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

This article contributes to the scant literature exploring the determinants of methane emissions. A lot is explored considering CO2 emissions, but fewer studies concentrate on the other most long-lived greenhouse gas (GHG), methane which contributes largely to climate change. For the empirical analysis, a large dataset is used considering 192 countries with data ranging from 1960 up to 2022 and considering a wide set of determinants (total central government debt, domestic credit to the private sector, exports of goods and services, GDP per capita, total unemployment, renewable energy consumption, urban population, Gini Index, and Voice and Accountability). Panel Quantile Regression (PQR) estimates show a non-negligible statistical effect of all the selected variables (except for the Gini Index) over the distribution's quantiles. Moreover, the Simple Regression Tree (SRT) model allows us to observe that the losing countries, located in the poorest world regions, abundant in natural resources, are those expected to curb methane emissions. For that, public interventions like digitalization, green education, green financing, ensuring the increase in Voice and Accountability, and green jobs, would lead losers to be positioned in the winner's rankings and would ensure an effective fight against climate change.

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

The presence of heavy metals in water pose a serious threat to both public and environmental health. However, the advances in the application of low cost biochar based adsorbent synthesize from various feedstocks plays an effective role in the of removal heavy metals from water. This study implies the introduction of novel method of converting food waste (FW) to biochar through pyrolysis, examine its physiochemical characteristics, and investigate its adsorption potential for the removal of heavy metals from water. The results revealed that biochar yield decreased from 18.4 % to 14.31 % with increase in pyrolysis temperature from 350 to 550 °C. Likewise, increase in the pyrolysis temperature also resulted in the increase in the ash content from 39.87 % to 42.05 % thus transforming the biochar into alkaline nature (pH 10.17). The structural and chemical compositions of biochar produced at various temperatures (350, 450, and 550 °C) showed a wide range of mineralogical composition, and changes in the concentration of surface functional groups. Similarly, the adsorption potential showed that all the produced biochar effectively removed the selected heavy metals from wastewater. However a slightly high removal capacity was observed for biochar produced at 550 °C that was credited to the alkaline nature, negatively charged biochar active sites due to O-containing functional groups and swelling behavior. The results also showed that the maximum adsorption was recorded at pH 8 at adsorbent dose of 2.5 g L-1 with the contact time of 120 min. To express the adsorption equilibrium, the results were subjected to Langmuir and Freundlich isotherms and correlation coefficient implies that the adsorption process follows the Freundlich adsorption isotherm. The findings of this study suggest the suitability of the novel FW derived biochar as an effective and low cost adsorbent for the removal of heavy metals form wastewater.

On the relationship between oil market and European stock returns

This paper investigates the dynamic relationship between the oil market and European stock market returns using monthly data from May 2007 to April 2022 for 27 European Union member countries. A novel approach is adopted by using the time-varying Granger causality test and the structural vector auto-regression model to examine the causal links. Empirical results reveal strong evidence of time-varying causation between the variables, considering the oil market from both the supply-side and demand-side perspectives. In light of these findings, numerous policy considerations emerge, including refining risk management strategies for investors, reformulating economic and energy policies, the potential impact on monetary policy decisions, the need for ad hoc market regulations, facilitating investor education initiatives, promoting international cooperation, and advancing the transition to sustainable energy sources.

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

There is a growing concern about inappropriate waste disposal and its negative impact on human health and the environment. The objective of this study is to understand household waste segregation intention considering psychological, institutional, and situational factors simultaneously. Insights into the motivations of household waste segregation drivers may assist in a better knowledge of how to pursue the most efficient and effective initiatives. For this purpose, data from a representative sample comprising 849 households is obtained from the twin cities of Islamabad and Rawalpindi (Pakistan). The empirical analysis employs a Structural Equation Modeling (SEM) approach, showing that policy instruments have significant direct and indirect impacts on households' segregation intentions. The results highlight that government policy instruments strengthen personal and perceived norms for waste segregation intentions, resulting in an external intervention that would encourage intrinsic motivation. Therefore, policy actions become the main entry point for initiating waste segregation behavior. Public policy must continue to emphasize waste segregation since it may help resource recovery. This is imperative because the environment is a shared resource, and its conservation increases social welfare.

