Search

Socio-ecological theories have long been in search of general principles to characterise anthropogenic activity-environmental change dynamics. Besides allowing for more flexible hypothesis testing, stochastic-extended IPAT and ImPACT baselines opened the door to multiple environmental applications in which solid waste generation took a growing stage. This paper surveys Waste Kuznets Curve's original foundations and underlines why the nature and shape of the hypothetical curvilinear income-waste response function tend to compare to a "black-box". It then stresses why diverging conclusions are linked to heterogeneous estimators' choices differing in their statistical assumptions and powers; whereas generic patterns hardly emerge (e.g., income elasticities of waste generation vary even when the mathematical functional form does not; population elasticities are sensitive to time-varying data and income groups). Next, we identify persisting biases of endogeneity which threaten the internal validity of WKC conclusions, if uncontrolled for (e.g., simultaneity, waste measurement errors and garbage policy confounding effect); along with other identification problems including within-panel heterogeneity with systematic slope variations and cross-sectional and spatially dependent income series. Although we propose a set of theoretically justified instrumental variables to exogenously predict income levels and ensure unbiased elasticities, we also detect and underline that additional threats to external invalidity do play out in practice (e.g., asymmetric geographical coverage and bias of case study selection due to environmental data constraints; missing policy-realm; within-waste heterogeneity hidden by widely aggregated indicators; and a non-systematic treatment of the technological effect). All prevent the waste literature from converging and should be considered by future empirical assessments. [ABSTRACT FROM AUTHOR]

Most national and international climate agendas promote energy efficiency and fossil to renewable energy substitution as key future policy directions. This paper surveys macro-energy-emission-output panel assessments and shows that previously estimated carbon response functions present diverging shapes with less evidence on the confounding role of development. This study applies a multivariate regression equation and both Pesaran (1995) and Pesaran (2006) mean group estimators with common correlated effects to illustrative samples of countries with data covering five decades. For all groups, long-run panel coefficients show that energy efficiency improvements associate with larger negative carbon responses than fossil-to-renewable energy shifts. Estimates derived from high-income economies are much smaller in magnitude and significance compared to those of developing countries, which is further corroborated by country-level parameters. This implies that least-energy efficient and -green economies can benefit from a wider set of carbon abatement policies. [ABSTRACT FROM AUTHOR]

The central problem is to shift from a rigid exclusive development to a redistributive inclusive one, more able to smooth development disparities among regions, without further hindering biodiversity and environmental quality. This leaves end-use innovation as the only source of technological progress that could reconcile infinite economic growth and biodiversity conservation. 26 Beckerman, W., 1992, Economic growth and the environment: Whose growth?. [Extracted from the article]

Summary: The EU countries are facing a burning debate with regard to the nature and amount of decarbonized energy used within the supply mix. This study highlights the role of nuclear power and renewable energy use in achieving economic growth in Europe. Capital, labour, exports, and trade openness series are included within a multivariate framework spanning the 1986 to 2019 period and a two‐step methodology is implemented. First, a panel causality analysis over the top 10 nuclear energy‐consuming EU countries is conducted using the panel non‐causality test from Juodis, Karavias and Sarafidis (2021), robust under large cross‐sectional and time‐series dimensions, as well as heterogeneous coefficients. Second, a sub‐set of three states (Germany, Spain, and France) that are different in terms of long‐run nuclear energy policies is selected and a single‐country time‐series assessment is performed using the vector error correction model, causality tests, as well as impulse response functions. Panel EU results support the "conservation hypothesis" for both nuclear and renewable resources, without feedback effect. The country‐level estimates fail to provide homogeneous results since no causal link is found between economic growth and renewable energy use for Spain and France (confirmation of "neutral hypothesis"). A weak causality from renewable energy use to output growth is observed for Germany, while economic growth enhances the nuclear energy consumption in Spain. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the relationships between population growth, energy needs, and environmental sustainability in Africa. To do so, we take Nigeria as an illustrative case and set-up a multivariate framework incorporating total population, electricity demand, per capita income and the amount of CO2 yearly emitted by the power and heating sector over the largest period: 1971–2018. Results confirm the existence of a one-way causality from population to electricity consumption, per capita income, and CO2 emissions without feedback. Besides, a unidirectional link running from electricity consumption to economic growth is depicted, congruent with the electricity-led growth hypothesis. Finally, electricity consumption is found to be a substantial environmental pollution-enabler in Nigeria. The provision of affordable electricity should be balanced by a trend from fossil-fuel supply to sustainable energy resources. [ABSTRACT FROM AUTHOR]

