An empirical investigation of the relationship between nuclear energy and environmental pollution in France: fresh evidence using asymmetric cointegration

An empirical investigation of the relationship between nuclear energy and environmental pollution in France: fresh evidence using asymmetric cointegration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An empirical investigation of the relationship between nuclear energy and environmental pollution in France: fresh evidence using asymmetric cointegration Emna Omri, Haifa Saadaoui This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1655777/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The intention behind the current analysis is to join the debate over the main factors to consider in the global fight against climate change. Thereby, we used the non-linear Autoregressive Distributed Lag (NARDL) cointegration approach to test the impacts of nuclear energy, fossil fuels, trade openness and economic growth on carbon emissions in France. In addition, we test the validity of the Environmental Kuznets Curve (EKC) assumption. Our results stipulate that nuclear power lessens CO 2 emissions in France. However, fossil fuels and trade openness enhance these emissions. On the other hand, the current analysis confirms the existence of an inverted U-shaped curve relying income with CO 2 emissions. Therefore, the EKC hypothesis is supported in our case. Indeed, by calculating the turning point, we can further extract the turning year which corresponds to the year 2012. carbon emissions EKC hypothesis nuclear energy fossil fuels trade Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Carbon emissions are considered as a menacing matter facing not only the ecosystem but also the economic development (Alola et al. 2019 ). In spite of international agreements and the strong awareness toward mitigating carbon emissions by most countries, the global carbon dioxide emissions augmented by 1.1% between 2008 and 2018 [British Petroleum (BP) Statistical Review of World Energy 2020]. However, in the European region, the carbon dioxide emissions have decreased slightly from 4246.12 million tons in 2018 to 4110.84 million tons in 2019 (BP Statistical Review of World Energy 2020). Climate change is manifested by several phenomena, including the rise in sea level and global temperatures. The objective of the Paris agreement is to keep the rise in global temperatures well below 2°C by 2100. The climb in temperature is due to polluting emissions, in particular CO 2 from combustion and the use of fossil fuels. Indeed, these emissions increased by 67% between 1990 and 2018 (Ministry of Ecological Transition 2021a ). In fact, since the global warming is becoming more and more critical, the issue of environment quality has received more consideration than ever from international organizations [United Nations Environment Programme (UNEP) and World Trade Organization (WTO) 2009 ; Intergovernmental Panel on Climate Change (IPCC) 2014 , 2021 ], politicians and researchers in order to understand the main causes of environment degradation and to predict its evolution over time. The consumption of fossil fuels is still prominent in industry, transport, building, and power in most countries (REN 21 2021). Nevertheless with the increasing awareness about climate change and the spreading perception of the damages of the excessive use of fossil fuels (IPCC 2021), many countries such as France have been making efforts in order to increase the share of nuclear and renewable energies. In fact, one of the main features of the energy system in France is the impressive progress made in nuclear energy generation since the oil crisis in 1973 (Millot et al. 2020 ). Furthermore, in 2019, French public spending on energy research and development (R&D) reached almost € 1.2 billion. Nuclear’s R&D accounts for 63% of this expenditure (Ministry of Ecological Transition 2021b ). On the other hand, GHG emissions in France have decreased by 19% between 1990 and 2018. However, this remains far from the ambitious objective of France which aims for a reduction of 40% of its GHG emissions during the period 1990–2030. To carry out its climate objectives, the French finance bill for the year 2021 allocated € 37 billion as climate-friendly spending (Ministry of Ecological Transition 2021a ). According to IEA (2019), nuclear energy played a crucial role in the global climate change engagement. In fact, nuclear power had contributed to the avoidance of 63 gigatonnes of carbon emissions during 1971–2018. In this regard, multiple studies have investigated the impact of nuclear power on environmental quality in general and CO 2 emissions in particular (Mahmoud et al. 2020; Nathaniel et al. 2021 ; Hassan et al. 2020 ; Pan and Zhang 2020 ). The foremost objective of the current study is to take part in the debating over the impact of nuclear power on the environment quality. Therefore, the NARDL method of cointegration is applied to detect the asymmetric effects of nuclear energy and income along with trade and fossil energy consumption on carbon emissions in France. In addition, the relationship between income and CO 2 emissions is carried out by testing the EKC hypothesis. We think France presents an interesting case to study as the country is a net importer of oil and has invested in nuclear energy since the 1970s in order to reduce its energy dependence and diversify its electricity mix. Moreover, France has signed many agreements to bring down its CO 2 emissions. The current paper contributes to the actual literature in two ways. Firstly, it analyzes the impact of nuclear energy, fossil fuels and trade within the framework of the EKC hypothesis for the case of France. In fact, most of the existing studies in this issue treat France as a part of the panel and not as a country specific case. Secondly, it is the first study to use the NARDL approach in order to determine the asymmetric links between CO 2 emissions in France and its drivers. To respond to the issue of this paper, we structure it in 5 interrelated sections. Section 2 looks at both the past and current energy situation in France while emphasizing the predominance of nuclear energy. Section 3 provides the data and the model specification. The main findings are included in Section 4. Finally, Section 5 presents the conclusions and offers the preeminent policy suggestions. 2. The Evolvement Of The Energy System In France: The Prominence Of Nuclear Power The energy system in France has been characterized by the preeminence of nuclear energy for many years and the steady progress of renewable energies. Primary national production represents a little more than half of France’s energy supply (Ministry of Ecological Transition, 2021b ). According to Fig. 1 , following the implementation of the French nuclear program, primary energy production increased from 514 TWh in 1973 (including 9% nuclear) to 1,423 TWh in 2020 (including 75% nuclear). However, primary energy production decreased by 8.7% in 2020 compared to 2019, which is explained by the decline in nuclear production (− 11.3%, to 1,072 TWh against 1,209 TWh). This decline is mainly due to the pandemic context which led to delays in scheduled maintenance and closure of the last two reactors at the Fessenheim nuclear power plant on June 29, 2020. Nuclear production is thus falling to a level not seen since the early 1990s (1,026 TWh in 1992 and 1,091 TWh in 1994). Currently, France has 56 reactors in service that were commissioned between the end of the 1970s and the beginning of the 2000s (Ministry of Ecological Transition 2021b ). Concerning fossil fuels, their extraction has declined sharply, this is mainly due to the extraction of coal and natural gas, which has been almost zero since 2015. The decline in gas production has been remarkable since 1984. In fact, it fell from 61.38 TWh in 1984 to 17.5 TWh in 2000 to reach only 0.22 TWh in 2020. On the other hand, France has stopped coal mining since 2015 and plans to stop producing electricity from coal in 2022 (Ministry of Ecological Transition 2021b ). Furthermore, the production of crude oil in France stands at 9.75 TWh in 2020 against 34.46 in 1970 and 44.56 in 1988, thus achieving a decrease of 72% compared to 1970. Production in 2020 only represents about 1% of national oil consumption. Since oil production in France is very limited, its supply of crude oil relies almost entirely on imports from Saudi Arabia and Kazakhstan. At the same time, production from renewable sources (wind power, biofuels, biogas, etc.) has been growing steadily since the mid-2000s. In 2020, the primary production of renewable energy amounts to 340.76 TWh. The main sources remain biomass, hydro and wind power. Between 2019 and 2020, the primary production of renewable energies increased slightly, by 2.79 TWh (or + 0.82%). According to Fig. 2 , energy consumption has tended to decrease slightly for several years. In fact, after steadily increasing until 2005 to reach a peak of 3,155 TWh, primary energy consumption has declined slightly. The long-term trend is contrasted between energies: since 1990, the consumption of coal and oil has fallen by 72% and 27% respectively. Conversely, during the same period, those of nuclear and natural gas increased by 14.5% and 44% respectively, while that of renewable energies almost doubled. In 2020, the drop in primary energy consumption is historic, falling by 8.3%. It is mainly explained by the health crisis and the associated travel limitations. France’s primary energy consumption stands at 2,650.43 TWh in 2020. France’s real primary energy mix consists of 39.22% nuclear, 27.53% petroleum, 16.85% gas, 13.87% renewable energies and waste, and 2.52% coal (Fig. 2 ). Concerning the French electricity mix in 2020, it is dominated by the nuclear energy as follows : 65.76% nuclear, 13.02% hydro, 10.45% conventional thermal, 7.98% wind, 2.67% PV, and 0.12% other sources. In 2020, net electricity production amounted to 510 TWh, down 6.8% from the previous year. This decrease is largely explained by the decline in nuclear production, which is at its lowest level since the early 1990s. On the other hand, renewable electricity production increased compared to 2019. Due to favorable weather conditions and the growth of the park, wind power production increased by 17.2% in particular. Photovoltaic and hydraulic productions are also up, respectively by 11.1% and 8.3% over one year (Ministry of Ecological Transition 2021b ). It should be noted that France is a net exporter of electricity to its European neighbors through the border interconnection lines, the most important part of its exports is satisfied by nuclear energy. In 2019, France imported 16 Tera-watt hour (TWh) and exported 73 TWh, i.e. an electricity export balance of 58 TWh, registering a decrease of 8% compared to 2018 due to the drop in nuclear and hydro production (Ministry of Ecological Transition 2019 ). 3. Literature Survey The links between nuclear energy, income, and CO 2 emissions under the EKC hypothesis is well recognized in previous studies (for instance, Dong et al. 2018 ; Mahmood et al. 2020 ; Syed et al. 2021 ). However, we incorporate different control variables such as fossil fuel energy generation and trade openness in the case of France for the period 1980–2019. We include fossil fuel generation in our model as a proxy for non-renewable energy. In fact, fossil fuel energy is perceived as the most ponderous component in the global energy mix (REN 21 2021). Concerning the empirical findings, multitude studies have demonstrated the negative effect of fossil fuels on the environment quality through the pollutant emissions (Lau et al. 2019 ; Bélaid and Zrelli 2019; Ma et al. 2021 ; Haldar and Sethi 2022 ). Moreover, we consider the substantial role of trade openness as a determinant for CO 2 emissions. In fact, several researches have realized that the transfer of goods and services between trading partners of different countries in the world has a substantial effect on the environment quality (Cristea et al. 2013 ; Shahbaz et al. 2013 ; Kim et al. 2019 ; Fang et al. 2020 ; Aslam et al. 2021 ; Pata and Caglar 2021 ). In general, the impact of trade openness on the environment reveals several opposite findings. Hence, trade openness and fossil fuel consumption are considered as control variables in our estimation and their impacts on CO 2 emissions were deeply detailed. For these reasons we will just focus on the nexuses between economic growth, nuclear energy and carbon emissions. 3.1. The nexus between economic growth and CO 2 emissions and the EKC hypothesis The controversy of whether the relationship between per capita income and environmental degradation follows an inverted-U-shaped form has been deeply analyzed by an impressive body of literature. In fact, since the pioneering studies of Grossman and Krueger ( 1993 , 1995 ) as well as Selden and Song ( 1994 ), many researchers have tried to discuss the presence of this relationship known as the EKC. Hence, since the 1990’s, many researchers have tested the validation of the EKC for the case of different countries by using different econometric methods. A recent review of the literature concerning the use of the EKC was given by Dinda ( 2004 ), Stern ( 2004 ), Kijima et al. ( 2010 ), Kaika and Zervas ( 2013a , 2013b ), and Sarkodie and Strezov ( 2019 ). They demonstrated that the EKC literature is very considerable and the results are mixed. The existing studies have used different indicators of environmental quality but the majority of them have employed carbon dioxide emissions as an indicator of pollution. The empirical findings concerning the EKC are mixed and there is a controversy about the validity of the EKC hypothesis. In fact, the EKC hypothesis was validated widely (Apergis 2016 ; Li et al. 2016 ; Churchill et al. 2018 ; Acheampong et al. 2019 ; Ghazouani 2021 ; Salari et al. 2021 ; Zhang et al. 2021 ). However, the EKC assumptions were rejected by a considerable number of studies (Ben Jebli and Ben Youssef 2015 ; Kang et al. 2016 ; Antonakakis et al. 2017 ; Amri et al. 2019 ; Lawson et al. 2020 ) The studies that rejected this hypothesis, found different patterns of the EKC, such as U- shaped form (Dinda et al. 2000 ; Pata and Caglar 2021 ), N-shaped form (Balsalobre-Lorente et al. 2018 ; Koc and Bulus 2020 ), and inverted N-shaped form (López-Menéndez et al. 2014 ). Multiple analyses were devoted to the case of a single country. For example, Ben Jebli and Ben Youssef ( 2015 ) as well as Amri ( 2018 ) found that the inverted U-shaped EKC hypothesis is not validated for the case of Tunisia. However, for the same country, Ghazouani ( 2021 ) demonstrated the validity of the EKC hypothesis. In addition, the results reveal that economic growth has a negative influence on the environment and the existence of a bidirectional relationship between economic growth and CO 2 emissions. Malik et al. ( 2020 ) and Zhang et al. ( 2021 ) validated the presence of the EKC for the case of Pakistan and Ali et al. ( 2017 ) for the case of Malaysia. In fact, Malik et al. ( 2020 ) demonstrated that economic growth increases CO 2 emissions. China, as a great emitter of CO 2 emissions has captivated the most attention (Jalil and Mahmud 2009 ; Dong et al. 2018 ; Wang and He 2019 ; Chen et al. 2020 ; Ahmad et al. 2021 ). The results for the case of China are also mitigated, for example, Pata and Caglar ( 2021 ) rejected the EKC hypothesis, when Zhang and Zhang ( 2018 ) validated it. Developed countries have also received great attention. For instance, Salari et al. ( 2021 ) validated the EKC hypothesis for the case of the USA. However, Shahbaz et al. ( 2017a ) found that the link between growth and CO 2 emissions follows an inverted-U shape and it is N-shaped in presence of structural breaks and biomass. The empirical results attest also that economic growth induces carbon emissions in the Granger sense. A recent study carried out by Hu et al. ( 2018 ) for a panel of 25 developing countries showed that economic growth has an important impact on CO 2 emissions and that the EKC hypothesis is validated. Arouri et al. ( 2012 ) analyzed the case of 12 countries of the Middle East and North Africa (MENA) region and found evidence of the existence of an inverted U-shaped curve which means that CO 2 emissions rise, become stable, and then decline with real GDP. However, for the same region, Gorus and Aydin ( 2019 ) indicated no causal link between economic growth and CO 2 emissions. Apergis and Ozturk ( 2015 ) used the generalized method of moments (GMM) and verified the validity of the EKC hypothesis in 14 Asian countries. Many other studies focused on a group of countries such as the European Union (López-Menéndez et al. 2014 ), OECD countries (Dogan et al. 2017 ; Churchill et al. 2018 ; Lau et al. 2019 ; Ng et al. 2019 ; Isik et al. 2021 ). As an example, Acaravci and Ozturk ( 2010 ) analyzed the case of Europe and revealed that the EKC hypothesis is valid in Denmark and Italy. Ben Youssef et al. ( 2016 ) analyzed the case of fifty-six countries divided into three panels according to their income level, they found that a bi-directional causality exists between economic growth and CO 2 emissions and this relationship indicates an inverted U-shaped curve. Lawson et al. ( 2020 ) used a semi-parametric dynamic panel data model in order to concomitantly test the environmental convergence and EKC assumptions for the case of 106 countries (85 non OECD and 21 OECD) over the period 1970–2015. Although the results reject the validity of the EKC hypothesis, they confirm the existence of the phenomenon of convergence of CO 2 emissions between countries with different income levels. An innovative study carried out by Kacprzyk and Kuchta ( 2020 ) used three conventional measures of GDP and a new proxy of GDP based on satellite nighttime data in order to test the validity of the EKC hypothesis for the case of a panel of 161 countries during 1992–2012. The results confirm the existence of the EKC and the turning point is lower than previous studies. Concerning the case of France, the EKC hypothesis was validated by various studies (Ang 2007 ; Iwata et al. 2010 ; Can and Gozgor 2016 ; Shahbaz et al. 2018 ). For instance, Shahbaz et al. ( 2018 ) investigated the contributory factors to CO 2 emissions for the case of France from 1955 to 2016. They applied the bootstrapping ARDL bounds testing approach, and Granger causality test in order to study the impacts of foreign direct investment (FDI), economic growth, energy consumption, financial development, and energy R&D on CO 2 emissions. The empirical results confirm that an increase in FDI and energy consumption boost carbon emissions and the EKC hypothesis is confirmed. However, financial development and energy research innovations decrease carbon emissions. In addition, the bootstrapping ARDL Granger causality test reveals the presence of bidirectional causality between CO 2 emissions and all the variables. On the other hand, Ang ( 2007 ) studied the links between carbon emissions, energy consumption and growth from 1960 to 2000 using multivariate vector error correction model (VECM). They demonstrate that the EKC hypothesis is validated. The results of cointegration analysis indicate the presence of a strong long-term relationship between all variables. The results of the causality test suggest that output causes carbon emissions and energy use in the long term, however in the short term, there is a unidirectional causality running from energy consumption to output. Moreover, Can and Gozgor ( 2016 ) used the error correction model (ECM) and the dynamic ordinary least squares (DOLS) estimation techniques for the case of France from 1964 to 2011 in order to investigate the relationships among output, energy consumption, economic complexity, and CO 2 emissions. The results attested the validation of the EKC hypothesis. In addition, the results proved the existence of a positive effect of energy use on carbon emissions and a negative effect of economic complexity on CO 2 emissions in the long term. A comparison between France and Germany was carried out in a recent study by Ma et al. ( 2021 ). The results confirm the existence of an inverted U-shaped curve between real GDP and CO 2 emissions which confirms the validity of the EKC hypothesis. In addition to the EKC hypothesis, the relationship between growth and CO 2 emissions was also analyzed by using the decoupling concept (Zhang and Wang 2013 ; Zhao et al. 2017 ; Cohen et al. 2018 ; Wu et al. 2018 ). For example, Song et al. ( 2019 ) used the EKC hypothesis and the decoupling concept to compare between China and the USA. 3.2. The nexus between nuclear energy and CO 2 emissions Varied analyses were concerned with the impact of nuclear energy on the mitigation of carbon emissions. A review of literature was given by van der Zwaan ( 2013 ) and Savacool (2008). The studies concerned with the relationship between nuclear power and carbon emissions are multiple and offer mixed findings. In fact, some studies confirmed the positive effect of nuclear energy on CO 2 emissions (Mahmoud et al. 2020; Ishida 2018 ; Pan and Zhang 2020 ; Sarkodie and Adams 2018 ), but other studies demonstrated that the use of nuclear energy mitigates carbon emissions (Saidi and Omri 2020 ; Apergis et al. 2010 ; Baek 2015 , 2016 ; Lee et al. 2017 ; Dong et al. 2018 ; Nathaniel et al. 2021 ; Hassan et al. 2020 ; Menyah and Wolde-Rufael 2010 ). Other studies suggested that nuclear energy consumption has no effect on the environment quality (Al-Mulali 2014 ; Jaforullah and King 2015 ). The lion’s share of the studies is dedicated to a panel of countries such as developed and developing countries (Akhmat et al. 2014 ; Alam 2013 ; Apergis et al. 2010 ; Ben Mbarek et al. 2018 ), or OECD countries (Lau et al. 2019 ; Saidi and Omri 2020 ). For instance, Azam et al. ( 2021 ) studied the case of a panel of ten countries with the highest CO 2 emissions from 1990 to 2014. The results of the panel fully modified ordinary least squares (FMOLS) revealed that the expansion of nuclear energy consumption is an efficient way to fight climate change. In fact a 1% augmentation in nuclear energy consumption decreases carbon emissions by 0.012%. In addition, the causality test showed the existence of a two-way causality between nuclear energy consumption and carbon emissions. In the same line of research, Vo et al. ( 2020 ) treated the case of a panel of nine countries by using FMOLS and ordinary least squares (DOLS) estimations and found that nuclear energy consumption contributes to the mitigation of carbon emissions. The expansion of nuclear power for electricity generation in many countries has prompted numerous researchers to explore the effect of such a transition on the environment quality in a country specific case. In fact, this issue was treated for the case of China (Dong et al. 2018 ), Japan (Ichida, 2018), Pakistan (Mahmood et al. 2020 ; Zaidi et al. 2018 ), Iran (Kargari and Mastouri 2011 ), Republic of Korea (Kim 2020 ), South Africa (Sarkodie and Adams 2018 ), United Arab of Emirates (AlFarra and Abu-Hijleh 2012 ), and Spain (Pilatowska et al. 2020 ). Other countries such as India (Bandyopadhyay and Rej 2021 ; Danish et al. 2021 ; Syed et al. 2021 ), and USA (Menyah and Wolde-Rufael 2010 ; Jaforullah and King 2015 ; Baek 2016 ; Pan and Zhang 2020 ) have attracted the most consideration from scholars. For the case of India, Bandyopadhyay and Rej ( 2021 ), Danish et al. ( 2021 ), and Syed et al. ( 2021 ) used the ARDL, the DARL and the NARDL approaches, respectively and found that nuclear energy is a contributor to the environment amelioration by reducing CO 2 emissions. Concerning the case of the USA, Pan and Zhang ( 2020 ) employed the extended STIRPAT model along with the Ridge regression to study the effects of many factors, notably nuclear energy, on carbon emissions in the USA. The results showed that alternative and nuclear energy of total energy use has a positive incidence on carbon emissions. However, for the same case of the USA, Baek ( 2016 ) used the ARDL approach and confirmed that nuclear consumption decreases CO 2 emissions. On the other hand, Jaforullah and King ( 2015 ) found no interlinkage between nuclear energy consumption and carbon emissions by using a VECM model. France, as one of the major nuclear energy producers, was also treated by many scholars (Iwata et al. 2010 ; Marques et al. 2016 ; Cany et al. 2018 ; Poinssot et al. 2014 ). While the studies are multiple and the approaches used are different, the results remain quite similar. In fact, Iwata et al. ( 2010 ) and Marques et al. ( 2016 ) used the ARDL approach and demonstrated that the use of nuclear energy is beneficial to the environment quality. In addition, using the pair-wise Granger causality test, Iwata et al. ( 2010 ) found a one-way causality linkage from nuclear power to carbon emissions. On the other hand, Poinssot et al. ( 2014 ) applied the Process Chain Analysis (PCA) in order to analyze the impacts of the nuclear fuel cycle on many environmental indicators in France. The results confirmed that nuclear power is one of the least impacting energies. Many other interesting studies deal with the case of France in the context of a comparison with other countries such as Sweden (Millot et al. 2020 ) or Sweden and Spain (Pilatowska and Geise 2021 ). For instance, Pilatowska and Geise ( 2021 ) studied the impacts of nuclear energy along with fossil energies and renewable energies on carbon emissions and growth in three European countries by using the vector autoregression (VAR) model. The results of the Granger causality revealed that, in the formative phase, there is no causality between carbon emissions and nuclear energy in three countries. Concerning the expansion phase, there is a unidirectional causality from nuclear to CO 2 in Spain, bidirectional causality in France, and no significant causality in Sweden. The studies that are interested in the case of France are not numerous compared to those that study France as part of a panel such as major nuclear generating countries (Al-Mulali 2014 ; Baek and Pride 2014 ; Baek 2015 ), the group of seven (Nathaniel et al. 2021 ), BRICS countries (Hassan et al. 2020 ), etc. Baek ( 2015 ) used the FMOLS and DOLS approaches for estimation for the case of 12 principal nuclear producing countries. The results revealed that the use of nuclear energy serves in the abatement of carbon emissions. In fact, a 1% increase in electricity produced from nuclear power abates carbon emissions by 0.12%. 4. Methodology 4.1. Data and descriptive analysis The data concerning the generations of nuclear and fossil fuel energies measured by Million Tonnes of Oil Equivalent (Mtoe) are collected from the British Petroleum Company (BP). The economic income is represented by the real GDP per capita and is obtained from the World Development Indicators (WDI). From the previous source, we used the ratio of trade in a percentage of GDP as a proxy for trade openness. Concerning the data of carbon emissions in million tonnes of CO 2 , they are obtained from the US EIA database. The data covers the period 1980–2019. A detailed description of the statistics is presented in Table 1 . We can notice from Table 1 that non-renewable energies have the lowest volatility, while nuclear energy generation is the most volatile. The statistic of the Skewness shows that CO 2 emissions (CO2), nuclear energy (NUC), and the real GDP per capita (GDP) are negatively skewed, which means that they present longer left tails in comparison to a normal distribution. In contrast, fossil fuel energies (NRE) and trade openness (TR) are positively skewed. In addition, the Kurtosis coefficient exceeds 3 for CO2, NRE, and NUC distributions which means that they are leptokurtic and have heavier tails compared with a normal distribution. The Jarque-Bera statistic attests that NUC is not normally distributed unlike all other variables. Table 1 Statistics lnCO 2 lnGDP lnNRE ln NUC lnTR Mean 5.902 10.483 11.754 11.356 3.918 Maximum 6.182 10.695 11.989 11.675 4.167 Minimum 5.680 10.199 11.606 9.677 3.686 Std. dev. 0.098 0.157 0.074 0.458 0.149 Skewness -0.008 -0.459 0.421 -2.131 0.088 Kurtosis 3.897 1.816 4.288 7.005 1.677 Jarque-Bera 1.343 (0.510) 3.737 (0.154) 3.947 (0.138) 57.029 ( 0.000) 2.965 (0.227) Note: the p-values are presented between parentheses. Moreover, Fig. 3 depicts the dynamics of the series, and shows that all explicative variables have an increasing trend except of NRE. Concerning the dependent variable, it shows a strong decrease of carbon emissions, mainly, during the period 1980–1986. To assess the validity of the environmental Kuznets curve, we add the GDP and the GDP squared term (GDP 2 ). Accordingly, the specific role of the environmental Kuznets curve is to specify the impact of the economic income on the environment quality. Therefore, we adopt the following model specification: $${lnCO2}_{t}={}_{0}+{{}_{1}lnGDP}_{t}+{}_{2}{\left(lnGDP\right)}_{t}^{2}+{{}_{3}lnNUC}_{t}+{{}_{4}lnNRE}_{t}+{{}_{5}lnTR}_{t}+{\mu }_{t}$$ 1 Where lnCO2, lnGDP, (lnGDP) 2 , lnNUC, lnNRE, and lnTR represent the natural logarithms of carbon emissions, GDP per capita, the square term of GDP per capita, nuclear energy generation, fossil fuel energy generation and trade openness, respectively. The year is represented by the subscript “t”, µ t represents the error term, and α 0 illustrates the constant term. The coefficients (α 1 , α 2 , α 3 , α 4, and α 5) present the elasticities corresponding to exogenous variables. 4.2. Non-linear autoregressive distributed lag methodology (NARDL) Recent studies consider that the reaction process of the determinants of CO 2 emissions is non stable and non-linear (see for example, Lahiani, 2020 ; Shahbaz et al. 2021 , etc). In this case, the standard linear cointegration becomes inadequate. For this reason, it was necessary to adopt another cointegration approach that is able to capture the non-linearity associated with the dynamics of the different variables. Thus, we adopt the asymmetric cointegration approach suggested by Shin et al. ( 2014 ), which allows considering the asymmetric links between the variables. In fact, the NARDL model is an extension of the linear ARDL model proposed by Pesaran et al. ( 2001 ). We first present the linear form (ARDL) of our model in the following equation: In order to highlight the asymmetric links between nuclear energy, non-renewable energy, trade openness, and CO 2 emissions by taking into account the EKC model, we use the NARDL model presented in Eq. ( 3 ): Where \({{\rho }}_{\text{G}\text{D}\text{P}}\) and \({{\rho }}_{\text{G}\text{D}\text{P}2}\) are the long run parameters associated to the GDP and the GDP 2 .The EKC hypothesis check is based on the verification of the positive sign of \({{\rho }}_{\text{G}\text{D}\text{P}}\) and the negative sign of \({{\rho }}_{\text{G}\text{D}\text{P}2}\) . This relationship implies that economic growth increases polluting emissions, in a first phase, and brings them down when the economy is mature. \({{\gamma }}_{\text{N}\text{U}\text{C}}^{+}\) , \({{\gamma }}_{\text{N}\text{R}\text{E}}^{+}\) , and \({{\gamma }}_{\text{T}\text{R}}^{+}\) ( \({{\gamma }}_{\text{N}\text{U}\text{C}}^{-}\) , \({{\gamma }}_{\text{N}\text{R}\text{E}}^{-}\) , and \({{\gamma }}_{\text{T}\text{R}}^{-}\) ) designate the long-run parameters attributed to positive alterations (negative alterations), respectively. \({{\sigma }}_{\text{i}}^{+}\) , \({{\phi }}_{\text{i}}^{+}\) , and \({{\omega }}_{\text{i}}^{+}\) ( \({{\sigma }}_{\text{i}}^{-}\) , \({{\phi }}_{\text{i}}^{-}\) , \({{\omega }}_{\text{i}}^{-}\) ) are the short-run parameters attributed to the positive alterations (negative alterations), respectively. Positive ( \({lnNUC}_{t}^{+},ln{NRE}_{t}^{+},\) \(ln{TR}_{t}^{+}\) ) as well as negative ( \({lnNUC}_{t}^{-}, { lnNRE}_{t}^{-}, ln{TR}_{t}^{-}\) ) partial sums relative to nuclear energy, non-renewable energy, and trade are presented in the following lines: \({lnNUC}_{t}^{+}\) = \({\sum }_{i=1}^{t}{\varDelta lnNUC}_{i}^{+}\) = \(\sum _{i=1}^{t}\text{m}\text{a}\text{x}(\varDelta {lnNUC}_{i}\) , 0) \(ln{NUC}_{t}^{-}\) = \({\sum }_{i=1}^{t}{\varDelta lnNUC}_{i}^{-}\) = \(\sum _{i=1}^{t}\text{m}\text{i}\text{n}(\varDelta {lnNUC}_{i}\) , 0) \({lnNRE}_{t}^{+}\) = \({\sum }_{i=1}^{t}{\varDelta lnNRE}_{i}^{+}\) = \(\sum _{i=1}^{t}\text{m}\text{a}\text{x}(\varDelta {lnNRE}_{i}\) , 0) \(ln{NRE}_{t}^{-}\) = \({\sum }_{i=1}^{t}{\varDelta lnNRE}_{i}^{-}\) = \(\sum _{i=1}^{t}\text{m}\text{i}\text{n}(\varDelta {lnNRE}_{i}\) , 0) \(ln{TR}_{t}^{+}\) = \({\sum }_{i=1}^{t}{\varDelta lnTR}_{i}^{+}\) = \(\sum _{i=1}^{t}\text{m}\text{a}\text{x}(\varDelta {lnTR}_{i}\) , 0) \({lnTR}_{t}^{-}\) = \({\sum }_{i=1}^{t}{\varDelta lnTR}_{i}^{-}\) = \(\sum _{i=1}^{t}\text{m}\text{i}\text{n}(\varDelta {lnTR}_{i}\) , 0) Then, we can also extract the negative and positive long-term coefficients for each CO 2 emission determinant in the NARDL model framework. The following formulas provide the coefficients for the variables NUC, NRE, and TR, respectively: \({{\rho }}_{\text{N}\text{U}\text{C}}^{+}=-\frac{{{\gamma }}_{\text{N}\text{U}\text{C}}^{+}}{{{\rho }}_{\text{C}\text{O}2}}\) and \({{\rho }}_{\text{N}\text{U}\text{C}}^{-}=-\frac{{{\gamma }}_{\text{N}\text{U}\text{C}}^{-}}{{{\rho }}_{\text{C}\text{O}2}}\) ; \({{\rho }}_{\text{N}\text{R}\text{E}}^{+}=-\frac{{{\gamma }}_{\text{N}\text{R}\text{E}}^{+}}{{{\rho }}_{\text{C}\text{O}2}}\) and \({{\rho }}_{\text{N}\text{R}\text{E}}^{-}=-\frac{{{\gamma }}_{\text{N}\text{R}\text{E}}^{-}}{{{\rho }}_{\text{C}\text{O}2}}\) ; \({{\rho }}_{\text{T}\text{R}}^{+}=-\frac{{{\gamma }}_{\text{T}\text{R}}^{+}}{{{\rho }}_{\text{C}\text{O}2}}\) and \({{\rho }}_{\text{T}\text{R}}^{-}=-\frac{{{\gamma }}_{\text{T}\text{R}}^{-}}{{{\rho }}_{\text{C}\text{O}2}}\) . The last step in the NARDL model involves a presentation of the dynamic multipliers related to favorable and unfavorable fluctuations, as follows: \({m}_{h,NUC}^{+}=\sum _{j=0}^{h}\frac{\partial {CO2}_{t+j}}{{\partial NUC}_{t}^{+}}\) ; \({m}_{h,NUC}^{-}=\sum _{j=0}^{h}\frac{\partial {CO2}_{t+\text{j}}}{{\partial NUC}_{t}^{-}}\) \({\text{m}}_{\text{h},\text{N}\text{R}\text{E}}^{+}=\sum _{\text{j}=0}^{\text{h}}\frac{\partial {\text{C}\text{O}2}_{\text{t}+\text{j}}}{{\partial \text{N}\text{R}\text{E}}_{\text{t}}^{+}}\) ; \({\text{m}}_{\text{h},\text{N}\text{R}\text{E}}^{-}=\sum _{\text{j}=0}^{\text{h}}\frac{\partial {\text{C}\text{O}2}_{\text{t}+\text{j}}}{{\partial \text{N}\text{R}\text{E}}_{\text{t}}^{-}}\) \({\text{m}}_{\text{h},\text{T}\text{R}}^{+}=\sum _{\text{j}=0}^{\text{h}}\frac{\partial {\text{C}\text{O}2}_{\text{t}+\text{j}}}{{\partial \text{T}\text{R}}_{\text{t}}^{+}}\) ; \({\text{m}}_{\text{h},\text{T}\text{R}}^{-}=\sum _{\text{j}=0}^{\text{h}}\frac{\partial {\text{C}\text{O}2}_{\text{t}+\text{j}}}{{\partial \text{T}\text{R}}_{\text{t}}^{-}}\) When \(\text{h}\to {\infty }\) ; \({\text{m}}_{\text{h},\text{N}\text{U}\text{C}}^{+}\to {{\rho }}_{\text{N}\text{U}\text{C}}^{+}\) ; \({\text{m}}_{\text{h},\text{N}\text{U}\text{C}}^{-}\to {{\rho }}_{\text{N}\text{U}\text{C}}^{-}\) (Shin et al. 2014 ). 