Spatiotemporal Dynamics of Electricity Consumption in China

Nighttime light (NTL) data from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) provide information on nighttime luminosity, a correlation of built environment and energy consumption. This research intends to estimate spatial distribution of electricity consumption (EC) in mainland China, and analyze the temporal and spatial change of electricity consumption during 2000–2012. Nighttime light vegetation index (NVI), ratio nighttime light vegetation index (RNVI), difference nighttime light vegetation index (DNVI), normalized difference nighttime light vegetation index (NDNVI), soil adjusted nighttime light vegetation index (SANVI), and modified difference nighttime light vegetation index (MDNVI) were used to compensate for shortages in DMSP/OLS data. Moderate resolution imaging spectroradiometer (MODIS) NDVI products, China GIS database, and socioeconomic statistical data were also considered. An EC estimation model was used to obtain EC during 2000–2012. We divided EC into four ratings and analyzed spatiotemporal patterns using exploratory spatial data analysis tools (e.g., Moran’s I and local indicators of spatial association-LISA statistics). Then we built a linear regression model of EC, and correlated with DMSP/OLS data to produce China’s EC spatially. We used mean relative error (MRE) to compare our results and related research outcomes. Our result showed lower MRE, i.e., superior accuracy. EC grew quickly in China from 2000 to 2012 increasing from 6.79 to 14.82 M kWh. Generating capacity and EC of 32 provinces, municipalities and autonomous regions have a strong spatial correlation. The proposed index combines information from DMSP/OLS NTL data and MODIS NDVI data for more detailed characterization of nighttime luminosity, and reduced NTL saturation. The index simplicity enables rapid characterization and monitoring of EC.

[1]  K. Seto,et al.  The Vegetation Adjusted NTL Urban Index: A new approach to reduce saturation and increase variation in nighttime luminosity , 2013 .

[2]  T. Croft Nighttime Images of the Earth from Space , 1978 .

[3]  Jianping Wu,et al.  Evaluation of NPP-VIIRS night-time light composite data for extracting built-up urban areas , 2014 .

[4]  Yun Chen,et al.  Modeling and mapping total freight traffic in China using NPP-VIIRS nighttime light composite data , 2015 .

[5]  C. P. Lo Urban Indicators of China from Radiance-Calibrated Digital DMSP-OLS Nighttime Images , 2002 .

[6]  Xi Li,et al.  Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China , 2013, Remote. Sens..

[7]  Bo Huang,et al.  Estimating CO2 (carbon dioxide) emissions at urban scales by DMSP/OLS (Defense Meteorological Satellite Program's Operational Linescan System) nighttime light imagery: Methodological challenges and a case study for China , 2014 .

[8]  Wang Mei-hong An Analysis on the Industrial and Regional Demand Structure of Electric Power in Recent 20 Years in China , 2008 .

[9]  Christopher D. Elvidge,et al.  Overview of DMSP nightime lights and future possibilities , 2009, 2009 Joint Urban Remote Sensing Event.

[10]  Wai Ming To,et al.  Modeling of electricity consumption in the Asian gaming and tourism center—Macao SAR, People's Republic of China , 2008 .

[11]  V. Bianco,et al.  Linear Regression Models to Forecast Electricity Consumption in Italy , 2013 .

[12]  Xifan Wang,et al.  A comprehensive study on low-carbon impact of distributed generations on regional power grids: A case of Jiangxi provincial power grid in China , 2016 .

[13]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[14]  Chen Peng,et al.  Urban Built-Up Area Extraction From Log- Transformed NPP-VIIRS Nighttime Light Composite Data , 2018, IEEE Geoscience and Remote Sensing Letters.

[15]  Zhifeng Liu,et al.  Extracting the dynamics of urban expansion in China using DMSP-OLS nighttime light data from 1992 to 2008 , 2012 .

[16]  C. P. Lo Modeling the population of China using DMSP operational linescan system nighttime data , 2001 .

[17]  Alfred Stein,et al.  Characterization and spatial modeling of urban sprawl in the Wuhan Metropolitan Area, China , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[18]  The Keqiang Index: A New Benchmark for China’s Development , 2015 .

[19]  Zhifeng Liu,et al.  Modeling the spatiotemporal dynamics of electric power consumption in Mainland China using saturation-corrected DMSP/OLS nighttime stable light data , 2014, Int. J. Digit. Earth.

[20]  Chunyang He,et al.  Quantifying spatiotemporal patterns of urban impervious surfaces in China: An improved assessment using nighttime light data , 2014 .

[21]  Elizabeth A. Mack,et al.  Spatio-Temporal Interaction of Urban Crime , 2008 .

[22]  Mark Z. Jacobson,et al.  Review of solutions to global warming, air pollution, and energy security , 2009 .