This study investigates the relationship among electricity consumption, per capita GDP, and CO2 emissions for the Kingdom of Saudi Arabia. Trade openness and urbanisation are included within a multivariate approach. Using data covering the 1971–2018 period, the wavelet-coherence analysis is applied to assess the interactions amongst variables. All variables except trade openness appear highly correlated with CO2 emissions. This is further confirmed by our causality testing procedure (i.e. the Johansen and Juselius test for cointegration and the Granger Causality test applied on a Vector Auto-Regressive model) which shows that electricity consumption, per capita income and urbanisation are crucial determinants of CO2 emissions. Accordingly, adequate policy measures are proposed to turn the Saudi power sector towards a sustainable path. [ABSTRACT FROM AUTHOR]

Producers do interpret environmental regulations as direct threats to resource scarcity rents. Sir, The Conference of Parties (COP) 26 ended on 12 November 2021 and yet, much remains unknown on how and whether the international community will solve the climate problem. Many have demonstrated how abandoning fossil fuels requires forecasting fossil fuels producers' anticipations [[2]]. [Extracted from the article]

This paper reviews and discusses methodological choices and statistical inferences relevant to the literature that has worked at identifying long-memory behaviours within the stochastic features of electricity series (consumption and prices). We argue that persisting divergences reported in this literature can be linked to the (non)-treatment of key identification challenges in time-series econometrics. We synthetized those problems in a formalized fashion focusing on the followings: presence of structural breaks in the series (i); inferences affecting panel data structure such as cross-sectional dependence among the series, heterogeneous slope coefficients as well as the problem of missing observations (ii); the issue of integrated time-series observed with noise and some implications for cointegrated relations (iii). [ABSTRACT FROM AUTHOR]

The logic of identifying the stationary features of energy series lays in the policy potentials contained within unit root-related information. By providing an overview of unit root testing frameworks applied on electricity data, this paper underlines why knowledge derived from integration order can benefit to electricity consumption/production forecasting, modelling, and planning. In a methodological discussion, empirical divergences reported in the literature are linked to the variety of unit root testing baselines employed, and the heterogeneous manners through which past stationarity analyses approached identification caveats in time-series econometrics, and threats to internal validity. Further, we argue that time-series model specifications and data choices do matter. They co-determine the external validity of the empirics and may hinder the quality of policy recommendations if mis-considered. Doors for future examinations of stochastic features of electricity series are finally opened, with research opportunities for both theorists and empiricists. [ABSTRACT FROM AUTHOR]

Kreike asserts that war reveals the fallacy in such a divide, as the human costs of war are often symptoms of war's environmental injury. Emmanuel Kreike traces the use of environmental destruction as military option from the 16 SP th sp to the 20 SP th sp Century. Kreike challenges the narrow interpretation of environmental destruction as an externality of war, insisting that it is a deliberate war strategy. [Extracted from the article]

We present a consistent Chebyshev–Fourier–Fourier pseudo-spectral algorithm for the numerical solution of anelastic models. These models satisfy the constraint ∂ i ( ρ ˜ u i ) = 0 , where ρ ˜ is a mean density profile and u the velocity field. The choice of the decoupling method is discussed and the Uzawa approach is generalized to such a constraint. The pressure operator properties are detailed and the solution of the zeroth Fourier mode case is discussed. The solution of linear systems is obtained and refined, using an iterative method. The Uzawa algorithm is embedded in an auto-adaptive multidomain approach in order to handle steep evolving gradients. The algorithm is reduced to the Boussinesq approximation. The characteristics and performance of the resulting numerical code are analyzed. The validation includes comparisons with linear stability results and with the Waddell et al. [55] single-mode experiment. We also compute the nonlinear growth rate of a Boussinesq turbulent mixing layer. A comparison between Boussinesq and anelastic models is then sketched out. Finally four 3D simulations are carried out (with the anelastic and Boussinesq models). These results illustrate the capability of the entire method to handle stiff problems such as the Rayleigh–Taylor configuration. [ABSTRACT FROM AUTHOR]

