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.
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Some or all data, models, or code generated or used during the study are available from the corresponding author by request.
Abbreviations
- DA:
-
decomposition analysis
- CI:
-
carbon emission intensity
- P:
-
population
- PGDP:
-
economic development
- TP:
-
technological progress
- EI:
-
energy intensity
- OPEN:
-
economic opening rate
- UR:
-
urbanization rate
- IS:
-
industrial structure
- GI:
-
government intervention
- FD:
-
financial development
- ESDA:
-
exploratory spatial data analysis
- SDM:
-
Spatial Durbin Model
- SAR:
-
Spatial Autoregressive Model
- SEM:
-
Spatial Error Model
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We express sincere gratitude to the potential reviewers for their help to improve the quality of the manuscript.
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This research was supported by the innovation and entrepreneurship training programs for Chinese college students (201910488002X) and graduate students (JCX201958).
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Yingdong Wang is responsible for the original draft editing and reviewing; Yueming Zheng is responsible for the project administration and reviewing.
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Wang, Y., Zheng, Y. Spatial effects of carbon emission intensity and regional development in China. Environ Sci Pollut Res 28, 14131–14143 (2021). https://doi.org/10.1007/s11356-020-11557-7
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DOI: https://doi.org/10.1007/s11356-020-11557-7