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Prof. Cosimo Magazzino
Roma Tre University

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0 Energy
0 Energy & the Environment
0 Public Finance
0 Applied Economics and Finance

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Short Biography

Cosimo Magazzino was born in Grottaglie (Italy) in 1980. He has obtained his BA in Public Administration and his Ph.D. in Political Sciences at Roma Tre University (Rome, Italy). POSITIONS Currently he is Professor of Environmental and Energy Economics and European Economic Policy at the Department of Political Sciences (DISCIPOL), Roma Tre University (Rome, Italy). He is Director of the Master on Management of Energy and Environment organized by Roma Tre University and Italian Association of Energy Economists (AIEE), Italy. He is member of the Ph.D. Council on Government of the Enterprise, Administration and Society in the International Dimension, University of Teramo (Italy). RESEARCH INTERESTS His research interests include: public finance; energy econometrics; environmental Kuznets curve; time series econometrics; panel data models. His current research activity is focused on the econometric analysis of the sustainability of public finances, the relationship among CO2 emissions, GDP and energy consumption, and the twin deficits. TEACHING He has taught and he is teaching Econometrics, Introductory Econometrics, Economic Policy, European Economic Policy, Energy and Environmental Economics, Economics and Development Policy, Mathematics for Social Sciences, and Health Economics in: the undergraduate and graduate programmes in the Department of Political Sciences, Roma Tre University (Italy); Department of Economics, Management and Business Law, University of Bari, Italy

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Research paper
Published: 24 May 2021 in Italian Economic Journal
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Research article
Published: 29 March 2021 in Environmental Science and Pollution Research
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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.

ACS Style

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 Style

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 (30):41127-41134.

Chicago/Turabian Style

Marco 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.

Journal article
Published: 18 March 2021 in Energy Reports
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This paper investigates the relationship between energy consumption and economic growth with over eighty decades of Italian dataset. The wavelet analysis is applied to decompose series into different time scales whereas the frequency domain technique is used to examine time-specific shocks. Results of both unit root and stationarity tests indicate all series are integrated of order one, however, no evidence of long-run relationship is reported between energy consumption and economic development. We observe that the causal flow from economic growth to energy consumption becomes dominant at lower scales (up to 4 years), while at higher scales the strength of causality from energy use to growth declines. Therefore, the influence of energy consumption on economic growth can significantly be detected only at lower scales. If only original series and lower scales are considered, causal findings lean towards the feedback mechanism, with bidirectional causal relationship. This bidirectional causality is reinforced at all frequency bands, thus, causality from energy consumption to economic growth is observed only at frequencies between 1.3–1.8 (3.49–4.83 years) and 2.2–2.4 (2.61–2.85 years). However, when higher scales are considered, the causality test results are in line with the conservation hypothesis. More precisely, causality from economic growth to energy consumption is reinforced by frequency technique at higher time scales (8–32 years) but only at a frequency more than 0.6 (more than 10.47 years). The differences in the applied results provide alternative policy implications, justifying the use of wavelet approach to decompose time series into various time scales.

ACS Style

Cosimo Magazzino; Mihai Mutascu; Marco Mele; Samuel Asumadu Sarkodie. Energy consumption and economic growth in Italy: A wavelet analysis. Energy Reports 2021, 7, 1520 -1528.

AMA Style

Cosimo Magazzino, Mihai Mutascu, Marco Mele, Samuel Asumadu Sarkodie. Energy consumption and economic growth in Italy: A wavelet analysis. Energy Reports. 2021; 7 ():1520-1528.

Chicago/Turabian Style

Cosimo Magazzino; Mihai Mutascu; Marco Mele; Samuel Asumadu Sarkodie. 2021. "Energy consumption and economic growth in Italy: A wavelet analysis." Energy Reports 7, no. : 1520-1528.

Journal article
Published: 10 March 2021 in Journal of Environmental Management
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Journal article
Published: 05 March 2021 in Sustainability
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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.

