Mitigation policy trust on fossil energy consumption in G7 countries: insight from avoidable policy thresholds

doi:10.21203/rs.3.rs-2100858/v1

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Mitigation policy trust on fossil energy consumption in G7 countries: insight from avoidable policy thresholds

https://doi.org/10.21203/rs.3.rs-2100858/v1

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This study investigated the nexuses between fossil energy consumption, economic growth, trade openness, and environmental degradation in G7 countries over the period 1965–2021. The empirical strategy for the study includes dynamic panel threshold regression (TR) analysis and quantile regression (QR) analysis. For clarity, TR is used to uncover the actionable and complementary policy thresholds in the nexuses between fossil energy consumption, trade openness, green growth, and environmental degradation. QR is utilized to explore the conditional distribution between growth outcome variables. The empirical evidence based is on TR and QR. First, using threshold regression the study revealed an actionable threshold for carbon dioxide emissions (CO₂) not beyond 6.75 mts, along with a complementary threshold of 3.05bcm pd natural gas consumption, and another complementary policy threshold of 5.60% of the share of trade to GDP, respectively. The policy relevance of the thresholds is apparent to policymakers in the cartel and for policy formulation. The policy implication of this study is straightforward. The novelty of this study stalk in the extant literature on providing policymakers an actionable threshold for CO₂ emissions with the corresponding complementary threshold for natural gas consumption and trade policies in the nexuses between green growth and the environment.

Fossil energy consumption

economic growth

trade openness

and CO2 emissions

The pathway from the coronavirus 2019 (COVID-19) pandemic recovery and the quest for further pursuit of higher growth in the Group of Seven (i.e., the USA, UK, Japan, Italy, Germany, France, and Canada “G7”) countries would interest policymakers contingent on the climate change mitigation strategy given by the rising trend of global warming (Wu et al., 2021). A cleaner environment even though G7 countries are the globally energy-producing economies and also the largest CO₂ emissions source around the globe with an estimated above 60% of total global CO₂ emissions (Ye et al., 2020). In the recent contemporary debates, there has been growing interest in G7 economies with a particular focus on the role of globalization, renewable energy, international trade, green growth, inequality, policy uncertainty, economic consequences, and economic growth in CO₂ emissions (Banday and Aneja, 2019; Destek et al., 2020; Hao et al., 2021; He et al., 2019; Khan et al., 2020; Liu et al., 2020; Pirgaip and Dinçergök, 2020; Uddin et al, 2020; Wu et al., 2021), and the topic is still open for discussion especially as it pertains to the pollution control during recovery from COVID-19 pandemic. In this article, we investigate the nexuses between CO₂ emissions, fossil energy consumption, and environmental degradation in G7 economies and provide avoidable policy thresholds underlying the relationships between the variables leveraging on promoting a policy response to a cleaner environment.

The global policy response on CO₂ emissions control is critical and provides an excellent rationale for debate as well as research focus during the recovery path from COVID-19. Whether the topic has some level of significant scrutiny or not, highlights the global concern on fossil energy consumption and its attendant economic and environmental consequences, the policymakers require a limit to these externalities. Building on the above perspective, the study attempts to explore a relationship between CO₂ emission on fossil energy consumption in G7 countries. The objective of the study is to investigate a threshold effect of CO₂ emission on fossil energy consumption in G7 countries, as this topic has not been given the exhaustive scholastic thought it deserves. Knowing the limits/thresholds of the CO₂ emission from fossil energy consumption will either positively/negatively drive cleaner environments, these governments would tailor policies toward sustainable economic growth.

The studies closest to the aligning of this research are Wu et al., (2021), Martins et al., (2021), and Banday and Aneja, (2019). The previous two have examined the relationships between climate change, green financing, cleaner environment, coal, oil, natural gas consumption, and CO₂ emission, while later have focused on the relationship between energy consumption, economic growth, and CO₂ emissions in G7 countries, using autoregressive distributed lagged (ARDL), vector error (VECM), granger causality test respectively. The current study departs from the underlying studies by exploring the limits to CO₂ emissions from energy consumption across G7 countries, or the threshold effect of CO₂ emission on energy consumption, a gap in the literature. Conversely, the corresponding research question follows; is there any avoidable limits of energy consumption that could dampen the CO₂ emissions in G7 countries?

The present study also departs from Martins et al., (2021) by exploring a threshold effect of CO₂ emission on energy consumption across G7 countries for the period 1965 to 2021 as the underlying research has focused only on the intertemporal relationship between fossil energy consumption and CO₂ emission for the period 1965–2018. Accordingly, it is apparent when engaged with the earlier studies that there are differences in concept, methodologies, specifications as well as periodicities between the present study and Martins et al., (2021), Wu et al., (2021), and Banday and Aneja, (2019) among others. However, while the current study concludes on the underlying presence of infliction point in CO₂ emission on fossil energy consumption, coupled with, the present study provides minimum avoidable CO₂ emission thresholds effect 6.75 on economic growth as well as the effect of the complementary threshold on natural gas consumption and trade openness for complementary policies needed across the G7 countries. To sum it up, the objectives and findings of the preceding studies are not like the present study.

