Mapping high-resolution energy consumption CO2 emissions in China by integrating nighttime lights and point source locations

Mengdi Wang; Rong Li; Meigen Zhang; Liangfu Chen; Fan Zhang; Congwu Huang

doi:10.1016/j.scitotenv.2023.165829

Mapping high-resolution energy consumption CO₂ emissions in China by integrating nighttime lights and point source locations

Sci Total Environ. 2023 Nov 20:900:165829. doi: 10.1016/j.scitotenv.2023.165829. Epub 2023 Jul 26.

Authors

Mengdi Wang¹, Rong Li², Meigen Zhang³, Liangfu Chen⁴, Fan Zhang⁵, Congwu Huang⁵

Affiliations

¹ Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China.
² Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China. Electronic address: rongli@hubu.edu.cn.
³ State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China.
⁴ State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China.
⁵ Faculty of Resources and Environmental Science, Hubei University, Wuhan 430062, China; Hubei Key Laboratory of Regional Development and Environmental Response, Hubei University, Wuhan 430062, China.

PMID: 37499816
DOI: 10.1016/j.scitotenv.2023.165829

Abstract

High-resolution CO₂ emission inventories are essential to accurately assess spatiotemporal patterns of carbon emissions, analyze factors affecting carbon emissions, and develop sound emission reduction policies. The top-down approach is often used to map CO₂ emissions from energy consumption due to its simplicity. However, the spatial proxy variables commonly used in this method, such as nighttime light (NL), land use, and population, are difficult to reflect the spatial distribution of CO₂ emissions from large point sources. Therefore, this study uses the active fire product provided by Visible Infrared Imaging Radiometer Suite (VIIRS) sensors on Suomi National Polar-Orbiting Partnership (Suomi-NPP) satellite to extract the location of industrial heat sources in China, and then develops an improved CO₂ emission estimation model by integrating industrial heat sources, Global Energy Monitor (GEM) power plant location and nighttime lights. The model is used to map CO₂ emissions from energy consumption at a resolution of 1 km*1 km from 2012 to 2019 in China. It is found that the overall accuracy of the model is greatly improved at the provincial level, the R² value is >0.75, and RMSE is distributed in 40-110 Mt. At the grid level, the improved model allocates more carbon emissions to the grid where the point source is located, which makes the spatial distribution of CO₂ emissions more reasonable.

Keywords: CO(2) emissions; China; Emission inventory; Point source; Spatiotemporal pattern; VIIRS.