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

This research aims to examine the validity of the Environmental Kuznets Curve (EKC) hypothesis in 37 Organization for Economic Co-operation and Development (OECD) countries over the period from 1960 to 2019. Panel Quantile Regressions (QR) show that for the lower quartile, economic growth does not impact emissions; for the central quartile a U-shaped curve emerges; while for the upper quartile, an N-shaped curve is found. In addition, cointegrating regressions highlight that economic growth, fossil fuel consumption, and population exert a detrimental effect on the environment, while renewable energy consumption reduces carbon dioxide (CO2) emissions. These results are confirmed by panel causality tests since a feedback mechanism is found between CO2 emissions and the remaining series. Furthermore, single-country estimates provide evidence of great variability in the sample.

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

This study seeks to address pertinent economic and environmental issues associated with natural gas flaring, especially for the world's leading natural gas flaring economies (i.e. Russia, Iraq, Iran, the United States, Algeria, Venezuela, and Nigeria). By applying relevant empirical panel and country-specific approaches, the study found that fuel energy export positively impacts economic growth with elasticity of ~ 0.22 to ~ 0.24 for the panel examination. It is further revealed that environmental quality in the panel is hampered by increase in economic growth, gas flaring, fuel energy export, and urbanization. Moreover, for the country-wise inference, government quality desirably moderates economic and environmental aspects of gas flaring in Venezuela and Nigeria, and in Russia and Iran respectively. However, government quality moderates gas flaring to cause economic downturn in the USA. Additionally, economic growth increased with increase in urbanisation (in Iraq and the USA), gas flaring (in Iran and the USA), government quality (only in the USA), and fuel energy export (only in Algeria) while economic growth downturn is due to increase urbanisation in Russia and the USA, increase in fuel energy export in the USA, and increase in government quality in Russia. Meanwhile, environmental quality is worsened through intense carbon dioxide emission from increased urbanisation activity (in Iraq, Iran, Algeria, and Nigeria), increased fuel energy export (in Nigeria), increased natural gas flaring (in Algeria and Nigeria), increased GDP (in Russia, Iran, USA, Algeria, and Venezuela), and high government quality (in Iran). Interestingly, the result revealed that increase in GDP (in Nigeria), increase in urbanisation (in the USA), and increase in gas flaring (in Algeria and Nigeria) dampens environmental quality. Importantly, this study offers policy insight into sustainable approaches in natural gas production, government effectiveness, and regulatory quality.

The trilemma among CO2 emissions, energy use, and economic growth in Russia

This paper examines the relationship among CO₂ emissions, energy use, and GDP in Russia using annual data ranging from 1990 to 2020. We first conduct time-series analyses (stationarity, structural breaks, cointegration, and causality tests). Then, we performed some Machine Learning experiments as robustness checks. Both approaches underline a bidirectional causal flow between energy use and CO₂ emissions; a unidirectional link running from CO₂ emissions to real GDP; and the predominance of the "neutrality hypothesis" for energy use-GDP nexus. Therefore, energy conservation measures should not adversely affect the economic growth path of the country. In the current geopolitical scenario, relevant policy implications may be derived.

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

This study investigates the co-movements of gasoline and diesel prices in three European countries (i.e. Germany, France, and Italy) with different fuel tax systems in place. The methodology follows a time-frequency approach, allowing us to analyse the co-movements at different frequencies and moments in time. As a novelty, we study the impact of fuel tax systems and international oil price dynamics on gasoline and diesel price co-movement. Using weekly data spanning the period from January 2005 to June 2021, the wavelet coherence analysis shows co-movements between gasoline and diesel at all frequencies, as well as during specific periods, but stronger in the long run. This evidence is recorded across all three countries, regardless of their tax systems. However, in decoupling the effect of international oil prices, the partial wavelet coherence analysis shows co-movements emerging also in the short run, with them being stronger around the global financial crisis (2008-2009). Although gasoline taxes are generally higher than diesel taxes, the analysis highlights that fuel tax systems do not influence the co-movements of fuel prices. Thus, shedding new light on the co-movement between commodity prices is fundamental, particularly in light of the current international geopolitical scene.

A Dynamic Factor and Neural Networks Analysis of the Co-movement of Public Revenues in the EMU

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.

