Impact of green energy production for sustainable economic growth and green economic recovery.

Green energy (GE) is frequently associated with sustainable development, which seeks to reduce global warming and its adverse effects on the environment, economy, and social justice. This study examines the impact of green energy on economic prosperity, green economic recovery, and long-term sustainability. This study analyses 33 industrialized and developing nations between 1991 and 2022 in the context of green energy, sustainable economic growth, and green economic recovery.

Evolving waste management: The impact of environmental technology, taxes, and carbon emissions on incineration in EU countries.

Amid the urgent global imperatives concerning climate change and resource preservation, our research delves into the critical domains of waste management and environmental sustainability within the European Union (EU), collecting data from 1990 to 2022. The Method of Moments Quantile Regression (MMQR) results reveal a resounding commitment among EU member states to diminish their reliance on incineration, which is evident through adopting green technologies and environmentally conscious taxation policies, aligning with the European Union's sustainability objectives. However, this transition presents the intricate task of harmonizing industrial emissions management with efficient waste disposal.

Assessing the drivers and solutions of green innovation influencing the adoption of renewable energy technologies.

The degradation of the environment in China is accelerating along with economic expansion. Adoption of renewable energy technologies (RETs) is crucial for reducing the adverse impacts of economic growth on the environment and fostering sustainable development. This study attempts to identify the green innovation drivers and sub-drivers that affect the adoption of RETs in China and provide solutions for boosting their implementation.

Untapped potential of food waste derived biochar for the removal of heavy metals from wastewater.

The presence of heavy metals in water pose a serious threat to both public and environmental health. However, the advances in the application of low cost biochar based adsorbent synthesize from various feedstocks plays an effective role in the of removal heavy metals from water. This study implies the introduction of novel method of converting food waste (FW) to biochar through pyrolysis, examine its physiochemical characteristics, and investigate its adsorption potential for the removal of heavy metals from water.

Exploring the determinants of methane emissions from a worldwide perspective using panel data and machine learning analyses.

This article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO 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).

On the relationship between oil market and European stock returns.

This paper investigates the dynamic relationship between the oil market and European stock market returns using monthly data from May 2007 to April 2022 for 27 European Union member countries. A novel approach is adopted by using the time-varying Granger causality test and the structural vector auto-regression model to examine the causal links. Empirical results reveal strong evidence of time-varying causation between the variables, considering the oil market from both the supply-side and demand-side perspectives.

Assessing Household's Municipal Waste Segregation Intentions in Metropolitan Cities of Pakistan: A Structural Equation Modeling Approach.

There is a growing concern about inappropriate waste disposal and its negative impact on human health and the environment. The objective of this study is to understand household waste segregation intention considering psychological, institutional, and situational factors simultaneously. Insights into the motivations of household waste segregation drivers may assist in a better knowledge of how to pursue the most efficient and effective initiatives.

The effects of gas flaring as moderated by government quality in leading natural gas flaring economies.

This study seeks to address pertinent economic and environmental issues associated with natural gas flaring, especially for the world's leading natural gas flaring economies (i.e. Russia, Iraq, Iran, the United States, Algeria, Venezuela, and Nigeria).

Examining the Energy-Environmental Kuznets Curve in OECD Countries Considering their Population.

This research aims to examine the validity of the Environmental Kuznets Curve (EKC) hypothesis in 37 Organization for Economic Co-operation and Development (OECD) countries over the period from 1960 to 2019. Panel Quantile Regressions (QR) show that for the lower quartile, economic growth does not impact emissions; for the central quartile a U-shaped curve emerges; while for the upper quartile, an N-shaped curve is found. In addition, cointegrating regressions highlight that economic growth, fossil fuel consumption, and population exert a detrimental effect on the environment, while renewable energy consumption reduces carbon dioxide (CO) emissions.

The trilemma among CO emissions, energy use, and economic growth in Russia.

This paper examines the relationship among CO emissions, energy use, and GDP in Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses (stationarity, structural breaks, cointegration, and causality tests). Then, we performed some Machine Learning experiments as robustness checks.

The trilemma of innovation, logistics performance, and environmental quality in 25 topmost logistics countries: A quantile regression evidence.

