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This paper shows that the co-movement of public revenues in the European Monetary Union (EMU) is driven by an unobserved common factor. Our empirical analysis uses yearly data covering the period 1970–2014 for 12 selected EMU member countries. We have found that this common component has a significant impact on public revenues in the majority of the countries. We highlight this common pattern in a dynamic factor model (DFM). Since this factor is unobservable, it is difficult to agree on what it represents. We argue that the latent factor that emerges from the two different empirical approaches used might have a composite nature, being the result of both the more general convergence of the economic cycles of the countries in the area and the increasingly better tuned tax structure. However, the original aspect of our paper is the use of a back-propagation neural networks (BPNN)-DF model to test the results of the time-series. At the level of computer programming, the results obtained represent the first empirical demonstration of the latent factor’s presence.
Cosimo Magazzino; Marco Mele. A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU. Italian Economic Journal 2021, 1 -50.
AMA StyleCosimo Magazzino, Marco Mele. A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU. Italian Economic Journal. 2021; ():1-50.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele. 2021. "A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU." Italian Economic Journal , no. : 1-50.
Marco Mele; Cosimo Magazzino; Nicolas Schneider; Floriana Nicolai. Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm. Environmental Science and Pollution Research 2021, 1 .
AMA StyleMarco Mele, Cosimo Magazzino, Nicolas Schneider, Floriana Nicolai. Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm. Environmental Science and Pollution Research. 2021; ():1.
Chicago/Turabian StyleMarco Mele; Cosimo Magazzino; Nicolas Schneider; Floriana Nicolai. 2021. "Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm." Environmental Science and Pollution Research , no. : 1.
Although the literature on the relationship between economic growth and CO2 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). Despite the phase of low Italian economic growth, results reveal that CO2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.
Marco Mele; Cosimo Magazzino; Nicolas Schneider; Floriana Nicolai. Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm. Environmental Science and Pollution Research 2021, 1 -14.
AMA StyleMarco Mele, Cosimo Magazzino, Nicolas Schneider, Floriana Nicolai. Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm. Environmental Science and Pollution Research. 2021; ():1-14.
Chicago/Turabian StyleMarco Mele; Cosimo Magazzino; Nicolas Schneider; Floriana Nicolai. 2021. "Revisiting the dynamic interactions between economic growth and environmental pollution in Italy: evidence from a gradient descent algorithm." Environmental Science and Pollution Research , no. : 1-14.
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. Using data on Brazilian economy, we test the contribution of renewables on the economy via a ML architecture (through a LSTM model). Empirical findings show that an ever-greater use of renewables may sustain the economic growth recovery, generating a better performing GDP acceleration vs. other energy variables.
Marco Mele; Antonia Rosa Gurrieri; Giovanna Morelli; Cosimo Magazzino. Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources. Environmental Science and Pollution Research 2021, 28, 41127 -41134.
AMA StyleMarco Mele, Antonia Rosa Gurrieri, Giovanna Morelli, Cosimo Magazzino. Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources. Environmental Science and Pollution Research. 2021; 28 (30):41127-41134.
Chicago/Turabian StyleMarco Mele; Antonia Rosa Gurrieri; Giovanna Morelli; Cosimo Magazzino. 2021. "Nature and climate change effects on economic growth: an LSTM experiment on renewable energy resources." Environmental Science and Pollution Research 28, no. 30: 41127-41134.
This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO2 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 empirical findings support the powerful role of biomass energy in reducing carbon dioxide emissions, although the effect of renewable energy technology displays a much stronger magnitude. Moreover, income remains an important determinant of environmental pollution in Germany.
Cosimo Magazzino; Marco Mele; Nicolas Schneider; Muhammad Shahbaz. Can biomass energy curtail environmental pollution? A quantum model approach to Germany. Journal of Environmental Management 2021, 287, 112293 .
AMA StyleCosimo Magazzino, Marco Mele, Nicolas Schneider, Muhammad Shahbaz. Can biomass energy curtail environmental pollution? A quantum model approach to Germany. Journal of Environmental Management. 2021; 287 ():112293.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Nicolas Schneider; Muhammad Shahbaz. 2021. "Can biomass energy curtail environmental pollution? A quantum model approach to Germany." Journal of Environmental Management 287, no. : 112293.
