A Panel Data Analysis of the Net Zero trajectory of Seven Industrialized Economies.  

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David Alemzero, Fredrick Darimeh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5286720/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The study examines the correlation between energy consumption and the journey towards achieving net zero emissions in G7 nations spanning from 2002 to 2022. The study reveals a decline in environmental performance in certain G7 economies, primarily attributed to high carbon emissions from sectors such as manufacturing, construction, and transportation. The United States demonstrates the highest levels of emissions, with Japan and Germany following closely behind. The United Kingdom and Italy exhibit the lowest levels of emissions. The transportation industry plays a substantial role in the generation of carbon emissions. The emissions of methane resulting from energy consumption are also significant. Cross-sectional interdependence is present within the G7 nations, refuting cross-sectional independence. The Pesaran Panel Unit Root Test has confirmed the stationarity in all panels. The analysis using the 2SLS method uncovers a statistically significant and positive impact of emissions from the transport sector on total carbon emissions. The under-identification test and the Cragg-Donald Wald F statistic provide substantial evidence of strong identification, whereas the Sargan test rejects the null hypothesis in the over-identifying constraint test. The study recommends that G7 nations adopt customized policy measures, prioritizing non-fiscal strategies to efficiently mitigate carbon emissions and attain net zero objectives.. JEL classification: O50, Q56, P1 Business and commerce/Finance Social science/Economics Social science/Environmental studies Social science/Social policy G7 Panel Fixed Effect Models Energy and the Environment Figures Figure 1 Figure 2 Figure 3 1.0 Introduction Developed countries face the challenge of transitioning to clean and sustainable energy sources while achieving net zero emissions(Guenedal, Lombard et al. 2022, Haddad, Pagone et al. 2022). This transition requires significant changes in energy production, consumption, and infrastructure to reduce greenhouse gas emissions and mitigate climate change.(Haddad, Pagone et al. 2022) Achieving net zero emissions involves reducing carbon dioxide and other greenhouse gas emissions to a level where they are balanced by removal or offsetting measures, such as carbon capture and storage or reforestation(Haddad, Pagone et al. 2022, removal 2023). The shift towards renewable energy sources, such as solar and wind power, is crucial in reducing reliance on fossil fuels and achieving net zero emissions. Policy measures, technological advancements, and international cooperation are necessary to support the transition to clean energy and achieve net zero emissions in developed countries. (Ozcan 2013, Villanthenkodath and Pal 2024). Consequently, the aim of this research is to evaluate the energy use and the net zero trajectory within the G7 economies, while pinpointing the primary drivers behind emissions levels that may impede the realization of their net zero targets. Climate change, largely fueled by carbon dioxide emissions, is causing severe consequences that the world is witnessing. The global community established the Paris Accord to keep global temperature increase to beneath 1.5 degrees Celsius by 2030. Considering that these countries have some of the highest emission levels globally, how they address these increases will be crucial in achieving the goals outlined in the Paris Agreement and reaching net zero targets by the middle of this century through their nationally determined contributions (NDCs). In June 2020, the leaders of the G7 made a commitment to achieve Net Zero Emissions (NZE) with environmentally friendly technologies and strong political support. Collectively, the G7 countries have control over 40% of the world’s GDP, account for nearly thirty percent of the world’s energy demand, and handle about 25% of global emissions(Oluc, Can et al. 2024). They also agreed to decarbonize the power sector by 2035 (Ashokan, Jaganathan et al. 2024). The power industry presently represents about 33% of the G7's total releases, which is a decrease from the 40% reported in 2007. Instead of coal, negative emissions technologies are now being used. The utilization of renewable energy and the presence of cost-competitive natural gas in the markets have contributed to both the growth and reduction of emissions levels in these countries. As of 2020, natural gas and renewable energy resources (RES) were the preferred sources of energy generation in the G7, each accounting for 30% of the total, followed by nuclear power and coal at 20% (EL-Karimi and El-houjjaji 2022, IEA 2022). The expansion of low carbon technologies is essential in attaining net zero emissions. In a similar vein, an analysis using panel data has been carried out to examine the net zero trajectory of seven industrialized economies. The analysis includes data on carbon emissions and introduces innovative metrics for assessing carbon emissions reduction goals and relative positioning in relation to the net zero emissions (NZE) scenario (Hsiao 2022). The study presents a definition of static and dynamic NZE measures that can be utilized to evaluate the performance of countries as in the NZE scenario. (Guenedal, Lombard et al. 2022). Additionally, the study applies a PAC framework to analyze the participation, ambition, and credibility of issuers' NZE policies. The findings reveal that achieving net zero carbon emissions is challenging for many issuers due to a lack of ambition and targets that are not compatible with past trends(Agrawal, Priyadarshinee et al. 2023, Khosla, Lezaun et al. 2023). This research provides insights into the feasibility and progress of achieving net zero emissions in industrialized economies(OECD 2022) . Wind and solar power are becoming more prevalent among the G7 countries, with a projected increase from 14% in 2020 to 40% in 2030 and two-thirds by the middle of the century. According to IEA (IEA, IRENA et al. 2023), wind and solar received 60% of investments in the power sector in 2021. To decrease emissions, the G7 nations have set their sights on achieving extensive decarbonization in their power systems. Experts predict that this ambitious aim will generate about 2.6 million job opportunities in the industry within the next ten years. In addition, there will be a decline of over one million jobs in the fossil fuel sector during the same period. The transition to renewable energy sources will lead to a reduction in investments in the energy sector, declining from 7% of GDP at present to 4% by the mid-century. To reduce emissions, we need innovation. Estimates suggest that new technologies could decrease G7 electricity emissions by up to 30%. The IEA's "Net Zero Emissions" (NZE) trajectory shows that the power sector receives a greater amount of investment compared to the energy sector(IEA, IRENA et al. 2023). To accomplish this transition, experts estimate it will require a total investment of $ 2 trillion, and they expect it to increase to $ 3 trillion by 2030 (IEA, IRENA et al. 2023, Dai, Sun et al. 2024). By 2030, scholars predict investors will allocate $ 1.3 trillion to the renewable energy industry, compared to $ 1.2 trillion for fossil fuels, as a significant portion of this investment. Several studies have investigated how energy use, GDP, trade openness, urbanization, and carbon emissions are related in the G7 countries using panel ARDL models (Altın 2024, Pham, Li et al. 2024, Xing, Husain et al. 2024). One study investigated the influence of urbanization on carbon footprint, while others examined the impact of an ecological tax on carbon emissions(Xing, Husain et al. 2024, Zhang, Lau et al. 2024). What sets this study apart is its use of panel data models to address omitted variable biases in heterogeneity analysis. It also controls for unmeasured variables that are linked to the variables being studied. This approach allows for the identification and measurement of effects that may not be apparent in a pure cross-sectional or time series analysis. 2SLS models address OLS overestimation by accounting for the correlation between energy use and the stochastic term. Finding instruments becomes challenging. These variables will be uncorrelated with the error term yet contribute to predicting the G7 emissions levels. The study period was from 2002 to 2022. Contribution of the Study The results contribute to the existing literature on energy use and CO2 emissions in the G7 economies. The results further demonstrated that (1) Carbon emissions from manufacturing and construction is significant contributing to growing emissions levels among the study countries. The aforementioned discovery is substantiated in (Davis, Lewis et al. 2018), where it was determined that 40% of carbon emissions resulting from cement production are derived from fossil fuel energy inputs, while the remaining CO2 emissions originate from the calcination process of calcium carbonate. ( 2 ) The study uncovered the significant carbon emissions from the transportation sector, highlighting the limited technology readiness of the sector for decarbonization. Additionally, ( 3 ) the study revealed that the power sector holds the highest emission rates, with a significant correlation coefficient. This confirmation is stated in (Davis, Lewis et al. 2018), where it is reported that the power sector accounted for 26% of global fossil fuel use and industry emissions in 2014. Additionally, the emission of methane is a significant contributor to the escalating levels of emissions and their achievement of net zero emissions. Methane is released through biological respiration, degradation, and combustion, and the decomposition of biomass under anaerobic conditions may result in significant CH4 emissions. The findings emphasize the need for an alternative policy approach in G7 countries to achieve net zero by the middle of the century by focusing policies on the difficult-to-eliminate emissions sectors such as transport sector, manufacturing and construction. The rest of the paper organizes: Section two analyzes the literature associated with the study. Section three of the paper discusses the materials and methods. Section four discusses results and findings. In section five, the study wraps up with conclusion and recommendations. 2.0 Literature Review In their pursuit of net zero objectives, the G7 nations have put into effect a range of policies and initiatives. In order to foster coordination and integration of support instruments along the innovation chain, mission-oriented innovation policies have been implemented, although their influence beyond science, technology, and innovation fields is constrained (OECD 2023). In the electric power sector, utilities are committing to net-zero goals by transitioning away from fossil fuels and decarbonizing their operations (Clémençon-Charles and Baranek 2023). Countries in the Asia-Pacific region, such as Japan, Korea, China, and Taiwan, have enacted laws and policies to achieve carbon neutrality by 2050 or 2060 (2023). Carbon pricing has been used as an instrument to attract private investments and cut greenhouse gas emissions, but there are gaps in its implementation that need to be addressed(Acharya 2022). The negotiation of free trade agreements, such as the one between Australia and the UK, aims to integrate trade and climate policies, but its contribution to climate change mitigation is minimal(Pareliussen, Crowe et al. 2022). The principal aim of negotiating free trade agreements, such as the Australia-UK deal, is to synchronize trade and climate policies, although their impact on addressing climate change is insignificant(Victoria 2024). This could have a significant impact on the rest of the world's efforts towards deep decarbonization. Their action plan is an example for other economies, especially developing countries, looking to reduce their carbon footprint, according to (Bennich, Persson et al. 2023). Another study by (IEA 2022, Saqib, Ozturk et al. 2023) concluded that transitioning to renewable energy sources will require scaling up renewable energy systems, negative emissions technologies, and gradually phasing out fossil fuels. Against this backdrop, (Ahmed, Zafar et al. 2020, Hoa, Xuan et al. 2024) found that per capita renewable energy consumption in G7 economies is on the rise. Also, (Borozan 2022, Borozan, Bayar et al. 2023) found that the Group of Seven economies have already passed the peak of their inverted U-shaped emissions curve. Their research showed that GDP and renewable energy consumption are linked in the G7 economies over a long period. It is clear that human-caused anthropogenic gas emissions contribute to temperature rise and climate change’. Furthermore, nearly 60% of global greenhouse gas emissions come from ten countries, two of which are G7 members (Tugcu and Menegaki 2024). The United States, for instance, has committed to reducing its greenhouse gas emissions by 50–52% below 2005 levels by 2030 (Abeysekara, Siriwardana et al. 2024, Ritchie, Rosado et al. 2024). In contrast, France's aggregate emissions have plummeted to 352.10 Mt CO2e (Baidya and Saha 2024, Ritchie and Roser 2024). France has set a goal of becoming carbon neutral by 2050, which was mooted in 2019(Ritchie and Roser 2024). Similarly, Germany's cumulative greenhouse gas pollution has been on a downward trend, standing at 720.23 MtCO2e in 2019(Ritchie and Roser 2024). Italy's emissions levels have taken an interesting turn, peaking in 2003 and then steadily declining. They emitted between 772 Gg and 7020 Gg per year (Ritchie and Roser 2024). In 2019, Italy's total greenhouse gas emissions from global warming were 376.19 Mt CO2e (Buchner, Tonkonogy et al. 2022). Japan's greenhouse gas emissions have stayed the same since the early 1900s, with a brief spike in 2010 before going back to normal (Ceglia, Marrasso et al. 2022, Murshed, Saboori et al. 2022). It is crucial to expand existing negative emissions technologies and invest in innovative solutions to reduce emissions, as this could save many lives from untimely deaths because of enclosed and out-of-door air pollution (Borozan, Bayar et al. 