Drafts by Nicolas Schneider
EnerarXiv, 2020
We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationshi... more We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the COVID-19 outbreak and air pollution. Using Artificial Neural Networks 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 in spreading the epidemic. The underlying hypothesis is that a predetermined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID19: 17.4 µg/m 3 (PM2.5) and 29.6 µg/m 3 (PM10) for Paris; 15.6 µg/m 3 (PM2.5) and 20.6 µg/m 3 (PM10) for Lyon; 14.3 µg/m 3 (PM2.5) and 22.04 µg/m 3 (PM10) for Marseille.
Papers by Nicolas Schneider
Journal of Cleaner Production, 2021
While the deployment of technological innovation was able to avert a devastating global supply ch... more 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. Empirical estimates from Fully Modified Ordinary Least Squares (FMOLS), Generalized Method of Moments (GMM), and Quantile Regression (QR) model suggest that technological innovation, Human Development Index (HDI), urbanization, and trade openness significantly boost logistic performance, whereas employment and Gross Fixed Capital Formation (GFCF) fail to respond in such a desirable path. In turn, an increase in the Logistic Performance Index (LPI) is found to worsen economic growth. Finally, LPI exhibits a large positive effect on carbon emissions, which is congruent with a strand of the literature highlighting that the modern supply chain is far from being decarbonized. Thus, this evidence further suggest that more global efforts should be geared to attain a sustainable logistics.
Utilities Policy, 2021
This paper examines the linkages among Information and Communication Technologies (ICT) penetrati... more This paper examines the linkages among Information and Communication Technologies (ICT) penetration, electricity consumption, economic growth, urbanization, and environmental pollution for 25 OECD countries over the 1990-2017 period. We first conduct several panel data analyses and then write and apply a new Machine Learning (ML) algorithm. Empirical findings show that ICT usage enhances economic growth, and it is also a crucial driver of electricity consumption, which, in turn, translates into polluting emissions. The ML results highlight internet usage emerges as a substantial CO 2 emissions-enabler, thus corroborating our panel data findings. Potential policy measures are discussed.
Journal of Economic Studies, 2021
Purpose-The purpose of this paper is to empirically test the economic convergence that operate be... more Purpose-The purpose of this paper is to empirically test the economic convergence that operate between five selected Asian countries (namely Thailand, Singapore, Malaysia, the Philippines and Indonesia). In particular, it seeks to investigate how increased economic integration has impacted the inter-country income levels among the five founding members of ASEAN. Design/methodology/approach-A new Machine Learning (ML) approach is applied along with a panel data analysis (GMM), and the application of KOF Globalization Index. Findings-The Generalized Method of Moments (GMM) results highlight that the endogenous growth theory seems to be supported for the selected Asian countries, indicating evidence of diverging forces resulting from unequal growth and polarization dynamics. Overcoming the technical issues raised by the econometric approach, the new ML algorithm brings contrasted but interesting results. Using the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members. Originality/value-Using the KOF Globalization Index, the authors confirm how the last phase of globalization set the conditions for an economic convergence among sample members. As a matter of fact, the new LSTM algorithm has provided consistent evidence supporting the existence of converging forces. In fact, the results highlighted the effectiveness of the experiments and the algorithm we chose. The high predictability of the authors' model and the absence of self-alignment in the values showed a convergence between the economies.
Environmental Science and Pollution Research, 2021
Although the literature on the relationship between economic growth and CO 2 emissions is extensi... more Although the literature on the relationship between economic growth and CO 2 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 CO 2 emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO 2 increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.
Journal of Environmental Management, 2021
This paper aims to investigate the causal relationship among renewable energy technologies, bioma... more 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.
Energy Sources, Part B: Economics, Planning, and Policy, 2021
This study investigates the relationship between Information and Communication Technology (ICT) p... more 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.
Environmental Research, 2021
This study represents the first empirical estimation of threshold values between nitrogen dioxide... more 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.
Energy, 2021
While Germany and Japan are going through major energy reforms, natural gas consumption is taking... more 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.
International Review of Environmental and Resource Economics, 2020
The empirical relationship among energy and economic growth has been abundantly studied in the li... more The empirical relationship among energy and economic growth has been abundantly studied in the literature. In this paper, we provide a state-of-the-art review of the topic, and highlight the main methodological issues that previous studies have attempted to address so far. Since Israel is experiencing profound energy changes, we take this case as an illustration and investigate the causal link between primary energy consumption and economic growth. Capital and labour are included in the model with multivariate framework. A cointegrating relationship is found among the variables. Causality tests results display both short and long-run relationship be-tween economic growth and primary energy consumption. Besides, a unidirectional causality running from economic growth to primary energy consumption is supported. Since the primary energy consumed in Israel is overwhelmingly oil, natural gas, and coal, we support the econom-ic growth-led-primary energy consumption hypothesis. In line with previous studies, our find-ings suggest that promoting low-carbon energy sources through fossil conservation policies may not significantly hinder economic growth.
Renewable Energy, 2021
China, India, and the USA are the world’s biggest energy consumers and CO2 emitters. Being the le... more 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.
Science of the Total Environment, 2021
Municipal solid waste (MSW) is one of the most urgent issues associated with economic growth and ... more 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.
Applied Energy, 2020
Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector... more 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.
Waste Management, 2020
Municipal solid waste generation is becoming a prominent issue in the environmental arena. The ai... more 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%.
Environmental Research Letters, 2020
This study aims to investigate the relationship between nuclear energy consumption and economic g... more This study aims to investigate the relationship between nuclear energy consumption and economic growth in Switzerland over the period 1970-2018. We use data on capital, labour, and exports within a multivariate framework. Starting from the consideration that Switzerland has decided to phase out nuclear energy by 2034, we examine the effect of this structural economic-energy change in the country. To do so, two distinct estimation tools are performed. The first model, using a time-series approach, analyze the relationship between bivariate and multivariate causality. The second, using a Machine Learning methodology, test the results of the econometric modelling through an Artificial Neural Networks process. This last empirical procedure represents our original contribution with respect to the previous energy-GDP papers. The results, in the logarithmic propagation of neural networks, suggest a careful analysis of the process that will lead to the abandonment of nuclear energy in Switzerland to avoid adverse effects on economic growth.
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Drafts by Nicolas Schneider
Papers by Nicolas Schneider