5. Results And Interpretations 5.1. Non linearity and stationarity tests We adopt the Broock–Dechert–Scheinkman (BDS) test developed by Broock et al. ( 1996 ) in order to check the nonlinearity in the data series. The null hypothesis is that the data are independently and identically distributed. The results of the non-linearity BDS test are reported in Table 2 . The results attest that for all series, the null hypothesis is rejected, meaning that all variables are not identically and independently distributed which proves the presence of asymmetries. For this reason, it is necessary to use an asymmetric integration model to analyze the non-linear interactions. Table 2 Results of BDS nonlinearity test Variables BDS statistic at different dimensions m = 2 m = 3 m = 4 m = 5 m = 6 lnCO 2 0.138 * 0.222* 0.289* 0.311* 0.298* lnGDP 0.199* 0.332* 0.426* 0.496* 0.549* lnGDP 2 0.196* 0.329* 0.423* 0.493* 0.546* lnNUC 0.131* 0.246* 0.345* 0.429* 0.501* lnNRE 0.102* 0.149* 0.190* 0.207* 0.185* lnTR 0.143* 0.228* 0.276* 0.299* 0.3001* Notes: * indicates the rejection of the null hypothesis at 1% level of significance and “m” designs the embedding dimension. We apply the Augmented Dickey Fuller (Dickey and Fuller 1979 ) and Zivot-Andrews (Zivot and Andrews 1992 ) tests to examine the order of integration. Concerning, the null hypothesis of the Augmented Dickey Fuller and Zivot-Andrews tests is the existence of unit root which indicates the non-stationarity of the series. Table 3 reports the results of both Augmented Dickey Fuller and Zivot-Andrews tests for all variables in levels and in first differences. The Augmented Dickey Fuller test reveals that the null assumption is rejected for CO2, NUC and NRE in levels. However, at first difference, the null hypothesis is rejected for all variables, which means that the nuclear energy, the fossil fuel energy and the CO 2 emission are I(0), while the economic income and the trade openness are I(1). Furthermore, previous studies have highlighted that classic unit root tests such as the Augmented Dickey Fuller are unable to detect the structural changes in the series that can lead to a misspecification of the variables’ integration order (Perron 1989 ). Therefore, we follow Syed et al. ( 2021 ) and Lahiani et al. ( 2019 ) in adopting the Zivot-Andrews test which is considered appropriate in the presence of non-linearity in the time series. The Zivot -Andrews test shows that all series are stationary in I(1) except for NRE which is I(0). The results of the precedent tests showed that all the variables are integrated in order I(0) and I(1), and no variables are I(2). This finding leads us to apply the non-linear ARDL model since the conditions of order of stationarity and linearity are valid. Table 3 Unit Root Analysis Variables Augmented Dickey–Fuller Zivot-Andrews Level First difference Level Break year First difference Break year lnCO2 -2.194** -5.716* -4.544 2011 -6.859 * 1998 lnNUC -10.009* -4.731* -3.475 2007 -7.348* 1988 lnGDP -1.768 -4.099* -3.630 2008 -4.727*** 2001 lnNRE -2.971** -7.283* -4.779*** 1998 -8.124* 2006 lnTR -0.560 -6.007* -4.559 1986 -6.433* 1994 Note: *, **,*** represent the rejection of the null assumption at the significance levels of 1%, 5%, and 10%, respectively. Critical values for the Zivot-Andrews test are − 5.34, -4.80 and − 4.58 for the levels of significance 1%, 5%, and 10%, respectively. 5.2. Results of the NARDL cointegration The results of the NARDL bounds estimation are presented in Table 4 . The results express that the speed of adjustment (-1.507) is negatively significant at the 1% level, meaning that the estimated NARDL model is stable. Furthermore, the t-statistic (T BDM ) suggested by Banerjee et al. ( 1998 ), and the F-statistic (F PSS ) presented by Pesaran et al. ( 2001 ) validate the presence of asymmetric long run cointegration between the selected variables at 1% significance level. The R 2 value indicates that 98.74% of the data fit the regression model. In other terms, it indicates that 98.74% of the variance in CO2 is collectively explained by the independent variables. Concerning the sensitivity analysis of the model, the Durbin-Watson statistic (2.431) specifies the non-existence of autocorrelation. In addition, the Breusch-Godfrey serial correlation test (2.088) confirms the null assumption of the absence of serial correlation of error terms, since the p-value of the serial correlation test is insignificant at the different levels of significance. On the other hand, the Autoregressive Conditional Heteroskedasticity (ARCH) test denies the existence of any conditional heteroskedasticity. Furthermore, the functional form is well-conceived and verified by the Ramsey Regression Equation Specification Error Test (RESET), at the different levels of significance. Finally, we perform the CUSUM and CUSUMSQ tests in order to check the model stability. Figure 4 presents the CUSUM and CUSUMSQ tests, and indicates that the estimated lines are between the critical bounds at the 5% threshold. This means that the estimated parameters in the model are stable over the period 1980–2019. Table 4 provides also the long-run and short run coefficients estimated by applying the NARDL cointegration for the determinants of CO 2 emissions. In our case, nuclear energy, nonrenewable energy and trade are decomposed into positive and negative changes, while the income and the square of GDP are non-decomposed in order to test the EKC model. The first long-term result to be retained from this model is the following: the impact of income on CO 2 is positive (49.252). This implies that a 1% increase in the level of economic growth increases CO 2 emissions by 49.252%. Concerning the squared real GDP per capita, it decreases the level of pollution by about 2.334%. The significant positive and negative signs of the coefficients of lnGDP and (lnGDP) 2 , respectively, confirm the existence of an inverted U-shaped curve relying income with CO 2 emissions. Accordingly, the EKC hypothesis is supported in our case, which means that over the earliest phases of development, an increase in economic growth is accompanied with a rise of pollutant emissions till a specific threshold level of GDP is reached when an increase of growth is followed by a decrease of pollutant emissions. These results are consistent with divers researches dealing with the case of France and confirming the EKC hypothesis, such as Iwata et al. ( 2010 ), Can and Gozgor ( 2016 ), Shahbaz et al. ( 2018 ), Ang ( 2007 ), and Shahbaz et al. ( 2017b ), Ma et al. ( 2021 ). Concerning other countries, the EKC is validated by Malik et al. ( 2020 ) and Zhang et al. ( 2021 ) for the case of Pakistan, Salari et al., ( 2021 ) in the USA. However, our results are inconsistent with those of Ben Jebli and Ben Youssef ( 2015 ) and Amri et al. ( 2019 ) which rejected the EKC hypothesis for the case of Tunisia and Pata and Caglar ( 2021 ) for the case of China. Based on the coefficients related to the variable GDP and its square, we can extract the turning point of the EKC curve and the turning year. The calculated turning point value is of the order of 40473.292 (US constant) which corresponds to the logarithm value equal to 10.608. This value is lower than the highest real value over the sample period. In fact, Ang ( 2007 ) and Iwata et al. ( 2010 ) have supported this result for the case of France. This value is included in our sample. Indeed, by calculating the turning point, we can further extract the turning year which corresponds to the year 2012. Practically these results are not surprising since France is a developed country and its growth is mature. This result is inconsistent with the work of Dong et al. ( 2018 ) which consider that after the year 2028, which corresponds to the turning point, China will realize its mature economic growth. We have also focused on the potential role of nuclear energy on the environment quality. The results reveal that a favorable variation in generated nuclear energy has a negative influence on carbon emissions. In other words, a 1% upturn in nuclear energy decreases CO 2 emissions by 0.12%. Moreover, a 1% decline in nuclear energy drops the CO 2 emissions by 0.322%. The negative change in nuclear energy has a more important effect than a positive change in declining the level of CO 2 emissions into the atmosphere. Our findings support the argument that nuclear energy as a green technology can help in reducing CO 2 emissions in the long run, confirming the findings of IEA (2019) which points out that nuclear energy makes a substantial contribution to enhancing the global fight against climate change. Our results are in harmony with the findings of Iwata et al. ( 2010 ) and Marques et al. ( 2016 ) who stipulated the positive role of nuclear energy in reducing the pollutant emissions for the case of France. Our results are also consistent with Nathaniel et al. ( 2021 ) for the group of seven and Hassan et al. ( 2020 ) for the BRICS countries, and Syed et al. ( 2021 ) for the case of India. However, results are in contradiction with those of Mahmoud et al. (2020) for the case of Pakistan, Pan and Zhang ( 2020 ) for the case of the USA, and Sarkodie and Adams ( 2018 ) for the case of South Africa. On the other hand, the estimated coefficients related to fossil fuel energy appear positive and significant. Specifically, a 1% increase of fossil fuel production leads to an increase of the CO 2 emissions by 1.409%, similarly a decrease of 1% of this variable increases CO 2 emissions by 1.381%. The results support the fact that CO 2 emissions are driven by fossil fuel production in France. These results are in accordance with the reported results of Ma et al. ( 2021 ) study comparing between France and Germany and Martins et al. ( 2021 ) for the case of G7 countries. Likewise, Kartal ( 2022 ) for the case of the top 5 carbon emitting countries and Lawson ( 2020 ) for the case of 41 Sub-Saharan African countries are confirming these results. The results also attest that the estimated coefficients for trade openness are positive. More specifically, the results reveal that the downside variations in trade openness have a positive impact (0.363), while the positive variations in the trade openness have no significant impact in controlling CO 2 emissions in France. Consequently, we can consider the positive effect of the flows of international trade in France for both exports and imports on CO 2 emissions. In this regard, policy makers in France should enhance the use of clean technologies, by implementing incentive policies toward environmentally friendly industries and it should also penalize polluting industries by involving taxes and norms. This finding is analogous to Mutascu ( 2018 ) for the case of France, and Aslam et al. ( 2021 ) for the case of Malaysia. However, it is inconsistent with the results of Iwata et al. ( 2010 ) in the case of France, who found that trade has an insignificant impact on CO 2 emissions. The short run estimation in Table 4 confirms the EKC hypothesis. The income per capita increases the CO 2 emissions by 41.944% at 10% of significance level, and its squared curtails the level of CO 2 by -1.979% at 10% of significance level. Regarding nuclear energy, its positive and negative changes have no significant impact on the CO 2 emissions in the short run. The positive and negative changes in fossil fuels have positive repercussions on CO 2 emissions. Besides, both positive and negative changes of trade openness have no significant impacts, in the short run. The next step is to analyze the long-term asymmetric responses of CO 2 emissions to positive and negative variations in nuclear, fossil fuel and trade openness, while the GDP per capita and its squared followed a symmetrical approach. Table 4 also details these asymmetric long term parameters. We conclude that a 1% increase of nuclear energy leads to a decrease of CO 2 emissions by 0.080%. Similarly, a 1% decrease of nuclear energy eases off CO 2 emissions by 0.214%. However, a 1% increase of non-renewable energy leads to a significant increase of CO 2 emissions by 0.935%. Likewise, a 1% decrease of this variable has the same impact on the environment quality (0.917%). The trade openness has a negative effect on CO 2 emission in the case of positive shock (-0.113%). However, it leads to a raise on CO 2 emissions by 0.241% in the case of a negative shock. Table 4 NARDL long-run and short-run estimations Long term analysis t-Statistics P-value Variables coefficients \({\text{l}\text{n}\text{C}\text{O}2}_{\text{t}-1}\) -1.507 * -5.08 0.000 \({\text{l}\text{n}\text{G}\text{D}\text{P}}_{\text{t}-1}\) 49.252* 3.95 0.002 \({\left(\text{l}\text{n}\text{G}\text{D}\text{P}\right)}_{\text{t}-1}^{2}\) -2.334* -3.93 0.002 \(\text{l}{\text{n}\text{N}\text{U}\text{C}}_{\text{t}-1}^{+}\) -0.120*** -2.07 0.063 \({\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}-1}^{-}\) -0.322*** -1.93 0.080 \({\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}-1}^{+}\) 1.409* 4.54 0.001 \({\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}-1}^{-}\) 1.381* 5.48 0.000 \({\text{l}\text{n}\text{T}\text{R}}_{\text{t}-1}^{+}\) -0.170 -1.59 0.139 \({\text{l}\text{n}\text{T}\text{R}}_{\text{t}-1}^{-}\) 0.363* 3.84 0.003 Short term analysis \({{\Delta }\text{l}\text{n}\text{G}\text{D}\text{P}}_{\text{t}}\) 41.944*** 1.85 0.091 \({{\Delta }\text{l}\text{n}\text{G}\text{D}\text{P}}_{\text{t}-1}\) -74.00** -2.70 0.020 \({{\Delta }\left(\text{l}\text{n}\text{G}\text{D}\text{P}\right)}_{\text{t}}^{2}\) -1.979*** -1.84 0.093 \({{\Delta }\left(\text{l}\text{n}\text{G}\text{D}\text{P}\right)}_{\text{t}-1}^{2}\) 3.507** 2.70 0.021 \({{\Delta }\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}}^{+}\) 0.003 0.04 0.966 \({{\Delta }\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}-1}^{+}\) -0.050 -0.99 0.343 \({{\Delta }\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}}^{-}\) 0.067 0.39 0.703 \({{\Delta }\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}-1}^{-}\) 0.031 0.20 0.848 \({{\Delta }\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}}^{+}\) 0.688* 4.17 0.002 \({{\Delta }\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}-1}^{+}\) -0.248 -1.05 0.318 \({{\Delta }\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}}^{-}\) 0.977* 7.16 0.000 \({{\Delta }\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}-1}^{-}\) -0.187 -0.88 0.400 \({{\Delta }\text{l}\text{n}\text{T}\text{R}}_{\text{t}}^{+}\) 0.054 0.45 0.660 \({{\Delta }\text{l}\text{n}\text{T}\text{R}}_{\text{t}-1}^{+}\) 0.146 1.17 0.266 \({{\Delta }\text{l}\text{n}\text{T}\text{R}}_{\text{t}}^{-}\) -0.028 -0.29 0.775 \({{\Delta }\text{l}\text{n}\text{T}\text{R}}_{\text{t}-1}^{-}\) -0.229*** -2.06 0.064 \(\text{c}\text{o}\text{n}\text{s}\text{t}\text{a}\text{n}\text{t}\) 9.366* 5.08 0.000 \({R}^{2}\) 0.9874 Bound Testing for Asymmetric cointegration F PSS 4.1206* T BDM -5.0776 * Long-run parameters \({{\rho }}_{\text{l}\text{n}\text{G}\text{D}\text{P}}\) 32.679* 27.96 0.000 \({{\rho }}_{(\text{l}\text{n}\text{G}\text{D}{\text{P})}^{2}}\) -1.549* 27.31 0.000 \({{\rho }}_{{\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}}^{+}}\) -0.080** 6.559 0.026 \({{\rho }}_{{\text{l}\text{n}\text{N}\text{U}\text{C}}_{\text{t}}^{-}}\) -0.214*** 3.221 0.100 \({{\rho }}_{{\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}}^{+}}\) 0.935* 84.21 0.000 \({{\rho }}_{{\text{l}\text{n}\text{N}\text{R}\text{E}}_{\text{t}}^{-}}\) 0.917* 66.33 0.000 \({{\rho }}_{{\text{l}\text{n}\text{T}\text{R}}_{\text{t}}^{+}}\) -0.113*** 3.342 0.095 \({{\rho }}_{\text{l}\text{n}{\text{T}\text{R}}_{\text{t}}^{-}}\) 0.241* 14.42 0.003 Sensitivity analysis D.W 2.431 SERIAL 2.088 0.148 ARCH 1.249 0.1008 RESET 1.39 0.268 CUSUM Stable CUSUMQ Stable Note: The asterisks (*), (**), and (***) refer to 1%, 5%, and 10% significance levels, sequentially. Figure 5 presents the results of the dynamic asymmetric multiplier, which characterizes the dynamic adjustment process between the variables in the model, except for the case of real GDP per capita, and its square which are selected to have a linear reaction during the whole period in order to test the EKC hypothesis. Clearly, it designs the asymmetric adjustment path of CO 2 emissions following one unit change of nuclear energy, nonrenewable energy, and trade openness. The analysis of this step allows French policy makers to integrate and design effective strategies to accomplish their objectives concerning the protection of the environment through the control of CO 2 emissions. It can be observed from Fig. 5 that negative shocks on both nuclear energy and trade openness have a deeper effect on CO 2 emissions than positive ones in the long run. Controversially, the impact of non-renewable energy on CO 2 emissions is equivalent in the case of positive and negative changes. 