Chongmei Wang1, Chonghong Wang2
1School of Management, Shandong Technology and Business University, Yantai, 264005, China
2Yantai Institute of Technology, Yantai, 264005, China
We studied the difference in carbon emission intensity by the Oaxaca blinder decomposition method with the panel data of 17 prefecture level cities in Shandong Province from 2007 to 2016. Per capita GDP, industrial structure, urbanization level, and energy consumption intensity are important factors affecting carbon emission intensity. The increase in urbanization rate and energy consumption intensity leads to an increase in carbon emission intensity. The reduction of the proportion of secondary industry in low-level cities is conducive to the reduction of carbon emission intensity. At the same time, we selected factors from the economic, energy, and social indicators in the low-level city rating system to reveal the contribution of different factors to the difference in carbon emission intensity. This is helpful to narrow the gap among the cities, accelerate the transformation from low-carbon cities to non-carbon cities, and realize the economic development of low-level cities.
low-carbon pilot cities, carbon emission intensity, the big data
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