A Multiscale Analysis On Spatiotemporal Pattern Of Carbon Emissions And Its Impact Factors In China Using DMSP-OLS Data

According to the statistics of the International Energy Agency,China has surpassed the United States and become the largest carbon emitter of the world in 2007.Due to the long-term durative impact of economic growth and industrial transformation,carbon emissions will undoubtedly continue to increase in China.In November 2015,Chinese government pledged to reduce 60%-65%of carbon intensity by 2030 compared the 2005 level at United Nations Climate Change Conference in Paris.In order to make reasonable carbon reduction polices and develop the low carbon economy under the national framework of carbon mitigation,it is important to accurately estimate carbon emissions at the fine scale,clarify spatiotemporal pattern of carbon emissions at the different scale,and identify the main impact factors of carbon emissions in different regions in China.Previous studies have evaluated carbon emissions in China in several ways.However,most previous investigations have used statistical data based on administrative units.The statistics are not only always significant discrepancies in the different administrative unit,but they are also blank in the small administrative unit.The nighttime light data obtained by the Defense Meteorological Satellite Program's Operational Linescan System(DMSP-OLS)have been demonstrated that they have a great potential to estimate carbon emissions,and thus made up for the shortcomings of statistical data.However,the estimation of carbon emissions was always derived from a single year,few studies have focused on carbon emissions estimation for a long time.Based on the data availability,a number of previous studies have evaluated spatiotemporal dynamics of carbon emissions at the provincial scale in China,and less attention was paid to the spatiotemporal dynamics of carbon emissions at the small scale,especially at the prefectural or county scales.These studies always ignore the sensibility of scale change.Recently,most studies have focused on the impact factors of carbon emissions at the provincial scale,but there still is a blank space in the differences of the impact factors at the different scales.In view of the above questions,this study firstly aimed to estimate carbon emissions(1 km spatial resolution)in China used DMSP-OLS data.Then,we explored spatiotemporal pattern of carbon emissions at the provincial,prefectural and county scales.Finally,we quantified the main impact factors of carbon emissions at the different scales in China.The main contents and contributions of this study were summarized as follows:1)A novel method was developed to accurately estimate carbon emissions using time series DMSP-OLS data.Firstly,China's 30 provinces were divided into three regions(Eastern region,Central region and Western region)based on the overall trend of socioeconomic development.Then,statistical carbon emissions from these provinces were calculated used IPCC(Intergovernmental Panel on Climate Change)reference approach.Finally,panel data analysis was adopted to establish a series of regression models for estimating carbon emissions at 1 km resolution from 1997 to 2012 in China.The procedures were including:panel unit root test,panel co-integration test,selection of panel regression model,and establishment of carbon estimation model.The results showed that the DN values and statistical carbon emissions rejected the null hypothesis of non-stationary at the 1%significance level,and maintained a long-term equilibrium relationship during the study period.In addition,the estimation results demonstrated that the high carbon emissions were clearly identified in coastal region,capital cities,and industrial cities.To assess the accuracy of our estimation results,comparisons among panel data analysis and linear regression model for carbon emission estimation were evaluated in this study using statistical data and land use/cover data.The compared results suggested panel data analysis performed better than linear regression model to estimate carbon emissions at 1 km resolution in China.2)The spatiotemporal pattern of carbon emissions was measured and compared in China at the provincial,prefectural and county scales.The variation coefficient index increased gradually from 1997 to 2012 at the provincial scale,while it appeared to decrease during the study period at the prefectural and county scales.In addition,the time-series global Moran's I indices showed that there were significant positive spatial autocorrelations of carbon emissions at the different scales,especially at the county scale.The High-High cluster phenomenon at the provincial scale was clearly identified in the eastern region,such as Hebei,Shandong,He Nan.While the Low-Low spatial clusters at the prefectural scale mainly were located at the central and western regions.The spatial autocorrelation distribution of carbon emissions in China at the different scales might be caused by the spatial spillover effect of long-term socioeconomic development and the advantages of economy,technology,and population in some developed areas of the central and western regions.3)The impact factors of carbon emissions were explored at the different scales in China.We firstly used spatial panel model to quantify the impact factors of carbon emissions at the provincial and prefectural scales.The results showed that the proportion of second industry(PSI)had positive effect on carbon emissions at the provincial scale because they have passed the test of significance.Population,GDP,PSI,and urbanization rate have also passed the test of significance at the prefecture scale in 2002,2007 and 2012,indicating that these indices could partly promote carbon emissions.Moreover,the geographically weighted regression has used to demonstrated that the impact of PSI on carbon emissions was much higher than that of the other three factors at the provincial scale.Population,PSI,and urbanization rate were the positive factors for carbon emissions.The high values of these factors were mainly located in eastern region,while the low values were largely distributed in western regions.Additionally,the effect of GDP on carbon emissions was different in China,which spatial distribution was "west high,east low".The effect of population on carbon emissions had a decreasing tendency at the prefectural scale in Gansu,Shanxi,and Shaanxi,but it presented a slight increasing tendency in other areas.The effect of GDP on carbon emissions showed the high-high and low-low patterns in China at the prefectural level.The effect of PSI on carbon emissions presented a decreasing trend from north China to other areas.The effect of urbanization rate on carbon emissions was generally high in the north and low in the south.