中国电力消费多尺度时空格局分析——基于DMSP-OLS夜间灯光数据

doi:10.3969/j.issn.0255-8297.2021.03.015

摘要/Abstract

摘要： 对美国国防气象卫星计划（defense meteorological satellite program，DMSP）上的操作线性扫描系统（operational linescan system，OLS）所获取的夜间灯光数据进行校正，结合省级电力消费量构建2000—2013年省级尺度电力消费模拟模型。模型的拟合优度为0.714 9，这表明模拟模型有效。在此基础上反演出市级、县级和栅格级电力消费量，最后采用全局空间自相关和局部空间自相关分析多尺度电力消费时空演变格局。结果表明： 2000—2013年中国电力消费呈现显著增长趋势。电力消费总量由2000年的13 606.29亿kWh增加为2013年的53 423.39亿kWh。京津冀地区、长三角地区和珠三角地区是高电力消费聚集区域，西部地区则呈现低电力消费集聚态势，可见电力消费量分布呈现胡焕庸线特征。栅格、省级、市级和县级尺度电力消费量呈现出一致的分布趋势，不同地区电力消费存在着差异性。市级尺度是模拟电力消费的有效行政单元。2013年12.9%的省份、16.56%的城市和40.95%的县域电力消费呈现出局域空间相关性。

关键词: 多尺度, 电力消费, 夜间灯光数据, 全局空间自相关, 局部空间自相关

Abstract: In combination of the corrected operational linescan system on defense meteorological satellite program (DMSP-OLS) nighttime light data with provincial electric power consumptions, a provincial scale electric power consumption simulation model was structured for the years from 2000 to 2013. The fitting goodness of the model was tested to be 0.714 9, and this proved the effectiveness of the proposed model. On this basis, the electric power consumptions of prefectural, county and pixel scales were calculated, and the spatiotemporal pattern of multiscale electric power consumption was analyzed by global spatial autocorrelation and local spatial autocorrelation. Computation results indicated that the electric power consumption in China showed a significant growth trend from 2000 to 2013. Total electric power consumption increased from 1 360.629 billion kWh in 2000 to 5 342.339 billion kWh in 2013. It is also found that the Beijing-Tianjin-Hebei region, the Yangtze River Delta and the Pearl River Delta were the high electric power consumption zones, while the western region presented low electric power consumption. This power consumption distribution showed the characteristics of Hu line. The power consumptions of all different scales presented a consistent spatial distribution trend, and there were differences of electric power consumption in different regions. Prefectural scale was an effective administrative unit to simulate electric power consumption. 12.9% of provinces, 16.56% of prefectural, and 40.95% of counties in 2013 showed local spatial correlation in electric power consumption.

Key words: multiscale, electric power consumption, nighttime light data, global spatial autocorrelation, local spatial autocorrelation

中图分类号:

F407.61

王涛, 冯志畅, 罗健, 孟庆涛. 中国电力消费多尺度时空格局分析——基于DMSP-OLS夜间灯光数据[J]. 应用科学学报, 2021, 39(3): 508-520.

WANG Tao, FENG Zhichang, LUO Jian, MENG Qingtao. Multiscale Analysis on Spatiotemporal Pattern of Electric Power Consumption in China——Based on DMSP-OLS Nighttime Light Data[J]. Journal of Applied Sciences, 2021, 39(3): 508-520.

