| Global climate warming caused by carbon emissions from fossil fuels was a common challenge for current human society.As the largest developing country and a major responsible country,Chinese government had pledged to reduce 40%-45%of the unit GDP carbon emissions by 2020 year than that in 2005,and 60%-65%of the unit GDP carbon emissions by 2030 year than that in 2005.The government also set a target that the carbon emissions will peak around 2030 as well as strive to reach the peak as soon as possible.Facing the unprecedented pressure of emissions reduction and the urgent transformation need of economic and social development,it was necessary to explore energy conservation and emissions reduction strategies for Chinese national conditions.In order to achieve this goal,this study constructed a provincial-level carbon emissions estimation model based on the corrected nighttime light datasets,then systematically analyzed the spatiotemporal dynamics characteristics of multiscale carbon emissions from 1995 to 2016.The carbon emissions influencing factor model was constructed to analyze the common characteristics and regional disparity of key factors on carbon emissions.The carbon emissions prediction model was constructed to predict the dynamic of carbon emissions in the medium and long term from 2017 to 2050.The carbon emissions peak path and target implementation of carbon emissions intensity from national,regions and provincial scale were analyzed.The differentiated emissions reduction strategies were proposed on this basis.(1)On the basis of the DMSP-OLS data correction and NPP-VIIRS data correction,respectively,this study integrated correction of the two nighttime light datasets.The R2 of the regression equation was 0.8354,and the fitting effect was well.Then the corrected nighttime light datasets of DMSP-OLS scale from 1992 to 2016 was constructed.Combined with energy consumption statistical carbon emissions,a provincial-level carbon emissions estimation model based on the corrected nighttime light datasets were constructed.The fitting accuracy of the estimation model was 0.7138,the fitting effect was well,and the estimation accuracy meet the requirements.(2)The provincial energy consumption statistical carbon emissions were used to make linear adjustments to the preliminary carbon emissions at pixel scale,and the provincial-level zero-error grid map of national energy consumption carbon emissions(1kmx1km spatial resolution)was generated.On this basis,the carbon emissions at prefectural and county scale were calculated.By the methods of trend analysis,global spatial correlation and local spatial correlation,the spatiotemporal dynamics characteristics of carbon emissions was analyzed at provincial,prefectural and county scale,respectively.At the national scale,the energy consumption carbon emissions showed a rapid growth trend,and increased from 1889 Mt in 1995 to 4683 Mt in 2016,with an average annual growth rate of 6.72%.The carbon emissions in three regions showed the same increasing trend as national scale,and the eastern region accounted for the highest share of carbon emissions as well as the central region accounted for the lowest share of carbon emissions.The high carbon emissions areas were mainly concentrated in Around Bohai Bay,Yangtze River Delta and Pearl River Delta at provincial,prefectural and county scale.Hebei and Shandong belonged to the provinces of rapid-growth type,and 11 prefectures and 77 counties belonged to the regions of rapid-growth type.There existed the Hu Line in the distribution and growth type of energy consumption carbon emissions in different scales.The global Moran's I index all showed a growth trend at provincial,prefectural and county scale,and the global spatial correlation of carbon emissions at prefectural scale was the strongest.The local spatial correlation of carbon emissions at provincial,prefectural and county scale showed an expanding trend,and the significant type of cluster distribution was mainly positive correlation.(3)According to energy consumption carbon emissions at provincial scale from 1995 to 2016,combined with the influence factors such as population size,per capita GDP,energy intensity,urbanization level and industrial structure,utilized the dynamic spatial Durbin model,geographic weighted regression model and STIRPAT model,this study constructed the SDM-STIRPAT model at the national,eastern,central and western China.The R2 of the models were 0.9894,0.9946,0.9938,and 0.9834,thus the model had good fitting accuracy.At the same time,this study constructed the GWR-STIRPAT model at provincial scale,the R2 of the models were all greater than 0.64,therefore,the model was reasonable and effective.In terms of common characteristics,the population in national,eastern and western China had a positive impact on the provincial carbon emissions.The Per capita GDP in national,eastern and central China had a positive impact on the provincial carbon emissions.The energy intensity had a positive impact on the provincial carbon emissions.The urbanization in national,central and western China had a positive impact on the provincial carbon emissions.The adjustment of industrial structure in national and western China had a positive impact on the provincial carbon emissions.From the perspective of population size,the total effect of short term in central China and long term in eastern China were the most significant.From the perspective of per capita GDP and energy intensity,the total effect of short term and long term in eastern China were the most significant.From the perspective of urbanization,the total effect of short term and long term in central China were the most significant.From the perspective of industrial structure,the total effect of short term in central China and long term in eastern China were the most significant.There existed the "Kuznets Curves" between carbon emissions and per capita GDP in national and eastern China,and the "Kuznets Curves" between carbon emissions and urbanization in the national,central and western China.In terms of regional disparity,the most significant impact of population size,energy intensity,and urbanization on carbon emissions were in northwest provinces.The most significant impact of per capita GDP on carbon emissions was in eastern China.The most significant impact of industrial structure on carbon emissions was in northeast China and north west energy provinces.