2023, Noussan, Negro et al. 2024). A study by (Sinel and Weis 2024) revealed that research and development funding significantly incentivizes the installation of new energy in Group of seven countries, as showed by their distributed lag nonlinear autoregressive model analysis. In addition, Huihui etal (Huihui, Alharthi et al. 2024) found that macroeconomic policies directly affect renewable energy adoption in G7 countries. Similarly, the findings of, (Jahanger, Awan et al., Tugcu and Menegaki 2024) suggest that the introduction of environmental taxes leads to a reduction in pollution levels within the G7 economies and encourages businesses to shift towards a low carbon economy (Cheng, Zhao et al. 2024). Furthermore, Wang et al (Wang, Xu et al. 2024) have advocated that developed nations, such as the G7, should strive to increase their mitigation commitments beyond the global average in order to significantly impact the decarbonization process. To meet the current commitments of limiting global warming to 1.5 degrees, advanced economies like the G7 must halve their emissions. In the Canadian context, it is worth noting that the cumulative global pollution level experienced a significant increase during the 2000s, lasting approximately ten years, before subsequently declining in 2010 to a level of 774g. Evaluating energy use and setting net zero goals is crucial for fighting climate change and building a sustainable future. With the global population and economies growing, the consumption of energy is increasing, causing higher carbon emissions. The diagram in Fig. 1 illustrates the global energy pathway towards achieving Net Zero Emissions (NZE) by the year 2050. To reach NZE, the International Energy Agency (IEA) has proposed a plan to halt the development of new electricity generated from coal in 2021. This strategy aims to attract investments in the energy sector. There are four key milestones that need to be met in order to achieve NZE. One of these milestones is the installation of 150 million tons of minimal emissions hydrogen by 2030, capturing approximately 4 gigatons of pollution by 2035, and increasing the adoption of EVs in new truck sales by 50% by 2035. The blueprint also highlights the importance of global collaborations in sectors such as innovation, comprising technology collaborations, to bring important technologies like hydrogen, enhanced biofuels, carbon capture, storage and use (CCUS) to commercial scale (Siriwardana and Nong 2021, Li and Haneklaus 2022, Borozan, Bayar et al. 2023). This will attract investments and technology help, and also transfer technology to other countries (IEA 2022). The IEA predicts a decrease in electricity emissions to 5.1 gigatons by 2030, with wind and solar providing over 40% of power generation (IEA 2022). Conventional energy sources handle the majority of global greenhouse gas emissions, accounting for 65% of total emissions excluding land use change (LUC)(IEA 2022). This is supported by various studies that have emphasized the need for developed economies, such as the G7, to hasten their efforts in meeting their Nationally Determined Contributions (NDCs) and the Paris Agreement's goal of limiting the global temperature rise to below 1.5 degrees Celsius. This study investigates how these advanced economies are using energy and producing carbon emissions. It offers evidence regarding their progress in achieving their net zero trajectories. 3.0 Methodology This study analyzes the energy usage and emission trends of seven developed countries, with a focus on their journey towards net zero. We conducted the study from 2002 to 2022 using panel data models with fixed effects. The World Bank Development Indicators (WDI) provided us with the data. The response variable, energy use, measures the amount of primary energy that is used before it is converted into various forms of end-use energy, including local supply, imports, stocks, and exports. This study takes into account methane pollution from conventional energy sources and biofuels. The measurement indicates the percentage change in cumulative greenhouse gas emissions since 1990, including carbon dioxide emissions (excluding biomass and short-term biomass burning) and cumulative human-made methane gases. Similarly, carbon dioxide emissions from transportation account for fuel combustion in all modes of transportation, excluding marine bunkering fuel and global aviation. The percentage of cumulative carbon dioxide emissions from solid fuels mainly refers to emissions from coal usage. The emissions from manufacturing and construction sectors include pollution from energy consumption in industries. CO2 emissions from transportation and heat supply include electricity used in transportation, utilities, and power plants. The study measures the carbon dioxide emissions per purchasing power parity of the gross domestic product. This includes emissions from burning traditional energy sources and producing cement. 3.2. Model 3.2.1 Panel The regression provides the fixed effect below, with lags in the predictors. $$\:{Y}_{it}={a}_{i}+{\beta\:}_{\:}{X}_{it-1}+{u}_{i}+{e}_{it}$$ 1 \(\:i\) =1…n; t=1….T Where \(\:i\) denotes countries under study and \(\:t\) denotes time The \(\:i\) subscript depicts the cross-section dimension, whereas \(\:t\) denotes the series dimension \(\:a\) is the intercept. \(\:B\:\times\:1\) and \(\:{X}_{it}\) depicts the \(\:{ith}_{\:}\) observation on \(\:{K}_{\:}\) explanatory variables (Baltagi,2008). $$\:{\mu\:}_{it}={u}_{i}+{v}_{it}$$ 2 \(\:{\mu\:}_{it}\) depicts the unobservable individual country specific fixed effects and \(\:{v}_{it}\) depicts the remainder of the error term. \(\:{u}_{i}\) is the individual is time invariant and account for individual fixed effects not included in the regression model(Baltagi, 2008). The model is reparametrized below with the given variables. $$\:{Eneu}_{it-1}=\:{a}_{i}+{\beta\:}_{1}{tghs}_{it-1}+{{\beta\:}_{2}enemethaneem\:}_{it-1}+{{\beta\:}_{3}co2trspt}_{it-1}+{{\beta\:}_{4}co2sf\:}_{it-1}+{{\beta\:}_{5}co2emfcons\:}_{it}-1+{{\beta\:}_{6}co2emhe}_{it-1}+{{\beta\:}_{7}co2gdp}_{it-1}$$ 3 Stationarity Test $$\:{y}_{it}={\varnothing\:}_{i}{y}_{i,\:t-1}+{Z{\prime\:}}_{it}{\gamma\:}_{it}+{ϵ}_{it}$$ 4 Where \(\:{ϵ}_{it}\) is independently distributed normal for \(\:i\) and \(\:t\) with panel specific variance \(\:{\sigma\:}_{i}^{2}\) (Im et al., 2003) Where initially values for \(\:{y}_{0}\) , are given consider the testing of the null hypothesis of unit roots \(\:{\varnothing\:}_{i}=1\) regarding all \(\:i\) (Im et al., 2003) Equation ( 4 ) can be stated as $$\:{\varDelta\:y}_{it}={a}_{i}+{\beta\:}_{i}{y}_{i,t-1}+{ϵ}_{it}$$ 5 , Where \(\:{a}_{i}+\left(1-{\varnothing\:}_{i}\right){u}_{i},\) \(\:{\beta\:}_{i}=1-\left(1-{\varnothing\:}_{i}\right)\) and \(\:{\varDelta\:{y}_{it}={y}_{it}}_{\:}-{y}_{i,t-1}.\) ( 6 ) The null hypothesis of unit root becomes. \(\:{H}_{o}:{\beta\:}_{i}\) for all \(\:i\) Against the alternative $$\:{H}_{1}={\beta\:}_{i}<0,\:i,\text{1,2}\dots\:,{N}_{i},{\:\:\:\beta\:}_{i}=0\:\:\:\:i={N}_{1}+1,{N}_{1}+2,\dots\:{N}_{\:}$$ 7 3.1 Stage Least Square Estimations(2SLS). The two stage Estimations (2LS) approximate two least square equations. The estimation approach contains an instrumental variable and four different types of variables, the endogenous, exogenous, and the dependent variable. This approach has the advantage of avoiding endogeneity and autocorrelation. Below is the model for evaluating the impact of the variables in undertaking the analysis. $$\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}{k}_{ij}+{\epsilon\:1}_{ij}$$ 8 The concern for this equation is that is correlated to the stochastic term, that is corr \(\:{\left(\:\right.k}_{ij}\) , \(\:{\epsilon\:1}_{ij})\ne\:0\) , to overcome the issue of endogeneity. \(\:{a}_{2}\) will generate unbiased and inefficient estimates if we proceed to use Eq. ( 8 ). This happens when there are other latent factors that might hinder the progress of the advanced countries to achieve their net zero (NZT), other than the indicators, such as used in the analysis, cultural and institutional performance of individual countries. We then instrument some variables such as emissions in the transport sector and emissions in the manufacturing sector and specify a model for below. $$\:{k}_{ij}={\beta\:}_{0}+{\beta\:}_{1}+{\beta\:}_{1}{x}_{1j}+{\beta\:}_{3}{q}_{ij}+{\epsilon\:2}_{ij}$$ 9 \(\:{q}_{ij}\) affects \(\:{k}_{ij}\) directly. It does not impact on \(\:{y}_{ij}\) , \(\:{k}_{ij}\) . \(\:{q}_{ij}\) has no influence on the other variables, is exogenous and is referred to as an instrumental variable. Then, we estimate Eq. ( 9 ) using OLS on the net zero trajectories. $$\:{k}_{ij}={\widehat{\beta\:}}_{0}+\widehat{\beta\:}{\:}_{1}+{\widehat{\beta\:}}_{2}{x}_{1j}+{\widehat{\beta\:}}_{3}{q}_{ij}$$ 10 \(\:{k}_{ij}\) from Eq. ( 10 ) is not associated with the stochastic term \(\:{\epsilon\:2}_{ij}\) in Eq. ( 9 ) which is, different latent factors cause endogeneity. $$\:{k}_{ij}=\widehat{{k}_{ij}}+{\epsilon\:2}_{ij}$$ 11 We then substitute \(\:{k}_{ij}\) using Eq. ( 8 ) within the first Eq. ( 11 ) $$\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}{k}_{ij}+{\epsilon\:1}_{ij}$$ $$\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}(\widehat{{k}_{ij}}+{\epsilon\:2}_{ij})+{\epsilon\:1}_{ij}$$ 12 $$\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}\widehat{{k}_{ij}}+\left({a}_{2}{\epsilon\:2}_{ij}+{\epsilon\:1}_{ij}\right)$$ 13 $$\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}{a}_{2}\widehat{{k}_{ij}}+\epsilon\:1{}_{ij}{}^{*}\:$$ 14 4.0 RESULTS AND DISCUSSIONS Table 1 presents the summary statistics for the parameters in the analysis. As expected, energy use has the maximum mean among the Group of Seven economies, which are highly industrialized and reliant on energy. To meet the Paris Agreement goals and prevent global temperatures from rising above 1.5 degrees Celsius, we must focus on sustainable energy consumption. Countries' Determined Contributions (NDCs) (Siriwardana and Nong 2021). The next highest average is for CO2 emissions from heat and electricity supply, showing that a greater share of CO2 emissions come from these industries. The G7 economies, which are in temperate climates, have a high demand for heating and cooling during extreme weather. This is evident from the current heat waves and the need for heating in winter and cooling in summer. As a result, the transport sector also has an above average for carbon dioxide emissions. Cutting emissions in this sector is challenging because people in these countries rely heavily on transportation services due to their high living standards. Air and road transport are notable sources of carbon dioxide emissions in these countries. The transport segment emits one quarter of global CO2 pollution (Dietz, Beaucamp et al. 2020). The total greenhouse gas emissions from the G7 economies are inverse, showing a minute impact on these gases. CO2 emissions from burning fossil fuels and making cement have the lowest average. This is because of the use of renewable energy and low-carbon building materials, as well as the economic decoupling in these countries. Table 1. Descriptive Statistics Variable Obs Mean Std. Dev. Min Max Eneu 140.00 80.03 61.34 0.00 212.42 Tghgs 139.00 -0.91 17.96 -26.91 70.88 Enemethaneem 140.00 7.31 13.06 0.00 55.14 co2trspt 140.00 18.07 13.97 0.00 42.41 co2sf 140.00 15.44 12.92 0.00 42.68 co2emfcons 140.00 8.09 6.24 0.00 17.88 co2emhe 140.00 25.27 20.03 0.00 51.18 co2gdp 140.00 0.20 0.11 0.00 0.54 Source. Authors' calculations Table 2 reveals a significant negative correlation between energy consumption and total greenhouse gas emissions. The decrease in emissions suggests a rise in the use of sustainable energy among the G7 economies, which explains this negative correlation. The transport sector and methane emissions are key areas to focus on for reducing carbon emissions because they are strongly corrected to energy use. However, the technological readiness level for decarbonizing the transport sector is not as advanced as that of the energy sector, which explains the significant correlation between the two. Various processes like biological respiration, degradation, and combustion release methane, which has a more long-term and devastating impact on the environment compared to carbon dioxide. Only 39% of transport firms have aligned with the Paris 2030 goals, and 18% have committed to reducing emissions by less than 2 degrees by 2030 (Sherwin, Rutherford et al. 2023). The relationship between solid fuels energy use, total greenhouse gas emissions, methane emissions, and carbon emissions from transport is also significant. The only variables that are not strongly correlated with carbon emissions from heat and electricity are total greenhouse emissions. This highlights the strong correlation between emissions as a percentage of GDP and all the other variables, indicating that economic growth is linked to higher emissions levels. Table correlation Matrix. Variables Eneu Tghg Enemeth co2trspt co2emfcons co2emhe co2gdp Anem Eneu 1 Tghg 0.237 1 -0.005 Enemethaneem 0.547 0.462 1 co2trspt 0.731 0.088 0.36 1 0.00 -0.301 0.00 co2sf 0.602 -0.279 0.317 0.499 1 0.000 -0.001 0.00 0.00 co2emfcons 0.701 0.093 0.487 0.952 0.563 1 0.00 -0.274 0.00 0.00 0.00 co2emhe 0.73 -0.126 0.421 0.781 0.778 0.818 1 0.00 -0.14 0.00 0.00 0.00 0.00 co2gdp 0.734 0.393 0.692 0.519 0.503 0.594 0.611 1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Source. Author's correlation Pesaran Panel Unit Root Test with Cross-sectional Dependence Following the footsteps of (Pesaran, 2007), we used CIPS in Stata, called xtcips. This command is designed for balanced panels. The results are shown in the following table, particularly in Table 3. Table 3 Pesaran Panel Unit Root Test with cross-sectional CIPS* = -2.511 N,T = (20,7) 10% 5% 1% Critical values at -2.730 -2.890 -3.200 Source. Author's Analysis Table 3 is Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for eneu with deterministic chosen: constant & trend dynamics: The lags criterion decision general to base on the F joint test. Individual ti was truncated during the aggregation process H0 (homogeneous non-stationary): bi = 0 for all I. As can be observed in table 4, the static value is -2.511, which is below the critical value at 1% significance level. Therefore, this second-generation unit root test refutes the null hypothesis of unit- root