参考文献

[1] 孙祥栋, 尹彦辉, 郑艳婷. 区域经济异质视角下电力消费因素分解及“拐点” 分析[J]. 技术经济, 2019, 38(7): 100-108. Sun X D, Yin Y H, Zheng Y T. Electricity consumption decomposition and “inflection point” analysis considering heterogeneity of China’s regional economic growth[J]. Technology Economics, 2019, 38(7): 100-108. (in Chinese)
[2] 单葆国, 孙祥栋, 李江涛, 等. 经济新常态下中国电力需求增长研判[J]. 中国电力, 2017, 50(1): 19-24. Shan B G, Sun X D, Li J T, et al. Analysis on the China’s electricity demand growth under the new economic norm[J]. Electric Power, 2017, 50(1): 19-24. (in Chinese)
[3] 许长新, 王豹, 林剑婷. 电力与东道国经济发展的关系研究——基于“一带一路”倡议背景[J]. 资源开发与市场, 2018, 34(8): 1080-1085. Xu C X, Wang B, Lin J T. Research on relationship between electricity and economic development of the host county—based on the “the belt and the road”[J]. Resource Development & Market, 2018, 34(8): 1080-1085. (in Chinese)
[4] 周月敏, 周翔. 中国高分辨率对地观测系统共性应用技术规范体系框架研究[J]. 地球信息科学学报, 2018, 20(9): 1298-1305. Zhou Y M, Zhou X. The system framework of technical standards for common applications in China high- resolution earth observation system[J]. Journal of Geo-information Science, 2018, 20(9): 1298-1305. (in Chinese)
[5] 王庆华, 肖宏伟. 中国电力消费对经济增长影响的空间溢出效应研究[J]. 宏观经济研究, 2018(2): 91-103. Wang Q H, Xiao H W. A study on the spatial spillover effect of electricity consumption on economic growth in China[J]. Macroeconomics, 2018(2): 91-103. (in Chinese)
[6] Elvidge C D, Baugh K E, Dietz J B, et al. Radiance calibration of DMSP-OLS low-light imaging data of human settlements[J]. Remote Sensing of Environment, 1999, 68(1): 77-88.
[7] Imhoff M L, Lawrence W T, Stutzer D C, et al. A technique for using composite DMSP/OLS “City Lights” satellite data to map urban area[J]. Remote Sensing of Environment, 1997, 61(3): 361-370.
[8] 马忠玉, 肖宏伟. 基于卫星灯光数据的中国省域电力消费模拟研究[J]. 中国能源, 2017,39(1): 6-10. Ma Z Y, Xiao H W. Simulation study of provincial electricity consumption based on satellite lighting data[J]. Energy of China, 2017, 39(1): 6-10. (in Chinese)
[9] 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. (in Chinese)
[10] 李峰, 孙广通, 王秋玲, 等. NPP-VIIRS夜间灯光数据的县级电力消费空间化研究[J]. 测绘与空间地理信息, 2018, 41(4): 8-11, 15, 18. Li F, Sun G T, Wang Q L, et al. Spatialization research of electricity consumptions at county level using NPP-VIIRS nighttime lights[J]. Geomatics & Spatial Information Technology, 2018, 41(4): 8-11, 15, 18. (in Chinese)
[11] Elvidge C D, Baugh K E, Kihn E A, et al. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption[J]. International Journal of Remote Sensing, 1997, 18(6): 1373-1379.
[12] Chand T R K, Badarinath K V S, Elvidge C D, et al. Spatial characterization of electrical power consumption patterns over India using temporal DMSP-OLS night-time satellite data[J]. International Journal of Remote Sensing, 2009, 30(3): 647-661.
[13] He C, Ma Q, Li T, et al. Spatiotemporal dynamics of electric power consumption in Chinese Mainland from 1995 to 2008 modeled using DMSP/OLS stable nighttime lights data[J]. Journal of Geographical Sciences, 2012, 22(1): 125-136.
[14] Letu H, Nakajima T Y, Nishio F. Regional-scale estimation of electric power and power plant CO₂ emissions using defense meteorological satellite program operational linescan system nighttime satellite data[J]. Environmental Science & Technology Letters, 2014, 1(5): 259-265.
[15] Cao X, Wang J, Chen J, et al. Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data[J]. International Journal of Applied Earth Observation and Geoinformation, 2014, 28: 193-200.
[16] Shi K, Chen Y, Yu B, et al. Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data[J]. Applied Energy, 2016, 184: 450-463.
[17] Anselin L. Under the hood issues in the specification and interpretation of spatial regression models[J]. Agricultural Economics, 2002(27): 247-267.
[18] 吕倩, 刘海滨. 京津冀县域尺度碳排放时空演变特征——基于DMSP/OLS夜间灯光数据[J]. 北京理工大学学报(社会科学版), 2019, 21(6): 41-50. Lü Q, Liu H B. Spatio-temporal evolution characteristics of county scale carbon emissions in Beijing-Tianjin-Hebei region—a study based on DMSP/OLS nighttime light data[J]. Journal of Beijing Institute of Technology (Social Sciences Edition), 2019, 21(6): 41-50. (in Chinese)