The effects of historical land-use changes on the Swiss Plateau on the near-surface atmospheric conditions of typical summer days in July were investigated in a case study. A mesoscale dynamical nonhydrostatic model with a horizontal resolution of 1 km2 was used. Two simulations of historical (around 1850) and present conditions as well as six sensitivity experiments were conducted in order to assess the importance of changes in the physiological, morphological, and soil properties. The modeled daily average temperature was 0.25°C lower under present than past land-use conditions. During the day, changes in temperature showed a heterogeneous spatial pattern that depended on the type of the land-use conversion and its geographic location. In areas where afforestation took place, an average warming of more than 1.0°C was observed. In contrast, deforestation resulted in a cooling of up to 2.0°C. Furthermore, an average increase in the water vapor mixing ratio of 0.2 g kg-1 was simulated during the day. During the night, the average temperature was up to 0.6°C lower under present land-use conditions. This nighttime cooling is mainly restricted to low-lying areas on the formerly frequently inundated plains. The diurnal temperature range in the area of land-use changes increased by 0.1°–0.3°C. The sensitivity experiments showed that the daytime temperature decrease was mainly caused by morphological changes, while the altered physiological properties are the dominant factor for the nighttime cooling. The water vapor mixing ratio during the day was mainly affected by changes in the physiological properties. [ABSTRACT FROM AUTHOR]

: BackgroundEndometriosis affecting surgical scars is a well-described entity that can complicate surgery involving hysterotomies. Malignant transformation is a rare event that has been reported complicating ovarian endometriosis mainly.: CaseA 60-year-old woman having in the past two uneventful caesarean deliveries presented with a solid lower abdominal mass. A surgical biopsy and then a radical resection of the lower half of the abdominal wall were performed, with a diagnosis of adenocarcinoma of endometrial origin. A diagnostic curettage excluded primary endometrial carcinoma. At 1 year she is NED.: ConclusionThis case report illustrates the carcinomatous transformation of an endometrial implant. Despite the rarity of such a diagnosis, it should be borne in mind when endometriosis in abdominal wall is suspected because an oncological resection is required. [Copyright &y& Elsevier]

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 be-tween the economies. [ABSTRACT FROM AUTHOR]

With the EU Green Deal initiatives, European members seek to launch the first climate neutral continent by 2050. This paper assesses the stochastic convergence of per capita energy consumption series for an illustrative sample of 15 EU countries with memberships prior to the 2004 enlargement, using data spanning the 1970–2018 period. Results from the confidence interval subsampling (asymmetric and equal-tailed) highlight that 11 out of the 15 EU series exhibit a long-run memory behaviour, while a diverging pattern was observed for the UK, Germany, Portugal, and Italy. Finally, per capita energy use series persist but fail to reveal one of the above dynamics for Ireland and Spain. Also, findings from the non-parametric Bayesian application (ANOVA/linear Dependent Dirichlet Process (DDP) mixture model) show how economic growth operates as a converging energy consumption-enabler over the long-run, from which the EU membership cannot be excluded. In particular, we highlight how the nature of energy-GDP hypotheses vary with the stochastic properties of energy use (converging behaviour with temporary shocks, converging pattern with permanent shocks, and diverging dynamic). Finally, our conclusions overcome the well-established development stage argument as we claim that countries with similar energy convergence patterns may need to adopt similar energy policies. [ABSTRACT FROM AUTHOR]

• Oil product consumption and CO 2 emissions sharply influence the transport logistics indexes. • A feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance. • We found empirical sustain for the "feedback hypothesis" for Germany and Japan. This paper critically assesses the effect of fossil fuel dependence and polluting emissions from the transport sector on the performance of logistics operations in the context of Green Supply Chain Management (GSCM). We collected macro-level time-series data for a sample of 27 European Union (EU) countries over the period 2007–2018. A new Artificial Neural Networks (ANNs) algorithm is adopted in a multivariate framework to investigate the dynamic interactions among a range of Logistics Performance Indexes (LPI), the demand for oil products, and carbon dioxide (CO 2) emissions from fuel combustion in the transport sector. Empirical findings show that oil product consumption and CO 2 emissions sharply influence the transport logistics indexes. However, a feedback relationship is discovered for environmental pollution, indicating that oil use is not significantly driven by supply chain performance. Based on our empirical insights, adequate policy recommendations are provided to help turning the logistics sector towards a more sustainable path in the European area. [ABSTRACT FROM AUTHOR]

