Journal of Geographical Sciences ›› 2022, Vol. 32 ›› Issue (10): 1886-1910.doi: 10.1007/s11442-022-2028-z

• Research Articles • Previous Articles     Next Articles

Spatio-temporal variations and influencing factors of energy-related carbon emissions for Xinjiang cities in China based on time-series nighttime light data

ZHANG Li1,2,3(), LEI Jun1,2,*(), WANG Changjian4, WANG Fei5, GENG Zhifei6, ZHOU Xiaoli7   

  1. 1.Xinjiang Institute of Ecology and Geography, CAS, Urumqi 830011, China
    2.University of Chinese Academy of Sciences, Beijing 100049, China
    3.Fujian Urban and Rural Planning & Design Institute, Fuzhou 350003, China
    4.Guangdong Provincial Key Laboratory of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, China
    5.School of Resources and Planning, Guangzhou Xinhua University, Guangzhou 510520, China
    6.Suzhou Honglan Data Technology Co., Ltd, Suzhou 215000, Jiangsu, China
    7.Urumqi Meteorological Satellite Ground Station, Urumqi 830011, China
  • Received:2021-11-01 Accepted:2022-03-14 Online:2022-10-25 Published:2022-12-25
  • Contact: LEI Jun;
  • About author:Zhang Li (1988-), Senior Engineer, specialized in urban and regional planning, regional sustainable development. E-mail:
  • Supported by:
    The Third Xinjiang Scientific Expedition Program(2021xjkk0905);GDAS Special Project of Science and Technology Development(2020GDASYL-20200301003);GDAS Special Project of Science and Technology Development(2020GDASYL-20200102002);National Natural Science Foundation of China(41501144);Project of Department of Natural Resources of Guangdong Province(GDZRZYKJ2022005)


This essay combines the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) nighttime light data and the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light data into a “synthetic DMSP” dataset, from 1992 to 2020, to retrieve the spatio-temporal variations in energy-related carbon emissions in Xinjiang, China. Then, this paper analyzes several influencing factors for spatial differentiation of carbon emissions in Xinjiang with the application of geographical detector technique. Results reveal that (1) total carbon emissions continued to grow, while the growth rate slowed down in the past five years. (2) Large regional differences exist in total carbon emissions across various regions. Total carbon emissions of these regions in descending order are the northern slope of the Tianshan (Mountains) > the southern slope of the Tianshan > the three prefectures in southern Xinjiang > the northern part of Xinjiang. (3) Economic growth, population size, and energy consumption intensity are the most important factors of spatial differentiation of carbon emissions. The interaction between economic growth and population size as well as between economic growth and energy consumption intensity also enhances the explanatory power of carbon emissions’ spatial differentiation. This paper aims to help formulate differentiated carbon reduction targets and strategies for cities in different economic development stages and those with different carbon intensities so as to achieve the carbon peak goals in different steps.

Key words: carbon emissions, nighttime light data, spatio-temporal variations, influencing factors, Xinjiang