6. Conclusions This research has analyzed the relation between CO 2 emissions, economic growth, and nuclear energy, taking into account the role of non-renewable energy and trade openness from 1980 to 2019, for the case of France. In this framework, we have tested the validity of the EKC hypothesis in France. The asymmetric ARDL approach suggested by Shin et al. ( 2014 ) was adopted to analyze the asymmetric cointegration in our model. The main findings of the asymmetric cointegration will be synthesized in what follows. First, the link between income and CO 2 emissions designs an inverted U-shaped relationship. More specifically, the increase in GDP leads to an increase in CO 2 emissions while the squared GDP per capita decreases these pollutant emissions. Accordingly, our results disclosed the validity of the EKC hypothesis. In addition, the turning point related to income and CO 2 emissions was reached in 2012, which means that since this year, the increase in GDP per capita has been coupled to a decrease in CO 2 emissions in France. Second, the asymmetric effect of nuclear energy on CO 2 emissions shows that the overall impact is negative. In addition, it was highlighted that the negative shock on nuclear energy has a deeper effect than the positive one, in the long run. However, the results in the short run revealed that both positive and negative shocks have no significant effect on CO 2 emissions. Given that nuclear power is reducing CO 2 emissions in France, consequently, the dependence of the French electricity mix to nuclear power is not in contrast with its ambitious targets of mitigating CO 2 emissions and its fight against climate change. However, the management of nuclear plants encompasses many risks as proved by many disasters (for example, Chernobyl in 1986 and Fukushima in 2011) and mentioned largely (Carless et al. 2021 ; Choi 2019 ). In addition, the perception of risks and acceptance of nuclear power by population is a very important issue that should be taken into account (Ho et al. 2014 ; Perez et al. 2020 ; Lee and Gloaguen 2015 ). In addition, Muellner et al. ( 2021 ) demonstrated that nuclear energy obviates only between 2% and 3% of GHG emissions and the tendency is decreasing by 2040. Hence, France should diversify its energy mix by introducing many types of renewable energy in order to mitigate its GHG emissions in the future. Third, the non-renewable energy negatively influences CO 2 emissions in France. In particular, positive and negative shocks on non-renewable energy are drivers to the environment pollution which confirms the fact that fossil fuels are among the main trigger factors of climate change. Fourth, trade openness is qualified as a driver of CO 2 emissions, more specifically only the downturn in this variable raises the level of CO 2 emission, while the upturn action has no significant effect on the environmental sustainability. In the short run, we can conclude the neutrality effect of trade on CO 2 emissions. In this case, the French government should adopt a strategy based on encouraging the use of clean industry. For example, it can impose taxes and standards on polluting industries, which can positively impact alternative industries based on clean technologies. Future research can contribute to the existing literature by analyzing the substitution possibility between nuclear energy and renewable energy in the case of France to put out useful policy suggestions. It will be interesting also to use a more general indicator as a proxy to environmental pollution such as the ecological footprint which is recently used by scholars such as Altintas and Kassouri (2020) and Bandyopadhyay et al. ( 2022 ). Declarations Author Contributions Emna Omri : Conceptualisation; Methodology; Writing, review & editing Haifa Saadaou i: Conceptualisation; Methodology; Data collection; Software Funding: The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests: The authors have no relevant financial or non-financial interests to disclose. 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J Clean Prod 142:3500–3516 Zivot E, Andrews DWK (1992) Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis. J Bus Econ Stat 10:251–270 Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 17 Jun, 2022 Reviewers invited by journal 17 Jun, 2022 Editor assigned by journal 23 May, 2022 First submitted to journal 14 May, 2022 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-1655777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":114406650,"identity":"d14d646b-5122-4b69-ac8d-05a9721740ec","order_by":0,"name":"Emna 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target=\"_blank\"\u003e[1]\u003c/a\u003e Data are collected from «\u0026nbsp;le service des données et études statistiques (SDES), chiffres clés de l’énergie 2021\u0026nbsp;», available online on\u0026nbsp;: https://www.statistiques.developpement-durable.gouv.fr/chiffres-cles-de-lenergie-edition-2021?rubrique=19\u0026amp;dossier=170 (accessed 15 february, 2022).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1655777/v1/2377fe5d315f1c15ba376d2c.jpg"},{"id":23133454,"identity":"806914f2-1cb9-45eb-b3cf-e4bdb7eb4f23","added_by":"auto","created_at":"2022-06-27 15:58:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":87162,"visible":true,"origin":"","legend":"\u003cp\u003ePrimary energy consumption in France\u003c/p\u003e\u003cp\u003eSource: SDES (2021)\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1655777/v1/0625e50a62cf543788d8a068.jpg"},{"id":23133456,"identity":"a6a8731d-8755-4d69-b93c-4c3d3878e7d9","added_by":"auto","created_at":"2022-06-27 15:58:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76511,"visible":true,"origin":"","legend":"\u003cp\u003eSeries trends\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1655777/v1/aabb7b3fdb5e782d5c7a58f1.jpg"},{"id":23133902,"identity":"b84dbb36-23e0-44eb-a3ee-2ee8b9f5402b","added_by":"auto","created_at":"2022-06-27 16:03:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55809,"visible":true,"origin":"","legend":"\u003cp\u003ePlots of CUSUM and CUSUMQ\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1655777/v1/6a08548d2d098d16493bd63a.jpg"},{"id":23133904,"identity":"b202e5eb-2146-468a-b96b-5a51fae7dca2","added_by":"auto","created_at":"2022-06-27 16:03:22","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":146707,"visible":true,"origin":"","legend":"\u003cp\u003eMultipliers of accumulative effects of NUC, GDP, GDP\u003csup\u003e2\u003c/sup\u003e, NRE, and TR on CO2\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-1655777/v1/a31aef78c9a47ac6142b9414.jpg"},{"id":23133906,"identity":"dd271071-5b44-4d37-90f6-b6226b9e5395","added_by":"auto","created_at":"2022-06-27 16:03:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":779114,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1655777/v1/811ef5ec-2fee-495b-aef1-ff1be807e139.pdf"}],"financialInterests":"","formattedTitle":"An empirical investigation of the relationship between nuclear energy and environmental pollution in France: fresh evidence using asymmetric cointegration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCarbon emissions are considered as a menacing matter facing not only the ecosystem but also the economic development (Alola et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn spite of international agreements and the strong awareness toward mitigating carbon emissions by most countries, the global carbon dioxide emissions augmented by 1.1% between 2008 and 2018 [British Petroleum (BP) Statistical Review of World Energy 2020].\u003c/p\u003e \u003cp\u003eHowever, in the European region, the carbon dioxide emissions have decreased slightly from 4246.12\u0026nbsp;million tons in 2018 to 4110.84\u0026nbsp;million tons in 2019 (BP Statistical Review of World Energy 2020).\u003c/p\u003e \u003cp\u003eClimate change is manifested by several phenomena, including the rise in sea level and global temperatures. The objective of the Paris agreement is to keep the rise in global temperatures well below 2\u0026deg;C by 2100. The climb in temperature is due to polluting emissions, in particular CO\u003csub\u003e2\u003c/sub\u003e from combustion and the use of fossil fuels. Indeed, these emissions increased by 67% between 1990 and 2018 (Ministry of Ecological Transition \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn fact, since the global warming is becoming more and more critical, the issue of environment quality has received more consideration than ever from international organizations [United Nations Environment Programme (UNEP) and World Trade Organization (WTO) \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Intergovernmental Panel on Climate Change (IPCC) \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e], politicians and researchers in order to understand the main causes of environment degradation and to predict its evolution over time.\u003c/p\u003e \u003cp\u003eThe consumption of fossil fuels is still prominent in industry, transport, building, and power in most countries (REN 21 2021). Nevertheless with the increasing awareness about climate change and the spreading perception of the damages of the excessive use of fossil fuels (IPCC 2021), many countries such as France have been making efforts in order to increase the share of nuclear and renewable energies.\u003c/p\u003e \u003cp\u003eIn fact, one of the main features of the energy system in France is the impressive progress made in nuclear energy generation since the oil crisis in 1973 (Millot et al. \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, in 2019, French public spending on energy research and development (R\u0026amp;D) reached almost \u0026euro; 1.2\u0026nbsp;billion. Nuclear\u0026rsquo;s R\u0026amp;D accounts for 63% of this expenditure (Ministry of Ecological Transition \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, GHG emissions in France have decreased by 19% between 1990 and 2018. However, this remains far from the ambitious objective of France which aims for a reduction of 40% of its GHG emissions during the period 1990\u0026ndash;2030. To carry out its climate objectives, the French finance bill for the year 2021 allocated \u0026euro; 37\u0026nbsp;billion as climate-friendly spending (Ministry of Ecological Transition \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e2021a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to IEA (2019), nuclear energy played a crucial role in the global climate change engagement. In fact, nuclear power had contributed to the avoidance of 63 gigatonnes of carbon emissions during 1971\u0026ndash;2018.\u003c/p\u003e \u003cp\u003eIn this regard, multiple studies have investigated the impact of nuclear power on environmental quality in general and CO\u003csub\u003e2\u003c/sub\u003e emissions in particular (Mahmoud et al. 2020; Nathaniel et al. \u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hassan et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Pan and Zhang \u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe foremost objective of the current study is to take part in the debating over the impact of nuclear power on the environment quality. Therefore, the NARDL method of cointegration is applied to detect the asymmetric effects of nuclear energy and income along with trade and fossil energy consumption on carbon emissions in France. In addition, the relationship between income and CO\u003csub\u003e2\u003c/sub\u003e emissions is carried out by testing the EKC hypothesis.\u003c/p\u003e \u003cp\u003eWe think France presents an interesting case to study as the country is a net importer of oil and has invested in nuclear energy since the 1970s in order to reduce its energy dependence and diversify its electricity mix. Moreover, France has signed many agreements to bring down its CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe current paper contributes to the actual literature in two ways. Firstly, it analyzes the impact of nuclear energy, fossil fuels and trade within the framework of the EKC hypothesis for the case of France. In fact, most of the existing studies in this issue treat France as a part of the panel and not as a country specific case. Secondly, it is the first study to use the NARDL approach in order to determine the asymmetric links between CO\u003csub\u003e2\u003c/sub\u003e emissions in France and its drivers.\u003c/p\u003e \u003cp\u003eTo respond to the issue of this paper, we structure it in 5 interrelated sections. Section 2 looks at both the past and current energy situation in France while emphasizing the predominance of nuclear energy. Section 3 provides the data and the model specification. The main findings are included in Section 4. Finally, Section 5 presents the conclusions and offers the preeminent policy suggestions.\u003c/p\u003e"},{"header":"2. The Evolvement Of The Energy System In France: The Prominence Of Nuclear Power","content":"\u003cp\u003eThe energy system in France has been characterized by the preeminence of nuclear energy for many years and the steady progress of renewable energies.\u003c/p\u003e\n\u003cp\u003e\u003ca class=\"FNLink\" href=\"#Fn1\" id=\"#FNLinkFn1\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003ePrimary national production represents a little more than half of France\u0026rsquo;s energy supply (Ministry of Ecological Transition, \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eAccording to Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, following the implementation of the French nuclear program, primary energy production increased from 514 TWh in 1973 (including 9% nuclear) to 1,423 TWh in 2020 (including 75% nuclear). However, primary energy production decreased by 8.7% in 2020 compared to 2019, which is explained by the decline in nuclear production (\u0026minus;\u0026thinsp;11.3%, to 1,072 TWh against 1,209 TWh). This decline is mainly due to the pandemic context which led to delays in scheduled maintenance and closure of the last two reactors at the Fessenheim nuclear power plant on June 29, 2020. Nuclear production is thus falling to a level not seen since the early 1990s (1,026 TWh in 1992 and 1,091 TWh in 1994).\u003c/p\u003e\n\u003cp\u003eCurrently, France has 56 reactors in service that were commissioned between the end of the 1970s and the beginning of the 2000s (Ministry of Ecological Transition \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConcerning fossil fuels, their extraction has declined sharply, this is mainly due to the extraction of coal and natural gas, which has been almost zero since 2015. The decline in gas production has been remarkable since 1984. In fact, it fell from 61.38 TWh in 1984 to 17.5 TWh in 2000 to reach only 0.22 TWh in 2020.\u003c/p\u003e\n\u003cp\u003eOn the other hand, France has stopped coal mining since 2015 and plans to stop producing electricity from coal in 2022 (Ministry of Ecological Transition \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eFurthermore, the production of crude oil in France stands at 9.75 TWh in 2020 against 34.46 in 1970 and 44.56 in 1988, thus achieving a decrease of 72% compared to 1970. Production in 2020 only represents about 1% of national oil consumption. Since oil production in France is very limited, its supply of crude oil relies almost entirely on imports from Saudi Arabia and Kazakhstan.\u003c/p\u003e\n\u003cp\u003eAt the same time, production from renewable sources (wind power, biofuels, biogas, etc.) has been growing steadily since the mid-2000s. In 2020, the primary production of renewable energy amounts to 340.76 TWh. The main sources remain biomass, hydro and wind power. Between 2019 and 2020, the primary production of renewable energies increased slightly, by 2.79 TWh (or +\u0026thinsp;0.82%).\u003c/p\u003e\n\u003cp\u003eAccording to Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, energy consumption has tended to decrease slightly for several years. In fact, after steadily increasing until 2005 to reach a peak of 3,155 TWh, primary energy consumption has declined slightly.\u003c/p\u003e\n\u003cp\u003eThe long-term trend is contrasted between energies: since 1990, the consumption of coal and oil has fallen by 72% and 27% respectively. Conversely, during the same period, those of nuclear and natural gas increased by 14.5% and 44% respectively, while that of renewable energies almost doubled.\u003c/p\u003e\n\u003cp\u003eIn 2020, the drop in primary energy consumption is historic, falling by 8.3%. It is mainly explained by the health crisis and the associated travel limitations.\u003c/p\u003e\n\u003cp\u003eFrance\u0026rsquo;s primary energy consumption stands at 2,650.43 TWh in 2020. France\u0026rsquo;s real primary energy mix consists of 39.22% nuclear, 27.53% petroleum, 16.85% gas, 13.87% renewable energies and waste, and 2.52% coal (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eConcerning the French electricity mix in 2020, it is dominated by the nuclear energy as follows : 65.76% nuclear, 13.02% hydro, 10.45% conventional thermal, 7.98% wind, 2.67% PV, and 0.12% other sources. In 2020, net electricity production amounted to 510 TWh, down 6.8% from the previous year. This decrease is largely explained by the decline in nuclear production, which is at its lowest level since the early 1990s. On the other hand, renewable electricity production increased compared to 2019. Due to favorable weather conditions and the growth of the park, wind power production increased by 17.2% in particular. Photovoltaic and hydraulic productions are also up, respectively by 11.1% and 8.3% over one year (Ministry of Ecological Transition \u003cspan class=\"CitationRef\"\u003e2021b\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eIt should be noted that France is a net exporter of electricity to its European neighbors through the border interconnection lines, the most important part of its exports is satisfied by nuclear energy. In 2019, France imported 16 Tera-watt hour (TWh) and exported 73 TWh, i.e. an electricity export balance of 58 TWh, registering a decrease of 8% compared to 2018 due to the drop in nuclear and hydro production (Ministry of Ecological Transition \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"3.\tLiterature Survey","content":"\u003cp\u003eThe links between nuclear energy, income, and CO\u003csub\u003e2\u003c/sub\u003e emissions under the EKC hypothesis is well recognized in previous studies (for instance, Dong et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Mahmood et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Syed et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eHowever, we incorporate different control variables such as fossil fuel energy generation and trade openness in the case of France for the period 1980\u0026ndash;2019.