China, India, and the USA are the world's biggest energy consumers and CO 2 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 CO 2 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 CO 2 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 CO 2 emissions. This indicates that curbing CO 2 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. • Renewable energies, coal consumption, economic growth, and CO 2 we examine. • We investigate the causal predicted relationship on China, India, and the USA. • An advanced methodology in Machine Learning is used. • India has a prediction of an increase in emissions of CO 2. • China and the U.S. present a more promising path to sustainability than India. [ABSTRACT FROM AUTHOR]

• A bidirectional causal relationship between municipal solid waste generation and GDP emerges. • Waste recovery (recycling and composting) is a key driver in mitigating greenhouse gas emissions. • The D2C algorithm shows that more waste coincides with higher consumption and more significant economic growth. 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%. [ABSTRACT FROM AUTHOR]

The increasing mismatch between the demand and supply of power in Nigeria raises concerns about the ability of this country to meet its vital energy security and sustainability targets in a demography-growing environment. This paper assesses how these three factors comove over the long run. While Nigeria provides an illustrative case, a multivariate framework including population dynamics, the demand for electricity, and CO 2 emissions from the power and heating sector is set with actual time-series data spanning the last five decades. Two independent estimation strategies are conducted: a time-series analysis (i.e., Least Squares with breaks regression) is complemented with Machine Learning experiments (i.e., ML Clustering method). In general, both methodologies' outputs stress the engine role of the population in driving the demand for power over the long run. • Some empirics on supply security and sustainability challenges facing Nigeria's power sector. • The relationship among population dynamics, demand for electricity, and CO 2 emissions in Nigeria is analyzed. • Both time-series and ML results highlight the role of the population in driving the demand for power over the long-run. • Economic, energy, and environmental factors seem to comove over time. [ABSTRACT FROM AUTHOR]

• The Belgium nuclear phase-out plan is analyzed. • A multivariate model through time-series analyses is implemented over the 1974–2019 period. • We develop and apply an innovative partial differential equations (PDE) algorithm. • Time-series and machine learning findings demonstrate suggests that the nuclear phase-out might neither adversely impact the economy's growth nor threaten the emission reduction target in the long-run. By 2025, Belgium will phase-out nuclear power. Unassessed so far, this policy reform may modify the economic and environmental channels through which energy and society interfere in this country. In this paper, we investigate whether this structural energy change may adversely impact the growth of the Belgian economy (i) and its ability to meet its long-term greenhouse gas emission targets (ii). A multivariate model comprising production factors (labor, capital, and exports), nuclear and renewable energy uses, total primary energy supply, economic growth, and CO 2 emissions from the power and heating sector is combined with real time-series data spanning the 1974–2019 period. The analysis consists in sequentially assessing two distinct nexuses (energy-economy and energy-economy-environment) over reduced- and augmented frameworks (excluding and including nuclear energy), and through a two-stage empirical strategy: time-series econometric estimations (Toda-Yamamoto causality test, Impulse Response Functions (IRFs), and the Auto-Regressive Distributed Lags (ARDL) and Machine Learning (ML) experiments with a Partial Differential Equations (PDEs) algorithm. For robustness purposes, we conduct two seminal tests which relate to dynamic predictive processes (T-Mat and Verticality tests). Besides confirming the time-series findings, our ML results highlight the necessity to timely manage the process of nuclear phase-out, along with a progressive deployment of installed renewable energy capacity. This should avoid additional economic costs, energy security threats, and undermining of climate targets. In doing so, this study combines macro-level nexus investigations with the politics and institutional determinants of nuclear energy reliance and seeks to bring inclusive knowledge on this topic. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the causal links among export diversification, per capita income, and energy demand for 20 Asia-Pacific Economic Cooperation (APEC) countries. Human capital, industry share, and Foreign Direct Investments (FDI) are included within a multivariate framework. Using data covering the 1995–2018 period, an innovative sequential Artificial Neural Networks experiment is developed. Empirical results highlight that the export diversification index negatively affects the neural target corresponding to energy demand. Conversely, per capita GDP displays a significant influence on the acceleration of the same energy target. Hence, findings suggest that export diversification plays a mitigating role in the demand for energy, which is congruent with the literature. Moreover, economic growth is found to be substantially driven by export diversification, while no evidence of a causal effect is found among export diversification, human capital, and FDI. Such inferences are finally corroborated by a Decision Tree model, conducted as a final robustness check. In line with the Sustainable Development Goals (SDGs), adequate measures are suggested to improve energy savings along the supply chain, without hindering the economic performance of the Asian region. [ABSTRACT FROM AUTHOR]

