates how the performance of the Logistics Performance Index (LPI) is correlated with a variety of ESG indicators. To enrich the analysis, machine learning models—models based upon regression, viz., Random Forest, k-Nearest Neighbors, Support Vector Machines, Boosting Regression, Decision Tree Regression, and Linear Regressions, and clustering, viz., Density-Based, Neighborhood-Based, and Hierarchical clustering, Fuzzy c-Means, Model-Based, and Random Forest—were applied to uncover unknown structures and predict the behavior of LPI. Empirical evidence suggests that higher improvements in the performance of logistics are systematically correlated with nascent developments in all three dimensions of the environment (E), social (S), and governance (G). The evidence from econometrics suggests that higher LPI goes with environmental trade-offs such as higher emissions of greenhouse gases but cleaner air and usage of resources. On the S dimension, better performance in terms of logistics is correlated with better education performance and reducing child labor, but also demonstrates potential problems such as social imbalances. For G, better governance of logistics goes with better governance, voice and public participation, science productivity, and rule of law. Through both regression and cluster methods, each of the respective parts of ESG were analyzed in isolation, allowing us to study in-depth how the infrastructure of logistics is interacting with sustainability research goals. Overall, the study emphasizes that while modernization is facilitated by the performance of the infrastructure of logistics, this must go hand in hand with policy intervention to make it socially inclusive, environmentally friendly, and institutionally robust.

This article investigates the macroeconomic and labor market conditions that shape the adoption of artificial intelligence (AI) technologies among large firms in Europe. Based on panel data econometrics and supervised machine learning techniques, we estimate how public health spending, access to credit, export activity, gross capital formation, inflation, openness to trade, and labor market structure influence the share of firms that adopt at least one AI technology. The research covers all 28 EU members between 2018 and 2023. We employ a set of robustness checks using a combination of fixed-effects, random-effects, and dynamic panel data specifications supported by Clustering and supervised learning techniques. We find that AI adoption is linked to higher GDP per capita, healthcare spending, inflation, and openness to trade but lower levels of credit, exports, and capital formation. Labor markets with higher proportions of salaried work, service occupations, and self-employment are linked to AI diffusion, while unemployment and vulnerable work are detractors. Cluster analysis identifies groups of EU members with similar adoption patterns that are usually underpinned by stronger economic and institutional fundamentals. The results collectively suggest that AI diffusion is shaped not only by technological preparedness and capabilities to invest but by inclusive macroeconomic conditions and equitable labor institutions. Targeted policy measures can accelerate the equitable adoption of AI technologies within the European industrial economy.

In this work, we examine the correlation between financial inclusion and the Environmental, Social, and Governance (ESG) factors of sustainable development with the assistance of an exhaustive panel dataset of 103 emerging and developing economies spanning 2011 to 2022. The “Account Age” variable, standing for financial inclusion, is the share of adults owning accounts with formal financial institutions or with the providers of mobile money services, inclusive of both conventional and digital entry points. Methodologically, the article follows an econometric approach with panel data regressions, supplemented by Two-Stage Least Squares (2SLS) with instrumental variables in order to control endogeneity biases. ESG-specific instruments like climate resilience indicators and digital penetration measures are utilized for the purpose of robustness. As a companion approach, the paper follows machine learning techniques, applying a set of algorithms either for regression or for clustering for the purpose of detecting non-linearities and discerning ESG-inclusion typologies for the sample of countries. Results reflect that financial inclusion is, in the Environmental pillar, significantly associated with contemporary sustainability activity such as consumption of green energy, extent of protected area, and value added by agriculture, while reliance on traditional agriculture, measured by land use and value added by agriculture, decreases inclusion. For the Social pillar, expenditure on education, internet, sanitation, and gender equity are prominent inclusion facilitators, while engagement with the informal labor market exhibits a suppressing function. For the Governance pillar, anti-corruption activity and patent filing activity are inclusive, while diminishing regulatory quality, possibly by way of digital governance gaps, has a negative correlation. Policy implications are substantial: the research suggests that development dividends from a multi-dimensional approach can be had through enhancing financial inclusion. Policies that intersect financial access with upgrading the environment, social expenditure, and institutional reconstitution can simultaneously support sustainability targets. These are the most applicable lessons for the policy-makers and development professionals concerned with the attainment of the SDGs, specifically over the regions of the Global South, where the trinity of climate resilience, social fairness, and institutional renovation most significantly manifests.