The remainder of this study is organized as follows. Section 2 presents the stylized facts, theoretical intuition, and literature. Section 3 presents the methodology and the data. Section 4 discloses the empirical findings while the section concludes the study with future directions.

2.1 Stylized facts

From the underlying studies above, we can observe the presence of a nonlinear relationship between CO₂ emissions and income in G7 countries as shown in the data over the period 1965 to 2021 obtained from the British Petroleum (BP), and World Development Indicators (WDI). Figure 1 presents the CO₂ emissions and Income relationship in G7 countries. Hence, the countries are the most advanced nation that contributed to the global greenhouse gases and climate change via fossil energy consumption. Accordingly, the rationale for the investigation of the threshold effect for CO₂ emissions is of three folds: (1) the greenhouse gas effect has a huge threat to the environment and human health and is difficult to manage. (2) CO₂ emissions driven mostly from production and consumption are produced and in large proportions by these seven nations. (3) CO₂ emissions are produced largely from fossil energy consumption. Considering these three reasons, policymakers and environmentalists would be concerned with the minimum avoidable policy threshold of CO₂ emissions for economic growth for policy formulation.

In panel 1 we can observe that the USA recorded the highest CO₂ emissions produced over the years. Panel 2 presents decades of GDP per capita among these giant nations with the emergence of the UK at the bottom growth. Panel 3 to panel 6 show the covariates of oil consumption, natural gas consumption, coal consumption, and trade, respectively.

2.2 Theoretical review

The theoretical supports inspiring this study are closely related to the contemporary academic debates that revealed economic activities such as production using fossil energy consumption increase environmental degradation (Chaudhry et al., 2020; Hao et al., 2021; Liu et al., 2020; Martins et al., 2021; Nathaniel et al., 2021; Qin et al., 2021; Wu et al., 2021; Ye et al., 2020).

Building from the above insight, the current study focuses on the carbon emission from fossil energy consumption should not exceed certain limits for economic growth to maintain its mitigation role in the world's largest emission producers the G7 countries. This is the premise of the testable hypothesis; (1) growth increases environmental pollution (first hypothesis), and the attendant relationship increases with the increase in fossil energy consumption (Second hypothesis). The third hypothesis states; that growth increases resulting from an increase in trade openness, inter alia, as how more fossil energy consumption affects the environment through greenhouse gases(GHGs) as trade causes natural resource scarcity and environmental degradation (Ding, Khattak, & Ahmad, 2021). This narration is consistent and corroborates with the Carbon Kuznets Curve (CKC) and the Environmental Kuznets Curve (EKC) Kuznets hypothesis (Guangyue and Deyong, 2011; Kuznets, 1955). Next, what follows is the extant literature review to support the underlying intuition positioned in this study and the need for an avoidable framework on fossil energy consumption in G7 countries.

2.3 Brief literature on CO₂ emission, fossil energy consumption, and economic growth

Fossil energy consumption has long-term implications on the environment quality such as climate change and also short-term effects on the habitant (He et al., 2019). Growth and fossil energy consumption have been a topic of great academic debate for decades and the underlying arguments in perspective of carbon dioxide emission mitigation have been investigated empirically. Given the Kuznets hypothesis, the subject could be categorized into two strands of studies based on the highly advanced countries and the rest of the world. The first strand of study has focused on the recent research in G7 countries as they are the largest emission producers (Chaudhry et al., 2020; Ding et al., 2021; Hao et al., 2021; Khan et al., 2020; Liu et al., 2020; Martins et al., 2021; Nathaniel et al., 2021; Pirgaip and Dinçergök, 2020; Qin et al., 2021; Wang et al., 2020; Wu et al., 2021) among many others. Briefly, Nathaniel et al., (2021) uncovered the validity of the EKC hypothesis in France, Canada, the USA, and the UK. Qin et al., (2021) unveiled the evidence that environmental policy, composite risk index, green innovation, and renewable energy reduce carbon emissions. Ding et al., (2021) discovered the evidence that trade openness, growth, eco-innovation, and energy productivity are the determinants of consumption-based CO2 (CCO₂)emissions in the G7 countries. Hao et al., (2021) found evidence of environmental tax, human capital, and renewable energy use reduces CO2 emissions in G7 countries. Conversely, in the perspective of the contemporary literature above, none of these studies positioned closest to the present study.