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. • Examining 25 topmost Logistics Countries on many fronts. • Technological innovation, HDI, urbanization, and trade openness spur logistic performance. • Logistic performance does not improve with employment rate and Gross Fixed Capital Formation. • Logistic performance increases carbon dioxide emissions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and CO 2 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. • An innovative Machine Learning algorithm (the Quantum model) has been constructed and applied. • The empirical findings support the powerful role of biomass energy in reducing CO 2 emissions. • GDP represents an important determinant of environmental pollution in Germany. [ABSTRACT FROM AUTHOR]

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. • The causal link between natural gas consumption and economic growth exists for both countries. • Predictive analysis highlights a propagation effect between natural gas consumption and economic growth for both economies. • We found empirical sustain for the "feedback hypothesis" for Germany and Japan. [ABSTRACT FROM AUTHOR]

This study represents the first empirical estimation of threshold values between nitrogen dioxide (NO 2) concentrations and COVID-19-related deaths in France. The concentration of NO 2 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 2 in spreading the epidemic. The underlying hypothesis is that NO 2 , 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 NO 2 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. • Machine Learning approach based on ANNs techniques is proposed on NO 2 concentrations and COVID-19 deaths. • Hourly data (41 consecutive days) for three French cities we use for our analysis. • Analysis on the effects of three different threshold levels of NO 2 compared to COVID-19 deaths. • New threshold values on NO 2 concentrations are proposed to policy makers. [ABSTRACT FROM AUTHOR]

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. Unlabelled Image • In-depth assessment of Municipal Solid Waste Sustainability is presented. • We assess the effect of wealth and urban sprawl on sustainable waste management. • We observe that urbanization is the backbone of GHG emissions from waste. • We find a gradual shift from linear economy to circular economy. • Other countries can learn from Denmark's path towards sustainable waste management. [ABSTRACT FROM AUTHOR]

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 2.5 and PM 10 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 PM 2.5 and PM 10 connected to COVID-19: 17.4 µg/m3 (PM 2.5) and 29.6 µg/m3 (PM 10) for Paris; 15.6 µg/m3 (PM 2.5) and 20.6 µg/m3 (PM 10) for Lyon; 14.3 µg/m3 (PM 2.5) and 22.04 µg/m3 (PM 10) 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. [ABSTRACT FROM AUTHOR]

Highlights • We developed a Decision Support System (DSS) that integrated eye-tracking measures. • Eye-fixations helped the DSS to capture the context that determined user decisions. • Due to this fact, the accuracy of consecutive DSS suggestions was improved. • The last fixations prior to the decision strongly aligned with the selected choice. Abstract We investigated the possibility of developing a decision support system (DSS) that integrates eye-fixation measurements to better adapt its suggestions. Indeed, eye fixation give insight into human decision-making: Individuals tend to pay more attention to key information in line with their upcoming selection. Thus, eye-fixation measures can help the DSS to better capture the context that determines user decisions. Twenty-two participants performed a simplified Air Traffic Control (ATC) simulation in which they had to decide to accept or to modify route suggestions according to specific parameter values displayed on the screen. Decisions and fixation times on each parameter were recorded. The user fixation times were used by an algorithm to estimate the utility of each parameter for its decision. Immediately after this training phase, the algorithm generated new route suggestions under two conditions: 1) Taking into account the participant's decisions, 2) Taking into account the participant's decisions plus their visual behavior using the measurements of dwell times on displayed parameters. Results showed that system suggestions were more accurate than the base system when taking into account the participant's decisions, and even more accurate using their dwell times. Capturing the crucial information for the decision using the eye tracker accelerated the DSS learning phase, and thus helped to further enhance the accuracy of consecutive suggestions. Moreover, exploratory eye-tracking analysis reflected two different stages of the decision-making process, with longer dwell times on relevant parameters (i.e. involved in a rule) during the entire decision time course, and frequency of fixations on these relevant parameters that increased, especially during the last fixations prior to the decision. Consequently, future DSS integrating eye-tracking data should pay specific care to the final fixations prior to the decision. In general, our results emphasize the potential interest of eye-tracking to enhance and accelerate system adaptation to user preference, knowledge, and expertise. [ABSTRACT FROM AUTHOR]

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. • A unidirectional causal link from urbanization to carbon emissions is discovered. • The constant variation in the ICT penetration generates a predictive causality variation of the electricity consumption. • A unidirectional relationship from ICT penetration to CO 2 emissions emerges. [ABSTRACT FROM AUTHOR]