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

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

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

Although the literature on the relationship between economic growth and CO₂ emissions is extensive, the use of machine learning (ML) tools remains seminal. In this paper, we assess this nexus for Italy using innovative algorithms, with yearly data for the 1960-2017 period. We develop three distinct models: the batch gradient descent (BGD), the stochastic gradient descent (SGD), and the multilayer perceptron (MLP). Despite the phase of low Italian economic growth, results reveal that CO₂ emissions increased in the predicting model. Compared to the observed statistical data, the algorithm shows a correlation between low growth and higher CO₂ increase, which contradicts the main strand of literature. Based on this outcome, adequate policy recommendations are provided.

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

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

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

This paper aims to investigate the causal relationship among renewable energy technologies, biomass energy consumption, per capita GDP, and 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.

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

The aim of this paper is to assess the relationship between COVID-19-related deaths, economic growth, PM₁₀, PM_2.5, and NO₂ concentrations in New York state using city-level daily data through two Machine Learning experiments. PM_2.5 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 PM_2.5 to Deaths, NO₂ to Deaths, and economic growth to both PM_2.5 and NO₂. Corroborating the first experiment, the causal results confirmed the capability of polluting variables (PM_2.5 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 (PM_2.5 and NO₂) in New York state.

NO2 levels as a contributing factor to COVID-19 deaths: The first empirical estimate of threshold values

This study represents the first empirical estimation of threshold values between nitrogen dioxide (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.

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

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

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

This study uses two different approaches to explore the relationship between pollution emissions, economic growth, and COVID-19 deaths in India. Using a time series approach and annual data for the years from 1980 to 2018, stationarity and Toda-Yamamoto causality tests were performed. The results highlight unidirectional causality between economic growth and pollution. 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 PM_2.5, CO₂, NO₂, and COVID-19 deaths. The implications require careful policy design.

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

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM_2.5 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 PM_2.5 and PM₁₀ connected to COVID-19: 17.4 µg/m³ (PM_2.5) and 29.6 µg/m³ (PM₁₀) for Paris; 15.6 µg/m³ (PM_2.5) and 20.6 µg/m³ (PM₁₀) for Lyon; 14.3 µg/m³ (PM_2.5) 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.

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

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

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

BACKGROUND

The coronavirus infection that emerged in China in the last few months of 2019 has now spread globally. Italy registered its first case in the second half of February, and in a short time period, it became the top country in Europe in terms of the number of infected people and the first in the world in terms of deaths. The medical and scientific community has been called upon to manage the emergency and to take measures. Dentists also need to take new precautions during their clinical activity to protect themselves, coworkers and patients from the risks of contagion and to avoid further spread of infection.

METHODS

Following the data published in the international literature as well as the guidelines and directives constantly updated by the WHO and by the national health authorities, a questionnaire to be completed anonymously was submitted online to Italian dentists using social tools and online professional platforms. The collected data were processed statistically, providing descriptive data and analysis of correlations of the most significant parameters using the Pearson's χ2, the Likelihood-Ratio χ2, Cramér's V, Fisher's exact test, Goodman and Kruskal's γ, and Kendall's τb (p < 0.05).

RESULTS

A total of 535 dentists from Italy participated in the survey. A good level of scientific knowledge about coronavirus and the extra precautionary measures needed to limit the spread was related to the age of respondents and their sex. Coming from areas with higher concentrations of cases affected knowledge, level of attention and perception of risk related to dental activity.

CONCLUSIONS

At the moment, there are no therapies or vaccines to contain the infection with the new coronavirus that is causing many infections, many of which are fatal, worldwide. Dentists are one of the categories at highest risk of encountering diseases and infections because they work in close proximity with patients, and in their procedures, there is always contact with aerosols with high bacterial and viral potential. Therefore, during this COVID-19 emergency, it is important that dentists are properly informed and take the appropriate precautionary measures.

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

Most nations are predominately preoccupied with the need to increase economic growth amidst pressure for increased energy consumption. However, higher energy consumption from fossil fuel has its environmental implication(s) especially in a high industrial economy like China. In this context, the current study explores the interaction between pollutant emission, foreign direct investment, energy consumption, tourism arrival, and economic growth for quarterly frequency data from 1995Q1 to 2016Q4 for econometrics analysis. Pesaran's autoregressive distributed lag-bound test traces long-run relationship between all outlined variables over the investigated period. Empirical results show positive relationship between pollutant emissions with all other variables with the exception of economic growth. This further exposes the environmental degradation in China with the curtailing strength from the GDP. The Granger causality analysis detects that CO₂ emissions and energy consumption show a two-way causality observed. Also, one-way causality existing between growth and foreign direct investment is seen running to pollutant emission. Furthermore, one-way causality is observed among foreign direct investment, energy consumption, pollutant emission, and tourism arrivals with economic growth, and this established their impact on the economic growth which will be a guide to the policy implication on how to ameliorate environmental degradation from the effect of consumption of fossil energy sources and foreign direct investment-induced pollutant emission.