in energy use (Eneu). Table 4 displays the findings of the panel analysis conducted on the environmental performance of the G7 economies and their net zero analysis. The first model used a multivariate analysis with the vce robust command to examine the variables. The results showed that carbon emissions from the transport sector are significant. This suggests that decarbonization efforts should prioritize the transport system, as it is a major contributor to emissions, rather than focusing on the supply of energy. To effectively decrease emissions in the transport sector, it is essential to reduce the consumption of fossil fuels, which currently dominate this sector (Dietz, Beaucamp et al. 2020, Furszyfer Del Rio, Sovacool et al. 2023). For example, the UK government has announced plans to ban the sale of petrol and diesel vehicles by 2030, ten years earlier than initially planned. Transportation accounts for approximately a quarter of global energy-related emissions and poses challenges in the form of transport poverty (Dietz, Beaucamp et al. 2020, Furszyfer Del Rio, Sovacool et al. 2023). To notably decrease emissions from the transport sector, it is crucial to employ renewable fuel options like bioethanol-gasoline blends, biodiesel, and green hydrogen, as suggested by(Molden 2023). Another key variable is the carbon emissions from the manufacturing and construction sectors, which are among the top contributors to global warming. It is worth noting that there is a negative correlation between energy consumption and emissions, suggesting a shift towards cleaner energy sources in the study countries, leading to reduced emissions in the building and construction industry. Conversely, an estimated global daily migration of 200,000 individuals to urban areas is occurring as a result of population growth. These individuals will require adequate housing infrastructure, provided by the construction sector, which will have a significant impact on air quality(C.D. Desouza a, et al. 2020). Consequently, emissions from the construction sector are expected to continue increasing, necessitating the implementation of a robust regulatory framework to control emissions(C.D. Desouza a, et al. 2020). The outsourcing of manufacturing industries to other countries may contribute to the decrease in emissions from this sector(Hanifa, Agarwal et al. 2023). The cement industry alone is responsible for about 7% of global carbon dioxide emissions (Hanifa, Agarwal et al. 2023). In 2019, the construction sector emitted roughly 9.95 Gt/y of carbon dioxide, making it the largest contributor to emissions. Researchers predict that the construction sector will cut emissions by 16% and become carbon neutral by 2050 (Hanifa, Agarwal et al. 2023). The respective contributions of the overall construction to NOX, PM10, and PM2.5 in London are 7%, 34%, and 15%(C.D. Desouza a, et al. 2020). A study has shown that the use of energy-intensive materials, such as bricks and cement, can effectively reduce embodied carbon emissions in buildings (Li Zheng a, Kashif Raza Abbasi b c et al. 2023). The percentage of GDP that is made up of carbon emissions is also a significant variable in the model, with a positive relationship with energy use, indicating that economic growth leads to increased emissions in the study countries. According to Al-Ayouty etal (Al-Ayouty 2023) , renewable energy consumption has a negative impact on carbon dioxide emissions, while Khalfaoui et al. (Zhao, Gozgor et al. 2023) observe a cyclical relationship between carbon emissions and per capita GDP, peaking during economic booms. Model 2 in Table 4 controls for time-invariant characteristics across countries to avoid any biased results. The results show total greenhouse gas emissions have a direct relationship with energy use, with a 65.8% increase in emissions. According to Shahzad et al (Avik Sinha a, Nicolas Schneider b et al. 2023), economic growth has a negative effect on the environment, while financial development can contribute to energy transition and lower greenhouse gas emissions (Atsu and Adams 2024). Raihan et al. (Raihan 2023) have also confirmed the link between economic growth and carbon emissions, with a 1% increase in economic growth leading to a 0.09% increase in emissions. Their research also suggests that adding value to agriculture can help enhance environmental quality by reducing carbon emissions. Model 3 uses the reghdfe command in stata to run a panel analysis with fixed effects models, similar to Guimarães and Portugal (Guimarães and Portugal 2010). The findings show a direct link between carbon dioxide emissions and energy use, with a substantial 14930% increase in emissions. Raihan etal (Raihan 2024) have documented the effectiveness of an environmental tax in reducing CO2 emissions, particularly when below the optimal point. They suggest that environmental policies, such as an environmental tax mechanism, can incentivize various economic sectors. In a study by Yousefi et al. In 2023, researchers found that BRICS countries outperformed G7 countries in renewable energy, while G7 countries made strides in reducing energy intensity. Table 4 Fixed effects Analysis Variables Model1_ eneu_ reg Model 2eneu__ xtreg Model _3eneu reghdfe Tghg 0.401 0.658 ** 0.401 (-1.6) (-3.35) -1.64 Enemethaneem 0.553 0.2 0.553 -1.77 (-1.01) -1.81 co2trspt 3.299 *** 3.746 *** 3.299 *** (-8.08) (-9.79) -8.27 co2sf 1.575 0.519 1.575 (-1.85) (-1.84) -1.9 co2emfcons -3.863 ** -12.78 *** -3.863 ** (-4.94) (-12.73) (-5.06) co2emhe -0.00555 0.121 -0.00555 (-0.02) (-0.49) (-0.02) co2gdp 149.3 ** 160.8 * 149.3 ** Cons (-5.61) (-2.68) 71.73*** (11.62) (-5.75) -6.418 (-0.72) Observations R-squared Country FE Number of country1 139 0.8408 YES 139 0.7276 YES 37 139 0.8408 YES Source. Author's estimation. t statistics in parentheses * p < 0.05, ** p < 0.01, *** p < 0.001 We augment the cross-sectional dependence diagnostics by conducting Frees and Friedman's test on the cross-section dependence in table 5. As we expect from the significant results of the CD test, both Frees, and Friedman's tests refute the null hypothesis of cross section independence because time is less than or equal to 30 years. Frees' s test provides the critical values at , , from the Q distribution. Free's statistics is more than the critical value with at least Friedman's test of cross-sectional independence = 40.916, Pr = 0.0025 Table 5. Cross sectional dependence test Frees' te't of cross-sectional independence = 2.927 Critical values from Frees' Q 'istribution alpha = 0.10 : 0.4127 alpha = 0.05 : 0.5676 alpha = 0.01 : 0.9027 Source. Authors’ estimation Table 6. presents the analysis of two stage least squares(2SLS) with over 300 observations. The aim here is to estimate energy used and the net zero trajectories (NZT) of the G7 economies by analyzing the carbon emissions parameters of the study countries. We estimate the coefficient of energy use in a regression equation alongside other explanatory variables. This is equally referred to as an energy use equation. People generally believe that there is a correlation between energy use and carbon emission levels within the equation. This will lead to the OLS overestimating the impact of carbon emissions on energy use. They need to be uncorrelated to the error term, to assist determine the net zero trajectories of the G7 economies. The 2SLS results estimate double equation with the explained parameter as energy use. We consider external factors like greenhouse gas emissions from methane energy, transportation, and solid fuel sources. And the regressors that endogenous are those to the left of the equation, emissions from manufacturing and construction. The equation takes into account factors such as carbon emissions from electricity and heat, and carbon emissions as a percentage of GDP on the right side. The key presumption is that energy use does not correspond to emissions levels but helps to determine when the G7 countries can attain their net zero goals. From the 2SLS results in model 1, emissions from transport sector is significant in a positive direction. This significance implies that carbon emissions is very high in the transport sector. This result is confirmed in (Borozan 2024) where they found fossil fuels to hinder environmental progress and the energy transition. In addition, energy emissions from methane are equally significant. Thus, energy use increases methane emissions(Tibrewal, Ciais et al. 2024).The Hausman test gives a chi-square value of negative 12.8, which disproves the consistency of the OLS and therefore uses the 2SLS model. The under-identification test demonstrates that the parameters correctly identify, with a significant value of 0.000. The weak identification is strongly estimated, as indicated by a Cragg-Donald Wald F statistic of 360.365, which accepts the null hypothesis. The Sargan test shows that the test of over-identifying restriction test has as a strong P value of 0.00 and rejected the null hypothesis. The R-squared reported of nearly 80% explains the model fitly estimated the analysis. Table 6. 2SLS Results. Variable Model 2 2SLS Model 2 OLS Co2trspt 2.485 *** 5.022 *** -9.67 -7.77 Tghg 0.119 0.362 -0.59 -1.77 Enemethaneem 1.531 *** 0.879 ** -5.18 -2.94 Co2sf 1.217 *** -3.65 Co2emfcons -9.296 *** (-5.92) Co2emhe 0.472 -1.57 Co2gdp 166.3 *** -4.38 Constant 24.20 *** -6.022 -4.47 (-1.03) Observations 139 139 R-squared 0.7943 Hausman test -174.28 Underidentification test 127.347 (Anderson canon. corr. LM statistic) Weak identification test 360.635 (Cragg-Donald Wald F statistic): Sargan statistic 54.261 (overidentification test of all instruments) Source. Authors' estimation t statistics in parentheses* p<0.05, ** p<0.01, *** p<0.001 Figure 2 shows the historical global gases of the G7 countries. The study countries' emissions levels continue to rise, even though these countries are signatories to the Paris Agreement. Historical and current emissions levels show that the United States is leading the way. The United States is the largest emitter per capita and the world's leading economy, making it the largest emitter among G7 countries. In the United States, long-distance transportation contributes significantly to global greenhouse gas emissions, more than the global average(Ritchie, Rosado et al. 2024). The other nation is Japan, which has the maximum global emissions. Due to Japan's rise as a manufacturing center and developed economy in Asia, it has become the second largest emitter of carbon emissions globally, following the United States, as depicted in the chart provided. Germany is followed by the United Kingdom and subsequently Italy. Borozan (Borozan, Bayar et al. 2023) states that the G7 renewable sources will generate 100 percent of electricity in a decade. The trend shows that all the various sectors continue to release emissions and so efforts must be redoubled to decrease emissions levels. Figure 3 depicts the box plot evaluation of the Group Seven countries parameters. The idea behind boxplot is to present a visual impression of the median, the interquartile range, and the range of data. Energy use is the most pronounced with the interquartile range (IRQ) of nearly 100 for the median range 25 th percentile, as shown in energy use boxplot for the lower boundary. This points to the reality that these are developed economies and require adequate energy to meet their development needs. This points to the reality that these advanced are economies and therefore need sufficient energy to keep up with development. Total greenhouse gas emissions is an outlier. This means that greenhouse gas emissions are on a growing trajectory among the study countries. The IQR is less than the 25 th percentile below the lower boundary. We observe another outlier in the next variable, methane emissions from energy, as the points lie outside the whisker. Carbon emissions from transport have the 25 th percentile for the lower boundary and nearly at the upper boundary. Carbon emissions from electricity and heat are the third largest emitters on a global scale as shown in the boxplot below. Its IQR is within the 25 th percentile range and the highest percentile for the upper boundary is at the 50th percentile. These countries are not doing well in terms of energy use and transport, as shown by the outliers in the analysis. These correlate to the significant levels of environmental pollution in these economies, as stated by (Li and Haneklaus 2022, Borozan, Bayar et al. 2023). The development of high emissions threatens energy security in their various economies(Programme 2015). 