The second strand in the category has focused on studies from emerging markets and developing countries (Adebayo and Rjoub, 2021; Agbede et al., 2021; Aziz et al., 2021; Chien et al., 2021; Doğanalp et al, 2021; Nawaz et al., 2021; Odugbesan and Rjoub, 2020; Rafique et al., 2020; Scherer et al., 2019; Shao et al., 2019) among many others. Adebayo and Rjoub (2021) found evidence in support of renewable energy and tradable exports to decrease CO₂ emissions in MINT countries while economic growth and tradable imports increase CO₂ emissions. Chien et al., (2021) uncovered the evidence in support of ICT to reduce CO₂ emissions in BRICS while economic growth and financial development contribute to CO₂ emissions. Nawaz et al., (2021) unveiled the evidence in support of the long-term correlation between energy consumption and CO₂ emissions in BRICS and OECD countries. Rafique et al., (2020) found evidence in support of long-run bidirectional causality between CO₂ emissions, economic growth, financial development, FDI, trade, energy use, and urbanization in BRICS countries. It is apparent and obvious when engaged with the extant literature that there are missing avoidable policy frameworks on fossil energy consumption and economic growth investigated. The present study intends to contribute to the field of knowledge and actionable policy framework on fossil energy consumption and economic growth using a dynamic threshold regression on the G7 countries.

In addition, the present study's focus is on complementing the previous literature on the need for a policy framework on fossil energy avoidable threshold policy for economic growth in the path of economic recovery from the COVID-19 pandemic discussed in the problem statements. It is worthwhile to provide insights into the limits to environmental pollution as well as the need requires a CO₂ emissions threshold and a gap in the literature.

3.1 Data

The data that support the investigation strategy in this study come from two databases; (1) Data on oil consumption, coal consumption, natural gas consumption, and carbon dioxide emission obtained from British Petroleum (2022). (2) Data on per capita GDP and Trade openness are obtained from World Development Indicator WDI (World Bank, 2022). The study adopted a panel analysis method to investigate the data for the fifty-seven years 1965–2021. The sample selection criteria are limited to only the G7 countries, the USA, UK, Japan, Italy, Germany, France, and Canada.

3.2 The models

The empirical strategy adopted models via leveraging on the review section and the gap created therein. Accordingly, the study utilized threshold regression to investigate the threshold effect test through the sample-spit approach. The study also used quantile regression to investigate the link between the conditional distribution of the economic growth outcome variables. All the variables are transformed into a natural logarithm.

3.2.1 Threshold Regression (TR)

The model is a nonlinear threshold regression approach by Hansen (1999,2000) but augmented with policy variables closest to a study by Abdulqadir (2020). The model as in Eq. (1), follows:

$\begin{gathered} GDPP{C_{it}}={\mu _i}+{{\beta ^{\prime}}_1}I({q_{it}} \leqslant \gamma )+{\delta _1}I({q_{it}} \leqslant \gamma )+{{\beta ^{\prime}}_2}I({q_{it}}>\gamma ) \hfill \\ ~~~~~~~~~~~~~~~~~+\lambda {X_{it}}+{\theta _t}+{e_{it}} \hfill \\ \end{gathered}$ $(1)$

Where ${q_{it}}$is the threshold variable (i.e., CO₂, NGas, and Trade), that split the sample into regimes, and $\gamma$is the unknown threshold parameter. The indicator function $I(\cdot )$ takes the value of 1 given by a valid argument in the parenthesis, and 0 otherwise. Uniquely, this technique allows the effect ${q_{it}}$ to differ depending on whether GPCPPC is below or above the unknown level of $\gamma$. The vector of regressors ${X_{it}}$ includes lagged values of GDPPC and other endogenous and exogenous variables of which the regime independent variable slope coefficient is assumed. In this study, we allow for differences in the regime intercepts$(\delta )$, and the effect of economic growth on the environment will be ${\beta ^{\prime}_1}({\beta ^{\prime}_2})$for countries in a (high)low level of the threshold variables. The model was re-parameterized as follows:

\(GDPPC=\left\{ {\begin{array}{*{20}{c}} {\psi _{0}^{1}+\psi _{1}^{1}Coa{l_{it}}+\psi _{2}^{1}NGa{s_{it}}+\psi _{3}^{1}Oi{l_{it}}+\psi _{4}^{1}Trad{e_{it}}+{u_{it}}~~~~~~~~~~~~~C{O_{2it}} \leqslant \gamma } \\ {\psi _{0}^{2}+\psi _{1}^{2}Coa{l_{it}}+\psi _{2}^{2}NGa{s_{it}}+\psi _{3}^{2}Oi{l_{it}}+\psi _{4}^{2}Trad{e_{it}}+{u_{it}}~~~~~~~~~~~~C{O_{2it}} \leqslant \gamma } \\ \vdots \\ {\lambda _{0}^{1}+\lambda _{1}^{1}C{O_{2it}}+\lambda _{2}^{1}Coa{l_{it}}+\lambda _{3}^{1}Oi{l_{it}}+\lambda _{4}^{1}Trad{e_{it}}+{e_{it}}~~~~~~~~~~~~~~~~NGa{s_{it}} \leqslant \gamma } \\ {\lambda _{0}^{2}+\lambda _{1}^{2}C{O_{2it}}+\lambda _{2}^{2}Coa{l_{it}}+\lambda _{3}^{2}Oi{l_{it}}+\lambda _{4}^{2}Trad{e_{it}}+{e_{it}}~~~~~~~~~~~~~~~~NGa{s_{it}} \leqslant \gamma } \\ \vdots \\ {\phi _{0}^{1}+\phi _{1}^{1}C{O_{2it}}+\phi _{2}^{1}Coa{l_{it}}+\phi _{3}^{1}Oi{l_{it}}+\phi _{4}^{1}NGa{s_{it}}+{e_{it}}~~~~~~~~~~~~~~~~Trad{e_{it}} \leqslant \gamma } \\ {\phi _{0}^{2}+\phi _{1}^{2}C{O_{2it}}+\phi _{2}^{2}Coa{l_{it}}+\phi _{3}^{2}Oi{l_{it}}+\phi _{4}^{2}NGa{s_{it}}+{e_{it}}~~~~~~~~~~~~~~~~Trad{e_{it}} \leqslant \gamma } \end{array}} \right.\) $(2)$

