Shaoxiong Wu1, Zheng Wei2
1School of Mathematics and Statistics, Xiamen Institute of Technology, Xiamen, Fujian, 361024, China
2College of Sciences, Northeastern University, Shenyang, Liaoning, 110004, China
Based on the data of China's provincial carbon emissions and provincial GDP from 2011 to 2019, the carbon emission intensity (t/w Chinese yuan) is calculated, a descriptive analysis of China's provincial carbon emission intensity is conducted to demonstrate its spatial and temporal distribution, and the spatial autocorrelation analysis and geographic detector methods are used to quantitatively analyze the spatial and temporal variation characteristics of China's provincial carbon emission intensity from 2011 to 2019. The spatial autocorrelation analysis and the geodetector method are used to quantify the spatial and temporal characteristics of China's provincial carbon emissions intensity from 2011 to 2019. Finally, the top nine factors with the greatest explanatory power selected by the geographic detector are used as input variables, and the GRNN neural network is used to forecast China's carbon emission intensity in 2020. The study shows that (1) China's provincial carbon emission intensity collated in a decreasing trend from 2011 to 2019, indicating the significant effect of environmental protection policy implementation in these years (2) the spatial agglomeration of China's provincial carbon emission intensity from 2011 to 2019 is obvious, with an expanding trend in both the H-H region and the L-L region, with H-H spreading from Northwest China to Northeast China and the L-L region expanding to surrounding cities, (3) There is a spatially heterogeneous pattern of inter-provincial carbon emission intensity in China from 2011 to 2019. (4) From 2011 to 2019, the factor with the greatest explanatory power for China's provincial carbon emission intensity is average temperature, followed by industrial output, topographic relief, and science and technology expenditure. (4) For the projection of China's inter-provincial carbon emission intensity in 2020, the trend of carbon emission intensity is greatly related to geographical differences, which indicates that inter-regional exchanges and cooperation have a facilitating effect on carbon reduction policies.
carbon emissions intensity, spatial and temporal distribution, spatial autocorrelation, geodetectors, GRNN
Shaoxiong Wu, Zheng Wei. Research on the Spatial and Temporal Evolution of Carbon Emission Intensity in China and its Influencing Factors from 2011 to 2020. Academic Journal of Environment & Earth Science (2023) Vol. 5 Issue 6: 35-43. https://doi.org/10.25236/AJEE.2023.050607.
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