5.0 Conclusion In conclusion, the assessment of energy use and carbon emissions is crucial in our efforts to create a sustainable future. Through the utilization of panel data analysis, this study has provided valuable insights into the energy consumption and carbon emissions of seven industrialized economies. The findings highlight the need for targeted policies and international cooperation to reduce energy consumption and carbon emissions, while also identifying opportunities for decoupling economic growth from environmental degradation. As we navigate the challenges of a rapidly developing world, it is essential that we continue to monitor and assess our energy use and carbon emissions. By doing so, we can develop strategies that promote economic growth while minimizing the impact on the environment. Together, we can pave the way for a more sustainable future for generations to come. The G7's ability to meet their climate targets is crucial for the success of the Paris Agreement, as they are major emitters and energy users. Carbon dioxide emissions from these countries are increasing, given that some of these countries continue to use fossil fuels despite the increasing consumption of renewable energy. The current energy crisis has caused governments to resort to solid fuels and coal because of the energy crises affecting the economies. The significance of CO2 emissions in G7 countries highlights the need for additional action to achieve the Paris Agreement goals. The analysis underscores the significance of carbon emissions in transportation, manufacturing, and construction, as well as the broader issue of greenhouse gases. Emissions in the transport sector continue to rise due to the low level of technological maturity. It calls on the G7 economies to implement measures to accelerate decarbonization in the transport sector. The energy crisis and pandemic have prompted a shift towards a low-carbon economy. G7 economies must align their NDCs with short-term and long-term policies to achieve net zero by mid-century. The G7 economies have the responsibility to meet their NDCs in order to limit their emissions levels and reach the Paris Agreement. These economies are experiencing an increase in carbon emissions because they have not widely implemented carbon markets in carbon-intensive sectors. Such sectors in power and heat, manufacturing and construction, power, and heat. The results show that most of the economies studied, particularly the United Kingdom, have undergone a structural change towards a service-oriented economy. This has led to a significant improvement in emissions levels in some economies studied, with the service economy emitting relatively lower carbon emissions than manufacturing. This can be observed with France, the United Kingdom, and Italy. Further technological advances have resulted in carbon emissions declining in some countries studied. The UK generates CO2 emissions from offshore wind energy from the North Sea and other renewable sources. France generates more of its energy from nuclear energy. In addition, the effects of globalization have resulted in service-based economies creating a system of indirect emissions levels, whereby they import carbon emissions from other economies or source or pass them on to other economies with lower environmental regulations and costs. The United Kingdom, for example, imports its emissions from China, where its manufacturing goods come from. In some of these countries, there was clearly an economic decoupling of emissions levels, as GDP growth increased while emissions fell. In the United Kingdom and France, emissions levels have continued to fall, while emissions levels continue to rise in the rest of the world. While our panel data analysis provides valuable insights into the energy use and carbon emissions of the seven industrialized economies, it is important to acknowledge the limitations of our study. First, the analysis is based on data, which may have limitations in terms of coverage and accuracy. The study focuses on a select group of economies and may not portray the diversity of energy consumption patterns and carbon emissions worldwide. Future research could expand the analysis to include a broader range of countries and explore additional variables, such as government policies and technological advancements. Incorporating more recent data could provide a more up-to-date assessment of energy use and carbon emissions trends. Declarations Compliance with ethical standards Consent for publication: We do not have any individual person’s data in Consent to Participate: Not applicable. Conflict of Interest: The authors declare no conflict of interests. Funding Source: No funding was received Author Contribution Contributions of Authors: David Alemzero: conceptualization, methodology, writing- original draft, Analysis, software Data curation, administration, supervision: Fredrick Darimeh: review. Acknowledgement No acknowledgement to make. Data Availability The data can be derived from the world bank development indicators via the link below. https://databank.worldbank.org/source/world-development-indicators References Moving Toward Net-Zero Carbon Society Challenges and Opportunities . Abeysekara, W. C. S. M., M. Siriwardana and S. Meng (2024). "Economic consequences of climate change impacts on South Asian agriculture: A computable general equilibrium analysis." Australian Journal of Agricultural and Resource Economics 68 (1): 77-100. Acharya, A. (2022). "Integrated Carbon Policy Design for Achieving Net-Zero Targets." Abhijeet Acharya 12 . Agrawal, R., P. Priyadarshinee, A. K. K, S. Luthra, J. A. Garza‐Reyes and S. Kadyan (2023). 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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-5286720","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":369025182,"identity":"97069d59-8cdd-4cdf-b85c-a36fb4ea247a","order_by":0,"name":"David Alemzero","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCElEQVRIiWNgGAWjYBACCSgtw8DA2HD4RwWQyczcQEALM5jmASptPMxwBqSFkWgt7M2HGdtAbAJaJNvPH/z4pcaOh1/sYMPhwnm10fztQC0/Krbh1CLNk8wsLXMsmUdydmLD4ZnbjufOOMzYwNhz5jZOLXIMyQzSEmzMPAa3ExsO8G47ltsA1MLM2IZHC/9j5t8S/+p57MFa5hzLnU9Ii7REMpvkx7bDPAbSQIfxNtTkbiCkRXLGYzNrxr7jPBJAWw7OOHYgdyNQy0F8fpE4n/j45o9v1XL8s9Mff/hQU5c77/zhgw9+VODWAgLMPAj2YTB5AK96IGD8gWDXEVI8CkbBKBgFIxAAAI/QXutdw91NAAAAAElFTkSuQmCC","orcid":"","institution":"Southwest University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"David","middleName":"","lastName":"Alemzero","suffix":""},{"id":369025183,"identity":"70e72cd5-15df-427d-9550-92ec7a990487","order_by":1,"name":"Fredrick Darimeh","email":"","orcid":"","institution":"University of International Business and Economics","correspondingAuthor":false,"prefix":"","firstName":"Fredrick","middleName":"","lastName":"Darimeh","suffix":""}],"badges":[],"createdAt":"2024-10-18 05:53:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5286720/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5286720/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":67353240,"identity":"4d201b6b-3b33-4401-b0d1-b594d0eb0e70","added_by":"auto","created_at":"2024-10-24 04:30:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":128621,"visible":true,"origin":"","legend":"\u003cp\u003eGlobal energy milestones for NZE development by mid-century Source. Adapted from(Birol 2022)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5286720/v1/8ed8af8b0f2988f81e6af146.png"},{"id":67353239,"identity":"3ef74ff2-97d6-468f-acae-a0cfe3776f8b","added_by":"auto","created_at":"2024-10-24 04:30:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52748,"visible":true,"origin":"","legend":"\u003cp\u003eHistorical global warming gases of the G7 countries\u003c/p\u003e\n\u003cp\u003eSource. Authors’ calculations.\u0026nbsp;\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5286720/v1/6ef53dce50c80b989e3069a3.png"},{"id":67353241,"identity":"4aadf3e2-2786-4d9e-a0ab-b05531ac340f","added_by":"auto","created_at":"2024-10-24 04:30:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":21032,"visible":true,"origin":"","legend":"\u003cp\u003eBox plot of variables.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5286720/v1/934752026e4c692fa47bebe6.png"},{"id":77695793,"identity":"508cd391-bad9-4e48-af9e-6f32de3ced2d","added_by":"auto","created_at":"2025-03-04 10:31:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1060488,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5286720/v1/47347d43-baa4-4a63-946a-f7876884f35d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":" A Panel Data Analysis of the Net Zero trajectory of Seven Industrialized Economies. ","fulltext":[{"header":"1.0 Introduction","content":"\u003cp\u003eDeveloped countries face the challenge of transitioning to clean and sustainable energy sources while achieving net zero emissions(Guenedal, Lombard et al. 2022, Haddad, Pagone et al. 2022). This transition requires significant changes in energy production, consumption, and infrastructure to reduce greenhouse gas emissions and mitigate climate change.(Haddad, Pagone et al. 2022) Achieving net zero emissions involves reducing carbon dioxide and other greenhouse gas emissions to a level where they are balanced by removal or offsetting measures, such as carbon capture and storage or reforestation(Haddad, Pagone et al. 2022, removal 2023). The shift towards renewable energy sources, such as solar and wind power, is crucial in reducing reliance on fossil fuels and achieving net zero emissions. Policy measures, technological advancements, and international cooperation are necessary to support the transition to clean energy and achieve net zero emissions in developed countries. (Ozcan 2013, Villanthenkodath and Pal 2024). Consequently, the aim of this research is to evaluate the energy use and the net zero trajectory within the G7 economies, while pinpointing the primary drivers behind emissions levels that may impede the realization of their net zero targets. Climate change, largely fueled by carbon dioxide emissions, is causing severe consequences that the world is witnessing. The global community established the Paris Accord to keep global temperature increase to beneath 1.5 degrees Celsius by 2030. Considering that these countries have some of the highest emission levels globally, how they address these increases will be crucial in achieving the goals outlined in the Paris Agreement and reaching net zero targets by the middle of this century through their nationally determined contributions (NDCs).\u003c/p\u003e \u003cp\u003eIn June 2020, the leaders of the G7 made a commitment to achieve Net Zero Emissions (NZE) with environmentally friendly technologies and strong political support. Collectively, the G7 countries have control over 40% of the world\u0026rsquo;s GDP, account for nearly thirty percent of the world\u0026rsquo;s energy demand, and handle about 25% of global emissions(Oluc, Can et al. 2024). They also agreed to decarbonize the power sector by 2035 (Ashokan, Jaganathan et al. 2024). The power industry presently represents about 33% of the G7's total releases, which is a decrease from the 40% reported in 2007. Instead of coal, negative emissions technologies are now being used. The utilization of renewable energy and the presence of cost-competitive natural gas in the markets have contributed to both the growth and reduction of emissions levels in these countries. As of 2020, natural gas and renewable energy resources (RES) were the preferred sources of energy generation in the G7, each accounting for 30% of the total, followed by nuclear power and coal at 20% (EL-Karimi and El-houjjaji 2022, IEA 2022). The expansion of low carbon technologies is essential in attaining net zero emissions.\u003c/p\u003e \u003cp\u003eIn a similar vein, an analysis using panel data has been carried out to examine the net zero trajectory of seven industrialized economies. The analysis includes data on carbon emissions and introduces innovative metrics for assessing carbon emissions reduction goals and relative positioning in relation to the net zero emissions (NZE) scenario (Hsiao 2022). The study presents a definition of static and dynamic NZE measures that can be utilized to evaluate the performance of countries as in the NZE scenario. (Guenedal, Lombard et al. 2022). Additionally, the study applies a PAC framework to analyze the participation, ambition, and credibility of issuers' NZE policies. The findings reveal that achieving net zero carbon emissions is challenging for many issuers due to a lack of ambition and targets that are not compatible with past trends(Agrawal, Priyadarshinee et al. 2023, Khosla, Lezaun et al. 2023). This research provides insights into the feasibility and progress of achieving net zero emissions in industrialized economies(OECD 2022) .\u003c/p\u003e \u003cp\u003eWind and solar power are becoming more prevalent among the G7 countries, with a projected increase from 14% in 2020 to 40% in 2030 and two-thirds by the middle of the century. According to IEA (IEA, IRENA et al. 2023), wind and solar received 60% of investments in the power sector in 2021. To decrease emissions, the G7 nations have set their sights on achieving extensive decarbonization in their power systems. Experts predict that this ambitious aim will generate about 2.6\u0026nbsp;million job opportunities in the industry within the next ten years. In addition, there will be a decline of over one million jobs in the fossil fuel sector during the same period. The transition to renewable energy sources will lead to a reduction in investments in the energy sector, declining from 7% of GDP at present to 4% by the mid-century. To reduce emissions, we need innovation. Estimates suggest that new technologies could decrease G7 electricity emissions by up to 30%. The IEA's \"Net Zero Emissions\" (NZE) trajectory shows that the power sector receives a greater amount of investment compared to the energy sector(IEA, IRENA et al. 2023). To accomplish this transition, experts estimate it will require a total investment of \u003cspan\u003e$\u003c/span\u003e2 trillion, and they expect it to increase to \u003cspan\u003e$\u003c/span\u003e3 trillion by 2030 (IEA, IRENA et al. 2023, Dai, Sun et al. 2024). By 2030, scholars predict investors will allocate \u003cspan\u003e$\u003c/span\u003e1.3 trillion to the renewable energy industry, compared to \u003cspan\u003e$\u003c/span\u003e1.2 trillion for fossil fuels, as a significant portion of this investment. Several studies have investigated how energy use, GDP, trade openness, urbanization, and carbon emissions are related in the G7 countries using panel ARDL models (Altın 2024, Pham, Li et al. 2024, Xing, Husain et al. 2024). One study investigated the influence of urbanization on carbon footprint, while others examined the impact of an ecological tax on carbon emissions(Xing, Husain et al. 2024, Zhang, Lau et al. 2024). What sets this study apart is its use of panel data models to address omitted variable biases in heterogeneity analysis. It also controls for unmeasured variables that are linked to the variables being studied. This approach allows for the identification and measurement of effects that may not be apparent in a pure cross-sectional or time series analysis. 2SLS models address OLS overestimation by accounting for the correlation between energy use and the stochastic term. Finding instruments becomes challenging. These variables will be uncorrelated with the error term yet contribute to predicting the G7 emissions levels. The study period was from 2002 to 2022.\u003c/p\u003e \u003cp\u003e \u003cb\u003eContribution of the Study\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe results contribute to the existing literature on energy use and CO2 emissions in the G7 economies. The results further demonstrated that (1) Carbon emissions from manufacturing and construction is significant contributing to growing emissions levels among the study countries. The aforementioned discovery is substantiated in (Davis, Lewis et al. 2018), where it was determined that 40% of carbon emissions resulting from cement production are derived from fossil fuel energy inputs, while the remaining CO2 emissions originate from the calcination process of calcium carbonate. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The study uncovered the significant carbon emissions from the transportation sector, highlighting the limited technology readiness of the sector for decarbonization. Additionally, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the study revealed that the power sector holds the highest emission rates, with a significant correlation coefficient. This confirmation is stated in (Davis, Lewis et al. 2018), where it is reported that the power sector accounted for 26% of global fossil fuel use and industry emissions in 2014. Additionally, the emission of methane is a significant contributor to the escalating levels of emissions and their achievement of net zero emissions. Methane is released through biological respiration, degradation, and combustion, and the decomposition of biomass under anaerobic conditions may result in significant CH4 emissions. The findings emphasize the need for an alternative policy approach in G7 countries to achieve net zero by the middle of the century by focusing policies on the difficult-to-eliminate emissions sectors such as transport sector, manufacturing and construction. The rest of the paper organizes: Section two analyzes the literature associated with the study. Section three of the paper discusses the materials and methods. Section four discusses results and findings. In section five, the study wraps up with conclusion and recommendations.\u003c/p\u003e"},{"header":"2.0 Literature Review","content":"\u003cp\u003eIn their pursuit of net zero objectives, the G7 nations have put into effect a range of policies and initiatives. In order to foster coordination and integration of support instruments along the innovation chain, mission-oriented innovation policies have been implemented, although their influence beyond science, technology, and innovation fields is constrained (OECD 2023). In the electric power sector, utilities are committing to net-zero goals by transitioning away from fossil fuels and decarbonizing their operations (Cl\u0026eacute;men\u0026ccedil;on-Charles and Baranek 2023). Countries in the Asia-Pacific region, such as Japan, Korea, China, and Taiwan, have enacted laws and policies to achieve carbon neutrality by 2050 or 2060 (2023). Carbon pricing has been used as an instrument to attract private investments and cut greenhouse gas emissions, but there are gaps in its implementation that need to be addressed(Acharya 2022). The negotiation of free trade agreements, such as the one between Australia and the UK, aims to integrate trade and climate policies, but its contribution to climate change mitigation is minimal(Pareliussen, Crowe et al. 2022). The principal aim of negotiating free trade agreements, such as the Australia-UK deal, is to synchronize trade and climate policies, although their impact on addressing climate change is insignificant(Victoria 2024). This could have a significant impact on the rest of the world\u0026apos;s efforts towards deep decarbonization. Their action plan is an example for other economies, especially developing countries, looking to reduce their carbon footprint, according to (Bennich, Persson et al. 2023). Another study by (IEA 2022, Saqib, Ozturk et al. 2023) concluded that transitioning to renewable energy sources will require scaling up renewable energy systems, negative emissions technologies, and gradually phasing out fossil fuels. Against this backdrop, (Ahmed, Zafar et al. 2020, Hoa, Xuan et al. 2024) found that per capita renewable energy consumption in G7 economies is on the rise. Also, (Borozan 2022, Borozan, Bayar et al. 2023) found that the Group of Seven economies have already passed the peak of their inverted U-shaped emissions curve. Their research showed that GDP and renewable energy consumption are linked in the G7 economies over a long period. It is clear that human-caused anthropogenic gas emissions contribute to temperature rise and climate change\u0026rsquo;. Furthermore, nearly 60% of global greenhouse gas emissions come from ten countries, two of which are G7 members (Tugcu and Menegaki 2024). The United States, for instance, has committed to reducing its greenhouse gas emissions by 50\u0026ndash;52% below 2005 levels by 2030 (Abeysekara, Siriwardana et al. 2024, Ritchie, Rosado et al. 2024). In contrast, France\u0026apos;s aggregate emissions have plummeted to 352.10 Mt CO2e (Baidya and Saha 2024, Ritchie and Roser 2024). France has set a goal of becoming carbon neutral by 2050, which was mooted in 2019(Ritchie and Roser 2024). Similarly, Germany\u0026apos;s cumulative greenhouse gas pollution has been on a downward trend, standing at 720.23 MtCO2e in 2019(Ritchie and Roser 2024). Italy\u0026apos;s emissions levels have taken an interesting turn, peaking in 2003 and then steadily declining. They emitted between 772 Gg and 7020 Gg per year (Ritchie and Roser 2024). In 2019, Italy\u0026apos;s total greenhouse gas emissions from global warming were 376.19 Mt CO2e (Buchner, Tonkonogy et al. 2022). Japan\u0026apos;s greenhouse gas emissions have stayed the same since the early 1900s, with a brief spike in 2010 before going back to normal (Ceglia, Marrasso et al. 2022, Murshed, Saboori et al. 2022). It is crucial to expand existing negative emissions technologies and invest in innovative solutions to reduce emissions, as this could save many lives from untimely deaths because of enclosed and out-of-door air pollution (Borozan, Bayar et al. 2023, Noussan, Negro et al. 2024). A study by (Sinel and Weis 2024) revealed that research and development funding significantly incentivizes the installation of new energy in Group of seven countries, as showed by their distributed lag nonlinear autoregressive model analysis. In addition, Huihui etal (Huihui, Alharthi et al. 2024) found that macroeconomic policies directly affect renewable energy adoption in G7 countries. Similarly, the findings of, (Jahanger, Awan et al., Tugcu and Menegaki 2024) suggest that the introduction of environmental taxes leads to a reduction in pollution levels within the G7 economies and encourages businesses to shift towards a low carbon economy (Cheng, Zhao et al. 2024). Furthermore, Wang et al (Wang, Xu et al. 2024) have advocated that developed nations, such as the G7, should strive to increase their mitigation commitments beyond the global average in order to significantly impact the decarbonization process. To meet the current commitments of limiting global warming to 1.5 degrees, advanced economies like the G7 must halve their emissions. In the Canadian context, it is worth noting that the cumulative global pollution level experienced a significant increase during the 2000s, lasting approximately ten years, before subsequently declining in 2010 to a level of 774g.\u003c/p\u003e\n\u003cp\u003eEvaluating energy use and setting net zero goals is crucial for fighting climate change and building a sustainable future. With the global population and economies growing, the consumption of energy is increasing, causing higher carbon emissions.\u003c/p\u003e\n\u003cp\u003eThe diagram in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e illustrates the global energy pathway towards achieving Net Zero Emissions (NZE) by the year 2050. To reach NZE, the International Energy Agency (IEA) has proposed a plan to halt the development of new electricity generated from coal in 2021. This strategy aims to attract investments in the energy sector. There are four key milestones that need to be met in order to achieve NZE. One of these milestones is the installation of 150 million tons of minimal emissions hydrogen by 2030, capturing approximately 4 gigatons of pollution by 2035, and increasing the adoption of EVs in new truck sales by 50% by 2035. The blueprint also highlights the importance of global collaborations in sectors such as innovation, comprising technology collaborations, to bring important technologies like hydrogen, enhanced biofuels, carbon capture, storage and use (CCUS) to commercial scale (Siriwardana and Nong 2021, Li and Haneklaus 2022, Borozan, Bayar et al. 2023). This will attract investments and technology help, and also transfer technology to other countries (IEA 2022). The IEA predicts a decrease in electricity emissions to 5.1 gigatons by 2030, with wind and solar providing over 40% of power generation (IEA 2022). Conventional energy sources handle the majority of global greenhouse gas emissions, accounting for 65% of total emissions excluding land use change (LUC)(IEA 2022). This is supported by various studies that have emphasized the need for developed economies, such as the G7, to hasten their efforts in meeting their Nationally Determined Contributions (NDCs) and the Paris Agreement\u0026apos;s goal of limiting the global temperature rise to below 1.5 degrees Celsius. This study investigates how these advanced economies are using energy and producing carbon emissions. It offers evidence regarding their progress in achieving their net zero trajectories.\u003c/p\u003e"},{"header":"3.0 Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003cp\u003eThis study analyzes the energy usage and emission trends of seven developed countries, with a focus on their journey towards net zero. We conducted the study from 2002 to 2022 using panel data models with fixed effects. The World Bank Development Indicators (WDI) provided us with the data. The response variable, energy use, measures the amount of primary energy that is used before it is converted into various forms of end-use energy, including local supply, imports, stocks, and exports. This study takes into account methane pollution from conventional energy sources and biofuels. The measurement indicates the percentage change in cumulative greenhouse gas emissions since 1990, including carbon dioxide emissions (excluding biomass and short-term biomass burning) and cumulative human-made methane gases. Similarly, carbon dioxide emissions from transportation account for fuel combustion in all modes of transportation, excluding marine bunkering fuel and global aviation. The percentage of cumulative carbon dioxide emissions from solid fuels mainly refers to emissions from coal usage. The emissions from manufacturing and construction sectors include pollution from energy consumption in industries. CO2 emissions from transportation and heat supply include electricity used in transportation, utilities, and power plants. The study measures the carbon dioxide emissions per purchasing power parity of the gross domestic product. This includes emissions from burning traditional energy sources and producing cement.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Model\u003c/h2\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003e3.2.1 Panel\u003c/h2\u003e\n \u003cp\u003eThe regression provides the fixed effect below, with lags in the predictors.\u003c/p\u003e\n \u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:{Y}_{it}={a}_{i}+{\\beta\\:}_{\\:}{X}_{it-1}+{u}_{i}+{e}_{it}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e=1\u0026hellip;n; t=1\u0026hellip;.T\u003c/p\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e denotes countries under study and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e denotes time\u003c/p\u003e\n \u003cp\u003eThe \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e subscript depicts the cross-section dimension, whereas \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e denotes the series dimension \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:a\\)\u003c/span\u003e\u003c/span\u003e is the intercept. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:B\\:\\times\\:1\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{X}_{it}\\)\u003c/span\u003e\u003c/span\u003e depicts the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ith}_{\\:}\\)\u003c/span\u003e\u003c/span\u003e observation on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{K}_{\\:}\\)\u003c/span\u003e\u003c/span\u003e explanatory variables (Baltagi,2008).\u003c/p\u003e\n \u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:{\\mu\\:}_{it}={u}_{i}+{v}_{it}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{\\mu\\:}_{it}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e depicts the unobservable individual country specific fixed effects and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{v}_{it}\\)\u003c/span\u003e\u003c/span\u003e depicts the remainder of the error term. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{u}_{i}\\)\u003c/span\u003e\u003c/span\u003e is the individual is time invariant and account for individual fixed effects not included in the regression model(Baltagi, 2008).\u003c/p\u003e\n \u003cp\u003eThe model is reparametrized below with the given variables.\u003c/p\u003e\n \u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:{Eneu}_{it-1}=\\:{a}_{i}+{\\beta\\:}_{1}{tghs}_{it-1}+{{\\beta\\:}_{2}enemethaneem\\:}_{it-1}+{{\\beta\\:}_{3}co2trspt}_{it-1}+{{\\beta\\:}_{4}co2sf\\:}_{it-1}+{{\\beta\\:}_{5}co2emfcons\\:}_{it}-1+{{\\beta\\:}_{6}co2emhe}_{it-1}+{{\\beta\\:}_{7}co2gdp}_{it-1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003cp\u003e\u003cstrong\u003eStationarity Test\u003c/strong\u003e\u003c/p\u003e\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e$$\\:{y}_{it}={\\varnothing\\:}_{i}{y}_{i,\\:t-1}+{Z{\\prime\\:}}_{it}{\\gamma\\:}_{it}+{ϵ}_{it}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{ϵ}_{it}\\)\u003c/span\u003e\u003c/span\u003e is independently distributed normal for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:t\\)\u003c/span\u003e\u003c/span\u003e with panel specific variance \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\sigma\\:}_{i}^{2}\\)\u003c/span\u003e\u003c/span\u003e (Im et al., 2003)\u003c/p\u003e\n \u003cp\u003eWhere initially values for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{0}\\)\u003c/span\u003e\u003c/span\u003e, are given consider the testing of the null hypothesis of unit roots \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varnothing\\:}_{i}=1\\)\u003c/span\u003e\u003c/span\u003e regarding all \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e (Im et al., 2003)\u003c/p\u003e\n \u003cp\u003eEquation (\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e) can be stated as\u003c/p\u003e\n \u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e$$\\:{\\varDelta\\:y}_{it}={a}_{i}+{\\beta\\:}_{i}{y}_{i,t-1}+{ϵ}_{it}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\n \u003c/div\u003e,\u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{i}+\\left(1-{\\varnothing\\:}_{i}\\right){u}_{i},\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\beta\\:}_{i}=1-\\left(1-{\\varnothing\\:}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varDelta\\:{y}_{it}={y}_{it}}_{\\:}-{y}_{i,t-1}.