However, this procedure involves the test for the linearity; thus, the null hypothesis thus ${H_0}:{\beta ^1}={\beta ^2}$as against the threshold model in Eq. (1). The p-value is computed via a fixed bootstrap and grid search technique. This procedure follows closely the asymptotically correct p-values ( Hansen, 2000).

Following an attempt to explore the hypothesized statements, an actionable policy CO₂ emission threshold effect on economic growth, we re-formulate the following Eq. (3), thus.

$\begin{gathered} GDPP{C_{it}}={\psi _i}+{\psi _1}C{O_{2,it}}I(C{O_{2,it}} \leqslant \gamma )+{\delta _1}I(C{O_{2,it}} \leqslant \gamma )+{\psi _2}C{O_{2,it}}I(C{O_{2it}}>\gamma ) \hfill \\ ~~~~~~~~~~~~~~~~~+{\psi _3}Oi{l_{it}}+{\psi _4}Coa{l_{it}}+{\psi _5}NGa{s_{it}}+{\psi _6}Trad{e_{it}}+{\theta _t}+{e_{it}} \hfill \\ \end{gathered}$ $(3)$

Similarly, Eq. (3) above is also re-parameterized to explore a complementary natural gas consumption policy threshold effect on economic growth. Eq. (4) is as follows:

$\begin{gathered} GDPP{C_{it}}={\lambda _i}+{\lambda _1}NGa{s_{2,it}}I(NGa{s_{it}} \leqslant \gamma )+{\delta _1}I(NGa{s_{it}} \leqslant \gamma )+{\lambda _2}NGa{s_{it}}I(NGa{s_{it}}>\gamma ) \hfill \\ ~~~~~~~~~~~~~~~~~+{\lambda _3}C{O_{2,it}}+{\lambda _4}Oi{l_{it}}+{\lambda _5}Coa{l_{it}}+{\lambda _6}Trad{e_{it}}+{\theta _t}+{e_{it}} \hfill \\ \end{gathered}$ $(4)$

Notwithstanding, Eq. (4) above is also re-parameterized to explore a complementary threshold effect of trade policies on economic growth. Eq. (5) is as follows:

$\begin{gathered} GDPP{C_{it}}={\phi _i}+{\phi _1}Trad{e_{2,it}}I(Trad{e_{it}} \leqslant \gamma )+{\delta _1}I(Trad{e_{it}} \leqslant \gamma )+{\phi _2}Trad{e_{it}}I(Trad{e_{it}}>\gamma ) \hfill \\ ~~~~~~~~~~~~~~~~~+{\phi _3}C{O_{2,it}}+{\phi _4}Oi{l_{it}}+{\phi _5}NGa{s_{it}}+{\phi _6}Coa{l_{it}}+{\theta _t}+{e_{it}} \hfill \\ \end{gathered}$ $(5)$

Following the approach by Hansen (2000) and Caner and Hansen (2004), we followed an empirical strategy for determining the 95% confidence interval from critical values of the threshold value given by

$\Gamma =\{ \gamma :LR(\gamma ) \leqslant C(\alpha )\}$ $(6)$

Where $C(\alpha )$is the 95% percentile of the asymptotic distribution of the likelihood ratio statistic $LR(\gamma )$(Hansen, 1999).

3.2.2 Quantile Regression (QR)

Following the contemporary literature, the study also adopted a quantile regression by Koenker and Bassett (1978) approach to the robust analysis of the conditional distribution of the growth outcome variables. The advantage of the model it allows the estimation of the investigation of the conditional distribution of means in a way that differentiates individual effects and how the entire conditional distribution is influenced by the regressors (Machado and Silva, 2019). The model takes the following form.

