|
|
Spatio-temporal simulation and differentiation pattern of carbon emissions in China based on DMSP/OLS nighttime light data |
ZHANG Yong-nian, PAN Jing-hu |
College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China |
|
|
Abstract A precise simulation and measurement of the time-resolved and spatial distribution characteristics of carbon dioxide (CO2) can help critical references to the formulation of reasonable and differential carbon emission reduction policies. Taking the DMSP/OLS nighttime light data as basic data, this paper extracted the urban built-up area in Chinese mainland on the basis of data rectification, pixel desaturation and outliers elimination. To simulate China's carbon emissions in the period of 2000 to 2013, the carbon emission panel data model was constructed according to the quantitative correlations between DMSP/OLS nighttime light image data and carbon emission statistics. Then the spatio-temporal evolving trend and spatial distribution characteristics of carbon emissions in the research period of 14 years were discussed using Theil-Sen Median trend analysis and Mann-Kendall test method. The results showed that: 1) by correcting the DMSP/OLS nighttime light image data systematically, the simulation here of long-time serial carbon emissions showed high accuracy. The determination coefficient value, R2, from the multiscale regression test for the year of 2002, 2007, 2012 were 0.893, 0.955 and 0.951, respectively. 2) It indicated that the overall carbon emissions from 2000 to 2013 in China have a significant characteristic of spatial-temporal evolution. The stable-slow rise type and rapid rise type carbon emission aeras accounted for 77.6% and 19.4% respectively of the total carbon emissions areas. It also showed that most regions in China were dominated by a stable-slow rise type, while the urban centers and its extended regions show a rapid rise type. 3) By the influence of city size and urbanization level, cities of the rapid rise type showed a clear directional difference with ‘hollow structure’ or ‘centered structure’. This study proposes that, the essential transformation of economic growth pattern and the development mode, as well as the implementation of different carbon emission reduction measures adapted to local conditions and provinces-regions linked strategy are the vital approach to achieve the “targeted emission alleviation”.
|
Received: 04 September 2018
|
|
|
|
|
[1] |
秦大河.气候变化科学的最新进展[J]. 科技导报, 2008,26(7):3. Qin D H. Latest progress on climate change research[J]. Science & Technology Review, 2008,26(7):3.
|
[2] |
BP石油公司.BP世界能源统计年鉴(2017)[EB/OL]. (2017-04-12). http://www.bp.com/zh_cn/china/reports-and-publications/bp2017-.html. The petroleum corporation of BP. The BP Statistical review of World Energy Published in 2017[EB/OL]. (2017-04-12). http://www.bp.com/zh_cn/china/reports-and-publications/bp2017-.html.
|
[3] |
程叶青,王哲野,张守志,等.中国能源消费碳排放强度及其影响因素的空间计量[J]. 地理学报, 2013,68(10):1418-1431. Cheng Y Q, Wang Z Y, Zhang S Z, et al. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China[J]. Acta Geographica Sinica, 2013,68(10):1418-1431.
|
[4] |
高长春,刘贤赵,李朝奎,等.近20年来中国能源消费碳排放时空格局动态[J]. 地理科学进展, 2016,35(6):747-757. Gao C C, Liu X Z, Li C K, et al. Spatiotemporal dynamics of carbon emissions by energy consumption in China from 1995 to 2014[J]. Progress in Geography, 2016,35(6):747-757.
|
[5] |
Ang B W. Decomposition analysis for policymaking in energy:which is the preferred method?[J]. Energy Policy, 2004,32(9):1131-1139.
|
[6] |
Diakoulaki D, Mavrotas G, Orkopoulos D, et al. A bottom-up decomposition analysis of energy-related CO2, emissions in Greece[J]. Energy, 2006,31(14):2638-2651.
|
[7] |
米红,张田田,任正委,等.城镇化进程中家庭CO2排放的驱动因素分析[J]. 中国环境科学, 2016,36(10):3183-3192. Mi H, Zhang T T, Ren Z W, et al. Driving factors of China's household CO2emissions in the process of urbanization[J]. China Environmental Science, 2016,36(10):3183-3192.
|
[8] |
Wang P, Wu W S, Zhu B Z, et al. Examining the impact factors of energy-related CO2, emissions using the STIRPAT model in Guangdong Province, China[J]. Applied Energy, 2013,106(11):65-71.
|
[9] |
Feng K S, Siu Y L, Guan D B, et al. Analyzing drivers of regional carbon dioxide emissions for China[J]. Journal of Industrial Ecology, 2012,16(4):600-611.
|
[10] |
计军平,马晓明.中国温室气体排放增长的结构分解分析[J]. 中国环境科学, 2011,31(12):2076-2082. Ji J P, Ma X M. Structural decomposition analysis of the increase in China's greenhouse gas emissions[J]. China Environmental Science, 2011,31(12):2076-3082.
|
[11] |
Diakoulaki D, Mandaraka M. Decomposition analysis for assessing the progress in decoupling industrial growth from CO2, emissions in the EU manufacturing sector[J]. Energy Economics, 2007,29(4):636-664.
|
[12] |
吕可文,苗长虹,尚文英.工业能源消耗碳排放行业差异研究——以河南省为例[J]. 经济地理, 2012,32(12):15-20. Lv K W, Miao C H, Shang W Y. Sectoral difference in carbon emission of industrial energy consumption:A case study of Henan province[J]. Economic Geography, 2012,32(12):15-20.
