Abstract:

Under the overall environment that the whole country is advocating energy conservation and emission reduction and seeking low-carbon economic development, exploring the correlation between various impact factors and carbon emissions at different income levels is conducive to the formulation of regional heterogeneous carbon emission reduction policies. Based on the data from 2002 to 2016, China's 30 provinces (except Tibet, HongKong, Macao and Taiwan) were divided into four income levels, and a panel vector autoregressive (PVAR) model was established. Moreover, Granger causality test, impulse response and variance decomposition were used to explore the correlation between urbanization, industrial structure, energy consumption, economic growth and carbon emissions. The results show that there is heterogeneity in the relationship between the impact factors and carbon emissions in the provinces at different income levels. The urbanization of provinces at higher income level has produced significant emission reduction effect, while the less developed areas are still in the stage which urbanization promotes carbon emissions. Energy consumption at all four income levels will affect carbon emissions in the long run. However, less developed areas have a more urgent need for getting rid of energy dependence for economic development, and should improve energy efficiency to reduce carbon emissions. The impact of industrial structure on carbon emissions in non-high-income provinces is significantly higher than that in high-income provinces. In addition, the empirical results show that China is still in the stage of synchronous growth of carbon emission and economic development, and emission reduction policies will generate negative feedback on economic growth. Therefore, the selection of existing emission reduction path needs highly prudent design and implementation.

Key words: Urbanization, Industrial structure, Energy consumption, Economic growth, Carbon emissions, Panel vector autoregression (PVAR)