$Q({\beta _q})=\sum\limits_{{i={y_i} \geqslant x_{i}^{i}\beta }} {q\left| {{y_i} - {{x^{\prime}}_i}{\beta _q}} \right|} +\sum\limits_{{i={y_i}<x_{i}^{i}\beta }} {(1 - q)\left| {{y_i} - {{x^{\prime}}_i}{\beta _q}} \right|}$ $(7)$

Where $0<q<1$the dependent variable ${y_i}$is economic growth and ${x_i}$represents CO₂ emission, oil consumption, natural gas consumption, coal consumption, trade openness, and a constant term. Unlike the Ordinary Least Squares (OLS) which minimize the total residuals squared, quantile regression minimizes the weighted total of absolute deviation. The standard linear specification of the conditional quantile is specified below:

${Q_q}(\left. {{y_i}} \right|{x_i})={x^{\prime}_i}{\beta _q}$ $(8)$

The marginal effect of the coefficient $qth$ is calculated by taking the partial derivate in Eq. (7) with respect to the regressors, thus finding the coefficient ${\beta _{qi}}$(Abdulqadir, 2020, 2022).

The empirical findings of the study are reported in tables 1–4. Table 1 presented the summary descriptive statistics of growth outcome variables and pairwise correlation of all the variables in G7 countries. Table 2 presents the panel unit roots tests conducted. Table 3 provides the results of the dynamic panel threshold regressions. Table 4 presents the quantile regression results.

INSERT TABLE 1 HERE

Table 1 reports the average value of GDP over the period 1956–2021 is $182.5, and the median is $182.5 with the minimum and maximum values of $2 and $363, respectively. The CO₂ emissions averaged 1241.76mts with minimum and maximum values of 204.87mts and 5884.22 mts, respectively. The oil consumption averaged values of 4554.69mts with the minimum and maximum values of 978.99 and 20531.48, respectively. In terms of natural gas consumption, the average values of 4.69bcm with minimum and maximum values being 0.03bcm and 30.63bcm, respectively. The level of coal consumption averaged 4.86mts with minimum and maximum values of 0.19mts and 22.85mts, respectively. The trade share of GDP averaged 187.5% with minimum and maximum values of 2% and 373%, respectively. The lower panel of Table 1 reports the significant pairwise correlations between the growth outcome variables.

INSERT TABLE 2 HERE

Table 2 reports the panel unit roots tests following a heterogenous panel technique developed by IPS (Im, Pesaran, & Shin, 2003), and ADF-Fisher recommended by (Maddala & Wu, 1999), respectively. All the variables are the stationary first difference. This is the preliminary data analysis just been the rule of thumb for empirical investigations.

Table 3 presents the threshold effect testes estimates for actionable CO₂ emission policy along with the complementarity thresholds for oil consumption, natural gas consumption, and trade policies for G7 countries. Columns 1–2, show policy regimes for CO₂ emission mitigation. Columns 3–4 show policy regimes for oil consumption and Columns 5–6 show policy regimes for natural gas consumption while columns 7–8 show the policy regimes corresponding to the trade policies in G7 countries. First, findings established that there is an overwhelming and statistically significant threshold effect of 6.75 CO₂ emissions in G7 countries. Second, findings also established that there is a statistically significant threshold effect of 3.05 natural gas consumption in G7 countries. Third, the finding revealed corresponding to the statistically significant threshold effect of 5.60% trade share of GDP in the nexuses between CO₂ emissions and growth in G7 countries. These findings are prior expectations while they responded to the primary objectives of the study.

INSERT TABLE 3 HERE

Figure 2 (avoidable threshold diagram) shows the graphical evidence suggesting that the G7 countries' data indicates an asymmetric trend in the nexuses between CO₂ emission and growth which corresponds to the initial results and best fit for the dynamic panel threshold regression model. The marginal effect of growth/CO₂ emission exhibits a nonlinear trend.

Figure 3 (complementary threshold diagram) shows the threshold diagram for natural gas consumption in G7 countries. The diagram also suggests that our data indicates an asymmetric trend in the nexuses between natural gas consumption and economic growth in G7 countries which corresponds to the initial results and best fit for the dynamic panel threshold regression model. The marginal effect of growth/natural gas consumption exhibits a nonlinear trend.

Figure 3 (complementary threshold diagram) shows the threshold diagram for the trade openness in G7 countries. The diagram also suggests that our data indicates an asymmetric trend in the nexuses between trade openness and economic growth in G7 countries which corresponds to the initial results and best fit for the dynamic panel threshold regression model. The marginal effect of growth/trade openness exhibits a nonlinear trend.

Table 4 presents the results of the panel quantile coefficients estimation for G7 countries. The table reports the links between economic growth, oil consumption, coal consumption, natural gas consumption, and trade openness. Columns 1–11, show a comparison between the static model (OLS and fixed effects) and quantile coefficients corresponding to the G7 countries. It is worthy of note that from columns 3–11 in Table 4, the levels of GDP increase in G7 countries. Conversely, it follows that in the 10th (70th), the level of per capita GDP is at its lowest(highest). These findings also revealed that the CO₂ emission unconditional(conditional) decreases (increases) economic growth in the 10th quantile of the conditional distribution of per capita GDP[1].

