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Driving factor analysis and dynamic forecast of industrial carbon emissions in resource-dependent cities: a case
Jing Li1, Zhuoya Ma1, Haowei Sun1
1School of Economics and Management, Beijing Forestry University, Beijing, 100083, China.
Abstract:
Urban carbon emissions are one of the most important areas contributing to the growth of carbon emissions, and resource-dependent cities with natural resource extraction and processing as their leading industries tend to have higher carbon emissions. Ordos is the city with the highest coal production in China, and its economic development is dominated by coal, oil and gas, and other resource extraction and processing industries, with industrial activities making a large contribution to carbon emissions. At the same time, Ordos has undergone rapid industrialization in recent years, but still faces the problem of environmental pollution, epitomizing a typical resource-dependent city in China. Therefore, this paper takes Ordos as an example and uses the Generalized Divisa Index Method (GDIM) to study the drivers of industrial carbon emissions in Ordos from 2005-2020, a typical resource-dependent city in China, and further analyzes are conducted in relation to the three phases of development. Based on the key drivers, the Monte Carlo method is used to forecast industrial carbon emissions from 2021 to 2030. The results show that the most important factors driving the growth of industrial carbon emissions are the scale of industrial output and industrial energy consumption, while the intensity of industrial energy investment is the most important factor mitigating industrial carbon emissions, and that energy efficiency and carbon intensity of energy consumption can also mitigate carbon emissions after economic transformation. At the same time, investment is the factor with the greatest potential for optimization on the path to emissions reduction.
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