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.
Marco Mele; Luciano Nieddu; Cristiana Abbafati; Angelo Quarto. An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration? Environmental Science and Pollution Research 2021, 1 -13.
AMA StyleMarco Mele, Luciano Nieddu, Cristiana Abbafati, Angelo Quarto. An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration? Environmental Science and Pollution Research. 2021; ():1-13.
Chicago/Turabian StyleMarco Mele; Luciano Nieddu; Cristiana Abbafati; Angelo Quarto. 2021. "An ANN experiment on the Indian economy: can the change in pollution generate an increase or decrease in GDP acceleration?" Environmental Science and Pollution Research , no. : 1-13.
Financial development, productivity, and growth are interconnected, but the direction of causality remains unclear. The relevance of these linkages is likely different for developing and developed economies, yet comparative cross-country studies are scant. The paper analyses the relationship among credit access, output and productivity in the agricultural sector for a large set of countries, over the period 2000–2012, using an Artificial Neural Networks approach. Empirical findings show that these three variables influence each other reciprocally, although marked differences exist among groups of countries. The role of credit access is more prominent for the OECD countries and less important for countries with a lower level of economic de-elopement. Our analysis allows us to highlight the specific effects of credit in stimulating the development of the agricultural sector: in developing countries, credit access significantly affects production, whereas in developed countries, it also has an impact on productivity.
Cosimo Magazzino; Marco Mele; Fabio Santeramo. Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture. Sustainability 2021, 13, 2828 .
AMA StyleCosimo Magazzino, Marco Mele, Fabio Santeramo. Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture. Sustainability. 2021; 13 (5):2828.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Fabio Santeramo. 2021. "Using an Artificial Neural Networks Experiment to Assess the Links among Financial Development and Growth in Agriculture." Sustainability 13, no. 5: 2828.
The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM10, PM2.5, and NO2 concentrations in New York state using city-level daily data through two Machine Learning experiments. PM2.5 and NO2 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. In line with the causal findings, a unidirectional causal effect is found from PM2.5 to Deaths, NO2 to Deaths, and economic growth to both PM2.5 and NO2. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM2.5 to Deaths, NO2 to Deaths) to accelerate COVID-19 deaths. In contrast, we found evidence that unsustainable economic growth predicts the dynamics of air pollutants. This shows how unsustainable economic growth could increase environmental pollution by escalating emissions of pollutant agents (PM2.5 and NO2) in New York state.
Cosimo Magazzino; Marco Mele; Samuel Asumadu Sarkodie. The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning. Journal of Environmental Management 2021, 286, 112241 .
AMA StyleCosimo Magazzino, Marco Mele, Samuel Asumadu Sarkodie. The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning. Journal of Environmental Management. 2021; 286 ():112241.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Samuel Asumadu Sarkodie. 2021. "The nexus between COVID-19 deaths, air pollution and economic growth in New York state: Evidence from Deep Machine Learning." Journal of Environmental Management 286, no. : 112241.
In this study, we used an image neural network model to assess the relationship between economic growth, pollution (PM2.5, PM10, and CO2), and deaths from COVID-19 in the Hubei area (China). Data analysis, neural network analysis, and deep learning experiments were carried out to assess the relationship among COVID-19 deaths, air pollution, and economic growth in China (Hubei province, the epicenter of the COVID-19 pandemic). We collected daily data at a city level from January 20 to July 31, 2020. We used main cities in the Hubei province, with data on confirmed COVID-19 deaths, air pollution (expressed in µg/m3 as PM2.5, PM10, and CO2), and per capita economic growth. Following the most recent contributions on the relationship among air pollution, GDP, and diffusion of COVID-19, we generated an algorithm capable of identifying a neural connection among these variables. The results confirmed a strong predictive relationship for the Hubei area between changes in the economic growth, fine particles, and deaths from COVID-19. These results would recommend adequate environmental reforms to policymakers to contain the spread and adverse effects of the virus. Therefore, there is a requirement to reconsider the system of transport and return to production by combining it with economic growth to protect the planet.
