Spatial effects of carbon emission intensity and regional development in China
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RESEARCH ARTICLE
Spatial effects of carbon emission intensity and regional development in China Yingdong Wang 1 & Yueming Zheng 1 Received: 7 August 2020 / Accepted: 4 November 2020 # Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China’s provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China’s 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China’s provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China’s carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity. Keywords Carbon emission intensity . Energy intensity . Economic development . Environmental Kuznets curve . Exploratory spatial data analysis . Spatial Durbin Model
Introduction At the 2015 Climate Change Conference in Paris, China announced that its carbon emissions would peak around 2030. Since then, the situation of China’s carbon emissions and the methods of reducing emissions have attracted extensive attention from all sectors of society especially some distinguished scholars (Haszeldine et al. 2018; Matthews et al. 2018). The carbon emission intensity index reflects the carbon emission efficiency in the process of economic development, that is the carbon dioxide emissions caused by the growth of GDP per unit. If the carbon emission intensity is low, the country has achieved a low-carbon development model. At present, there Responsible Editor: Philippe Garrigues * Yueming Zheng [email protected] 1
College of Literature Law & Economics, Wuhan University of Science & Technology, Wuhan 430065, Hubei, China
are sufficient research on carbon emission, but the carbon emission intensity is more objective and reasonable than the carbon emission (Shao et al. 2018; Zhou et al. 2018). Therefore,
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