\u003c/p\u003e\n\u003cp\u003eWe include fossil fuel generation in our model as a proxy for non-renewable energy. In fact, fossil fuel energy is perceived as the most ponderous component in the global energy mix (REN 21 2021). Concerning the empirical findings, multitude studies have demonstrated the negative effect of fossil fuels on the environment quality through the pollutant emissions (Lau et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; B\u0026eacute;laid and Zrelli 2019; Ma et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Haldar and Sethi \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\n\u003cp\u003eMoreover, we consider the substantial role of trade openness as a determinant for CO\u003csub\u003e2\u003c/sub\u003e emissions. In fact, several researches have realized that the transfer of goods and services between trading partners of different countries in the world has a substantial effect on the environment quality (Cristea et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Shahbaz et al. \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kim et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Fang et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Aslam et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pata and Caglar \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). In general, the impact of trade openness on the environment reveals several opposite findings.\u003c/p\u003e\n\u003cp\u003eHence, trade openness and fossil fuel consumption are considered as control variables in our estimation and their impacts on CO\u003csub\u003e2\u003c/sub\u003e emissions were deeply detailed. For these reasons we will just focus on the nexuses between economic growth, nuclear energy and carbon emissions.\u003c/p\u003e\n\u003cdiv class=\"Section2\" id=\"Sec3\"\u003e\n \u003ch2\u003e3.1. The nexus between economic growth and CO\u003csub\u003e2\u003c/sub\u003e emissions and the EKC hypothesis\u003c/h2\u003e\n \u003cp\u003eThe controversy of whether the relationship between per capita income and environmental degradation follows an inverted-U-shaped form has been deeply analyzed by an impressive body of literature. In fact, since the pioneering studies of Grossman and Krueger (\u003cspan class=\"CitationRef\"\u003e1993\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e1995\u003c/span\u003e) as well as Selden and Song (\u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e), many researchers have tried to discuss the presence of this relationship known as the EKC.\u003c/p\u003e\n \u003cp\u003eHence, since the 1990\u0026rsquo;s, many researchers have tested the validation of the EKC for the case of different countries by using different econometric methods. A recent review of the literature concerning the use of the EKC was given by Dinda (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), Stern (\u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), Kijima et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e), Kaika and Zervas (\u003cspan class=\"CitationRef\"\u003e2013a\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2013b\u003c/span\u003e), and Sarkodie and Strezov (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). They demonstrated that the EKC literature is very considerable and the results are mixed.\u003c/p\u003e\n \u003cp\u003eThe existing studies have used different indicators of environmental quality but the majority of them have employed carbon dioxide emissions as an indicator of pollution.\u003c/p\u003e\n \u003cp\u003eThe empirical findings concerning the EKC are mixed and there is a controversy about the validity of the EKC hypothesis. In fact, the EKC hypothesis was validated widely (Apergis \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Li et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Churchill et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Acheampong et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ghazouani \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Salari et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhang et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, the EKC assumptions were rejected by a considerable number of studies (Ben Jebli and Ben Youssef \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kang et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Antonakakis et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Amri et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Lawson et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eThe studies that rejected this hypothesis, found different patterns of the EKC, such as U- shaped form (Dinda et al. \u003cspan class=\"CitationRef\"\u003e2000\u003c/span\u003e; Pata and Caglar \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), N-shaped form (Balsalobre-Lorente et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Koc and Bulus \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), and inverted N-shaped form (L\u0026oacute;pez-Men\u0026eacute;ndez et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eMultiple analyses were devoted to the case of a single country. For example, Ben Jebli and Ben Youssef (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) as well as Amri (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that the inverted U-shaped EKC hypothesis is not validated for the case of Tunisia. However, for the same country, Ghazouani (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated the validity of the EKC hypothesis. In addition, the results reveal that economic growth has a negative influence on the environment and the existence of a bidirectional relationship between economic growth and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n \u003cp\u003eMalik et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Zhang et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) validated the presence of the EKC for the case of Pakistan and Ali et al. (\u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e) for the case of Malaysia. In fact, Malik et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) demonstrated that economic growth increases CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n \u003cp\u003eChina, as a great emitter of CO\u003csub\u003e2\u003c/sub\u003e emissions has captivated the most attention (Jalil and Mahmud \u003cspan class=\"CitationRef\"\u003e2009\u003c/span\u003e; Dong et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wang and He \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Chen et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Ahmad et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The results for the case of China are also mitigated, for example, Pata and Caglar (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) rejected the EKC hypothesis, when Zhang and Zhang (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) validated it.\u003c/p\u003e\n \u003cp\u003eDeveloped countries have also received great attention. For instance, Salari et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) validated the EKC hypothesis for the case of the USA. However, Shahbaz et al. (\u003cspan class=\"CitationRef\"\u003e2017a\u003c/span\u003e) found that the link between growth and CO\u003csub\u003e2\u003c/sub\u003e emissions follows an inverted-U shape and it is N-shaped in presence of structural breaks and biomass. The empirical results attest also that economic growth induces carbon emissions in the Granger sense.\u003c/p\u003e\n \u003cp\u003eA recent study carried out by Hu et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) for a panel of 25 developing countries showed that economic growth has an important impact on CO\u003csub\u003e2\u003c/sub\u003e emissions and that the EKC hypothesis is validated.\u003c/p\u003e\n \u003cp\u003eArouri et al. (\u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e) analyzed the case of 12 countries of the Middle East and North Africa (MENA) region and found evidence of the existence of an inverted U-shaped curve which means that CO\u003csub\u003e2\u003c/sub\u003e emissions rise, become stable, and then decline with real GDP. However, for the same region, Gorus and Aydin (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) indicated no causal link between economic growth and CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n \u003cp\u003eApergis and Ozturk (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) used the generalized method of moments (GMM) and verified the validity of the EKC hypothesis in 14 Asian countries. Many other studies focused on a group of countries such as the European Union (L\u0026oacute;pez-Men\u0026eacute;ndez et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), OECD countries (Dogan et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Churchill et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lau et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Ng et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Isik et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). As an example, Acaravci and Ozturk (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) analyzed the case of Europe and revealed that the EKC hypothesis is valid in Denmark and Italy.\u003c/p\u003e\n \u003cp\u003eBen Youssef et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) analyzed the case of fifty-six countries divided into three panels according to their income level, they found that a bi-directional causality exists between economic growth and CO\u003csub\u003e2\u003c/sub\u003e emissions and this relationship indicates an inverted U-shaped curve. Lawson et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) used a semi-parametric dynamic panel data model in order to concomitantly test the environmental convergence and EKC assumptions for the case of 106 countries (85 non OECD and 21 OECD) over the period 1970\u0026ndash;2015. Although the results reject the validity of the EKC hypothesis, they confirm the existence of the phenomenon of convergence of CO\u003csub\u003e2\u003c/sub\u003e emissions between countries with different income levels.\u003c/p\u003e\n \u003cp\u003eAn innovative study carried out by Kacprzyk and Kuchta (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) used three conventional measures of GDP and a new proxy of GDP based on satellite nighttime data in order to test the validity of the EKC hypothesis for the case of a panel of 161 countries during 1992\u0026ndash;2012. The results confirm the existence of the EKC and the turning point is lower than previous studies.\u003c/p\u003e\n \u003cp\u003eConcerning the case of France, the EKC hypothesis was validated by various studies (Ang \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e; Iwata et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Can and Gozgor \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Shahbaz et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor instance, Shahbaz et al. (\u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e) investigated the contributory factors to CO\u003csub\u003e2\u003c/sub\u003e emissions for the case of France from 1955 to 2016. They applied the bootstrapping ARDL bounds testing approach, and Granger causality test in order to study the impacts of foreign direct investment (FDI), economic growth, energy consumption, financial development, and energy R\u0026amp;D on CO\u003csub\u003e2\u003c/sub\u003e emissions. The empirical results confirm that an increase in FDI and energy consumption boost carbon emissions and the EKC hypothesis is confirmed. However, financial development and energy research innovations decrease carbon emissions. In addition, the bootstrapping ARDL Granger causality test reveals the presence of bidirectional causality between CO\u003csub\u003e2\u003c/sub\u003e emissions and all the variables.\u003c/p\u003e\n \u003cp\u003eOn the other hand, Ang (\u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e) studied the links between carbon emissions, energy consumption and growth from 1960 to 2000 using multivariate vector error correction model (VECM). They demonstrate that the EKC hypothesis is validated. The results of cointegration analysis indicate the presence of a strong long-term relationship between all variables. The results of the causality test suggest that output causes carbon emissions and energy use in the long term, however in the short term, there is a unidirectional causality running from energy consumption to output.\u003c/p\u003e\n \u003cp\u003eMoreover, Can and Gozgor (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) used the error correction model (ECM) and the dynamic ordinary least squares (DOLS) estimation techniques for the case of France from 1964 to 2011 in order to investigate the relationships among output, energy consumption, economic complexity, and CO\u003csub\u003e2\u003c/sub\u003e emissions. The results attested the validation of the EKC hypothesis. In addition, the results proved the existence of a positive effect of energy use on carbon emissions and a negative effect of economic complexity on CO\u003csub\u003e2\u003c/sub\u003e emissions in the long term.\u003c/p\u003e\n \u003cp\u003eA comparison between France and Germany was carried out in a recent study by Ma et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). The results confirm the existence of an inverted U-shaped curve between real GDP and CO\u003csub\u003e2\u003c/sub\u003e emissions which confirms the validity of the EKC hypothesis.\u003c/p\u003e\n \u003cp\u003eIn addition to the EKC hypothesis, the relationship between growth and CO\u003csub\u003e2\u003c/sub\u003e emissions was also analyzed by using the decoupling concept (Zhang and Wang \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Zhao et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Cohen et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Wu et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). For example, Song et al. (\u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) used the EKC hypothesis and the decoupling concept to compare between China and the USA.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"Section2\" id=\"Sec4\"\u003e\n \u003ch2\u003e3.2. The nexus between nuclear energy and CO\u003csub\u003e2\u003c/sub\u003e emissions\u003c/h2\u003e\n \u003cp\u003eVaried analyses were concerned with the impact of nuclear energy on the mitigation of carbon emissions. A review of literature was given by van der Zwaan (\u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Savacool (2008). The studies concerned with the relationship between nuclear power and carbon emissions are multiple and offer mixed findings. In fact, some studies confirmed the positive effect of nuclear energy on CO\u003csub\u003e2\u003c/sub\u003e emissions (Mahmoud et al. 2020; Ishida \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Pan and Zhang \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sarkodie and Adams \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), but other studies demonstrated that the use of nuclear energy mitigates carbon emissions (Saidi and Omri \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Apergis et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Baek \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Lee et al. \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e; Dong et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Nathaniel et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Hassan et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Menyah and Wolde-Rufael \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e). Other studies suggested that nuclear energy consumption has no effect on the environment quality (Al-Mulali \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jaforullah and King \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eThe lion\u0026rsquo;s share of the studies is dedicated to a panel of countries such as developed and developing countries (Akhmat et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Alam \u003cspan class=\"CitationRef\"\u003e2013\u003c/span\u003e; Apergis et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Ben Mbarek et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), or OECD countries (Lau et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saidi and Omri \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor instance, Azam et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the case of a panel of ten countries with the highest CO\u003csub\u003e2\u003c/sub\u003e emissions from 1990 to 2014. The results of the panel fully modified ordinary least squares (FMOLS) revealed that the expansion of nuclear energy consumption is an efficient way to fight climate change. In fact a 1% augmentation in nuclear energy consumption decreases carbon emissions by 0.012%. In addition, the causality test showed the existence of a two-way causality between nuclear energy consumption and carbon emissions.\u003c/p\u003e\n \u003cp\u003eIn the same line of research, Vo et al. (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) treated the case of a panel of nine countries by using FMOLS and ordinary least squares (DOLS) estimations and found that nuclear energy consumption contributes to the mitigation of carbon emissions.\u003c/p\u003e\n \u003cp\u003eThe expansion of nuclear power for electricity generation in many countries has prompted numerous researchers to explore the effect of such a transition on the environment quality in a country specific case. In fact, this issue was treated for the case of China (Dong et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), Japan (Ichida, 2018), Pakistan (Mahmood et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zaidi et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), Iran (Kargari and Mastouri \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), Republic of Korea (Kim \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), South Africa (Sarkodie and Adams \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), United Arab of Emirates (AlFarra and Abu-Hijleh \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e), and Spain (Pilatowska et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eOther countries such as India (Bandyopadhyay and Rej \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Danish et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Syed et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), and USA (Menyah and Wolde-Rufael \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Jaforullah and King \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Baek \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pan and Zhang \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) have attracted the most consideration from scholars.