\\)\u003c/span\u003e\u003c/span\u003e (\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\n \u003cp\u003eThe null hypothesis of unit root becomes.\u003c/p\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{H}_{o}:{\\beta\\:}_{i}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e for all \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:i\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eAgainst the alternative\u003c/p\u003e\n \u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e$$\\:{H}_{1}={\\beta\\:}_{i}\u0026lt;0,\\:i,\\text{1,2}\\dots\\:,{N}_{i},{\\:\\:\\:\\beta\\:}_{i}=0\\:\\:\\:\\:i={N}_{1}+1,{N}_{1}+2,\\dots\\:{N}_{\\:}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\n \u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Stage Least Square Estimations(2SLS).\u003c/h2\u003e\n \u003cp\u003eThe two stage Estimations (2LS) approximate two least square equations. The estimation approach contains an instrumental variable and four different types of variables, the endogenous, exogenous, and the dependent variable. This approach has the advantage of avoiding endogeneity and autocorrelation.\u003c/p\u003e\n \u003cp\u003eBelow is the model for evaluating the impact of the variables in undertaking the analysis.\u003c/p\u003e\n \u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e$$\\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}{k}_{ij}+{\\epsilon\\:1}_{ij}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eThe concern for this equation is that is correlated to the stochastic term, that is corr\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\left(\\:\\right.k}_{ij}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:1}_{ij})\\ne\\:0\\)\u003c/span\u003e\u003c/span\u003e, to overcome the issue of endogeneity. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{a}_{2}\\)\u003c/span\u003e\u003c/span\u003e will generate unbiased and inefficient estimates if we proceed to use Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). This happens when there are other latent factors that might hinder the progress of the advanced countries to achieve their net zero (NZT), other than the indicators, such as used in the analysis, cultural and institutional performance of individual countries.\u003c/p\u003e\n \u003cp\u003eWe then instrument some variables such as emissions in the transport sector and emissions in the manufacturing sector and specify a model for below.\u003c/p\u003e\n \u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e$$\\:{k}_{ij}={\\beta\\:}_{0}+{\\beta\\:}_{1}+{\\beta\\:}_{1}{x}_{1j}+{\\beta\\:}_{3}{q}_{ij}+{\\epsilon\\:2}_{ij}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{ij}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e affects \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{ij}\\)\u003c/span\u003e\u003c/span\u003e directly. It does not impact on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{y}_{ij}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{ij}\\)\u003c/span\u003e\u003c/span\u003e. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{q}_{ij}\\)\u003c/span\u003e\u003c/span\u003e has no influence on the other variables, is exogenous and is referred to as an instrumental variable.\u003c/p\u003e\n \u003cp\u003eThen, we estimate Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) using OLS on the net zero trajectories.\u003c/p\u003e\n \u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e$$\\:{k}_{ij}={\\widehat{\\beta\\:}}_{0}+\\widehat{\\beta\\:}{\\:}_{1}+{\\widehat{\\beta\\:}}_{2}{x}_{1j}+{\\widehat{\\beta\\:}}_{3}{q}_{ij}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{ij}\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e from Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e) is not associated with the stochastic term \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:2}_{ij}\\)\u003c/span\u003e\u003c/span\u003e in Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e) which is, different latent factors cause endogeneity.\u003c/p\u003e\n \u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e$$\\:{k}_{ij}=\\widehat{{k}_{ij}}+{\\epsilon\\:2}_{ij}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWe then substitute \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{k}_{ij}\\)\u003c/span\u003e\u003c/span\u003e using Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e) within the first Eq.\u0026nbsp;(\u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e\n \u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}{k}_{ij}+{\\epsilon\\:1}_{ij}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e$$\\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}(\\widehat{{k}_{ij}}+{\\epsilon\\:2}_{ij})+{\\epsilon\\:1}_{ij}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e$$\\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}\\widehat{{k}_{ij}}+\\left({a}_{2}{\\epsilon\\:2}_{ij}+{\\epsilon\\:1}_{ij}\\right)$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e$$\\:{y}_{ij}={a}_{0}+{a}_{1}{x1}_{ij}+{a}_{2}{a}_{2}\\widehat{{k}_{ij}}+\\epsilon\\:1{}_{ij}{}^{*}\\:$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"4.0 RESULTS AND DISCUSSIONS","content":"\u003cp\u003eTable 1 presents the summary statistics for the parameters in the analysis. As expected, energy use has the maximum mean among the Group of Seven economies, which are highly industrialized and reliant on energy. To meet the Paris Agreement goals and prevent global temperatures from rising above 1.5 degrees Celsius, we must focus on sustainable energy consumption. Countries\u0026apos; Determined Contributions (NDCs) (Siriwardana and Nong 2021). The next highest average is for CO2 emissions from heat and electricity supply, showing that a greater share of CO2 emissions come from these industries. The G7 economies, which are in temperate climates, have a high demand for heating and cooling during extreme weather. This is evident from the current heat waves and the need for heating in winter and cooling in summer. As a result, the transport sector also has an above average for carbon dioxide emissions. Cutting emissions in this sector is challenging because people in these countries rely heavily on transportation services due to their high living standards. Air and road transport are notable sources of carbon dioxide emissions in these countries. The transport segment \u0026nbsp;emits \u0026nbsp;one quarter \u0026nbsp;of global CO2 pollution (Dietz, Beaucamp et al. 2020). The total greenhouse gas emissions from the G7 economies are inverse, showing a minute impact on these gases. CO2 emissions from burning fossil fuels and making cement have the lowest average. This is because of the use of renewable energy and low-carbon building materials, as well as the economic decoupling in these countries.\u003c/p\u003e\n\u003cp\u003eTable 1. Descriptive Statistics\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"417\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003eObs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003eStd. Dev.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eEneu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e80.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e61.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e212.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eTghgs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e139.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e-0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e17.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e-26.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e70.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eEnemethaneem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e13.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e55.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eco2trspt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e18.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e13.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e42.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eco2sf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e15.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e12.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e42.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eco2emfcons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e8.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e6.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e17.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eco2emhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e25.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e20.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e51.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 23.2614%;\"\u003e\n \u003cp\u003eco2gdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e140.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15.3477%;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource. Authors\u0026apos; calculations\u003c/p\u003e\n\u003cp\u003eTable 2 reveals a significant negative correlation between energy consumption and total greenhouse gas emissions. The decrease in emissions suggests a rise in the use of sustainable energy among the G7 economies, which explains this negative correlation. The transport sector and methane emissions are key areas to focus on for reducing carbon emissions because they are strongly corrected to energy use. However, the technological readiness level for decarbonizing the transport sector is not as advanced as that of the energy sector, which explains the significant correlation between the two. Various processes like biological respiration, degradation, and combustion release methane, which has a more long-term and devastating impact on the environment compared to carbon dioxide. Only 39% of transport firms have aligned with the Paris 2030 goals, and 18% have committed to reducing emissions by less than 2 degrees by 2030 (Sherwin, Rutherford et al. 2023). The relationship between solid fuels energy use, total greenhouse gas emissions, methane emissions, and carbon emissions from transport is also significant. The only variables that are not strongly correlated with carbon emissions from heat and electricity are total greenhouse emissions. This highlights the strong correlation between emissions as a percentage of GDP and all the other variables, indicating that economic growth is linked to higher emissions levels. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable correlation Matrix.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"721\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 120px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 56px;\"\u003e\n \u003cp\u003eEneu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 54px;\"\u003e\n \u003cp\u003eTghg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003eEnemeth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 70px;\"\u003e\n \u003cp\u003eco2trspt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 97px;\"\u003e\n \u003cp\u003eco2emfcons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 77px;\"\u003e\n \u003cp\u003eco2emhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" style=\"width: 93px;\"\u003e\n \u003cp\u003eco2gdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 109px;\"\u003e\n \u003cp\u003eAnem\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;Eneu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;Tghg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e-0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eEnemethaneem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eco2trspt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;co2sf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eco2emfcons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.701\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.563\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eco2emhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e-0.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;co2gdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.734\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.692\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.611\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 56px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 77px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource. Author\u0026apos;s correlation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePesaran Panel Unit Root Test with Cross-sectional Dependence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFollowing the footsteps of (Pesaran, 2007), we used CIPS \u0026nbsp; in Stata, called xtcips. This command is designed for balanced panels. The results are shown in the following table, particularly in Table 3.\u003c/p\u003e\n\u003cp\u003eTable 3 Pesaran Panel Unit Root Test with cross-sectional\u003c/p\u003e\n\u003cp\u003eCIPS* = \u0026nbsp; \u0026nbsp;-2.511 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;N,T = (20,7)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e10%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e5%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 167px;\"\u003e\n \u003cp\u003eCritical values at\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e-2.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e-2.890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 133px;\"\u003e\n \u003cp\u003e-3.200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource. Author\u0026apos;s Analysis\u003c/p\u003e\n\u003cp\u003eTable 3 is Pesaran Panel Unit Root Test with cross-sectional and first difference mean included for eneu with deterministic chosen: constant \u0026amp; trend dynamics: The lags criterion decision general to base on the F joint test. Individual ti was truncated during the aggregation process H0 (homogeneous non-stationary): bi = 0 for all I. As can be observed in table 4, the static value is -2.511, which is below the critical value at 1% significance level. Therefore, this second-generation unit root test refutes the null hypothesis of unit- root in energy use (Eneu).