Cosimo Magazzino; Marco Mele. A Neural Network Evidence of the Nexus Among Air Pollution, Economic Growth, and COVID-19 Deaths in the Hubei Area. Advances in Environmental and Engineering Research 2021, 02, 1 -1.
AMA StyleCosimo Magazzino, Marco Mele. A Neural Network Evidence of the Nexus Among Air Pollution, Economic Growth, and COVID-19 Deaths in the Hubei Area. Advances in Environmental and Engineering Research. 2021; 02 (02):1-1.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele. 2021. "A Neural Network Evidence of the Nexus Among Air Pollution, Economic Growth, and COVID-19 Deaths in the Hubei Area." Advances in Environmental and Engineering Research 02, no. 02: 1-1.
This paper examines the relationship between renewable energy consumption and economic growth in Brazil, in the Covid-19 pandemic. Using an Artificial Neural Networks (ANNs) experiment in Machine Learning, we tried to verify if a more intensive use of renewable energy could generate a positive GDP acceleration in Brazil. This acceleration could offset the harmful effects of the Covid-19 global pandemic. Empirical findings show that an ever-greater use of renewable energies may sustain the economic growth process. In fact, through a model of ANNs, we highlighted how an increasing consumption of renewable energies triggers an acceleration of the GDP compared to other energy variables considered in the model.
Cosimo Magazzino; Marco Mele; Giovanna Morelli. The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy. Sustainability 2021, 13, 1285 .
AMA StyleCosimo Magazzino, Marco Mele, Giovanna Morelli. The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy. Sustainability. 2021; 13 (3):1285.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Giovanna Morelli. 2021. "The Relationship between Renewable Energy and Economic Growth in a Time of Covid-19: A Machine Learning Experiment on the Brazilian Economy." Sustainability 13, no. 3: 1285.
This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO2) concentrations and COVID-19-related deaths in France. The concentration of NO2 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 NO2 in spreading the epidemic. The underlying hypothesis is that NO2, as a precursor to secondary particulate matter formation, can foster COVID-19 and make the respiratory system more susceptible to this infection. Three different neural networks for the cities of Paris, Lyon and Marseille were built in this work, followed by the application of an innovative tool of cutting the signal from the inputs to the selected target. The results show that the threshold levels of NO2 connected to COVID-19 range between 15.8 μg/m3 for Lyon, 21.8 μg/m3 for Marseille and 22.9 μg/m3 for Paris, which were significantly lower than the average annual concentration limit of 40 μg/m³ imposed by Directive 2008/50/EC of the European Parliament.
Marco Mele; Cosimo Magazzino; Nicolas Schneider; Vladimir Strezov. NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values. Environmental Research 2021, 194, 110663 -110663.
AMA StyleMarco Mele, Cosimo Magazzino, Nicolas Schneider, Vladimir Strezov. NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values. Environmental Research. 2021; 194 ():110663-110663.
Chicago/Turabian StyleMarco Mele; Cosimo Magazzino; Nicolas Schneider; Vladimir Strezov. 2021. "NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values." Environmental Research 194, no. : 110663-110663.
While Germany and Japan are going through major energy reforms, natural gas consumption is taking a growing share in their energy supply. This paper adopts a Machine Learning approach to assess the causal link between natural gas consumption and economic growth for both economies. A Causal Direction from Dependency (D2C) algorithm with the interconnection of the sub-class is employed using yearly data from 1970 to 2018. The interconnections of the sub-classes are found for both economies, indicating evidence of causalities operating in both directions. In addition, the propagation over the seven eras is linear and homogeneously continue for Japan, while this effect meets a stabilization phase in the fifth era for Germany. The empirical findings claim strong support for the existence of a bidirectional causality between these variables in Germany and Japan, which is in line with the “feedback hypothesis”. Although the strength of this bidirectional relationship is clear for both economies, its time-propagation is expected to be longer for Japan. Accordingly, this study claims that the gas supply should be further strengthened to progressively replace the most polluting fuels (oil and coal) and ensure a feasible transition towards a renewable path.
Cosimo Magazzino; Marco Mele; Nicolas Schneider. A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan. Energy 2020, 219, 119586 .
AMA StyleCosimo Magazzino, Marco Mele, Nicolas Schneider. A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan. Energy. 2020; 219 ():119586.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Nicolas Schneider. 2020. "A D2C algorithm on the natural gas consumption and economic growth: Challenges faced by Germany and Japan." Energy 219, no. : 119586.
