科研之友
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摘要
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 CO2 emissions, economic growth and energy consumption (by decoupling greenhouse gas emission of CO2 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 CO2 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.
- 出版日期2017-11-10
- 单位华中科技大学