\u003c/p\u003e\n\u003cp\u003eTable 4 displays the findings of the panel analysis conducted on the environmental performance of the G7 economies and their net zero analysis. The first model used a multivariate analysis with the vce robust command to examine the variables. The results showed that carbon emissions from the transport sector are significant. This suggests that decarbonization efforts should prioritize the transport system, as it is a major contributor to emissions, rather than focusing on the supply of energy. To effectively decrease emissions in the transport sector, it is essential to reduce the consumption of fossil fuels, which currently dominate this sector (Dietz, Beaucamp et al. 2020, Furszyfer Del Rio, Sovacool et al. 2023). For example, the UK government has announced plans to ban the sale of petrol and diesel vehicles by 2030, ten years earlier than initially planned. Transportation accounts for approximately a quarter of global energy-related emissions and poses challenges in the form of transport poverty (Dietz, Beaucamp et al. 2020, Furszyfer Del Rio, Sovacool et al. 2023). To notably decrease emissions from the transport sector, it is crucial to employ renewable fuel options like bioethanol-gasoline blends, biodiesel, and green hydrogen, as suggested by(Molden 2023). Another key variable is the carbon emissions from the manufacturing and construction sectors, which are among the top contributors to global warming. It is worth noting that there is a negative correlation between energy consumption and emissions, suggesting a shift towards cleaner energy sources in the study countries, leading to reduced emissions in the building and construction industry. Conversely, an estimated global daily migration of 200,000 individuals to urban areas is occurring as a result of population growth. These individuals will require adequate housing infrastructure, provided by the construction sector, which will have a significant impact on air quality(C.D. Desouza a, \u0026nbsp;et al. 2020). Consequently, emissions from the construction sector are expected to continue increasing, necessitating the implementation of a robust regulatory framework to control emissions(C.D. Desouza a, \u0026nbsp;et al. 2020).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The outsourcing of manufacturing industries to other countries may contribute to the \u0026nbsp;decrease in emissions from this sector(Hanifa, Agarwal et al. 2023). The cement industry alone is responsible for about 7% of global carbon dioxide emissions (Hanifa, Agarwal et al. 2023). In 2019, the construction sector emitted roughly 9.95 Gt/y of carbon dioxide, making it the largest contributor to emissions. Researchers predict that the construction sector will cut emissions by 16% and become carbon neutral by 2050 (Hanifa, Agarwal et al. 2023). \u0026nbsp;The respective contributions of the overall construction to NOX, PM10, and PM2.5 in London are 7%, 34%, and 15%(C.D. Desouza a, \u0026nbsp;et al. 2020). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA study has shown that the use of energy-intensive materials, such as bricks and cement, can effectively reduce embodied carbon emissions in buildings \u0026nbsp;(Li Zheng a, Kashif Raza Abbasi b c et al. 2023). The percentage of GDP that is made up of carbon emissions is also a significant variable in the model, with a positive relationship with energy use, indicating that economic growth leads to increased emissions in the study countries. According to Al-Ayouty etal (Al-Ayouty 2023) , renewable energy consumption has a negative impact on carbon dioxide emissions, while Khalfaoui et al. (Zhao, Gozgor et al. 2023) observe a cyclical relationship between carbon emissions and per capita GDP, peaking during economic booms. Model 2 in Table 4 controls for time-invariant characteristics across countries to avoid any biased results. The results show total greenhouse gas emissions have a direct relationship with energy use, with a 65.8% increase in emissions. According to Shahzad et al (Avik Sinha a, Nicolas Schneider b et al. 2023), economic growth has a negative effect on the environment, while financial development can contribute to energy transition and lower greenhouse gas emissions (Atsu and Adams 2024). Raihan et al. (Raihan 2023) have also confirmed the link between economic growth and carbon emissions, with a 1% increase in economic growth leading to a 0.09% increase in emissions. Their research also suggests that adding value to agriculture can help enhance environmental quality by reducing carbon emissions. Model 3 uses the reghdfe command in stata to run a panel analysis with fixed effects models, similar to Guimar\u0026atilde;es and Portugal (Guimar\u0026atilde;es and Portugal 2010). The findings show a direct link between carbon dioxide emissions and energy use, with a substantial 14930% increase in emissions. Raihan etal (Raihan 2024) \u0026nbsp;have documented the effectiveness of an environmental tax in reducing CO2 emissions, particularly when below the optimal point. They suggest that environmental policies, such as an environmental tax mechanism, can incentivize various economic sectors. In a study by Yousefi et al. In 2023, researchers found that BRICS countries outperformed G7 countries in renewable energy, while G7 countries made strides in reducing energy intensity.\u003c/p\u003e\n\u003cp\u003eTable 4 Fixed effects\u0026nbsp;Analysis\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"615\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003eModel1_ eneu_ reg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003eModel 2eneu__ xtreg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003eModel _3eneu\u0026nbsp;reghdfe\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eTghg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e0.658\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e0.401\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e(-1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-3.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e-1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eEnemethaneem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-1.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e-1.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eco2trspt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e3.299\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e3.746\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e3.299\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e(-8.08)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-9.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e-8.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eco2sf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e1.575\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e1.575\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e(-1.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-1.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e-1.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eco2emfcons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e-3.863\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e-12.78\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e-3.863\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e(-4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-12.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e(-5.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eco2emhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e-0.00555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e-0.00555\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e(-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e(-0.02)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003eco2gdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e149.3\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e160.8\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e149.3\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e(-5.61)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e(-2.68)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; 71.73***\u003c/p\u003e\n \u003cp\u003e(11.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e(-5.75)\u003c/p\u003e\n \u003cp\u003e-6.418\u003c/p\u003e\n \u003cp\u003e(-0.72)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29.69%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003cp\u003eR-squared\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eCountry FE\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eNumber of country1 \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.6754%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003cp\u003e0.8408\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24.7961%;\"\u003e\n \u003cp\u003e\u0026nbsp;139\u003c/p\u003e\n \u003cp\u003e0.7276 \u0026nbsp;\u003c/p\u003e\n \u003cp\u003eYES\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22.8385%;\"\u003e\n \u003cp\u003e\u0026nbsp;139\u003c/p\u003e\n \u003cp\u003e0.8408\u003c/p\u003e\n \u003cp\u003eYES\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eSource. Author\u0026apos;s estimation. \u003cem\u003et\u003c/em\u003e statistics in parentheses\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e*\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, \u003csup\u003e**\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, \u003csup\u003e***\u003c/sup\u003e \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001\u003c/p\u003e\n\u003cp\u003eWe augment the cross-sectional dependence diagnostics by conducting Frees and Friedman\u0026apos;s test on the cross-section dependence in table 5. \u0026nbsp;As we expect from the significant results of the CD test, both Frees, and Friedman\u0026apos;s tests refute the null hypothesis of cross section independence because time is less than or equal to 30 years.\u003c/p\u003e\n\u003cp\u003eFrees\u0026apos; s test provides the critical values at \u0026nbsp; , \u0026nbsp;, \u0026nbsp; from the Q distribution. Free\u0026apos;s statistics is more than the critical value with at least \u0026nbsp; \u0026nbsp; \u0026nbsp;Friedman\u0026apos;s test of cross-sectional independence = \u0026nbsp; \u0026nbsp;40.916, Pr = 0.0025 \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Table 5. \u0026nbsp;Cross sectional dependence test\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"320\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; Frees\u0026apos; te\u0026apos;t of cross-sectional independence = \u0026nbsp; \u0026nbsp; 2.927\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97.8125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;Critical values from Frees\u0026apos; Q \u0026apos;istribution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2.1875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97.8125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; alpha = 0.10 : \u0026nbsp; 0.4127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2.1875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97.8125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; alpha = 0.05 : \u0026nbsp; 0.5676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2.1875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 97.8125%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; alpha = 0.01 : \u0026nbsp; 0.9027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 2.1875%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Source. Authors\u0026rsquo; estimation \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. presents the analysis of two stage least squares(2SLS) with over 300 observations. The aim here is to estimate energy used and the net zero trajectories (NZT) of the G7 economies by analyzing the carbon emissions parameters of the study countries. We estimate the coefficient of energy use in a regression equation alongside other explanatory variables. This is equally referred to as an energy use equation. People generally believe that there is a correlation between energy use and carbon emission levels within the equation. This will lead to the OLS overestimating the impact of carbon emissions on energy use. They need to be uncorrelated to the error term, to assist determine the net zero trajectories of the G7 economies. The 2SLS results estimate double equation with the explained parameter as energy use. We consider external factors like greenhouse gas emissions from methane energy, transportation, and solid fuel sources. And the regressors that endogenous are those to the left of the equation, emissions from manufacturing and construction. The equation takes into account factors such as carbon emissions from electricity and heat, and carbon emissions as a percentage of GDP on the right side. The key presumption is that energy use does not correspond to emissions levels but helps to determine when the G7 countries can attain their net zero goals. \u0026nbsp; From the 2SLS results in model 1, emissions from transport sector is significant in a positive direction. This significance implies that carbon emissions is very high in the transport sector. This result is confirmed in \u0026nbsp;(Borozan 2024) where they found fossil fuels to hinder environmental progress and the energy transition. In addition, energy emissions from methane are equally significant. Thus, energy use increases methane emissions(Tibrewal, Ciais et al. 2024).The Hausman test gives a chi-square value of negative 12.8, which disproves the consistency of the OLS and therefore uses the 2SLS model. The under-identification test demonstrates that the parameters correctly identify, with a significant value of 0.000. \u0026nbsp;The weak identification is strongly estimated, as indicated by a Cragg-Donald Wald F statistic of 360.365, which accepts the null hypothesis. The Sargan test shows that the test of over-identifying restriction test has as a strong P value of 0.00 and rejected the null hypothesis. \u0026nbsp;The R-squared reported of nearly 80% explains the model fitly estimated the analysis. \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6. 2SLS Results.\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"304\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003eModel 2 2SLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003eModel 2 OLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eCo2trspt\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e2.485\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e5.022\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e-9.