INSERT TABLE 4 HERE

Secondly, the levels of per capita GDP increase. Equally, it follows that in the 40th (90th), the level of oil consumption is highest(lowest). These findings revealed that oil consumption conditional(unconditional) increases (decreases) growth in the 90th quantile of the conditional distribution of the oil consumption in G7 countries[2]. Third, the levels of natural gas consumption GDP increase. It follows that in the 20th (90th), the level of natural gas consumption is highest(lowest). These findings revealed that natural gas consumption conditional(unconditional) increases (decreases) economic growth in the 90th quantile of the conditional distribution of the natural gas consumption in G7 countries[3]. Fourth, the level of coal consumption is highest(lowest). It follows that the coal consumption unconditional(conditional) increase(decrease) economic growth at the 10th (90th) in G7 countries[4]. Fifth, the level of trade openness is highest(lowest). This finding revealed that trade openness conditional(unconditional) decrease(increase) economic growth at the 30th (90th) in G7 countries[5].

Figure 5 shows the critical regions of the confidence interval of the conditional(unconditional) increase(decrease) of the static models against the quantile regression differences along the distribution of the economic growth outcome variable.

The current findings are novel to the extant literature as it provides the policymakers in these large producers of carbon dioxide emission with an avoidable threshold for CO₂ emissions along with the complementary thresholds on natural gas consumption and trade for complementary policies. These findings are statistically worthwhile and articulated toward CO₂ emission mitigations and provide a blueprint for policy formulation and implementation in these G7 countries being the major emissions producers.

[1] It is worthy to note that the coefficients CO₂ emissions are significantly different from the OLS coefficient at 70^th,80^th, and 90^th quantiles for the G7 countries in the nexuses between economic growth and CO₂ emission. At 80^th, and 90^th quantile the CO₂ emissions estimates are significantly different from the static models’ coefficients.

[2] Note: that the coefficients oil consumption are significantly different from the static models’ coefficients at 70^th,80^th, and 90^th quantiles for the G7 countries in the nexuses between economic growth and oil consumption. At 90^th quantile the oil consumption estimate is significantly different from the static models’ coefficients.

[3] It is worthy to note that the coefficients natural gas consumption are significantly different from the static models’ coefficients at 70^th,80^th, and 90^th quantiles for the G7 countries in the nexuses between economic growth and natural gas consumption.

[4] Note that the coefficients coal consumption are significantly different from the static models’ coefficients at 30^th,40^th, 50^th 60^th and 90^th quantiles for the G7 countries in the nexuses between economic growth and coal consumption.

[5] Note that the coefficient trade openness is significantly different from the static models’ coefficients only at 80^th quantile for the G7 countries in the nexuses between economic growth and coal consumption.

The study has explored the nexuses between CO₂ emissions, oil consumption, natural gas consumption, coal consumption, trade openness, and economic growth in the sample of G7 countries over the period 1965–2021. The study utilized the dynamic threshold regression as an empirical strategy via 1000 bootstrap replications and 400 grid searches to obtain an asymptotic confidence interval. The study revealed the following findings. First, avoidable thresholds of 6.75, on CO₂ emission metric tons per capita policy G7 countries. Second, the complementary threshold of 3.05 on natural gas consumption. Lastly, another complementary threshold of 5.60% on trade openness in G7 countries, respectively.

The study also investigated the conditional distribution the economic growth in G7 countries using quantile regression to examine the links between CO₂ emissions, oil consumption, natural gas consumption, coal consumption, trade openness, and economic growth in G7 countries. The following Findings established, that the coefficients of CO₂ emission were significantly different from the static models' coefficients at the 80th, and 90th quantiles for G7 countries. Also, the quantile coefficients of the oil consumption are significantly different from static models at the 90th quantile, while natural gas consumption is significantly different from static models at the 70th, 80th, and 90th quantiles, respectively. On the other hand, the quantile coefficients of coal consumption are significantly different from static models at the 30th, 40th, 50th, 60th, and 90th quantiles, while trade openness is significantly different from the static models at only the 80th quantile, respectively.

The policy implications of the findings are apparent premise in the light of the extant literature and novelty in providing the policymakers in these G7 countries with avoidable CO₂ emission thresholds and complementary thresholds on natural gas consumption and trade openness for complementary policies. Future studies can be oriented toward research on the individual country analysis of the nexus between growth and environmental quality.

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Funding: The research received no external funding

Authors’ Contribution: Abdulqadir I.A. does the conceptualization, writing, methodology, analysis, review, editing, and supervision. The first author makes all contributions to the research.

Availability of Data: Data is available on request from the corresponding author.

Code Availability: Not applicable

Conflict of Interest: The authors declare no conflicting interests.