[23]  Lin Ma,et al.  Evaluating Saturation Correction Methods for DMSP/OLS Nighttime Light Data: A Case Study from China's Cities , 2014, Remote. Sens..

[24]  C. Elvidge,et al.  Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption , 1997 .

[25]  Yang Yang,et al.  Application of DMSP/OLS Nighttime Light Images: A Meta-Analysis and a Systematic Literature Review , 2014, Remote. Sens..

[26]  Dengsheng Lu,et al.  Regional mapping of human settlements in southeastern China with multisensor remotely sensed data , 2008 .

[27]  Yun Chen,et al.  Modeling spatiotemporal CO2 (carbon dioxide) emission dynamics in China from DMSP-OLS nighttime stable light data using panel data analysis , 2016 .

[28]  D. Bruce,et al.  The use of night-time lights satellite imagery as a measure of Australia's regional electricity consumption and population distribution , 2010 .

[29]  Christopher D. Elvidge,et al.  Potential for global mapping of development via a nightsat mission , 2007 .

[30]  Qihao Weng,et al.  Detecting urban-scale dynamics of electricity consumption at Chinese cities using time-series DMSP-OLS (Defense Meteorological Satellite Program-Operational Linescan System) nighttime light imageries , 2016 .

[31]  Dolores Jane Forbes Multi-scale analysis of the relationship between economic statistics and DMSP-OLS night light images , 2013 .

[32]  G. Tana,et al.  Estimating energy consumption from night-time DMPS/OLS imagery after correcting for saturation effects , 2010 .

[33]  Huadong Guo Digital Earth: Big Earth Data , 2014, Int. J. Digit. Earth.

[34]  Bailang Yu,et al.  Detecting spatiotemporal dynamics of global electric power consumption using DMSP-OLS nighttime stable light data , 2016 .

[35]  Takashi Moriyama,et al.  PROGRESS FOR STABLE ARTIFICIAL LIGHTS DISTRIBUTION EXTRVCTION ACCURACY AND ESTIMATION OF ELECTRIC] POWER CONSUMPTION BY MEANS OF DMSP/OLS NIGHTTIME IMAGERY , 2010 .

[36]  K. Seto,et al.  Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data , 2011 .

[37]  Youngihn Kho,et al.  GeoDa: An Introduction to Spatial Data Analysis , 2006 .

[38]  Yeqing Cheng,et al.  Spatio-temporal dynamic of quality of life of residents, Northeast China , 2016, Chinese Geographical Science.

[39]  C. Elvidge,et al.  Spatial characterization of electrical power consumption patterns over India using temporal DMSP‐OLS night‐time satellite data , 2009 .

[40]  Jianping Wu,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-OLS Data , 2014, Remote. Sens..

[41]  P. Shi,et al.  Restoring urbanization process in China in the 1990s by using non-radiance-calibrated DMSP/OLS nighttime light imagery and statistical data , 2006 .

[42]  Zhifeng Liu,et al.  Detecting the 20 year city-size dynamics in China with a rank clock approach and DMSP/OLS nighttime data , 2015 .

[43]  Yaoqiu Kuang,et al.  China׳s 19-year city-level carbon emissions of energy consumptions, driving forces and regionalized mitigation guidelines , 2014 .

[44]  Luc Anselin,et al.  The Moran scatterplot as an ESDA tool to assess local instability in spatial association , 2019, Spatial Analytical Perspectives on GIS.

[45]  Christopher Doll,et al.  Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery , 2010 .

[46]  C. Jordan Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[47]  Jinpei Ou,et al.  Evaluation of NPP-VIIRS Nighttime Light Data for Mapping Global Fossil Fuel Combustion CO2 Emissions: A Comparison with DMSP-OLS Nighttime Light Data , 2015, PloS one.

[48]  F. Baret,et al.  Potentials and limits of vegetation indices for LAI and APAR assessment , 1991 .

[49]  Kwawu Mensan Gaba,et al.  Tracking Electrification in Vietnam Using Nighttime Lights , 2014, Remote. Sens..

[50]  C. Elvidge,et al.  Night-time lights of the world: 1994–1995 , 2001 .

[51]  Gilberto Câmara,et al.  Estimating population and energy consumption in Brazilian Amazonia using DMSP night-time satellite data , 2005, Comput. Environ. Urban Syst..

[52]  Zhifeng Liu,et al.  Spatiotemporal dynamics of electric power consumption in Chinese Mainland from 1995 to 2008 modeled using DMSP/OLS stable nighttime lights data , 2012, Journal of Geographical Sciences.

[53]  Feng Shi,et al.  Spatialization of electricity consumption of China using saturation-corrected DMSP-OLS data , 2014, Int. J. Appl. Earth Obs. Geoinformation.