1,721,143 research outputs found
Subjective well-being in Italian regions: a panel data approach
Full text linkWe use data from the new ISTAT-BES database to estimate the socio-economic determinants of subjective well-being in Italian regions between 2004 and 2016. Empirical findings show that subjective well-being is positively associated with education, income and social relations. Our findings imply that governments should improve subjective well-being increasing the level of investment in education, deepening economic growth, reducing income inequality and promoting social relations
The Role of Air Pollution in the ESG Model at the World Level
No full textIn the following chapter, the authors analyse the role of air pollution (AP) in the context of ESG (environmental, social, and governance) model. They use data from the World Banks' ESG Database for 193 countries during the period 2011-2020. They perform panel data with random effects, panel data with fixed effects, pooled OLS, and WLS. Results show that the level of PA is positively associated, among others, to “cooling degree days,” “CO2 emissions,” and “agriculture, forestry, and fishing, value added,” and negatively associated, among others, to “terrestrial and marine protected areas,” “proportion of seats held by women in national parliaments,” and “mammal species threatened.” Furthermore, they confront eight different machine learning algorithms for the prediction of the future value of AP. Polynomial regression is the best predictive algorithm in the sense either maximization of R-squared either minimization of MAE, MSR, and RMSE. The future value of AP is expected to reduce on average of -0,060% for the analysed countries
Exploring the determinants of methane emissions from a worldwide perspective using panel data and machine learning analyses
Full text linkThis article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO2 emissions, but fewer studies concentrate on the other most long-lived greenhouse gas (GHG), methane which contributes largely to climate change. For the empirical analysis, a large dataset is used considering 192 countries with data ranging from 1960 up to 2022 and considering a wide set of determinants (total central government debt, domestic credit to the private sector, exports of goods and services, GDP per capita, total unemployment, renewable energy consumption, urban population, Gini Index, and Voice and Accountability). Panel Quantile Regression (PQR) estimates show a non-negligible statistical effect of all the selected variables (except for the Gini Index) over the distribution's quantiles. Moreover, the Simple Regression Tree (SRT) model allows us to observe that the losing countries, located in the poorest world regions, abundant in natural resources, are those expected to curb methane emissions. For that, public interventions like digitalization, green education, green financing, ensuring the increase in Voice and Accountability, and green jobs, would lead losers to be positioned in the winner's rankings and would ensure an effective fight against climate change
Greenhouse gas emissions and road infrastructure in Europe: A machine learning analysis
No full textThis paper explores the determinants of greenhouse gas (GHG) emissions in Europe, focusing on transportation-related variables. By combining classical econometric models with Machine Learning (ML) techniques, we analyze data spanning from 2013 to 2021. The empirical findings highlight the complex relationship between newer passenger cars and GHG emissions, noting the significant impact of their production and increased usage. Conversely, the adoption of alternative fuel vehicles is found to significantly reduce emissions. This is further supported by ML models, which emphasize the critical role of car density and alternative fuel vehicles in determining emissions. Policy implications suggest the need for targeted interventions, including the promotion of electric and hybrid vehicles, enhancements in transportation infrastructure, and the implementation of economic incentives for clean technologies
Regional Disparities and Strategic Implications of Hydrogen Production in 27 European Countries
No full textThis study examines hydrogen production across 27 European countries, highlighting disparities due to varying energy policies and industrial capacities. Germany leads with 109 plants, followed by Poland, France, Italy, and the UK. Mid-range contributors like the Netherlands, Spain, Sweden, and Belgium also show substantial investments. Countries like Finland, Norway, Austria, and Denmark, known for their renewable energy policies, have fewer plants, while Estonia, Iceland, Ireland, Lithuania, and Slovenia are just beginning to develop hydrogen capacities. The analysis also reveals that a significant portion of the overall hydrogen production capacity in these countries remains underutilized, with an estimated 40% of existing infrastructure not operating at full potential. Many countries underutilize their production capacities due to infrastructural and operational challenges. Addressing these issues could enhance output, supporting Europe’s energy transition goals. The study underscores the potential of hydrogen as a sustainable energy source in Europe and the need for continued investment, technological advancements, supportive policies, and international collaboration to realize this potential
Methane emissions in the esg framework at the world level
Full text linkMethane 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
Waste Management and Innovation: Insights from Europe
Full text linkThis 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
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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