Ethics approval/declarations: Not Applicable

Consent to participate: Not Applicable

Consent for publication: Not Applicable

Table 1. Descriptive Statistics and Pairwise correlation

Variable	Mean	Median	St.Dev	CV	Min	Max
GDPPC	165.67	164	112.74	0.681	1	363
CO2 emission	1241.76	573.5	1552	1.25	204.87	5884.22
Oil consumption	4554.69	2049.32	5413.99	1.19	978.99	20531.48
Natural Gas	4.70	2.20	6.98	1.49	.03	30.63
Coal consumption	4.89	2.75	5.89	1.21	.194	22.85
Trade	174.88	174	113.92	0.65	1	373
	(1)	(2)	(3)	(4)	(5)	(6)
(1) GDPPC	1.000

(2) CO2 emission	0.381*	1.000
	0.000
(3) Coal consumption	0.368*	0.993*	1.000
	0.000	0.000
(4) Oil consumption	0.467*	0.962*	0.959*	1.000
	0.000	0.000	0.000
(5) Natural Gas	0.331*	0.972*	0.944*	0.891*	1.000
	0.000	0.000	0.000	0.000
(6) Trade	0.321*	-0.508*	-0.534*	-0.368*	-0.546*	1.000
	0.000	0.000	0.000	0.000	0.000

* Shows significance at the 0.05 level. St.Dev: standard deviation

Table 2. Panel Unit Roots Test

Series	(1)		(2)		(3)		(4)		(5)			(6)
	GDPPC		CO₂ emission		Coal cons.		Oil Cons.			Natural Gas cons.		Trade
	No Trend	Trend	No Trend	Trend	No Trend	Trend	No Trend	Trend	No Trend		Trend	No Trend	Trend
Level
IPS	-7.03***	-3.66***	-4.66***	-2.62**	-6.17***	-3.97***	-4.24***	-2.72**	-5.43***		-5.47***	-3.68***	-2.19**
	(0.00)	(0.00)	(0.09)	(0.02)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)		(0.00)	(0.31)	(0.04)
ADF-Fisher	-9.13***	-0.88	-3.12***	-37.83***	-2.54***	-5.65***	-2.82*	-44.76***	-3.19***		-10.63***	-2.38***	-21.41***
	(0.00)	(0.18)	(0.00)	(0.00)	(0.00)	(0.00)	(0.02)	(0.00)	(0.00)		(0.00)	(0.22)	(0.63)
First Difference
IPS	-4.42***		-5.57***		-5.86***		-6.24***		-5.83***			-4.85***
	(0.00)		(0.00)		(0.00)		(0.00)		(0.00)			(0.00)
ADF-Fisher	-5.10***		-5.84***		-5.50***		-12.31***		10.00***			-1.72**
	(0.00)		(0.00)		(0.00)		(0.00)		(0.00)			(0.04)

Note: Figure in parentheses are probability values, *** p<0.01, ** p<0.05, * p<0.1 denote rejection of non-stationary at the 1%, 5% and 10% levels of significance. The maximum number of lags is set to three. SBC is used to select the lag length.

Table 3. Threshold regression results for the G7 countries over the period (1965-2021)

Dependent variable: GDPPC		(1)	(2)	(3)	(4)	(5)		(6)
*Threshold variable*	*CO₂ emission*			*Natural Gas Consumption*			*Trade*
	Regime 1		Regime 2	Regime 1	Regime 2	Regime 1		Regime 2
CO₂ emission	---		---	2.669***	3.147***	4.075***		3.974***
	---		---	(0.829)	(0.803)	(0.789)		(0.784)
Oil Consumption	-2.379***		-2.458***	0.666	0.722	-0.639		-0.412
	(0.521)		(0.506)	(0.608)	(0.585)	(0.567)		(0.569)
Natural Gas Consumption	0.642***		0.669***	---	---	0.643***		0.616***
	(0.0749)		(0.0729)	---	---	(0.0794)		(0.0794)
Trade	0.163***		0.116**	0.0631	0.111**	---		---
	(0.0470)		(0.0465)	(0.0499)	(0.0489)	---		---
Coal Consumption	-1.637***		-1.718***	-1.148***	-1.227***	-0.850***		-0.815***
	(0.183)		(0.179)	(0.201)	(0.194)	(0.206)		(0.205)
H0: no threshold	3.417***		4.473***	0.452***	-0.279*	0.0188		0.0131
	(0.751)		(0.759)	(0.0818)	(0.153)	(0.0500)		(0.0496)
H0:at least one thresh.	3.848***		4.864***	0.790***	0.171*	0.290***		0.120*
	(0.744)		(0.750)	(0.0807)	(0.0935)	(0.0522)		(0.0627)
H0:at most two thresh.	---		4.717***	---	0.531***	---		0.298***
	---		(0.743)	---	(0.0907)	---		(0.0519)
Threshold estimate	6.20		6.75	4.36	3.05	5.65		5.60
F-statistics	19.80		55.01***	20.76	26.96***	11.70		22.73***
Confidence interval 1%	63.27		39.07	32.29	24.22	37.65		22.29
Confidence interval 5%	51.57		32.52	25.87	18.67	30.39		17.60
Confidence interval 10%	45.21		29.09	21.97	15.94	25.11		15.15
Constant	-10.17***		-16.03***	-18.29***	-20.68***	-21.67***		-22.06***
	(2.797)		(2.958)	(2.820)	(2.750)	(2.746)		(2.729)
Observations	399		399	399	399	399		399
R-squared	0.705		0.722	0.664	0.689	0.668		0.674
Number of idcode	7		7	7	7	7		7

Notes: Following Hansen (1999,2000), each regime contains at least 5% of all observations. Standard errors in parentheses; ***, ** and * designating statistical significance at 1%, 5% and 10%, respectively. GDPPC (per capita constant 2015 USD), Trade (% of GDP). 1,000 bootstraps were used to obtain the p-values to test for the number of thresholds.

