| Since the reform and opening up,the continuous advancement of industrialization,accelerated urbanization and large-scale utilization of fossil energy have led to the rapid increase of greenhouse gas（CO2-eq）emissions in China.The resulting resource and environmental problems will not only affect the sustainable development of the country,but also aggravate global warming.In order to meet the challenge of global climate change,how to reduce carbon emissions has become the focus of global environment and development.To this end,China has made a commitment to the "3060" dual carbon target.In order to achieve this target,how to effectively reduce China’s carbon emission intensity is an urgent problem to be solved.Based on this,a systematic study of the spatiotemporal evolution of carbon emission intensity at different spatial scales and the mechanism of driving factors is helpful to formulate more effective and reasonable emission reduction policies and measures,which is of great significance to achieve emission reduction targets and sustainable economic development.Based on remote sensing nighttime light inversion data,this paper applied Theil index,spatial autocorrelation analysis,LISA time paths and spatiotemporal transition.Firstly,the spatiotemporal dynamic evolution characteristics of carbon emission intensity at provincial and municipal levels in China were revealed from four dimensions: spatiotemporal evolution,spatial agglomeration,spatiotemporal dynamics and spatiotemporal comparison.Then,quantile regression and spatiotemporal transition nested model were used to analyze the influencing factors of carbon emission intensity of provinces and cities in China.The main research conclusions were as follows:（1）From 2000 to 2017,the total carbon emission of China’s provinces showed an evolution characteristic of "rising first and then stabilizing",and the carbon emission intensity showed a trend of decreasing year by year,showing strong regional characteristics.The difference of carbon emission intensity among provinces was enlarged,and the contribution of intra-region difference to the expansion of overall difference was greater than inter-region difference.Moran’s I index indicates that provincial carbon emission intensity had significant spatial agglomeration,forming two groups of "high" and "low".HH type provinces were stably distributed in Inner Mongolia,Gansu and Ningxia,while LL type provinces were concentrated in Jiangsu,Zhejiang,Anhui,Fujian,Jiangxi and Guangdong in southeastern coastal provinces.（2）The evolution characteristics of total carbon emissions at the municipal level were similar to those at the provincial level,but the decline rate of carbon emission intensity was higher than that at the provincial level.The spatial distribution of carbon emission intensity was high in the north and low in the south,and the distribution difference tended to decrease.The high-intensity cities in Shaanxi,Gansu,Ningxia,Shanxi,Heilongjiang,Jilin and Liao regions had been transformed into some cities in Ningxia and Inner Mongolia,and the number of low-intensity cities had increased rapidly and concentrated in the south.The degree of spatial agglomeration of urban carbon emission intensity increased first and then decreased.HH was mainly located in Ningxia,Northern Shanxi and Shaanxi and Eastern Gansu,while LL was mainly located in southeastern coastal cities of Jiangsu,Zhejiang,Fujian and Guangdong and some cities in the Yangtze River Basin.（3）The comparison of spatiotemporal evolution showed that the inequality of carbon emission intensity was more sensitive in geographical scale and more significant in spatial unit.The comparative analysis of spatial agglomeration showed that the smaller the spatial scale,the easier it is to reveal the spatial heterogeneity and spatial correlation of carbon emission intensity.The spatiotemporal dynamic contrast analysis showed that the spatial structure and spatial dependence of carbon emission intensity in provinces and prefecture-level cities had strong stability,and the spatial pattern evolution had strong integration.Type 0 was the most common among the four spatial form types of local correlation.The spatial agglomeration had strong path dependence and locking characteristics,and the locking characteristics at provincial scale showed an increasing trend,while the locking characteristics among prefecture-level cities showed a decreasing trend.（4）Provincial quantile regression and spatiotemporal transition nested showed that economic development level played a leading role in the low quantile restriction,and the industrial structure,investment intensity and energy intensity played a leading role in the low quantile drive.While the economic development level and population size had a significant effect on the constraint of high score,energy intensity,industrial structure and investment intensity had a promoting effect on the driving type of high score.（5）The quantile regression and spatiotemporal transition nesting showed that the economic development level and population size had significant effects on the low quantile constraint,and industrial structure,foreign capital intensity and energy intensity played a dominant role in the low quantile constraint.The level of economic development,population size and intensity of foreign investment had a significant effect on the constraint of high score,while the industrial structure,energy intensity and investment intensity had a promoting effect on the driving type of high score.（6）The comparative analysis of the influencing factors of carbon emission intensity at different scales showed that the level of economic development had a significant restricting effect on carbon emission intensity,and the industrial structure and energy intensity had a significant driving effect.There were differences in the significance and magnitude of the effects of other factors at different spatial scales,indicating that the same method and index had different results at different scales,that is,different influencing factors had different sensitivity to the scale of carbon emission intensity. |
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