This article proposes a complex solution to improve sustainable intelligent building management based on the principles of Environmental, Social, and Governance (ESG) factors. The ESG KPI Framework–Metaverse-Enabled Operations incorporates the latest digital twin solutions, IoT sensor systems, and metaverse platforms to deliver real-time management and optimization of ESG factors. A hybrid solution strategy has been used in this framework, focusing on auto-acquisition of information and multiple validations at different levels through correlation analysis, Principal Component Analysis (PCA), Ordinary Least Squares (OLS) regression, and Machine Learning. The designed prototype links all the solutions together in a multi-level dashboard to represent key performance factors such as carbon footprint, energy consumption, renewable energy use, and occupant wellness. Experiments conducted validate the effectiveness of the proposed solution in improving prediction efficiency and user interaction experience during metaverse simulations.

Addressing climate change requires a deeper understanding of all greenhouse gases, yet nitrous oxide (N[sub.2] O)—despite its significant global warming potential—remains underrepresented in sustainability analysis and policy discourse. The paper examines N[sub.2] O emissions from an environmental, social, and governance (ESG) standpoint with a combination of econometric and machine learning specifications to uncover global trends and policy implications. Results show the overwhelming effect of ESG factors on emissions, with intricate interdependencies between economic growth, resource productivity, and environmental policy. Econometric specifications identify forest degradation, energy intensity, and income inequality as the most significant determinants of N[sub.2] O emissions, which are in need of policy attention. Machine learning enhances predictive power insofar as emission drivers and country-specific trends are identifiable. Through the integration of panel data techniques and state-of-the-art clustering algorithms, this paper generates a highly differentiated picture of emission trends, separating country groups by ESG performance. The findings of this study are that while developed nations have better energy efficiency and environmental governance, they remain significant contributors to N[sub.2] O emissions due to intensive industry and agriculture. Meanwhile, developing economies with energy intensity have structural impediments to emission mitigation. The paper also identifies the contribution of regulatory quality in emission abatement in that the quality of governance is found to be linked with better environmental performance. ESG-based finance instruments, such as green bonds and impact investing, also promote sustainable economic transition. The findings have the further implications of additional arguments for mainstreaming sustainability in economic planning, developing ESG frameworks to underpin climate targets.

In this article, we investigate the impact of “Renewable Electricity Output” on the green economy in the context of the circular economy for 193 countries in the period 2011–2020. We use data from the World Bank ESG framework. We perform Panel Data with Fixed Effects, Panel Data with Random Effects, Weighted Last Squares-WLS, and Pooled Ordinary Least Squares-OLS. Our results show that Renewable Electricity Output is positively associated, among others, with “Adjusted Savings-Net Forest Depletion” and “Renewable Energy Consumption” and negatively associated, among others, with “CO2 Emission” and “Cooling Degree Days”. Furthermore, we perform a cluster analysis implementing the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. In adjunct, we confront seven different machine learning algorithms to predict the future level of “Renewable Electricity Output”. Our results show that Linear Regression is the best algorithm and that the future value of renewable electricity output is predicted to growth on average at a rate of 0.83% for the selected countries. Furthermore, we improve the machine learning analysis with a Deep Learning approach using Convolutional Neural Network-CNN but the algorithm is not appropriate for the analyzed dataset. Less complex machine learning algorithms show better statistical results.