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Estimating the carbon dioxide emission levels of G7 countries: A count data approach

Siele Jean TuoSchool of Accounting, Dongbei University of Finance and Economics, Dalian, ChinaChang LiSchool of Accounting, Dongbei University of Finance and Economics, Dalian, ChinaEttien Fulgence BrouSchool of Finance and Economics, Jiangsu University, Zhenjiang, ChinaDiby François KassiBusiness School, Henan University, Kaifeng, ChinaYobouet Thierry GnangoinSchool of Management Science and Economics, Felix Houphouet Boigny University, Abidjan, Côte d’Ivoire
2022en
ABI

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The G7 countries account for about 40% of the global gross domestic product and emit 25% of energy-related emissions due to their energy use, releasing so much carbon dioxide (CO 2 ) into the atmosphere. This study uses competing count data models to model carbon emissions between 2002 and 2021. All the hypotheses are significant and with varied effects on the energy use of the G7 economies, backing the increasing levels of emissions. The findings show that emissions from manufacturing and construction have mixed effects on the energy use of the G7 countries, increasing by 2.37% and reducing by 6.65% and 8.39% in some countries. The study proves that the transport sector is the hard-to-abate sector as the effects of the transport covariate impact minimally between 6.49% and 4.13% of the G7. This means that the transport sector is the high-hanging fruit for deep decarbonization due to the low level of technology readiness. Also, emissions from solid fuels increase between 6.49% and 4.13% in the G7 countries, implying that coal consumption has peaked due to the current energy crisis. The cross-sectional dependence (CD) analysis proves a strong significant dependence among the study countries depicting the global nature of pollution. The fitness of the model was performed using the Akaike and Bayesian information criteria to determine the appropriate method to present robust and consistent results. The panel's negative Poisson regression model obtains the lowest Akaike information criteria and Bayesian information criteria values and is therefore the most appropriate model for the analysis. This will serve as a rallying point for achieving net zero emission (NZE) targets by mid-century and for scaling technologies to achieve that goal.

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