(4)Utilized the scenario analysis method and STIRPAT model,combined the influence factors such as population size,per capita GDP,urbanization,industrial structure and energy intensity,this study constructed the carbon emissions prediction model.The R2 of the model was 0.9515,the prediction effect of the model was well,and the relative error met the accuracy requirement.Setting three scenarios of Business as Usual(BAU),Low Carbon(LC),and Enhanced Low Carbon(ELC),the carbon emissions trends were forecasted in the nation,three regions and 30 provinces in the medium and long term from 2017 to 2050.At the national scale,the carbon emissions peaking time were 2032,2037,and 2039 under the scenarios of ELC,LC,and BAU,and the peak levels increased under the three scenarios.The government would achieve the overall carbon emissions intensity reduction requirement under the three scenarios.At the regional scale,the total energy consumption carbon emissions were 99380Mt,52863Mt,and 59396Mt under BAU scenario in eastern,central and western China.The carbon emissions peaking time of eastern China were early,while the carbon emissions peaking time of western China were later.The peak levels of central China were small,and the peak levels of Eastern China were large.The government would achieve the overall carbon emissions intensity reduction requirement under the three scenarios.At the provincial scale,the carbon emissions peaking time varied from 2032 to 2046 under BAU scenario,and Guangdong,Tianjin,and Ningxia reached the peak levels as early as 2032.The carbon emissions peaking time varied from 2028 to 2045 under EC scenario,and Tianjin and Shandong reached the peak levels as early as 2028.The carbon emissions peaking time varied from 2022 to 2042 under ELC scenario,and Hebei and Xinjiang reached the peak levels as early as 2022.The government would achieve the overall carbon emissions intensity reduction requirement under three scenarios except Fujian,Henan,Hunan,Guangdong and Ningxia.(5)This study put forward carbon emissions reduction strategy from the perspective of the general,eastern,central and western China.In general,it was necessary to change the mode of economic development and choose a low-carbon path of social and economic development.The government should promote the rationalization and advanced development of industrial structure.It was necessary to promote the diversified development of energy and improve energy utilization efficiency.The government should build the carbon emissions statistical system,create the carbon emissions market,and foster the national awareness of energy conservation and emissions reduction.From the perspective of the eastern China,the industrial structure adjustment had a significant impact on the carbon emissions,then the carbon emissions reduction strategy should focus on optimizing the industrial layout,and the key emissions reduction counties in eastern China were concentrated in the Beijing-Tianj in-Hebei region,the Yangtze River Delta,the Pearl River Delta,Liaoning,Shandong and Fujian.From the perspective of the central China,the industrial structure adjustment had a significant impact on the carbon emissions,and the key emissions reduction counties in central China were concentrated in Wuhan city circle and Changshao-Zhuzhou-Xiangtan region,including Wuhan county,Zengdu district in Suizhou,Liuyang,Changshao,and Wangcheng in Hunan.From the perspective of the western China,the energy intensity had a significant impact on the carbon emissions,thus,energy structure optimization and energy utilization efficiency improvement were the important approaches to energy conservation and emission reduction,and the key emissions reduction counties in western China were concentrated in the Inner Mongolia,Shaanxi,Ningxia and Xinjiang in northwest China and Guangxi,Chongqing,Sichuan and Guizhou in southwest China.The innovation points of this study were summarized as follows:(1)This study corrected and integrated DMSP-OLS data and NPP-VIIRS data by adopting the four step method.A long time series nighttime light datasets of DMSP-OLS scale after correction from 1992 to 2016 was constructed.Then the quantitative estimation model of energy consumption carbon emissions from provincial,prefectural,county and pixel scale based on the corrected nighttime light datasets and provincial carbon emissions were constructed,the dynamic characteristics of carbon emissions from multiscale were researched,and the high carbon emissions areas and the key carbon emissions reduction regions were determined accurately.(2)This study constructed the SDM-STIRPAT model to analyze the regional common characteristics of influence factors on carbon emissions from the national,eastern,central and western China.At the same time,this study constructed the GWR-STIRPAT model to analyze the regional disparity of influence factors on carbon emissions from provincial scale.(3)This study built the medium and long-term carbon emissions prediction model,then determined the different carbon emissions peaking path and the implementation of carbon emissions intensity targets from multiscale,and determined the different emissions reduction strategies. |
PDF Full Text Request