Table 4. Quantile regression results for the G7 countries over the period (1965-2021)

	(1)		(2)	(3)	(4)	(5)	(6)		(7)	(8)	(9)	(10)	(11)
Dependent variable: GDPPC		Static OLS	Static fixed effect	Q 0.10	Q 0.20	Q 0.30	Q 0.40		Q MID	Q 0.60	Q 0.70	Q 0.80	Q 0.90

CO2 emission	1.576**		0.955	-5.698	0.394	0.0968	0.0160		0.0468	0.376	1.589***	1.355***⁺	0.552**⁺
	(0.793)		(0.764)	(4.922)	(2.988)	(0.401)	(0.315)		(0.294)	(0.442)	(0.468)	(0.315)	(0.237)
Oil consumption	-0.669		-0.437	4.072	-0.439	-0.0269	0.0338		-0.154	-0.345	-0.786***	-0.677***	-0.434***⁺
	(0.484)		(0.478)	(3.005)	(1.824)	(0.245)	(0.192)		(0.179)	(0.270)	(0.286)	(0.192)	(0.145)
Natural Gas	0.371***		-0.188	0.0292	0.914*	0.813***	0.783***		0.809***	0.715***	0.309***⁺	0.211***⁺	0.405***⁺
	(0.138)		(0.130)	(0.853)	(0.518)	(0.0696)	(0.0545)		(0.0510)	(0.0767)	(0.0811)	(0.0546)	(0.0411)
Coal consumption	-0.439		0.0362	3.414*	-0.281	-0.298**⁺	-0.260**⁺		-0.208*⁺	-0.273*⁺	-0.639***	-0.501***	-0.230***⁺
	(0.292)		(0.284)	(1.809)	(1.098)	(0.148)	(0.116)		(0.108)	(0.163)	(0.172)	(0.116)	(0.0871)
Trade	0.285***		-0.151*	0.603	0.354	0.374***	0.355***		0.306***	0.298***	0.295***	0.209***⁺	0.0906***
	(0.0668)		(0.0858)	(0.414)	(0.251)	(0.0338)	(0.0265)		(0.0247)	(0.0372)	(0.0394)	(0.0265)	(0.0200)
Constant	-1.929		2.432	1.778	3.120	1.990	2.246**		3.824***	3.419**	-0.293	0.955	4.657***
	(2.479)		(2.386)	(15.38)	(9.338)	(1.254)	(0.983)		(0.919)	(1.382)	(1.463)	(0.984)	(0.741)
Heteroskedasticity								31.16(0.000) ***
Observations	399		399	399	399	399	399		399	399	399	399	399
R-squared			0.167
Number of years	57		57

Notes: Standard errors in parentheses; ***, ** and * designating statistical significance at 1%, 5% and 10%, respectively. GDPPC (per capita constant 2015 USD), Trade (% of GDP).⁺Indicates a significantly different quantile regression coefficient from OLS and FE coefficients, when OLS and FE coefficients are outside of the quantile coefficient confidence interval. MID (median quantile).

No competing interests reported.

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Mitigation policy trust on fossil energy consumption in G7 countries: insight from avoidable policy thresholds

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1. Introduction

2. Stylized Facts, Theoretical Intuition, And Literature Review

2.1 Stylized facts

2.2 Theoretical review

2.3 Brief literature on CO₂ emission, fossil energy consumption, and economic growth

3. Data And Methods

3.1 Data

3.2 The models

3.2.1 Threshold Regression (TR)

3.2.2 Quantile Regression (QR)

4. Empirical Results And Discussions

5. Conclusion

References

Declarations

Tables

Additional Declarations

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Version 1

Mitigation policy trust on fossil energy consumption in G7 countries: insight from avoidable policy thresholds

Status:

Version 1

Abstract

Figures

1. Introduction

2. Stylized Facts, Theoretical Intuition, And Literature Review

2.1 Stylized facts

2.2 Theoretical review

2.3 Brief literature on CO2 emission, fossil energy consumption, and economic growth

3. Data And Methods

3.1 Data

3.2 The models

3.2.1 Threshold Regression (TR)

3.2.2 Quantile Regression (QR)

4. Empirical Results And Discussions

5. Conclusion

References

Declarations

Tables

Additional Declarations

Status:

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2.3 Brief literature on CO₂ emission, fossil energy consumption, and economic growth