\u003c/p\u003e\n \u003cp\u003eFor the case of India, Bandyopadhyay and Rej (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), Danish et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), and Syed et al. (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) used the ARDL, the DARL and the NARDL approaches, respectively and found that nuclear energy is a contributor to the environment amelioration by reducing CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e\n \u003cp\u003eConcerning the case of the USA, Pan and Zhang (\u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) employed the extended STIRPAT model along with the Ridge regression to study the effects of many factors, notably nuclear energy, on carbon emissions in the USA. The results showed that alternative and nuclear energy of total energy use has a positive incidence on carbon emissions.\u003c/p\u003e\n \u003cp\u003eHowever, for the same case of the USA, Baek (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) used the ARDL approach and confirmed that nuclear consumption decreases CO\u003csub\u003e2\u003c/sub\u003e emissions. On the other hand, Jaforullah and King (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) found no interlinkage between nuclear energy consumption and carbon emissions by using a VECM model.\u003c/p\u003e\n \u003cp\u003eFrance, as one of the major nuclear energy producers, was also treated by many scholars (Iwata et al. \u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e; Marques et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e; Cany et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Poinssot et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eWhile the studies are multiple and the approaches used are different, the results remain quite similar. In fact, Iwata et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Marques et al. (\u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e) used the ARDL approach and demonstrated that the use of nuclear energy is beneficial to the environment quality. In addition, using the pair-wise Granger causality test, Iwata et al. (\u003cspan class=\"CitationRef\"\u003e2010\u003c/span\u003e) found a one-way causality linkage from nuclear power to carbon emissions.\u003c/p\u003e\n \u003cp\u003eOn the other hand, Poinssot et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e) applied the Process Chain Analysis (PCA) in order to analyze the impacts of the nuclear fuel cycle on many environmental indicators in France. The results confirmed that nuclear power is one of the least impacting energies.\u003c/p\u003e\n \u003cp\u003eMany other interesting studies deal with the case of France in the context of a comparison with other countries such as Sweden (Millot et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) or Sweden and Spain (Pilatowska and Geise \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eFor instance, Pilatowska and Geise (\u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e) studied the impacts of nuclear energy along with fossil energies and renewable energies on carbon emissions and growth in three European countries by using the vector autoregression (VAR) model. The results of the Granger causality revealed that, in the formative phase, there is no causality between carbon emissions and nuclear energy in three countries. Concerning the expansion phase, there is a unidirectional causality from nuclear to CO\u003csub\u003e2\u003c/sub\u003e in Spain, bidirectional causality in France, and no significant causality in Sweden.\u003c/p\u003e\n \u003cp\u003eThe studies that are interested in the case of France are not numerous compared to those that study France as part of a panel such as major nuclear generating countries (Al-Mulali \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Baek and Pride \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e; Baek \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), the group of seven (Nathaniel et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), BRICS countries (Hassan et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e), etc.\u003c/p\u003e\n \u003cp\u003eBaek (\u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e) used the FMOLS and DOLS approaches for estimation for the case of 12 principal nuclear producing countries. The results revealed that the use of nuclear energy serves in the abatement of carbon emissions. In fact, a 1% increase in electricity produced from nuclear power abates carbon emissions by 0.12%.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Methodology","content":"\u003cdiv class=\"Section2\" id=\"Sec6\"\u003e\n \u003ch2\u003e4.1. Data and descriptive analysis\u003c/h2\u003e\n \u003cp\u003eThe data concerning the generations of nuclear and fossil fuel energies measured by Million Tonnes of Oil Equivalent (Mtoe) are collected from the British Petroleum Company (BP). The economic income is represented by the real GDP per capita and is obtained from the World Development Indicators (WDI). From the previous source, we used the ratio of trade in a percentage of GDP as a proxy for trade openness. Concerning the data of carbon emissions in million tonnes of CO\u003csub\u003e2\u003c/sub\u003e, they are obtained from the US EIA database. The data covers the period 1980\u0026ndash;2019.\u003c/p\u003e\n \u003cp\u003eA detailed description of the statistics is presented in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\n \u003cp\u003eWe can notice from Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e that non-renewable energies have the lowest volatility, while nuclear energy generation is the most volatile. The statistic of the Skewness shows that CO\u003csub\u003e2\u003c/sub\u003e emissions (CO2), nuclear energy (NUC), and the real GDP per capita (GDP) are negatively skewed, which means that they present longer left tails in comparison to a normal distribution. In contrast, fossil fuel energies (NRE) and trade openness (TR) are positively skewed. In addition, the Kurtosis coefficient exceeds 3 for CO2, NRE, and NUC distributions which means that they are leptokurtic and have heavier tails compared with a normal distribution. The Jarque-Bera statistic attests that NUC is not normally distributed unlike all other variables.\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eStatistics\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnGDP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnNRE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eln NUC\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003elnTR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.356\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMaximum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMinimum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.677\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStd. dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.098\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.149\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.459\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.816\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJarque-Bera\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.343\u003c/p\u003e\n \u003cp\u003e(0.510)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.737 (0.154)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.947\u003c/p\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e57.029\u003c/p\u003e\n \u003cp\u003e(\u0026nbsp;0.000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.965\u003c/p\u003e\n \u003cp\u003e(0.227)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: the p-values are presented between parentheses.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eMoreover, Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e depicts the dynamics of the series, and shows that all explicative variables have an increasing trend except of NRE. Concerning the dependent variable, it shows a strong decrease of carbon emissions, mainly, during the period 1980\u0026ndash;1986.\u003c/p\u003e\n \u003cp\u003eTo assess the validity of the environmental Kuznets curve, we add the GDP and the GDP squared term (GDP\u003csup\u003e2\u003c/sup\u003e). Accordingly, the specific role of the environmental Kuznets curve is to specify the impact of the economic income on the environment quality.\u003c/p\u003e\n \u003cp\u003eTherefore, we adopt the following model specification:\u003c/p\u003e\n \u003cdiv class=\"Equation\" id=\"Equ1\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$${lnCO2}_{t}={}_{0}+{{}_{1}lnGDP}_{t}+{}_{2}{\\left(lnGDP\\right)}_{t}^{2}+{{}_{3}lnNUC}_{t}+{{}_{4}lnNRE}_{t}+{{}_{5}lnTR}_{t}+{\\mu }_{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere lnCO2, lnGDP, (lnGDP)\u003csup\u003e2\u003c/sup\u003e, lnNUC, lnNRE, and lnTR represent the natural logarithms of carbon emissions, GDP per capita, the square term of GDP per capita, nuclear energy generation, fossil fuel energy generation and trade openness, respectively. The year is represented by the subscript \u0026ldquo;t\u0026rdquo;, \u0026micro;\u003csub\u003et\u003c/sub\u003e represents the error term, and \u0026alpha;\u003csub\u003e0\u003c/sub\u003e illustrates the constant term. The coefficients (\u0026alpha;\u003csub\u003e1\u003c/sub\u003e, \u0026alpha;\u003csub\u003e2\u003c/sub\u003e, \u0026alpha;\u003csub\u003e3\u003c/sub\u003e, \u0026alpha;\u003csub\u003e4,\u003c/sub\u003e and \u0026alpha;\u003csub\u003e5)\u003c/sub\u003e present the elasticities corresponding to exogenous variables.\u003c/p\u003e\u003c/div\u003e\u003cdiv class=\"Section2\" id=\"Sec7\"\u003e\u003ch2\u003e4.2. Non-linear autoregressive distributed lag methodology (NARDL)\u003c/h2\u003e\u003cp\u003eRecent studies consider that the reaction process of the determinants of CO\u003csub\u003e2\u003c/sub\u003e emissions is non stable and non-linear (see for example, Lahiani, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e; Shahbaz et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, etc). In this case, the standard linear cointegration becomes inadequate. For this reason, it was necessary to adopt another cointegration approach that is able to capture the non-linearity associated with the dynamics of the different variables. Thus, we adopt the asymmetric cointegration approach suggested by Shin et al. (\u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e), which allows considering the asymmetric links between the variables.\u003c/p\u003e\u003cp\u003eIn fact, the NARDL model is an extension of the linear ARDL model proposed by Pesaran et al. (\u003cspan class=\"CitationRef\"\u003e2001\u003c/span\u003e). We first present the linear form (ARDL) of our model in the following equation:\u003c/p\u003e\u003cdiv class=\"Equation\" id=\"Equa\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\u003cimg 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order to highlight the asymmetric links between nuclear energy, non-renewable energy, trade openness, and CO\u003csub\u003e2\u003c/sub\u003e emissions by taking into account the EKC model, we use the NARDL model presented in Eq. 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\"\u003e\u003c/p\u003e\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{G}\\text{D}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{G}\\text{D}\\text{P}2}\\)\u003c/span\u003e\u003c/span\u003e are the long run parameters associated to the GDP and the GDP\u003csup\u003e2\u003c/sup\u003e.The EKC hypothesis check is based on the verification of the positive sign of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{G}\\text{D}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e and the negative sign of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{G}\\text{D}\\text{P}2}\\)\u003c/span\u003e\u003c/span\u003e. This relationship implies that economic growth increases polluting emissions, in a first phase, and brings them down when the economy is mature.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{N}\\text{U}\\text{C}}^{+}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{N}\\text{R}\\text{E}}^{+}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{T}\\text{R}}^{+}\\)\u003c/span\u003e\u003c/span\u003e(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{N}\\text{U}\\text{C}}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{N}\\text{R}\\text{E}}^{-}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\gamma }}_{\\text{T}\\text{R}}^{-}\\)\u003c/span\u003e\u003c/span\u003e) designate the long-run parameters attributed to positive alterations (negative alterations), respectively. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\sigma }}_{\\text{i}}^{+}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\phi }}_{\\text{i}}^{+}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\omega }}_{\\text{i}}^{+}\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\sigma }}_{\\text{i}}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\phi }}_{\\text{i}}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\omega }}_{\\text{i}}^{-}\\)\u003c/span\u003e\u003c/span\u003e) are the short-run parameters attributed to the positive alterations (negative alterations), respectively.\u003c/p\u003e\n \u003cp\u003ePositive (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({lnNUC}_{t}^{+},ln{NRE}_{t}^{+},\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(ln{TR}_{t}^{+}\\)\u003c/span\u003e\u003c/span\u003e) as well as negative (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({lnNUC}_{t}^{-}, { lnNRE}_{t}^{-}, ln{TR}_{t}^{-}\\)\u003c/span\u003e\u003c/span\u003e ) partial sums relative to nuclear energy, non-renewable energy, and trade are presented in the following lines:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({lnNUC}_{t}^{+}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{t}{\\varDelta lnNUC}_{i}^{+}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{t}\\text{m}\\text{a}\\text{x}(\\varDelta {lnNUC}_{i}\\)\u003c/span\u003e\u003c/span\u003e, 0)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(ln{NUC}_{t}^{-}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{t}{\\varDelta lnNUC}_{i}^{-}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{t}\\text{m}\\text{i}\\text{n}(\\varDelta {lnNUC}_{i}\\)\u003c/span\u003e\u003c/span\u003e, 0)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({lnNRE}_{t}^{+}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{t}{\\varDelta lnNRE}_{i}^{+}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{t}\\text{m}\\text{a}\\text{x}(\\varDelta {lnNRE}_{i}\\)\u003c/span\u003e\u003c/span\u003e, 0)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(ln{NRE}_{t}^{-}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{t}{\\varDelta lnNRE}_{i}^{-}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{t}\\text{m}\\text{i}\\text{n}(\\varDelta {lnNRE}_{i}\\)\u003c/span\u003e\u003c/span\u003e, 0)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(ln{TR}_{t}^{+}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{t}{\\varDelta lnTR}_{i}^{+}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{t}\\text{m}\\text{a}\\text{x}(\\varDelta {lnTR}_{i}\\)\u003c/span\u003e\u003c/span\u003e, 0)\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({lnTR}_{t}^{-}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\sum }_{i=1}^{t}{\\varDelta lnTR}_{i}^{-}\\)\u003c/span\u003e\u003c/span\u003e=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sum _{i=1}^{t}\\text{m}\\text{i}\\text{n}(\\varDelta {lnTR}_{i}\\)\u003c/span\u003e\u003c/span\u003e, 0)\u003c/p\u003e\n \u003cp\u003eThen, we can also extract the negative and positive long-term coefficients for each CO\u003csub\u003e2\u003c/sub\u003e emission determinant in the NARDL model framework. The following formulas provide the coefficients for the variables NUC, NRE, and TR, respectively:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{N}\\text{U}\\text{C}}^{+}=-\\frac{{{\\gamma }}_{\\text{N}\\text{U}\\text{C}}^{+}}{{{\\rho }}_{\\text{C}\\text{O}2}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{N}\\text{U}\\text{C}}^{-}=-\\frac{{{\\gamma }}_{\\text{N}\\text{U}\\text{C}}^{-}}{{{\\rho }}_{\\text{C}\\text{O}2}}\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{N}\\text{R}\\text{E}}^{+}=-\\frac{{{\\gamma }}_{\\text{N}\\text{R}\\text{E}}^{+}}{{{\\rho }}_{\\text{C}\\text{O}2}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{N}\\text{R}\\text{E}}^{-}=-\\frac{{{\\gamma }}_{\\text{N}\\text{R}\\text{E}}^{-}}{{{\\rho }}_{\\text{C}\\text{O}2}}\\)\u003c/span\u003e\u003c/span\u003e ;\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{T}\\text{R}}^{+}=-\\frac{{{\\gamma }}_{\\text{T}\\text{R}}^{+}}{{{\\rho }}_{\\text{C}\\text{O}2}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003eand \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{T}\\text{R}}^{-}=-\\frac{{{\\gamma }}_{\\text{T}\\text{R}}^{-}}{{{\\rho }}_{\\text{C}\\text{O}2}}\\)\u003c/span\u003e\u003c/span\u003e .