Research on energy consumption of China’s prefecture-level scale based on DMSP/OLS night light data

Abstract

Energy is an important support for the development of national economy. Accurate and convenient access to space-time dynamic information of energy consumption is of great significance for the rational formulation of energy policy. Based on the quantitative correlation between DMSP/OLS night lighting data and energy statistics, the spatial pattern of energy consumption in China from 2000 to 2013 was simulated at the prefecture-level scale. The results show that it is feasible to simulate energy consumption in different cities in China based on DMSP/OLS night lighting data, which can reliably reflect the temporal and spatial dynamics of energy consumption. This study provides a new method support for dynamic monitoring of energy consumption.

Analysis of spatial inequality using DMSP‐OLS nighttime‐light satellite imageries: A case study of Thailand

“…The annual ALI value of each province was calculated for the period of 1994–2013 by using DMSP–OLS NTL imageries. Then, in accordance with the validation methodologies conventionally applied in Forbes (2013), Shi et al (2014), You, Chen, Du, Zhang, and Hu (2014), Liu et al (2015), Jing, Shao, Cao, Fu, and Yan (2016), Bagan, Borjigin, and Yamagata (2018), Ji, Zhao, Yang, Yue, and Wang (2018), Rafa, Moyer, Wang, and Sutton (2018), Yücer and Erener (2018), Xu, Shan, Zhang, and Duan (2019), Bergantino, Liddo, and Porcelli (2020), Hu and Zhang (2020) and Ma, Fu, He, and Fan (2020), the correlation with IPI was computed using Pearson's correlation coefficients and the regression analysis between IPI and ALI was conducted. As indicated in Table 2, the obtained Pearson's correlation coefficients were positive and statistically significant at the 0.01 level.…”