China, India, and the USA are the world’s biggest energy consumers and CO2 emitters. Being the leading contributors to climate change, these economies are also at the core of environmental solutions. This paper investigates the causal relationship among solar and wind energy production, coal consumption, economic growth, and CO2 emissions for these three countries. To do so, we use an advanced methodology in Machine Learning to verify the predictive causal linkages among variables. The Causal Direction from Dependency (D2C) algorithm set CO2 emissions as the target variable. The obtained results were disaggregated and estimated in a supervised prediction model. The findings, confirmed by three different Machine Learning procedures, showed an interesting output. While a reduction in overall carbon emissions is predicted in China and the US (resulting from the intensive use of renewable sources of energy), India displays critical predictions of a rise in CO2 emissions. This indicates that curbing CO2 emissions cannot be achieved without conducting a comprehensive shift from fossil to renewable resources, although China and the U.S. present a more promising path to sustainability than India. Being an emerging renewable energy leader, India should further enhance the use of low-carbon sources in its power supply and limit its dependence on coal.
Cosimo Magazzino; Marco Mele; Nicolas Schneider. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. Renewable Energy 2020, 167, 99 -115.
AMA StyleCosimo Magazzino, Marco Mele, Nicolas Schneider. A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions. Renewable Energy. 2020; 167 ():99-115.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Nicolas Schneider. 2020. "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions." Renewable Energy 167, no. : 99-115.
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. With its promising path to sustainability, the Danish case is of high interest since estimated results are thought to bring useful information for policy purposes. Here, we exploit the most recent and available data period (1994–2017) and investigate the causal relationship between MSW generation per capita, income level, urbanization, and GHG emissions from the waste sector in Denmark. We use an experiment based on Artificial Neural Networks and the Breitung-Candelon Spectral Granger-causality test to understand how the variables, object of the study, manage to interact within a complex ecosystem such as the environment and waste. Through numerous tests in Machine Learning, we arrive at results that imply how economic growth, identifiable by changes in per capita GDP, affects the acceleration and the velocity of the neural signal with waste emissions. We observe a periodical shift from the traditional linear economy to a circular economy that has important policy implications.
Cosimo Magazzino; Marco Mele; Nicolas Schneider; Samuel Asumadu Sarkodie. Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy? Science of The Total Environment 2020, 755, 142510 -142510.
AMA StyleCosimo Magazzino, Marco Mele, Nicolas Schneider, Samuel Asumadu Sarkodie. Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy? Science of The Total Environment. 2020; 755 ():142510-142510.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Nicolas Schneider; Samuel Asumadu Sarkodie. 2020. "Waste generation, wealth and GHG emissions from the waste sector: Is Denmark on the path towards circular economy?" Science of The Total Environment 755, no. : 142510-142510.
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 PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.
Cosimo Magazzino; Marco Mele; Nicolas Schneider. The relationship between air pollution and COVID-19-related deaths: An application to three French cities. Applied Energy 2020, 279, 115835 -115835.
AMA StyleCosimo Magazzino, Marco Mele, Nicolas Schneider. The relationship between air pollution and COVID-19-related deaths: An application to three French cities. Applied Energy. 2020; 279 ():115835-115835.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Nicolas Schneider. 2020. "The relationship between air pollution and COVID-19-related deaths: An application to three French cities." Applied Energy 279, no. : 115835-115835.
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. Then, a D2C algorithm on proportion-based causality is applied, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis is that a predetermined pollution concentration, caused by economic growth, could foster COVID-19 by making the respiratory system more susceptible to infection. We use data (from January 29 to May 18, 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verify a ML causal link between PM2.5, CO2, NO2, and COVID-19 deaths. The implications require careful policy design.
Marco Mele; Cosimo Magazzino. Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence. Environmental Science and Pollution Research 2020, 28, 2669 -2677.
AMA StyleMarco Mele, Cosimo Magazzino. Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence. Environmental Science and Pollution Research. 2020; 28 (3):2669-2677.