\u003c/p\u003e\n \u003cp\u003eThe last step in the NARDL model involves a presentation of the dynamic multipliers related to favorable and unfavorable fluctuations, as follows:\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({m}_{h,NUC}^{+}=\\sum _{j=0}^{h}\\frac{\\partial {CO2}_{t+j}}{{\\partial NUC}_{t}^{+}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e ; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({m}_{h,NUC}^{-}=\\sum _{j=0}^{h}\\frac{\\partial {CO2}_{t+\\text{j}}}{{\\partial NUC}_{t}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({\\text{m}}_{\\text{h},\\text{N}\\text{R}\\text{E}}^{+}=\\sum _{\\text{j}=0}^{\\text{h}}\\frac{\\partial {\\text{C}\\text{O}2}_{\\text{t}+\\text{j}}}{{\\partial \\text{N}\\text{R}\\text{E}}_{\\text{t}}^{+}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e ; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{m}}_{\\text{h},\\text{N}\\text{R}\\text{E}}^{-}=\\sum _{\\text{j}=0}^{\\text{h}}\\frac{\\partial {\\text{C}\\text{O}2}_{\\text{t}+\\text{j}}}{{\\partial \\text{N}\\text{R}\\text{E}}_{\\text{t}}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\({\\text{m}}_{\\text{h},\\text{T}\\text{R}}^{+}=\\sum _{\\text{j}=0}^{\\text{h}}\\frac{\\partial {\\text{C}\\text{O}2}_{\\text{t}+\\text{j}}}{{\\partial \\text{T}\\text{R}}_{\\text{t}}^{+}}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e ; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{m}}_{\\text{h},\\text{T}\\text{R}}^{-}=\\sum _{\\text{j}=0}^{\\text{h}}\\frac{\\partial {\\text{C}\\text{O}2}_{\\text{t}+\\text{j}}}{{\\partial \\text{T}\\text{R}}_{\\text{t}}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eWhen \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{h}\\to {\\infty }\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{m}}_{\\text{h},\\text{N}\\text{U}\\text{C}}^{+}\\to {{\\rho }}_{\\text{N}\\text{U}\\text{C}}^{+}\\)\u003c/span\u003e\u003c/span\u003e; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{m}}_{\\text{h},\\text{N}\\text{U}\\text{C}}^{-}\\to {{\\rho }}_{\\text{N}\\text{U}\\text{C}}^{-}\\)\u003c/span\u003e\u003c/span\u003e (Shin et al. \u003cspan class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\n \u003c/div\u003e"},{"header":"5. Results And Interpretations","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e5.1. Non linearity and stationarity tests\u003c/h2\u003e \u003cp\u003eWe adopt the Broock\u0026ndash;Dechert\u0026ndash;Scheinkman (BDS) test developed by Broock et al. (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1996\u003c/span\u003e) in order to check the nonlinearity in the data series. The null hypothesis is that the data are independently and identically distributed.\u003c/p\u003e \u003cp\u003eThe results of the non-linearity BDS test are reported in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The results attest that for all series, the null hypothesis is rejected, meaning that all variables are not identically and independently distributed which proves the presence of asymmetries.\u003c/p\u003e \u003cp\u003eFor this reason, it is necessary to use an asymmetric integration model to analyze the non-linear interactions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of BDS nonlinearity test\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eBDS statistic at different dimensions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003em\u0026thinsp;=\u0026thinsp;6\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnCO\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.138 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.289*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.311*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.298*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.199*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.332*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.426*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.496*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.549*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.196*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.329*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.423*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.493*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.546*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.131*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.246*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.345*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.429*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.501*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.102*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.149*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.190*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.207*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.185*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.143*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.228*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.276*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.299*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.3001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNotes: * indicates the rejection of the null hypothesis at 1% level of significance and \u0026ldquo;m\u0026rdquo; designs the embedding dimension.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe apply the Augmented Dickey Fuller (Dickey and Fuller \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1979\u003c/span\u003e) and Zivot-Andrews (Zivot and Andrews \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) tests to examine the order of integration. Concerning, the null hypothesis of the Augmented Dickey Fuller and Zivot-Andrews tests is the existence of unit root which indicates the non-stationarity of the series. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e reports the results of both Augmented Dickey Fuller and Zivot-Andrews tests for all variables in levels and in first differences.\u003c/p\u003e \u003cp\u003eThe Augmented Dickey Fuller test reveals that the null assumption is rejected for CO2, NUC and NRE in levels. However, at first difference, the null hypothesis is rejected for all variables, which means that the nuclear energy, the fossil fuel energy and the CO\u003csub\u003e2\u003c/sub\u003e emission are I(0), while the economic income and the trade openness are I(1).\u003c/p\u003e \u003cp\u003eFurthermore, previous studies have highlighted that classic unit root tests such as the Augmented Dickey Fuller are unable to detect the structural changes in the series that can lead to a misspecification of the variables\u0026rsquo; integration order (Perron \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Therefore, we follow Syed et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Lahiani et al. (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) in adopting the Zivot-Andrews test which is considered appropriate in the presence of non-linearity in the time series.\u003c/p\u003e \u003cp\u003eThe Zivot -Andrews test shows that all series are stationary in I(1) except for NRE which is I(0).\u003c/p\u003e \u003cp\u003eThe results of the precedent tests showed that all the variables are integrated in order I(0) and I(1), and no variables are I(2). This finding leads us to apply the non-linear ARDL model since the conditions of order of stationarity and linearity are valid.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnit Root Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eAugmented Dickey\u0026ndash;Fuller\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003eZivot-Andrews\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFirst difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBreak year\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFirst difference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBreak year\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnCO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.194**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-5.716*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.859 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNUC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-10.009*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.731*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.348*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1988\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-4.099*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-3.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.727***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnNRE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.971**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-7.283*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.779***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-8.124*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elnTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-6.007*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-4.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.433*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1994\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *, **,*** represent the rejection of the null assumption at the significance levels of 1%, 5%, and 10%, respectively. Critical values for the Zivot-Andrews test are \u0026minus;\u0026thinsp;5.34, -4.80 and \u0026minus;\u0026thinsp;4.58 for the levels of significance 1%, 5%, and 10%, respectively.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e5.2. Results of the NARDL cointegration\u003c/h2\u003e \u003cp\u003eThe results of the NARDL bounds estimation are presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The results express that the speed of adjustment (-1.507) is negatively significant at the 1% level, meaning that the estimated NARDL model is stable.\u003c/p\u003e \u003cp\u003eFurthermore, the t-statistic (T\u003csub\u003eBDM\u003c/sub\u003e) suggested by Banerjee et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and the F-statistic (F\u003csub\u003ePSS\u003c/sub\u003e) presented by Pesaran et al. (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) validate the presence of asymmetric long run cointegration between the selected variables at 1% significance level.\u003c/p\u003e \u003cp\u003eThe R\u003csup\u003e2\u003c/sup\u003e value indicates that 98.74% of the data fit the regression model. In other terms, it indicates that 98.74% of the variance in CO2 is collectively explained by the independent variables.\u003c/p\u003e \u003cp\u003eConcerning the sensitivity analysis of the model, the Durbin-Watson statistic (2.431) specifies the non-existence of autocorrelation. In addition, the Breusch-Godfrey serial correlation test (2.088) confirms the null assumption of the absence of serial correlation of error terms, since the p-value of the serial correlation test is insignificant at the different levels of significance.\u003c/p\u003e \u003cp\u003eOn the other hand, the Autoregressive Conditional Heteroskedasticity (ARCH) test denies the existence of any conditional heteroskedasticity. Furthermore, the functional form is well-conceived and verified by the Ramsey Regression Equation Specification Error Test (RESET), at the different levels of significance.\u003c/p\u003e \u003cp\u003eFinally, we perform the CUSUM and CUSUMSQ tests in order to check the model stability. Figure\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents the CUSUM and CUSUMSQ tests, and indicates that the estimated lines are between the critical bounds at the 5% threshold. This means that the estimated parameters in the model are stable over the period 1980\u0026ndash;2019.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides also the long-run and short run coefficients estimated by applying the NARDL cointegration for the determinants of CO\u003csub\u003e2\u003c/sub\u003e emissions. In our case, nuclear energy, nonrenewable energy and trade are decomposed into positive and negative changes, while the income and the square of GDP are non-decomposed in order to test the EKC model.\u003c/p\u003e \u003cp\u003eThe first long-term result to be retained from this model is the following: the impact of income on CO\u003csub\u003e2\u003c/sub\u003e is positive (49.252). This implies that a 1% increase in the level of economic growth increases CO\u003csub\u003e2\u003c/sub\u003e emissions by 49.252%. Concerning the squared real GDP per capita, it decreases the level of pollution by about 2.334%.\u003c/p\u003e \u003cp\u003eThe significant positive and negative signs of the coefficients of lnGDP and (lnGDP)\u003csup\u003e2\u003c/sup\u003e, respectively, confirm the existence of an inverted U-shaped curve relying income with CO\u003csub\u003e2\u003c/sub\u003e emissions. Accordingly, the EKC hypothesis is supported in our case, which means that over the earliest phases of development, an increase in economic growth is accompanied with a rise of pollutant emissions till a specific threshold level of GDP is reached when an increase of growth is followed by a decrease of pollutant emissions.\u003c/p\u003e \u003cp\u003eThese results are consistent with divers researches dealing with the case of France and confirming the EKC hypothesis, such as Iwata et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Can and Gozgor (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), Shahbaz et al. (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), Ang (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and Shahbaz et al. (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e2017b\u003c/span\u003e), Ma et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Concerning other countries, the EKC is validated by Malik et al. (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and Zhang et al. (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the case of Pakistan, Salari et al., (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) in the USA.\u003c/p\u003e \u003cp\u003eHowever, our results are inconsistent with those of Ben Jebli and Ben Youssef (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and Amri et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) which rejected the EKC hypothesis for the case of Tunisia and Pata and Caglar (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the case of China.\u003c/p\u003e \u003cp\u003eBased on the coefficients related to the variable GDP and its square, we can extract the turning point of the EKC curve and the turning year. The calculated turning point value is of the order of 40473.292 (US constant) which corresponds to the logarithm value equal to 10.608. This value is lower than the highest real value over the sample period.\u003c/p\u003e \u003cp\u003eIn fact, Ang (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) and Iwata et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) have supported this result for the case of France. This value is included in our sample. Indeed, by calculating the turning point, we can further extract the turning year which corresponds to the year 2012. Practically these results are not surprising since France is a developed country and its growth is mature. This result is inconsistent with the work of Dong et al. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) which consider that after the year 2028, which corresponds to the turning point, China will realize its mature economic growth.\u003c/p\u003e \u003cp\u003eWe have also focused on the potential role of nuclear energy on the environment quality. The results reveal that a favorable variation in generated nuclear energy has a negative influence on carbon emissions. In other words, a 1% upturn in nuclear energy decreases CO\u003csub\u003e2\u003c/sub\u003e emissions by 0.12%. Moreover, a 1% decline in nuclear energy drops the CO\u003csub\u003e2\u003c/sub\u003e emissions by 0.322%. The negative change in nuclear energy has a more important effect than a positive change in declining the level of CO\u003csub\u003e2\u003c/sub\u003e emissions into the atmosphere.\u003c/p\u003e \u003cp\u003eOur findings support the argument that nuclear energy as a green technology can help in reducing CO\u003csub\u003e2\u003c/sub\u003e emissions in the long run, confirming the findings of IEA (2019) which points out that nuclear energy makes a substantial contribution to enhancing the global fight against climate change.\u003c/p\u003e \u003cp\u003eOur results are in harmony with the findings of Iwata et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Marques et al. (\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) who stipulated the positive role of nuclear energy in reducing the pollutant emissions for the case of France. Our results are also consistent with Nathaniel et al. (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the group of seven and Hassan et al. (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for the BRICS countries, and Syed et al. (\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the case of India.\u003c/p\u003e \u003cp\u003eHowever, results are in contradiction with those of Mahmoud et al. (2020) for the case of Pakistan, Pan and Zhang (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for the case of the USA, and Sarkodie and Adams (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for the case of South Africa.\u003c/p\u003e \u003cp\u003eOn the other hand, the estimated coefficients related to fossil fuel energy appear positive and significant. Specifically, a 1% increase of fossil fuel production leads to an increase of the CO\u003csub\u003e2\u003c/sub\u003e emissions by 1.409%, similarly a decrease of 1% of this variable increases CO\u003csub\u003e2\u003c/sub\u003e emissions by 1.381%. The results support the fact that CO\u003csub\u003e2\u003c/sub\u003e emissions are driven by fossil fuel production in France.