基于夜间灯光数据的晋陕蒙能源消费碳排放时空格局
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摘要
晋陕蒙三省区既是能源生产基地又是碳排放主要地区。对晋陕蒙市县的碳排放估算难度较大,如何准确快捷地获取其碳排放时空动态信息,对于合理制定区域碳减排规划具有重要的应用价值。本文选取中国晋陕蒙三省作为研究对象,基于夜间灯光数据,模拟晋陕蒙地区碳排放空间分布,进而系统地刻画其碳排放空间分布特征和规律。研究结果表明:①1997-2016年,晋陕蒙三省区夜间灯光像元总值与能源消耗碳排放量之间的相关系数较高,均通过了1%的显著性检验;②1997-2016年,晋陕蒙地区的CO2排放总量呈逐年增长趋势,鄂尔多斯市属于"高碳"地区;铜川市、安康市、商洛市、汉中市、阿拉善盟和阳泉市属于"低碳"地区;③陕西省碳排放清晰地呈现出"陕北>关中>陕南"的格局。晋陕蒙地区碳排放空间分布规律分析为该区域制定切实可行的碳减排政策提供了重要的理论依据。
The Shanxi-Shaanxi-Inner Mongolia region is China's energy production base and also a major carbon emission area. It is very difficult to estimate the carbon emissions of in Shanxi, Shaanxi, Inner Mongolia. How to obtain the spatiotemporal dynamic information of carbon emissions accurately and quickly has important application value for making more informed regional carbon emission reduction plans. In this paper, the ShanxiShaanxi-Inner Mongolia region was selected as the study area. Based on nighttime light imagery, the spatial distribution of carbon emissions in Shanxi、Shaanxi and Inner Mongolia were simulated, and then the spatial distribution characteristics and rules of carbon emissions were systematically described. The results showed that the correlation coefficient between the total value of night light pixels and energy consumption carbon emissions was relatively high in the three provinces of Shanxi, Shaanxi and Mongolia during 1997-2016, which all passed the significance test of 1%. From 1997 to 2016, the carbon emissions increased year by year in Shanxi、Shaanxi and Inner Mongolia. Ordos is a "high carbon" area; Tongchuan, Ankang, Shangluo, Hanzhong, Alashan, and Yangquan are "low carbon" areas. The distribution of the carbon emissions presented a distinct pattern-smaller in southern Shaanxi than in the central areas. The analysis of spatial distribution patterns of carbon emissions in energy rich areas can better inform the formulation of feasible carbon emission reduction policies.
The Shanxi-Shaanxi-Inner Mongolia region is China's energy production base and also a major carbon emission area. It is very difficult to estimate the carbon emissions of in Shanxi, Shaanxi, Inner Mongolia. How to obtain the spatiotemporal dynamic information of carbon emissions accurately and quickly has important application value for making more informed regional carbon emission reduction plans. In this paper, the ShanxiShaanxi-Inner Mongolia region was selected as the study area. Based on nighttime light imagery, the spatial distribution of carbon emissions in Shanxi、Shaanxi and Inner Mongolia were simulated, and then the spatial distribution characteristics and rules of carbon emissions were systematically described. The results showed that the correlation coefficient between the total value of night light pixels and energy consumption carbon emissions was relatively high in the three provinces of Shanxi, Shaanxi and Mongolia during 1997-2016, which all passed the significance test of 1%. From 1997 to 2016, the carbon emissions increased year by year in Shanxi、Shaanxi and Inner Mongolia. Ordos is a "high carbon" area; Tongchuan, Ankang, Shangluo, Hanzhong, Alashan, and Yangquan are "low carbon" areas. The distribution of the carbon emissions presented a distinct pattern-smaller in southern Shaanxi than in the central areas. The analysis of spatial distribution patterns of carbon emissions in energy rich areas can better inform the formulation of feasible carbon emission reduction policies.
引文
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[24]Shi K F,Yu B L,Huang Y X,et al.Evaluating the ability of NPP-VIIRS nighttime light data to estimate the Gross domestic product and the electric power consumption of China at multiple scales:A comparison with DMSP-OLSdata[J].Remote Sensing,2014,6(2):1705-1724.
[25]Li X,Li D,Xu H,et al.Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria's major human settlement during Syrian Civil War[J].International Journal of Remote Sensing,2017,38(1):1-18.
[26]杨任飞.基于DMSP/OLS与NPP/VIIRS整合数据的城市群发育过程研究[D].成都:西南大学,2018.[Yang R F.Integrating DMSP/OLS&NPP/VIIRS nighttime light data to the application research of urban agglomeration growth process[D].Chengdu:Southwest University,2018.]
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[30]苏泳娴,陈修治,叶玉瑶,等.基于夜间灯光数据的中国能源消费碳排放特征及机理[J].地理学报,2013,68(11):1513-1526.[Su Y X,Chen X Z,Ye Y Y,et al.The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries[J].Acta Geographica Sinica,2013,68(11):1513-1526.]