Chicago/Turabian StyleMarco Mele; Cosimo Magazzino. 2020. "Pollution, economic growth, and COVID-19 deaths in India: a machine learning evidence." Environmental Science and Pollution Research 28, no. 3: 2669-2677.
This paper aims to explore the impact of transportation infrastructure on economic growth in China at different levels: aggregate and regional. Using a time series approach and panel data for 28 regions (where there are provinces also) over the time 1990–2017, the experimental findings confirms the economic theory of development choices. Although other studies have addressed this problem with the same data, our contribution has been to combine the aggregated results with the regional ones for policy analysis. In addition, we combine a Machine Learning technique capable of verifying causality through a supervised and an econometric approach. The results show that the contribution to the growth of transport investments is different from region to region, but we have highlighted how transport affects economic growth at the aggregate level. However, the lack of infrastructure maintenance eliminates the positive effects of investments over time in the medium term.
Cosimo Magazzino; Marco Mele. On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics 2020, 100947 .
AMA StyleCosimo Magazzino, Marco Mele. On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics. 2020; ():100947.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele. 2020. "On the relationship between transportation infrastructure and economic development in China." Research in Transportation Economics , no. : 100947.
The aim of this paper is to analyze the relationship among iron and steel industries, air pollution and economic growth in China. Using monthly time series from 2000 to 2017, we adopt a Long Short Term Memory (LSTM) approach. The empirical results show that the relationship between economic growth and steel production is very strong in the first stage. Furthermore, in our model, we can see that the reduction of polluting emissions is linked to the principle of sustainable development. In particular, this phenomenon lies in the economic growth model responsible for the future generation.
Marco Mele; Cosimo Magazzino. A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China. Journal of Cleaner Production 2020, 277, 123293 .
AMA StyleMarco Mele, Cosimo Magazzino. A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China. Journal of Cleaner Production. 2020; 277 ():123293.
Chicago/Turabian StyleMarco Mele; Cosimo Magazzino. 2020. "A Machine Learning analysis of the relationship among iron and steel industries, air pollution, and economic growth in China." Journal of Cleaner Production 277, no. : 123293.
This study used two different approaches to demonstrate the relationship between pollution emissions, economic growth and COVID-19 deaths in India. Using a time series method and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto tests were completed. The results from our analysis highlight unidirectional causality between economic growth and pollution variables. We then used a D2C algorithm on proportion-based causality, implementing the Oryx 2.0.8 protocol in Apache. The underlying hypothesis was that a predetermined pollution concentration, caused by economic growth, could foster COVID-19, by making the respiratory system more susceptible to infection. We used data (29th January to 18th May 2020) on confirmed deaths (total and daily) and air pollution concentration levels for 25 major Indian cities. We verified an ML causal link between PM2.5, CO2, NO2 and COVID-19 deaths. The implications require careful policy design.
Marco Mele; Cosimo Magazzino. Pollution, Economic Growth and COVID-19 Deaths in India: Machine Learning Evidence. 2020, 1 .
AMA StyleMarco Mele, Cosimo Magazzino. Pollution, Economic Growth and COVID-19 Deaths in India: Machine Learning Evidence. . 2020; ():1.
Chicago/Turabian StyleMarco Mele; Cosimo Magazzino. 2020. "Pollution, Economic Growth and COVID-19 Deaths in India: Machine Learning Evidence." , no. : 1.
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. Empirical findings underline a bidirectional causal relationship between municipal solid waste generation and GDP, indicating that the Environmental Kuznets Curve hypothesis holds for Switzerland. Moreover, we found that waste recovery (recycling and composting) is a key driver in mitigating greenhouse gas emissions. In fact, in the Tree Model, the probability that a change in the waste recovery variable could lead to a reduction in the greenhouse gas emissions registered a value of 87%.
Cosimo Magazzino; Marco Mele; Nicolas Schneider. The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland. Waste Management 2020, 113, 508 -520.
AMA StyleCosimo Magazzino, Marco Mele, Nicolas Schneider. The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland. Waste Management. 2020; 113 ():508-520.
Chicago/Turabian StyleCosimo Magazzino; Marco Mele; Nicolas Schneider. 2020. "The relationship between municipal solid waste and greenhouse gas emissions: Evidence from Switzerland." Waste Management 113, no. : 508-520.