Multiscale analysis on spatiotemporal dynamics of energy consumption CO2 emissions in China: Utilizing the integrated of DMSP-OLS and NPP-VIIRS nighttime light datasets

Abstract

CO2 emissions caused by socioeconomic development and energy consumption in China have put enormous pressure on emissions reduction for Chinese government. In response to CO2 emissions reduction in China, this study integrated the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) stable nighttime light (SNL) data and Suomi National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) composite data, and established the integrated nighttime light datasets from 1992 to 2016. The estimated CO2 emissions model utilizing the integrated nighttime light datasets and statistical CO2 emissions at the provincial level from 1995 to 2016 were established. Finally, spatiotemporal dynamics of CO2 emissions were simulated from multiscale. The results clearly showed that: (1) The fitting results of regression relationship between DMSP-OLS SNL data and NPP-VIIRS composite data met the accuracy requirements. The CO2 emissions estimated model was valid. (2) The total amount of energy consumption CO2 emissions in China had increased from 1889.3340 Mt in 1995 to 4683.3165 Mt in 2016, with a total growth of 2.47 times. (3) The high CO2 emissions regions were clearly agglomerated in eastern coastal China from the pixel scale, the highest CO2 emissions provinces were concentrated in Hebei and Shandong, the high CO2 emissions prefecture cities were concentrated in Around Bohai Gulf area, eastern coastal China and some developed cities, and the high CO2 emissions counties were concentrated in eastern coastal China and western energy intensive counties. (4) The relatively-slow growth accounted for the highest proportion among the five growth types, and the CO2 emissions rapid growth regions were concentrated in eastern China at provincial, prefectural and county scale. The western regions accounted for the largest area proportion in five growth types at prefectural scale. We provided policy implications based on the results, which was beneficial to propose mitigation CO2 emissions reduction in China.