Rasterizing CO2 emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013-2019

Bin Guo; Tingting Xie; Wencai Zhang; Haojie Wu; Dingming Zhang; Xiaowei Zhu; Xuying Ma; Min Wu; Pingping Luo

doi:10.1016/j.scitotenv.2023.167309

Rasterizing CO₂ emissions and characterizing their trends via an enhanced population-light index at multiple scales in China during 2013-2019

Sci Total Environ. 2023 Dec 20:905:167309. doi: 10.1016/j.scitotenv.2023.167309. Epub 2023 Sep 22.

Authors

Bin Guo¹, Tingting Xie², Wencai Zhang³, Haojie Wu², Dingming Zhang², Xiaowei Zhu⁴, Xuying Ma², Min Wu², Pingping Luo⁵

Affiliations

¹ College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China. Electronic address: guobin12@xust.edu.cn.
² College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
³ College of Land Science and Technology, China Agricultural University, Beijing 100193, China.
⁴ Department of Mechanical and Materials Engineering, Portland State University, Portland, OR 97207, USA.
⁵ School of Water and Environment, Chang'an University, Xi'an, Shaanxi 710054, China. Electronic address: lpp@chd.edu.cn.

PMID: 37742983
DOI: 10.1016/j.scitotenv.2023.167309

Abstract

Climate change caused by CO₂ emissions (CE) has received widespread global concerns. Obtaining precision CE data is necessary for achieving carbon peak and carbon neutrality. Significant deficiencies of existing CE datasets such as coarse spatial resolution and low precision can hardly meet the actual requirements. An enhanced population-light index (RPNTL) was developed in this study, which integrates the Nighttime Light Digital Number (DN) Value from the National Polar-orbiting Partnership (NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) and population density to improve CE estimation accuracy. The CE from the Carbon Emission Accounts & Datasets (CEAD_S) was divided into three sectors, namely urban, industrial, and rural, to differentiate the heterogeneity of CE in each sector. The ordinary least square (OLS), geographically weighted regression (GWR), temporally weighted regression (TWR), and geographically and temporally weighted regression (GTWR) models were employed to establish the quantitative relationship between RPNTL and CE for each sector. The optimal model was determined through model comparison and precision evaluation and was utilized to rasterize CE for urban, industrial, and rural areas. Additionally, hot spot analysis, trend analysis, and standard deviation ellipses were introduced to demonstrate the spatiotemporal dynamic characteristics of CE at multiple scales. The performance of the GTWR outperformed other methods in estimating CE. The enhanced RPNTL demonstrated a higher coefficient of determination (R² = 0.95) than the NTL (R² = 0.92) in predicting CE, particularly in rural regions where the R² value increased from 0.76 to 0.81. From 2013 to 2019, high CE was observed in eastern and northern China, while a decreasing trend was detected in northeastern China and Chengdu-Chongqing. Conversely, the Yangtze River Delta, Pearl River Delta, Fenwei Plain, and Henan Province showed an increasing trend. The center of gravity for industrial and rural CE is shifting towards western regions, whereas that for urban CE is moving northward. This study provides valuable insights for decision-making on CE control.

Keywords: CO(2); Data-driven models; Mapping; Nightlight remote sensing; Rasterization.