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Fig. 3. Empirical analysis procedure.

Figure 3 Empirical analysis procedure.

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Fig. 2. Progress of disaggregated level clean EG Note: The unit is Gigawatt-hours/day. The total EG is shown on the right axis. Source: Carbonmonitor [17].
Fig. 2. Progress of disaggregated level clean EG Note: The unit is Gigawatt-hours/day. The total EG is shown on the right axis. Source: Carbonmonitor [17].
Variables. Notes: * and ** show the dependent and independent variables, respectively. Table 1
Variables. Notes: * and ** show the dependent and independent variables, respectively. Table 1
Fundamental statistics. Notes: Std. Dev. and JB imply the standard deviation and Jarque-Bera. Table 2
Fundamental statistics. Notes: Std. Dev. and JB imply the standard deviation and Jarque-Bera. Table 2
Correlation matrix. Notes: Values denote correlation coefficients.
Correlation matrix. Notes: Values denote correlation coefficients.
BDS nonlinearity test outcomes. Notes: Values show the p-values. DIM and NL denote dimension and nonlinear, respectively.
BDS nonlinearity test outcomes. Notes: Values show the p-values. DIM and NL denote dimension and nonlinear, respectively.
Fig. 4. GB effect on sectoral CO2 emissions. By benefitting from quantile-based approaches, the study answers the research questions, in order, as follows: (i) GBs have a decreasing effect on some sectors’ COz emissions; (ii) GBs stimulate generally clean EG at higher quantiles; (iii) the effect of GBs on sectoral CO2 emissions and clean EG varies across quantiles by following a nonlinear pattern; and (iv) the outcomes are robust based on alternative economics approach. Thus, the study partially verifies the first hypothesis, almost completely verifies the second hypothesis, and completely verifies the third hypothesis. which reveals the robustness. Thus, various policy endeavors can be discussed by relying on the outcomes collected.
Fig. 4. GB effect on sectoral CO2 emissions. By benefitting from quantile-based approaches, the study answers the research questions, in order, as follows: (i) GBs have a decreasing effect on some sectors’ COz emissions; (ii) GBs stimulate generally clean EG at higher quantiles; (iii) the effect of GBs on sectoral CO2 emissions and clean EG varies across quantiles by following a nonlinear pattern; and (iv) the outcomes are robust based on alternative economics approach. Thus, the study partially verifies the first hypothesis, almost completely verifies the second hypothesis, and completely verifies the third hypothesis. which reveals the robustness. Thus, various policy endeavors can be discussed by relying on the outcomes collected.
Summary of QQR and QR comparison. Table 5 emissions and clean EG across quantiles.
Summary of QQR and QR comparison. Table 5 emissions and clean EG across quantiles.
Fig. 5. GB effect on clean EG.
Fig. 5. GB effect on clean EG.
Annex-1. QQR and QR comparison for GB effect on sectoral CO, emissions
Annex-1. QQR and QR comparison for GB effect on sectoral CO, emissions
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