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(1)https://www.ngdc.noaa.gov/ngdc.html。
(2)http://data.stats.gov.cn/index.htm。
(3)http://www.resdc.cn/。
[2]Mbarek M B,Boukarraa B,Saidi K.Role of energy consumption and economic growth in the spread of greenhouse emissions:empirical evidence from Spain[J].Environmental Earth Sciences,2016,75(16):1161.
[3]Liu Y,Chen S Y,Chen B,et al.Analysis of CO2emissions embodied in China's bilateral trade:A non-competitive import input-output approach[J].Journal of Cleaner Production,2017,163:410-419.
[4]Moutinho V,Moreira A C,Silva P M.The driving forces of change in energy-related CO2,emissions in Eastern,Western,Northern and Southern Europe:The LMDI approach to decomposition analysis[J].Renewable&Sustainable Energy Reviews,2015,50:1485-1499.
[5]王丽萍,刘明浩.基于投入产出法的中国物流业碳排放测算及影响因素研究[J].资源科学,2018,40(1):195-206.[Wang L P,Liu M H.Carbon emission measurement for China's logistics industry and its influence factors based on input-output method[J].Resources Science,2018,40(1):195-206.]
[6]李翔,朱江,尹向东.基于夜间灯光数据的中国经济发展时空格局特征[J].地球信息科学学报,2019,21(3):417-426.[Li X,Zhu J,Yin X D.Spatio-temporal pattern of Chinese economy development based on nightlight data[J].Journal of Geo-information Science,2019,21(3):417-426.]
[7]卢秀,李佳,段平,等.基于夜间灯光和土地利用数据的云南沿边地区GDP空间差异性分析[J].地球信息科学学报,2019,21(3):455-466.[Lu X,Li J,Duan P,et al.Spatial difference of GDP in Yunnan border area based on nighttime light and land use data[J].Journal of Geo-information Science,2019,21(3):455-466.]
[8]周翼,谢保鹏,陈英,等.基于灯光数据的中国县域城镇建设用地产出效率时空演变特征[J].地球信息科学学报,2018,20(12):1733-1744.[Zhou Y,Xie B P,Chen Y,et al.The spatial-temporal pattern of the construction land output efficiency in China at county level based on nighttime light[J].Journal of Geo-information Science,2018,20(12):1733-1744.]
[9]邓刘洋,沈占锋,柯映明.城市建成区遥感影像边界提取与扩张分析[J].地球信息科学学报,2018,20(7):996-1003.[Deng L Y,Shen Z F,Ke Y M.Built-up area extraction and urban expansion analysis based on remote sensing images[J].Journal of Geo-information Science,2018,20(7):996-1003.]
[10]李通,何春阳,杨洋,等.1995-2008年中国大陆电力消费量时空动态[J].地理学报,2011,66(10):1403-1412.[Li T,He C Y,Yang Y,et al.Understanding electricity consumption changes in Chinese mainland from 1995 to 2008 by using DMSP/OLS stable nighttime light time series data[J].Acta Geographica Sinica,2011,66(10):1403-1412.]
[11]吴健生,牛妍,彭建,等.基于DMSP/OLS夜间灯光数据的1995-2009年中国地级市能源消费动态[J].地理研究,2014,33(4):625-634.[Wu J S,Niu Y,Peng J,et al.Research on energy consumption dynamic among prefecture-level cities in China based on DMSP/OLS nighttime light[J].Geograhical Research,2014,33(4):625-634.]
[12]何则,杨宇,宋周莺,等.中国能源消费与经济增长的相互演进态势及驱动因素[J].地理研究,2018,37(8):1528-1540.[He Z,Yang Y,Song Z Y,et al.Analyzing regional economic disparities based on ESDA[J].Geograhical Research,2018,37(8):1528-1540.]
[13]王婧,刘奔腾,李裕瑞.京津冀人口时空变化特征及其影响因素[J].地理研究,2018,37(9):1802-1817.[Wang J,Liu B T,Li Y R.Spatial-temporal characteristics and influencing factors of population distribution and floating changes in Beijing-Tianjin-Hebei region[J].Geograhical Research,2018,37(9):1802-1817.]
[14]Su Y,Chen X,Li Y,et al.China's 19-year city-level carbon emissions of energy consumptions,driving forces and regionalized mitigation guidelines[J].Renewable&Sustainable Energy Reviews,2014,35(C):231-243.
[15]郭忻怡,闫庆武,谭晓悦,等.基于DMSP/OLS与NDVI的江苏省碳排放空间分布模拟[J].世界地理研究,2016,25(4):102-110.[Guo X Y,Yan Q W,Tan X Y,et al.Spatial distribution of carbon emissions based on DMSP/OLSnighttime light data and NDVI in Jiangsu province[J].World Regional Studies,2016,25(4):102-110.]