While the deployment of technological innovation was able to avert a devastating global supply chain fallout arising from the impact of ravaging COronaVIrus Disease 19 (COVID-19) pandemic, little is known about potential environmental cost of such achievement. The aim of this paper is to identify the determinants of logistics performance and investigate its empirical linkages with economic and environmental indicators. We built a macro-level dataset for the top 25 ranked logistics countries from 2007 to 2018, conducting a set of panel data tests on cross-sectional dependence, stationarity and cointegration, to provide preliminary insights.

A machine learning algorithm to analyse the effects of vaccination on COVID-19 mortality.

The coronavirus disease 2019 (COVID-19), with new variants, continues to be a constant pandemic threat that is generating socio-economic and health issues in manifold countries. The principal goal of this study is to develop a machine learning experiment to assess the effects of vaccination on the fatality rate of the COVID-19 pandemic. Data from 192 countries are analysed to explain the phenomena under study.

Do gasoline and diesel prices co-move? Evidence from the time-frequency domain.

This study investigates the co-movements of gasoline and diesel prices in three European countries (i.e. Germany, France, and Italy) with different fuel tax systems in place.

Assessing a fossil fuels externality with a new neural networks and image optimisation algorithm: the case of atmospheric pollutants as confounders to COVID-19 lethality.

This paper demonstrates how the combustion of fossil fuels for transport purpose might cause health implications. Based on an original case study [i.e.

Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm.

Although the literature on the relationship between economic growth and CO emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960-2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP).

Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources.

Global energy demand increases overtime, especially in emerging market economies, producing potential negative environmental impacts, particularly on the long term, on nature and climate changes. Promoting renewables is a robust policy action in world energy-based economies. This study examines if an increase in renewables production has a positive effect on the Brazilian economy, partially offsetting the SARS-CoV2 outbreak recession.

Can biomass energy curtail environmental pollution? A quantum model approach to Germany.

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO emissions for Germany. We constructed an innovative algorithm, the Quantum model, and applied it with Machine Learning experiments - through a software capable of emulating a quantum system - to data over the period of 1990-2018. This process is possible after eliminating the "irreversibility" of classical computations (unitary transformations) by making the process "reversible".

The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning.

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM, PM and NO concentrations in New York state using city-level daily data through two Machine Learning experiments. PM and NO are the most significant pollutant agents responsible for facilitating COVID-19 attributed death rates. Besides, we found only six out of many tested causal inferences to be significant and true within the AUPRC analysis.

NO levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values.

This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO) concentrations and COVID-19-related deaths in France. The concentration of NO linked to COVID-19-related deaths in three major French cities were determined using Artificial Neural Networks experiments and a Causal Direction from Dependency (D2C) algorithm. The aim of the study was to evaluate the potential effects of NO in spreading the epidemic.

Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy?

Municipal solid waste (MSW) is one of the most urgent issues associated with economic growth and urban population. When untreated, it generates harmful and toxic substances spreading out into the soils. When treated, they produce an important amount of Greenhouse Gas (GHG) emissions directly contributing to global warming.

The relationship between air pollution and COVID-19-related deaths: An application to three French cities.

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM and PM linked to COVID-19-related deaths.

Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence.

This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution.

Coronavirus (COVID-19) in Italy: knowledge, management of patients and clinical experience of Italian dentists during the spread of contagion.

Background: The coronavirus infection that emerged in China in the last few months of 2019 has now spread globally. Italy registered its first case in the second half of February, and in a short time period, it became the top country in Europe in terms of the number of infected people and the first in the world in terms of deaths. The medical and scientific community has been called upon to manage the emergency and to take measures.

The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland.

Municipal solid waste generation is becoming a prominent issue in the environmental arena. The aim of this paper is to investigate the relationship among municipal waste generation, greenhouse gas emissions, and GDP in Switzerland over the period 1990-2017. We apply both time series procedures (stationarity and causality tests) and a Machine Learning approach.

Modeling the nexus between pollutant emission, energy consumption, foreign direct investment, and economic growth: new insights from China.

Most nations are predominately preoccupied with the need to increase economic growth amidst pressure for increased energy consumption. However, higher energy consumption from fossil fuel has its environmental implication(s) especially in a high industrial economy like China. In this context, the current study explores the interaction between pollutant emission, foreign direct investment, energy consumption, tourism arrival, and economic growth for quarterly frequency data from 1995Q1 to 2016Q4 for econometrics analysis.