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Abstract: As world's top two carbon emitters, driver analysis of China and the USA helped the governments to develop policies to cut or slow down carbon emission. Many studies identified the factors affecting carbon emission in China and the USA (emitting more than 40% of the global CO 2 emission), however, few studies considered stratified heterogeneity or the interactions of factors. Here, we adopted the modified Geographical Detector tool to investigate the main drivers of carbon emission from the perspective of stratified heterogeneity. The results of this analysis showed that human economic activities in China were the dominant effect of carbon emission changes, while energy intensity contributed toward controlling the carbon emission in China. Furthermore, population growth was the most significant driving force followed by energy intensity toward controlling the carbon emission of the USA. All these factors are mutually enhancing in changing carbon emissions, while oil share with energy intensity and coal share were more significantly enhanced in China's carbon emission than other interactions. The factors of human activities and energy mix posed a more powerful effect when they mutually enhanced each other to change carbon emission compared to other enhancing interactions. This work represents a pilot scheme for a carbon dioxide emission analysis from the categorical stratified heterogeneity based on statistical methods. [ABSTRACT FROM AUTHOR]

China is one of the highest growth economies in the world, and it has experienced a significant rise in energy consumption and greenhouse gas emissions in recent decades. Its economic growth heavily depends on fossil fuels of coal and oil which led to huge amount of greenhouse gas emissions. This study logically analyzes the nexus of CO 2 emissions, economic growth and energy consumption (by decoupling greenhouse gas emission of CO 2 and economic growth) to provide more conclusive evidence on the phenomenon of the environmental Kuznets curve (EKC) over the period 1970–2015. To achieve the objective of consistency in the estimation results, the study applies different estimation techniques such as ARDL (Auto regressive Distributed Lag) model, FMOLS (Fully Modified Ordinary Least Squares, DOLS (Dynamic Ordinary Least Squares), and impulse response and variance decomposition. In addition, ‘the business as usual' inverted U-shaped relationship between GDP per capita and an economy-related CO 2 emission is hypothesized. The result supports the Environmental Kuznets Curve (EKC) hypothesis from different techniques, the turning point hovers around $744665. The estimation result indicates that China EKC turning point shows some inconsistencies when compared to other turning points obtained from different studies. The inconsistency of the EKC turning point is attributed to the sensitivity of the result to different data source, variables selection, different pollutants and scope of the data. Although the EKC turning point of this present research differs from other China EKC turning points, it gives a clear policy roadmap on the pursuance of long run economic growth in favour of environmental quality. Besides, significance of the turning point lies with the need to explore other structural policies such as articulated demographic and energy policies rather than passively waiting for the arrival of the inflexion point. One interesting findings of the analysis is the consistency of the estimation results from the different estimation techniques. [ABSTRACT FROM AUTHOR]