Spatial Quantitative Analysis of Urban Energy Consumption based on Night-Time Remote Sensing Data and POI

Climate change has become a major global environmental issue that is widely concerned by countries around the world. It has been a very clear scientific consensus that the global carbon emission has to be cut urgently under the context of the global warming and extreme climate. Currently, few studies on the urban energy consumption have been performed, especially the quantitative research on the scale of urban blocks, which is actually required by cities in order to adopt precise control, optimize energy structure, and reduce carbon emissions. This paper took Jingmen, a resource-based city, as a case city, and applied night-time remote sensing data, POI, and other big data. Quantitative analysis of the spatial data on key factors affecting carbon emissions in transportation, industry, and construction sectors, respectively, was applied to realize block-scale spatial visualization of urban energy consumption, and furthermore, to discuss the impact of urbanization and industrialization on urban energy consumption. It is found that the continuous growth of energy consumption in the industrial sector was the main driving factor of the city's total energy consumption growth. Among the 72 towns (blocks), 10 towns (blocks) were dominated by industrial energy consumption which accounted for up to 68% the energy consumption of Jingmen. From 2005 to 2015, the total energy consumption of Jingmen City increased by 828, 200 tons of standard coal equivalent(tce), while the number of towns (blocks) with more than 10, 000 tons of standard coal equivalent(tce) decreased by 4. Therefore, the energy consumption of Jingmen City showed a trend of increase and concentration. The conclusions of this study can fill up the problems that cannot be found in the energy consumption statistics of cities, and propose a more accurate way to reduce energy consumption in Jingmen City, which provide a reference for the green transformation of similar small and medium-sized resource-based cities. © 2021, Science Press. All right reserved.