[16]马忠玉,肖宏伟.基于卫星夜间灯光数据的中国分省碳排放时空模拟[J].中国人口·资源与环境,2017,27(9):143-150.[Ma Z Y,Xiao H W.Spatiotemporal simulation study of China's provincial carbon emissions based on satellite night lighting data[J].China Population,Resources and Environment,2017,27(9):143-150.]
[17]Shi K,Chen Y,Yu B,et al.Modeling spatiotemporal CO2,(Carbon Dioxide)emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis[J].Applied Energy,2016,168:523-533.
[18]李海萍,龙宓,李光一.基于DMSP/OLS数据的区域碳排放时空动态研究[J].中国环境科学,2018,38(7):2777-2784.[Li H P,Long M,Li G Y.Spatial-temporal dynamics of carbon dioxide emissions in China based on DMSP/OLS nighttime stable light data[J].China Environmental Science,2018,38(7):2777-2784.]
[19]宋金超,李新虎,吝涛,等.基于夜晚灯光数据和Google Earth的城市建成区提取分析[J].地球信息科学学报,2015,17(6):750-756.[Song J C,Li X H,Nin T,et al.Analysis of urban built-up district based on night light data and Google earth[J].Journal of Geo-information Science,2015,17(6):750-756.]
[20]沈镭,刘立涛,高天明,等.中国能源资源的数量、流动与功能分区[J].资源科学,2012,34(9):1611-1621.[Shen L,Liu L T,Gao T M,et al.The quantity,flow and functional zoning of energy resources in China[J].Resources Science,2012,34(9):1611-1621.]
[21]顾羊羊,乔旭宁,樊良新,等.夜间灯光数据的区域能源消费碳排放空间化[J].测绘科学,2017,42(2):140-146.[Gu Y Y,Qiao X N,Fan L X,et al.Spatial analysis of carbon emissions from region energy consumption based on night light data[J].Science of Surveying and Mapping,2017,42(2):140-146.]
[22]曹子阳,吴志峰,匡耀求,等.DMSP/OLS夜间灯光影像中国区域的校正及应用[J].地球信息科学学报,2015,17(9):1092-1102.[Cao Z Y,Wu Z F,Kuang Y Q,et.al.Correction of DMSP/OLS night-time light Images and its application in China[J].Journal of Geo-information Science,2015,17(9):1092-1102.]
[23]Li X,Xu H,Chen X,et al.Potential of NPP-VIIRSnighttime light imagery for modeling the regional economy of China[J].Remote Sensing,2013,5(6):3057-3081.
[24]Shi K F,Yu B L,Huang Y X,et al.Evaluating the ability of NPP-VIIRS nighttime light data to estimate the Gross domestic product and the electric power consumption of China at multiple scales:A comparison with DMSP-OLSdata[J].Remote Sensing,2014,6(2):1705-1724.
[25]Li X,Li D,Xu H,et al.Intercalibration between DMSP/OLS and VIIRS night-time light images to evaluate city light dynamics of Syria's major human settlement during Syrian Civil War[J].International Journal of Remote Sensing,2017,38(1):1-18.
[26]杨任飞.基于DMSP/OLS与NPP/VIIRS整合数据的城市群发育过程研究[D].成都:西南大学,2018.[Yang R F.Integrating DMSP/OLS&NPP/VIIRS nighttime light data to the application research of urban agglomeration growth process[D].Chengdu:Southwest University,2018.]
[27]IPCC.2006 IPCC Guidelines for National Greenhouse Gas Inventories[EB/OL].(2008-07-20)[2017-11-13].http://www.ipccnggip.iges.or.jp/public/2006gl/index.html.
[28]国家统计局能源统计司.中国能源统计年鉴2013[M].北京:中国统计出版社,2014.[Department of energy statistics,National bureau of statistics of the People's Republic of China.China Energy Statistical Yearbook of 2013[M].Beijing:China Statistics Press,2014.]
[29]国家发展和改革委员会应对气候变化司.省级温室气体清单编制指南(试行)[EB/OL].(2011-05)[2017-11-13].http://qhs.ndrc.gov.cn/zcfg/index.html.[National Development and Reform Commission for Addressing Climate Change.Notice on Issuing Guidelines for the Compilation of Provincial Greenhouse Gas Inventories(for Trial Implementation)[EB/OL].(2011-05)[2017-11-13].http://qhs.ndrc.gov.cn/zcfg/index.html.]
[30]苏泳娴,陈修治,叶玉瑶,等.基于夜间灯光数据的中国能源消费碳排放特征及机理[J].地理学报,2013,68(11):1513-1526.[Su Y X,Chen X Z,Ye Y Y,et al.The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries[J].Acta Geographica Sinica,2013,68(11):1513-1526.]
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(1)https://www.ngdc.noaa.gov/ngdc.html。
(2)http://data.stats.gov.cn/index.htm。
(3)http://www.resdc.cn/。