Angelo Quarto

Aim: The association between particulate air pollution and increased risk of type 2 diabetes (T2DM) is well established. Air pollution, a major public health concern worldwide, affects several noncommunicable diseases, including T2DM. Italy faces significant challenges in relation to both particulate air pollution and T2DM. There are no studies in Italy assessing the association between particulate matter and T2DM in a large dataset of patients with clinically diagnosed T2DM. Subject and methods: This study aims to assess the association between particulate matter (PM2.5 and PM10) and T2DM prevalence and incidence rates in Lombardy compared with the rest of Italy from 2006 to 2019. The association with years lived with disability (YLDs) was assessed using data from 2006 to 2016. Data were obtained from the AMD dataset, a comprehensive outpatient longitudinal dataset, while particulate matter data were obtained from the European Environment Agency and ARPA Lombardy, the regional environmental protection agency for Lombardy. The association was assessed using mixed-effects models. Results: The mixed-effects model showed a significant positive association between particulate matter and T2DM incidence in Italy, with notable variations over time and between regions (Lombardy vs. the rest of Italy). While no significant effect of particulate matter was observed with respect to prevalence and YLD rates, a significant positive association was found between particulate matter and incidence rates for Italy, with the effect increasing over time. The opposite tendency was observed for Lombardy, with a negative association between particulate matter and incidence, and a decreases in the effect over time. Conclusion: Particulate matter pollution, specifically PM2.5 and PM10, appears to be significantly associated with T2DM incidence rates in Italy. However, the effect varies between regions, with Lombardy showing a complex relationship influenced by socioeconomic factors. This study highlights the importance of addressing air pollution as a public health priority, particularly in regions with high levels of pollution such as Lombardy, in order to reduce the risk of T2DM and its associated burden. Graphical

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.

This article affords the question of methane emissions in the context of the ESG framework at the global level. The World Bank ESG-Environmental, Social, and Governance database was used. The data refers to 193 countries for a period between 2011 and 2020. The econometric models used are Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, and Weighted Least Squares-WLS. The impact of methane on the ESG model was calculated through three equations capable of analyzing the impact of methane emissions, respectively, con-cerning the E-Environmental, S-Social, and G-Governance components of the ESG model. The re-sults show that methane emissions tend to increase even in the improvement of ESG conditions at world level.

In recent years, the concept of sustainable development has enriched numerous scientific researches. Therefore, the combination of economic growth and the environment has been the subject of numerous econometric and statistical models. They demonstrated that there is a two-way relationship between economic growth and pollution. So, we use data from the World Bank database (1971–2014) to assess the possibility that a change (positive or negative) in pollution in India generates a gross domestic product acceleration. Through a Machine Learning approach via artificial neural network analysis, empirical findings show that a deep neural network can predict the outcome under study. The novelty of this paper is to have determined the results through a model based on a comparison with a highly developed country (Japan). The results obtained show that in a country like India, 76% of the time, a change in pollution evolves into a change in the acceleration of the economic growth.