ACS Style

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 Style

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 (5):2828.

Chicago/Turabian Style

Cosimo 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.

Journal article
Published: 02 March 2021 in Journal of Environmental Management
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Journal article
Published: 19 February 2021 in Advances in Environmental and Engineering Research
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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.

ACS Style

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 Style

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 (02):1-1.

Chicago/Turabian Style

Cosimo 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.

Journal article
Published: 16 February 2021 in Science of The Total Environment
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The impact of climate change has resulted in several long-term events including extreme temperatures. Besides, the occurrence of climate events impedes economic progress––affecting economic readiness of climate mitigation. However, the effect of climatic factors on economic productivity has not been extensively covered in existing literature, especially among climate regimes. Here, we use sophisticated panel and time series techniques to examine the heterogeneous effects of temperature and emissions on income from 1960 to 2014. Our empirical results indicate a 1% rise in temperature declines income by 0.39% whereas 1% increase in emission levels stimulates income by 0.22%. This implies a mutual relationship between income and emissions––where environmental pollution supports wealth creation and vice versa. We find that a shift from optimal temperature levels to extreme patterns hamper economic productivity. Extreme temperatures affect heating and cooling degree days due to increased energy requirements, hence, escalating energy demand and emissions. With the agenda towards emission reduction, this study emphasizes economic structural change through transition from brown to green growth.

ACS Style

Cosimo Magazzino; Mihai Mutascu; Samuel Asumadu Sarkodie; Festus Fatai Adedoyin; Phebe Asantewaa Owusu. Heterogeneous effects of temperature and emissions on economic productivity across climate regimes. Science of The Total Environment 2021, 775, 145893 .

AMA Style

Cosimo Magazzino, Mihai Mutascu, Samuel Asumadu Sarkodie, Festus Fatai Adedoyin, Phebe Asantewaa Owusu. Heterogeneous effects of temperature and emissions on economic productivity across climate regimes. Science of The Total Environment. 2021; 775 ():145893.

Chicago/Turabian Style

Cosimo Magazzino; Mihai Mutascu; Samuel Asumadu Sarkodie; Festus Fatai Adedoyin; Phebe Asantewaa Owusu. 2021. "Heterogeneous effects of temperature and emissions on economic productivity across climate regimes." Science of The Total Environment 775, no. : 145893.

Journal article
Published: 26 January 2021 in Sustainability
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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.

ACS Style

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 Style

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 (3):1285.

Chicago/Turabian Style

Cosimo 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.

Research article
Published: 07 January 2021 in Energy Sources, Part B: Economics, Planning, and Policy
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This study investigates the relationship between Information and Communication Technology (ICT) penetration, electricity consumption, economic growth, and environmental pollution within a multivariate framework. A panel of 16 EU countries was analyzed over the 1990–2017 period. The results of the Dumitrescu-Hurlin panel causality tests reveal the existence of a one-way causality running from ICT usage and electricity consumption and which, in turn, causes a rise in CO2 emissions and improves GDP. Panel Mean-Group regression results highlight that economic growth is also an important driver of electricity demand as a 1% economic growth rate is associated with a 0.13% increase in per capita electricity consumption. These results demonstrate for the first time in the literature a single assessment on the linkages among ICT, electricity use and environmental pollution with a novel focus on the EU. Based on these results, adequate measures should encompass the adverse environmental effects of ICT, while energy saving policies must be carefully implemented in order not to hinder economic growth.

ACS Style

Cosimo Magazzino; Donatella Porrini; Giulio Fusco; Nicolas Schneider. Investigating the link among ICT, electricity consumption, air pollution, and economic growth in EU countries. Energy Sources, Part B: Economics, Planning, and Policy 2021, 1 -23.

AMA Style

Cosimo Magazzino, Donatella Porrini, Giulio Fusco, Nicolas Schneider. Investigating the link among ICT, electricity consumption, air pollution, and economic growth in EU countries. Energy Sources, Part B: Economics, Planning, and Policy. 2021; ():1-23.

