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 25th 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 25th 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 25th 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 25th 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).