|
[13] |
田云,李波.中国农业碳排放研究:测算、时空比较及脱钩效应[J]. 资源科学, 2012,34(11):2097-2105. Tian Y, Li B. Agricultural carbon emissions in China:calculation, spatial-temporal comparison and decoupling effects[J]. Resources Science, 2012,34(11):2097-2105.
|
[14] |
袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J]. 资源科学, 2017,39(4):687-697. Yuan C W, Zhang S, Jiao P, et al. Temporal and spatial variation and influencing factors research on total factor efficiency for transportation carbon emissions in China[J]. Resources Science, 2017,39(4):687-697.
|
[15] |
Yang X M. The household carbon emission analysis under individual consumer behavior[J]. China Population Resources & Environment, 2010,20(5):35-40.
|
[16] |
付允,马永欢,刘怡君,等.低碳经济的发展模式研究[J]. 中国人口·资源与环境, 2008,18(3):14-19. Fu Y, Ma Y H, Liu Y J, et al. Development patterns of low carbon economy[J]. China Population, Resources and Environment, 2008, 18(3):14-19.
|
[17] |
Tu Z. Strategic measures to reduce China's carbon emissions:based on an index decomposition analysis of carbon emissions in eight industries[J]. Social Sciences in China, 2014,35(3):158-173.
|
[18] |
李广东,方创琳.中国区域经济增长差异研究进展与展望[J]. 地理科学进展, 2013,32(7):1102-1112. Li G D, Fang C L. A review on divergence of regional economic growth in China[J]. Progress in Geography, 2013,32(7):1102-1112.
|
[19] |
Raupach M R, Rayner P J, Paget M. Regional variations in spatial structure of nightlights, population density and fossil-fuel CO2, emissions[J]. Energy Policy, 2010,38(9):4756-4764.
|
[20] |
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.
|
[21] |
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.
|
[22] |
NOAA. National Centers for Environmental Information.[EB/OL]. (2017-06-20). http://ngdc.noaa.gov/eog/down-load.html.
|
[23] |
NASA. NASA's Earth Observing System Data and Information System.[EB/OL]. (2017-08-15). https://search.earthdata.nasa.gov/search.
|
[24] |
CEADS. China Emission Accounts and Datasets.[EB/OL]. (2017-04-15).http://www.ceads.net/.
|
[25] |
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.
|
[26] |
潘竟虎,李俊峰.基于夜间灯光影像的中国电力消耗量估算及时空动态[J]. 地理研究, 2016,35(4):627-638. Pan J H, Li J F. Estimate and spatio-temporal dynamics of electricity consumption in China based on DMSP/OLS images[J]. Geographical Research, 2016,35(4):627-638.
|
[27] |
卓莉,张晓帆,郑璟,等.基于EVI指数的DMSP/OLS夜间灯光数据去饱和方法[J]. 地理学报, 2015,70(8):1339-1350. Zhuo L, Zhang X F, Zheng J, et al. An EVI-based method to reduce saturation of DMSP/OLS nighttime light data[J]. Acta Geographica Sinica, 2015,70(8):1339-1350.
|
[28] |
IEA. 2008. World energy outlook 2008[EB/OL]. 2008-05-01[2014-07-01]. http://www.worldenergyoutlook.org/media/weowebsite/2008-1994/weo2008.pdf.
|
[29] |
舒松,余柏蒗,吴健平,等.基于夜间灯光数据的城市建成区提取方法评价与应用[J]. 遥感技术与应用, 2011,26(2):169-176. Shu S, Yu B L, Wu J P, et al. Methods for deriving urban built-up area using night-light data:assessment and application[J]. Remote Sensing Technology and Application, 2011,26(2):169-176.
|
[30] |
Doll C N H, Elvidge C D. Night-time imagery as a tool for global mapping of socioeconomic parameters and greenhouse gas emissions[J]. Ambio, 2000,29(3):157-162.
|
[31] |
袁丽华,蒋卫国,申文明,等.2000-2010年黄河流域植被覆盖的时空变化[J]. 生态学报, 2013,33(24):7798-7806. Yuan L H, Jiang W G, Shen W M, et al. The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010[J]. Acta Ecologica Sinica, 2013,33(24):7798-7806.
|
[32] |
方利,王文杰,蒋卫国,等.2000~2014年黑龙江流域(中国)植被覆盖时空变化及其对气候变化的响应[J]. 地理科学, 2017,37(11):1745-1754. Fang L, Wang W J, Jiang W G, et al. Spatio-temporal variations of vegetation cover and its responses to climate change in the Heilongjiang basin of China from 2000 to 2014[J]. Scientia Geographica Sinica, 2017,37(11):1745-1754.
|
[33] |
刘宪锋,潘耀忠,朱秀芳,等.2000~2014年秦巴山区植被覆盖时空变化特征及其归因[J]. 地理学报, 2015,70(5):705-716. Liu X F, Pan Y Z, Zhu X F, et al. Spatiotemporal variation of vegetation coverage in Qinling-DabaMountains in relation to environmental factors[J]. Acta Geographica Sinica, 2015,70(5):705-716.
|
[34] |
范丹,王维国,梁佩凤.中国碳排放交易权机制的政策效果分析——基于双重差分模型的估计[J]. 中国环境科学, 2017,37(6):2383-2392. Fan D, Wang W G, Liang P F. Analysis of the performance of carbon emissions trading right in China-The evaluation based on the difference-in-difference model.[J]. China Environmental Science, 2017,37(6):2383-2392.
|
|
|
|