Estimate And Spatiotemporal Dynamics Of Electricity Consumption In China Based On DMSP/OLS

Energy consumption is directly related to economic growth, CO 2 emissions and global warming, and so on. Electricity consumption is an important part of the energy consumption, which plays an important role in the national economy and social life.The study area in this paper is the Chinese mainland. Firstly, this paper presents NVI(Nighttime-light Vegetation Index) for the first time, which compensate for the shortage of DMSP/OLS data. DMSP/OLS night lights data, MODIS NDVI products, China GIS database and socio-economic statistical data are also taken into consideration. An electricity consumption estimation model is used to obtain a figure for electricity consumption from 2000 to 2012. Lastly, we divide electricity consumption into four ratings and analyze the spatiotemporal patterns by using ESDA method.Below are the results:(1) This paper is the first time we have used NVI(Nighttime-light Vegetation Index). RNVI(Ratio Nighttime-light Vegetation Index), DNVI(Difference Nighttime-light Vegetation Index), NDNVI(Normalized Difference Nighttime-light Vegetation Index), SANVI(Soil-adjusted Nighttime-light Vegetation Index), MDNVI(Modified Difference Nighttime-light Vegetation Index). All of them can compensate the shortage of DMSP/OLS data. But MDNVI is the best model. We reduced space overflow of night lights data by using MDNVI model.(2) In the study, we found there is a strong correlation between total DN value of night lights data and provincial electricity consumption through correlation analysis. Then we build a linear regression model of electricity consumption by regression analysis, and we used it for DMSP/OLS data to reverse China electricity consumption space layout. We compared the MRE(mean relative error) between the result and related research, prooving that our result have a lower MRE and a higher accuracy. Finally we find a way to get China electricity consumption date from 2000 to 2012 quickly and effectively.(3) Electricity consumption trends: electric consumption grew quickly in China from 2000 to 2012; on the whole, maximum electric consumption of pixel increased from 6.79 M kW?h to14.82 M kW?h. We analyzed county-level electric consumption trends and found that no-change type mainly occurred in the Qinghai-Tibet plateau. On the other hand, the rapid growth type was mainly distributed in Beijing, Shanghai, Guangzhou and the capital cities of the eastern provinces. Electric consumption growth rate was higher in southern China than in northern China. Based on China’s estimated spatial electricity consumption data, we compared electricity consumption data with county-level population density in 2012, and found that consumption is consistent with population density distribution. While we expected capital cities to have higher electricity consumption and this proved to be true, we also analyzed electricity consumption in provincial prefecture-level cities and counties. We discovered, using downscaling analysis, electricity consumption showed significant differences within regions. We analyzed electricity consumption levels, Moran’s I and LISA cluster in the study area from 2000 to 2012 by using statistical data. Results showed that generating capacity and electricity consumption of 31 provinces have a strong space correlation. There are gradually formed four "HH" cluster areas, such as Langfang-Tianjin, the Pearl River Delta, Shanghai-Hangzhou-Nanjing area and the West Bank of the Taiwan Straits from 2000 to 2012. County scale of spatial agglomeration of electricity consumption is significant, with "HH" cluster areas mainly located in Beijing-Tianjin, the Yangtze River Delta, Pearl River Delta, Shandong Peninsula, Changchun-Harbin-Dalian area, North Tianshan Mountain area. “LL" show a graduated trend moving from the South-Eastern border of Qinghai- Tibet plateau to the Tibetan plateau.(4) We used an ArcGIS-based hydrological model, through basin approach, and setting thresholds, to quantitatively define the boundaries of urban agglomerations in China, and the result roughly matched Fang Chuanglin’s "15+8" system of urban agglomerations.