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.

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.

The paper analyzes the relationship between urban waste recycling and technological innovation systems in Europe. Data from the Global Innovation Index for 34 European countries in the period 2013-2022 was used. To analyze the characteristics of European countries in terms of waste recy-cling capacity, the k-Means algorithm optimized with the Elbow method and the Silhouette Coef-ficient was used. The results show that the optimal number of clusters is 3. Panel data results show that waste recycling increases with domestic market scale, gross capital formation, and the diffu-sion of Information and Communication Technologies (ICTs), while it decreases with the infra-structure index, business sophistication index, and the average expenditure in research and devel-opment of large companies.

Methane is a strong green gas that has higher GWP. Methane emissions, therefore, form one of the critical focuses within climate change mitigation policy. Indeed, the present study represents a very novel analysis of methane emission within the ESG framework by using the data across 193 countries within the period of 2011–2020. Methane reduction on account of ESG delivers prompt climate benefits and thereby preserves the core environment, social, and governance objectives. In spite of its importance, the role of methane remains thinly explored within ESG metrics. This study analyzes how factors like renewable energy use, effective governance, and socioeconomic settings influence the emission rate of the study subject, as many previous ESG studies are deficient in considering methane. By using econometric modeling, this research identifies that increasing methane emissions remain unabated with the improvement of ESG performances around the world, particularly within key agricultural and fossil fuel-based industrial sectors. Renewable energy cuts emissions, but energy importation simply transfers the burdens to exporting nations. It therefore involves effective governance and targeted internationational cooperation, as socioeconomic elements act differently in different developed and developing countries to drive various emission sources. These findings strongly call for balanced, targeted strategies to integrate actions of mitigation into ESG goals related to methane abatement.

In this article we analyze the determinants and the export trend of European countries of medium and high technology products. The data were analyzed using various econometric models, namely WLS, Pooled OLS, Dynamic Panel, Panel Data with Fixed Effects, Panel Data with Random Effects. The results show that exports of medium and high-tech products are positively associated, among other variables, with the value of “Average Annual GDP Growth”, “Total Entrepreneurial Activity” and “Sales Impacts”, and negatively associated with, among other variables, “Human Resources”, “Government and Procurement of Advanced Technology Products” and “Buyer Sophistication”. A cluster analysis was realized with the k-Means algorithm optimized with the Silhouette coefficient. The result showed the presence of only two clusters. Since this result was considered poorly representative of the industrial complexity of the European Union countries, a further analysis was carried out with the Elbow method. The result showed the presence of 6 clusters with the dominance of Germany and the economies connected to the German economy. In addition, a network analysis was carried out using the distance to Manhattan. Four complex network structures and two simplified network structures were detected. A comparison was then made between 10 machine learning algorithms for predicting the value of exports of medium and high-tech products. The result shows that the best performing algorithm is the SGD. An analysis with Augmented Data-AD was implemented with a comparison between 10 machine learning algorithms for prediction and the result shows that the Linear Regression algorithm is the best predictor. The prediction with the Augmented Data-AD allows to reduce the MAE by about 0.0022131 compared to the prediction with the Original Data-OD.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%. JEL CODE: D7, D70, D72, D73, D78.