Modeling the dynamic Nexus among coal consumption, pollutant emissions and real income: empirical evidence from South Africa

This study explores the interaction among coal consumption, pollutant emissions, and real income for South Africa in a multivariate setting. To achieve this objective, annual frequency data spanning from 1965 to 2017 is used for analysis. A series of econometrics tests were conducted ranging from stationarity and non-stationarity tests for unit root properties of the variables under consideration. Empirical evidence finds support for the inverted U-shaped pattern between energy consumption and environmental degradation in South Africa. The Toda-Yamamoto Granger causality test shows a feedback causality between economic growth and carbon dioxide emissions, as well as between GDP and coal consumption. All these highlighted findings have inherent environmental implications. Based on these outcomes, policy directions such as diversification of the South Africa energy mix to renewables and cleaner energy sources and also the adoption of carbon capturing and storage techniques were suggested to engender a cleaner and friendlier environment.

Testing the stationarity and convergence of CO2 emissions series in MENA countries

Purpose

This study aims to investigate the stationarity and convergence of CO2 emissions series in MENA countries. The stationarity and unit root properties of per capita carbon dioxide (CO2) emissions series are explored by an increasing amount of studies, which use different methodologies. Examining the time series properties of energy and environmental series is crucial for both researchers and the policymakers, given the close link between energy, environment and the real economy. In fact, if energy exhibits the presence of a unit root, this suggests that this series does not revert to its equilibrium level after being hit by a shock.

Design/methodology/approach

The contribution of this work is twofold. First, to the author’s knowledge, a very little attention has been paid to the topics of stationarity and convergence of CO2 emissions in the case of Middle East and North Africa (MENA) member states, especially in a panel context. Convergence analyses of CO2 emissions for MENA countries can improve the knowledge of energy and environmental scenario of the area, giving some ideas for appropriate future policies. Second, this is the first study that jointly analyzed time series and panel data properties of emissions series for these countries.

Findings

The author finds that relative per capita CO2 emissions in the 19 MENA countries are a mixture of I(0) and I(1) processes and there is a weak evidence to support the stationarity of CO2 emissions. After having verified the presence of cross-sectional dependence in the series, the panel unit root tests in presence of cross-section dependence show strong evidence in favor of non-stationarity. In addition, after performing tests for ß-convergence, it is also found that per capita CO2 emissions are converging on average in 11 out of 19 sample’s countries, while s-convergence analysis reveals that the variance of per capita CO2 emissions decreased over time, which is an indication of convergence.

Originality/value

Important policy implications emerge from the empirical results. Sustainable environmental and energy policies rely heavily on the CO2 series’ properties. In this regard, determining whether shocks to CO2 emissions are permanent or transitory is important for setting feasible goals for sustainable environmental policies. Given that per capita CO2 emissions are essentially associated with a quality of life, the issues of their reduction have been the leading agenda in energy and environmental management over the past two decades.

GDP, energy consumption and financial development in Italy

Purpose

This study aims to explore the relationship among energy consumption, real income, financial development and oil prices in Italy over the period 1960-2014.

Design/methodology/approach

Different econometric techniques – such as the General Methods of Moment (GMM) or the AutoRegressive Distributed Lags (ARDL) bounds test – are usually used in the empirical analysis. Moreover, both the Toda and Yamamoto causality tests and the Granger causality tests are applied to the data.

Findings

The results of unit root and stationarity tests show that the variables are non-stationary at levels, but stationary in first-differences form, or I(1). The ARDL bounds F-test reveals an evidence of a long-run relationship among the four variables at 1% significance level. Moreover, an increase in real GDP and oil prices has a significant effect on energy consumption in the long run. The coefficients of estimated error correction term are also negative and statistically significant. In addition, the paper explores the causal relationship between the variables by using a VAR framework, with Toda and Yamamoto but also Granger causality tests, within both multivariate and bivariate systems. The findings indicate that energy consumption is affected by real GDP.

Originality/value

The study also filled the literature gap of applying ARDL technique to examine this relevant issue for Italy.