Chicago/Turabian Style

Cosimo Magazzino; Donatella Porrini; Giulio Fusco; Nicolas Schneider. 2021. "Investigating the link among ICT, electricity consumption, air pollution, and economic growth in EU countries." Energy Sources, Part B: Economics, Planning, and Policy , no. : 1-23.

Journal article
Published: 05 January 2021 in Environmental Research
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Marco 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.

Journal article
Published: 16 December 2020 in Energy
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Preprint content
Published: 03 December 2020
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We 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.

ACS Style

Cosimo Magazzino; Angelo Leogrande. Subjective Well-being in Italian Regions: A Panel Data Approach. 2020, 1 .

AMA Style

Cosimo Magazzino, Angelo Leogrande. Subjective Well-being in Italian Regions: A Panel Data Approach. . 2020; ():1.

Chicago/Turabian Style

Cosimo Magazzino; Angelo Leogrande. 2020. "Subjective Well-being in Italian Regions: A Panel Data Approach." , no. : 1.

Journal article
Published: 20 November 2020 in Renewable Energy
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Journal article
Published: 25 September 2020 in Science of The Total Environment
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Journal article
Published: 12 September 2020 in Applied Energy
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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.

ACS Style

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 Style

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.

Chicago/Turabian Style

Cosimo 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.

Research article
Published: 04 September 2020 in Environmental Science and Pollution Research
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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.

ACS Style

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 Style

Marco 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 Style

Marco 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.

Journal article
Published: 01 September 2020 in Environmental Research Letters
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ACS Style

Cosimo Magazzino; Marco Mele; Nicolas Schneider; Guillaume Vallet. The relationship between nuclear energy consumption and eco-nomic growth: evidence from Switzerland. Environmental Research Letters 2020, 15, 1 .

AMA Style

Cosimo Magazzino, Marco Mele, Nicolas Schneider, Guillaume Vallet. The relationship between nuclear energy consumption and eco-nomic growth: evidence from Switzerland. Environmental Research Letters. 2020; 15 (9):1.

Chicago/Turabian Style

Cosimo Magazzino; Marco Mele; Nicolas Schneider; Guillaume Vallet. 2020. "The relationship between nuclear energy consumption and eco-nomic growth: evidence from Switzerland." Environmental Research Letters 15, no. 9: 1.

Journal article
Published: 26 August 2020 in Research in Transportation Economics
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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.

ACS Style

Cosimo Magazzino; Marco Mele. On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics 2020, 100947 .

AMA Style

Cosimo Magazzino, Marco Mele. On the relationship between transportation infrastructure and economic development in China. Research in Transportation Economics. 2020; ():100947.

Chicago/Turabian Style

Cosimo Magazzino; Marco Mele. 2020. "On the relationship between transportation infrastructure and economic development in China." Research in Transportation Economics , no. : 100947.

Research article
Published: 17 August 2020 in International Journal of Finance & Economics
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In this paper we analyse the relationship between public primary deficit and debt for Italian sustainability over the 1862–2013 years. Our empirical strategy uses the wavelet analysis. The evidence confirms the absence of fiscal sustainability in the long‐run for Italy, reinforcing the need for a rebalancing of the public accounts.

ACS Style

Cosimo Magazzino; Francesco Forte; Lorenzo Giolli. On the Italian public accounts' sustainability: A wavelet approach. International Journal of Finance & Economics 2020, 1 .

AMA Style

Cosimo Magazzino, Francesco Forte, Lorenzo Giolli. On the Italian public accounts' sustainability: A wavelet approach. International Journal of Finance & Economics. 2020; ():1.

Chicago/Turabian Style

Cosimo Magazzino; Francesco Forte; Lorenzo Giolli. 2020. "On the Italian public accounts' sustainability: A wavelet approach." International Journal of Finance & Economics , no. : 1.