\u003c/p\u003e \u003cp\u003eThese results are in accordance with the reported results of Ma et al. (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) study comparing between France and Germany and Martins et al. (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the case of G7 countries. Likewise, Kartal (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) for the case of the top 5 carbon emitting countries and Lawson (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) for the case of 41 Sub-Saharan African countries are confirming these results.\u003c/p\u003e \u003cp\u003eThe results also attest that the estimated coefficients for trade openness are positive. More specifically, the results reveal that the downside variations in trade openness have a positive impact (0.363), while the positive variations in the trade openness have no significant impact in controlling CO\u003csub\u003e2\u003c/sub\u003e emissions in France. Consequently, we can consider the positive effect of the flows of international trade in France for both exports and imports on CO\u003csub\u003e2\u003c/sub\u003e emissions. In this regard, policy makers in France should enhance the use of clean technologies, by implementing incentive policies toward environmentally friendly industries and it should also penalize polluting industries by involving taxes and norms.\u003c/p\u003e \u003cp\u003eThis finding is analogous to Mutascu (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) for the case of France, and Aslam et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) for the case of Malaysia. However, it is inconsistent with the results of Iwata et al. (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) in the case of France, who found that trade has an insignificant impact on CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eThe short run estimation in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e confirms the EKC hypothesis. The income per capita increases the CO\u003csub\u003e2\u003c/sub\u003e emissions by 41.944% at 10% of significance level, and its squared curtails the level of CO\u003csub\u003e2\u003c/sub\u003e by -1.979% at 10% of significance level.\u003c/p\u003e \u003cp\u003eRegarding nuclear energy, its positive and negative changes have no significant impact on the CO\u003csub\u003e2\u003c/sub\u003e emissions in the short run.\u003c/p\u003e \u003cp\u003eThe positive and negative changes in fossil fuels have positive repercussions on CO\u003csub\u003e2\u003c/sub\u003e emissions. Besides, both positive and negative changes of trade openness have no significant impacts, in the short run.\u003c/p\u003e \u003cp\u003eThe next step is to analyze the long-term asymmetric responses of CO\u003csub\u003e2\u003c/sub\u003e emissions to positive and negative variations in nuclear, fossil fuel and trade openness, while the GDP per capita and its squared followed a symmetrical approach.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e also details these asymmetric long term parameters. We conclude that a 1% increase of nuclear energy leads to a decrease of CO\u003csub\u003e2\u003c/sub\u003e emissions by 0.080%. Similarly, a 1% decrease of nuclear energy eases off CO\u003csub\u003e2\u003c/sub\u003e emissions by 0.214%.\u003c/p\u003e \u003cp\u003eHowever, a 1% increase of non-renewable energy leads to a significant increase of CO\u003csub\u003e2\u003c/sub\u003e emissions by 0.935%. Likewise, a 1% decrease of this variable has the same impact on the environment quality (0.917%).\u003c/p\u003e \u003cp\u003eThe trade openness has a negative effect on CO\u003csub\u003e2\u003c/sub\u003e emission in the case of positive shock (-0.113%). However, it leads to a raise on CO\u003csub\u003e2\u003c/sub\u003e emissions by 0.241% in the case of a negative shock.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eNARDL long-run and short-run estimations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong term analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et-Statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecoefficients\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{C}\\text{O}2}_{\\text{t}-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.507 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}}_{\\text{t}-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e49.252*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\left(\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}\\right)}_{\\text{t}-1}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2.334*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{l}{\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}-1}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.120***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}-1}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.322***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}-1}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.409*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}-1}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.381*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}-1}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}-1}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.363*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eShort term analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}}_{\\text{t}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41.944***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.091\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}}_{\\text{t}-1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-74.00**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\left(\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}\\right)}_{\\text{t}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.979***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\left(\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}\\right)}_{\\text{t}-1}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.507**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}-1}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.343\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}-1}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.848\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.688*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}-1}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.318\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.977*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}-1}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.660\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}-1}^{+}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.775\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\Delta }\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}-1}^{-}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.229***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{c}\\text{o}\\text{n}\\text{s}\\text{t}\\text{a}\\text{n}\\text{t}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.366*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({R}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.9874\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eBound Testing for Asymmetric cointegration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF\u003csub\u003ePSS\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.1206*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT\u003csub\u003eBDM\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.0776 *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLong-run parameters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{l}\\text{n}\\text{G}\\text{D}\\text{P}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.679*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{(\\text{l}\\text{n}\\text{G}\\text{D}{\\text{P})}^{2}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.549*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{{\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}}^{+}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.080**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{{\\text{l}\\text{n}\\text{N}\\text{U}\\text{C}}_{\\text{t}}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.214***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{{\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}}^{+}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.935*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e84.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{{\\text{l}\\text{n}\\text{N}\\text{R}\\text{E}}_{\\text{t}}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.917*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e66.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{{\\text{l}\\text{n}\\text{T}\\text{R}}_{\\text{t}}^{+}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.342\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{\\text{l}\\text{n}{\\text{T}\\text{R}}_{\\text{t}}^{-}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.241*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eSensitivity analysis\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eD.W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSERIAL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.148\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eARCH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.1008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRESET\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.268\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUSUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCUSUMQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote: The asterisks (*), (**), and (***) refer to 1%, 5%, and 10% significance levels, sequentially.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \n\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the results of the dynamic asymmetric multiplier, which characterizes the dynamic adjustment process between the variables in the model, except for the case of real GDP per capita, and its square which are selected to have a linear reaction during the whole period in order to test the EKC hypothesis.\u003c/p\u003e\n\u003cp\u003eClearly, it designs the asymmetric adjustment path of CO\u003csub\u003e2\u003c/sub\u003e emissions following one unit change of nuclear energy, nonrenewable energy, and trade openness. The analysis of this step allows French policy makers to integrate and design effective strategies to accomplish their objectives concerning the protection of the environment through the control of CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eIt can be observed from Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e that negative shocks on both nuclear energy and trade openness have a deeper effect on CO\u003csub\u003e2\u003c/sub\u003e emissions than positive ones in the long run.\u003c/p\u003e \u003cp\u003eControversially, the impact of non-renewable energy on CO\u003csub\u003e2\u003c/sub\u003e emissions is equivalent in the case of positive and negative changes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"6. Conclusions","content":"\u003cp\u003eThis research has analyzed the relation between CO\u003csub\u003e2\u003c/sub\u003e emissions, economic growth, and nuclear energy, taking into account the role of non-renewable energy and trade openness from 1980 to 2019, for the case of France. In this framework, we have tested the validity of the EKC hypothesis in France. The asymmetric ARDL approach suggested by Shin et al. (\u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) was adopted to analyze the asymmetric cointegration in our model. The main findings of the asymmetric cointegration will be synthesized in what follows.\u003c/p\u003e \u003cp\u003eFirst, the link between income and CO\u003csub\u003e2\u003c/sub\u003e emissions designs an inverted U-shaped relationship. More specifically, the increase in GDP leads to an increase in CO\u003csub\u003e2\u003c/sub\u003e emissions while the squared GDP per capita decreases these pollutant emissions. Accordingly, our results disclosed the validity of the EKC hypothesis. In addition, the turning point related to income and CO\u003csub\u003e2\u003c/sub\u003e emissions was reached in 2012, which means that since this year, the increase in GDP per capita has been coupled to a decrease in CO\u003csub\u003e2\u003c/sub\u003e emissions in France.\u003c/p\u003e \u003cp\u003eSecond, the asymmetric effect of nuclear energy on CO\u003csub\u003e2\u003c/sub\u003e emissions shows that the overall impact is negative. In addition, it was highlighted that the negative shock on nuclear energy has a deeper effect than the positive one, in the long run. However, the results in the short run revealed that both positive and negative shocks have no significant effect on CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eGiven that nuclear power is reducing CO\u003csub\u003e2\u003c/sub\u003e emissions in France, consequently, the dependence of the French electricity mix to nuclear power is not in contrast with its ambitious targets of mitigating CO\u003csub\u003e2\u003c/sub\u003e emissions and its fight against climate change.\u003c/p\u003e \u003cp\u003eHowever, the management of nuclear plants encompasses many risks as proved by many disasters (for example, Chernobyl in 1986 and Fukushima in 2011) and mentioned largely (Carless et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Choi \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). In addition, the perception of risks and acceptance of nuclear power by population is a very important issue that should be taken into account (Ho et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Perez et al. \u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lee and Gloaguen \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, Muellner et al. (\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that nuclear energy obviates only between 2% and 3% of GHG emissions and the tendency is decreasing by 2040. Hence, France should diversify its energy mix by introducing many types of renewable energy in order to mitigate its GHG emissions in the future.\u003c/p\u003e \u003cp\u003eThird, the non-renewable energy negatively influences CO\u003csub\u003e2\u003c/sub\u003e emissions in France. In particular, positive and negative shocks on non-renewable energy are drivers to the environment pollution which confirms the fact that fossil fuels are among the main trigger factors of climate change.\u003c/p\u003e \u003cp\u003eFourth, trade openness is qualified as a driver of CO\u003csub\u003e2\u003c/sub\u003e emissions, more specifically only the downturn in this variable raises the level of CO\u003csub\u003e2\u003c/sub\u003e emission, while the upturn action has no significant effect on the environmental sustainability. In the short run, we can conclude the neutrality effect of trade on CO\u003csub\u003e2\u003c/sub\u003e emissions.\u003c/p\u003e \u003cp\u003eIn this case, the French government should adopt a strategy based on encouraging the use of clean industry. For example, it can impose taxes and standards on polluting industries, which can positively impact alternative industries based on clean technologies.\u003c/p\u003e \u003cp\u003eFuture research can contribute to the existing literature by analyzing the substitution possibility between nuclear energy and renewable energy in the case of France to put out useful policy suggestions. It will be interesting also to use a more general indicator as a proxy to environmental pollution such as the ecological footprint which is recently used by scholars such as Altintas and Kassouri (2020) and Bandyopadhyay et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEmna Omri\u003c/strong\u003e: Conceptualisation; Methodology; Writing, review \u0026amp; editing\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaifa Saadaou\u003c/strong\u003ei: Conceptualisation; Methodology; Data collection; Software\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe links to the data sources are indicated in the sub-section \u0026ldquo;Data and descriptive analysis\u0026rdquo;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAcaravci A, Ozturk I (2010) On the relationship between energy consumption, CO\u003csub\u003e2\u003c/sub\u003e emissions and economic growth in Europe. 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J Bus Econ Stat 10:251\u0026ndash;270\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"environmental-science-and-pollution-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"espr","sideBox":"Learn more about [Environmental Science and Pollution Research](https://www.springer.com/journal/11356)","snPcode":"11356","submissionUrl":"https://submission.nature.com/new-submission/11356/3","title":"Environmental Science and Pollution Research","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"carbon emissions, EKC hypothesis, nuclear energy, fossil fuels, trade","lastPublishedDoi":"10.21203/rs.3.rs-1655777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1655777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe intention behind the current analysis is to join the debate over the main factors to consider in the global fight against climate change. Thereby, we used the non-linear Autoregressive Distributed Lag (NARDL) cointegration approach to test the impacts of nuclear energy, fossil fuels, trade openness and economic growth on carbon emissions in France. In addition, we test the validity of the Environmental Kuznets Curve (EKC) assumption.\u003c/p\u003e \u003cp\u003eOur results stipulate that nuclear power lessens CO\u003csub\u003e2\u003c/sub\u003e emissions in France. However, fossil fuels and trade openness enhance these emissions. On the other hand, the current analysis confirms the existence of an inverted U-shaped curve relying income with CO\u003csub\u003e2\u003c/sub\u003e emissions. Therefore, the EKC hypothesis is supported in our case. 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