Guo Bin, Xie Tingting, Zhang Wencai, Wu Haojie, Zhang Dingming, Zhu Xiaowei, Ma Xuying, Wu Min, Luo Pingping
College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
College of Geomatics, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China.
Sci Total Environ. 2023 Dec 20;905:167309. doi: 10.1016/j.scitotenv.2023.167309. Epub 2023 Sep 22.
Climate change caused by CO 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) 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 (R = 0.95) than the NTL (R = 0.92) in predicting CE, particularly in rural regions where the R 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.
由碳排放（CE）引起的气候变化已受到全球广泛关注。获取精确的碳排放数据对于实现碳达峰和碳中和至关重要。现有碳排放数据集存在显著不足，如空间分辨率粗糙和精度低，难以满足实际需求。本研究开发了一种增强型人口-夜光指数（RPNTL），它整合了来自国家极地轨道伙伴关系（NPP）可见红外成像辐射计套件（VIIRS）的夜间灯光数字值（DN）和人口密度，以提高碳排放估算精度。碳排放账户与数据集（CEAD）中的碳排放被分为三个部门，即城市、工业和农村，以区分各部门碳排放的异质性。采用普通最小二乘法（OLS）、地理加权回归（GWR）、时间加权回归（TWR）和地理与时间加权回归（GTWR）模型，建立每个部门RPNTL与碳排放之间的定量关系。通过模型比较和精度评估确定最优模型，并将其用于对城市、工业和农村地区的碳排放进行栅格化。此外，引入热点分析、趋势分析和标准差椭圆来展示多尺度碳排放的时空动态特征。在估算碳排放方面，GTWR的性能优于其他方法。增强型RPNTL在预测碳排放方面的决定系数（R = 0.95）高于夜光指数（NTL，R = 0.92），特别是在农村地区，R值从0.76增加到0.81。2013年至2019年，中国东部和北部碳排放较高，而东北地区和成渝地区碳排放呈下降趋势。相反，长江三角洲、珠江三角洲、汾渭平原和河南省呈上升趋势。工业和农村碳排放的重心向西部地区转移，而城市碳排放的重心向北移动。本研究为碳排放控制决策提供了宝贵的见解。