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-7.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eTghg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e0.362\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e-0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-1.77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eEnemethaneem\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e1.531\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e0.879\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e-5.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eCo2sf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e1.217\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-3.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eCo2emfcons\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-9.296\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e(-5.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eCo2emhe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eCo2gdp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e166.3\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-4.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eConstant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e24.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-6.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 38.1579%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e-4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e(-1.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eObservations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e139\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eR-squared\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e0.7943\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eHausman test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e-174.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 65.4605%;\"\u003e\n \u003cp\u003eUnderidentification test\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e127.347\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e(Anderson canon. corr. LM statistic)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 65.4605%;\"\u003e\n \u003cp\u003eWeak identification test\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e360.635\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 65.4605%;\"\u003e\n \u003cp\u003e(Cragg-Donald Wald F statistic):\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 38.1579%;\"\u003e\n \u003cp\u003eSargan statistic\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 27.3026%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 34.5395%;\"\u003e\n \u003cp\u003e54.261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 100%;\"\u003e\n \u003cp\u003e(overidentification test of all instruments)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSource. Authors\u0026apos; estimation t statistics in parentheses* p\u0026lt;0.05, ** p\u0026lt;0.01, *** p\u0026lt;0.001\u003c/p\u003e\n\u003cp\u003eFigure 2 shows the historical global gases of the G7 countries. The study countries\u0026apos; emissions levels continue to rise, even though these countries are signatories to the Paris Agreement. Historical and current emissions levels show that the United States is leading the way. The United States is the largest emitter per capita and the world\u0026apos;s leading economy, making it the largest emitter among G7 countries. In the United States, long-distance transportation contributes significantly to global greenhouse gas emissions, more than the global average(Ritchie, Rosado et al. 2024). The other nation is Japan, which has the maximum global emissions. Due to Japan\u0026apos;s rise as a manufacturing center and developed economy in Asia, it has become the second largest emitter of carbon emissions globally, following the United States, as depicted in the chart provided.\u003c/p\u003e\n\u003cp\u003eGermany is followed by the United Kingdom and subsequently Italy. Borozan (Borozan, Bayar et al. 2023) states that the G7 \u0026nbsp;renewable sources will generate 100 percent of electricity in a decade. The trend shows that all the various sectors continue to release emissions and so efforts must be redoubled to decrease emissions levels.\u003c/p\u003e\n\u003cp\u003eFigure 3 depicts the box plot evaluation of the Group Seven countries parameters. The idea behind boxplot is to present a visual impression of the median, the interquartile range, and the range of data. Energy use is the most pronounced with the interquartile range (IRQ) of nearly 100 for the median range 25\u003csup\u003eth\u003c/sup\u003e percentile, as shown in energy use boxplot for the lower boundary. This points to the reality that these are developed economies and require adequate energy to meet their development needs. \u0026nbsp;This points to the reality that these advanced are economies and therefore need sufficient energy to keep up with development. Total greenhouse gas emissions is an outlier. This means that greenhouse gas emissions are on a growing trajectory among the study countries. The IQR is less than the 25\u003csup\u003eth\u003c/sup\u003e percentile below the lower boundary. We observe another outlier in the next variable, methane emissions from energy, as the points lie outside the whisker. Carbon emissions from transport have the 25\u003csup\u003eth\u003c/sup\u003e percentile for the lower boundary and nearly at the upper boundary. Carbon emissions from electricity and heat are the third largest emitters on a global scale as shown in the boxplot below. Its IQR is within the 25\u003csup\u003eth\u003c/sup\u003e percentile range and the highest percentile for the upper boundary is at the 50th percentile.\u003c/p\u003e\n\u003cp\u003eThese countries are not doing well in terms of energy use and transport, as shown by the outliers in the analysis. These correlate to the significant levels of environmental pollution in these economies, as stated by (Li and Haneklaus 2022, Borozan, Bayar et al. 2023). The development of high emissions threatens energy security in their various economies(Programme 2015). \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eIn conclusion, the assessment of energy use and carbon emissions is crucial in our efforts to create a sustainable future. Through the utilization of panel data analysis, this study has provided valuable insights into the energy consumption and carbon emissions of seven industrialized economies. The findings highlight the need for targeted policies and international cooperation to reduce energy consumption and carbon emissions, while also identifying opportunities for decoupling economic growth from environmental degradation.\u003c/p\u003e \u003cp\u003eAs we navigate the challenges of a rapidly developing world, it is essential that we continue to monitor and assess our energy use and carbon emissions. By doing so, we can develop strategies that promote economic growth while minimizing the impact on the environment. Together, we can pave the way for a more sustainable future for generations to come. The G7's ability to meet their climate targets is crucial for the success of the Paris Agreement, as they are major emitters and energy users. Carbon dioxide emissions from these countries are increasing, given that some of these countries continue to use fossil fuels despite the increasing consumption of renewable energy. The current energy crisis has caused governments to resort to solid fuels and coal because of the energy crises affecting the economies. The significance of CO2 emissions in G7 countries highlights the need for additional action to achieve the Paris Agreement goals. The analysis underscores the significance of carbon emissions in transportation, manufacturing, and construction, as well as the broader issue of greenhouse gases. Emissions in the transport sector continue to rise due to the low level of technological maturity. It calls on the G7 economies to implement measures to accelerate decarbonization in the transport sector. The energy crisis and pandemic have prompted a shift towards a low-carbon economy. G7 economies must align their NDCs with short-term and long-term policies to achieve net zero by mid-century. The G7 economies have the responsibility to meet their NDCs in order to limit their emissions levels and reach the Paris Agreement.\u003c/p\u003e \u003cp\u003eThese economies are experiencing an increase in carbon emissions because they have not widely implemented carbon markets in carbon-intensive sectors. Such sectors in power and heat, manufacturing and construction, power, and heat.\u003c/p\u003e \u003cp\u003eThe results show that most of the economies studied, particularly the United Kingdom, have undergone a structural change towards a service-oriented economy. This has led to a significant improvement in emissions levels in some economies studied, with the service economy emitting relatively lower carbon emissions than manufacturing. This can be observed with France, the United Kingdom, and Italy. Further technological advances have resulted in carbon emissions declining in some countries studied. The UK generates CO2 emissions from offshore wind energy from the North Sea and other renewable sources. France generates more of its energy from nuclear energy.\u003c/p\u003e \u003cp\u003eIn addition, the effects of globalization have resulted in service-based economies creating a system of indirect emissions levels, whereby they import carbon emissions from other economies or source or pass them on to other economies with lower environmental regulations and costs. The United Kingdom, for example, imports its emissions from China, where its manufacturing goods come from. In some of these countries, there was clearly an economic decoupling of emissions levels, as GDP growth increased while emissions fell. In the United Kingdom and France, emissions levels have continued to fall, while emissions levels continue to rise in the rest of the world.\u003c/p\u003e \u003cp\u003eWhile our panel data analysis provides valuable insights into the energy use and carbon emissions of the seven industrialized economies, it is important to acknowledge the limitations of our study. First, the analysis is based on data, which may have limitations in terms of coverage and accuracy. The study focuses on a select group of economies and may not portray the diversity of energy consumption patterns and carbon emissions worldwide.\u003c/p\u003e \u003cp\u003eFuture research could expand the analysis to include a broader range of countries and explore additional variables, such as government policies and technological advancements. Incorporating more recent data could provide a more up-to-date assessment of energy use and carbon emissions trends.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompliance with ethical standards\u003c/h2\u003e\n\u003ch2\u003eConsent for publication:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eWe do not have any individual person\u0026rsquo;s data in\u003c/p\u003e\n\u003ch2\u003eConsent to Participate:\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003e\u0026nbsp;Conflict of Interest:\u003c/h2\u003e\n\u003cp\u003eThe authors declare no conflict of interests.\u003c/p\u003e\n\u003ch2\u003eFunding Source:\u003c/h2\u003e\n\u003cp\u003eNo funding was received\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eContributions of Authors: David Alemzero: conceptualization, methodology, writing- original draft, Analysis, software Data curation, administration, supervision: Fredrick Darimeh: review.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eNo acknowledgement to make.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data can be derived from the world bank development indicators via the link below. https://databank.worldbank.org/source/world-development-indicators\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cu\u003eMoving Toward Net-Zero Carbon Society Challenges and Opportunities\u003c/u\u003e.\u003c/li\u003e\n\u003cli\u003eAbeysekara, W. 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Khalfaoui (2023). \u0026quot;The impact of geopolitical risks on renewable energy demand in OECD countries.\u0026quot; \u003cu\u003eEnergy Economics\u003c/u\u003e \u003cstrong\u003e122\u003c/strong\u003e: 106700.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"G7, Panel Fixed Effect Models, Energy, and the Environment","lastPublishedDoi":"10.21203/rs.3.rs-5286720/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5286720/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe study examines the correlation between energy consumption and the journey towards achieving net zero emissions in G7 nations spanning from 2002 to 2022. The study reveals a decline in environmental performance in certain G7 economies, primarily attributed to high carbon emissions from sectors such as manufacturing, construction, and transportation. The United States demonstrates the highest levels of emissions, with Japan and Germany following closely behind. The United Kingdom and Italy exhibit the lowest levels of emissions. The transportation industry plays a substantial role in the generation of carbon emissions. The emissions of methane resulting from energy consumption are also significant. Cross-sectional interdependence is present within the G7 nations, refuting cross-sectional independence. The Pesaran Panel Unit Root Test has confirmed the stationarity in all panels. The analysis using the 2SLS method uncovers a statistically significant and positive impact of emissions from the transport sector on total carbon emissions. The under-identification test and the Cragg-Donald Wald F statistic provide substantial evidence of strong identification, whereas the Sargan test rejects the null hypothesis in the over-identifying constraint test. The study recommends that G7 nations adopt customized policy measures, prioritizing non-fiscal strategies to efficiently mitigate carbon emissions and attain net zero objectives..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL classification: O50, Q56, P1\u003c/strong\u003e\u003c/p\u003e","manuscriptTitle":" A Panel Data Analysis of the Net Zero trajectory of Seven Industrialized Economies. 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