College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China; Chinese Academy of Surveying & Mapping, Beijing 100830, China; Jiangsu Laboratory of Lake Environment Remote Sensing Technologies, Huaiyin Institute of Technology, Huai'an 223003, China.
College of Geoscience and Surveying Engineering, China University of Mining and Technology, Beijing 100083, China.
Sci Total Environ. 2022 Nov 20;848:157630. doi: 10.1016/j.scitotenv.2022.157630. Epub 2022 Jul 26.
Accurate mapping spatiotemporal patterns of CO emissions and understanding its driving factors are very important, it is useful for the scientific and rational formulation of carbon emission reduction policies. Nevertheless, due to data availability issues, most studies have been limited to the global and national scales, and the models used were relatively simple. In this paper, we used the 500 m Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS-DNB) data and the 250 m Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (MODIS NDVI) and proposed an improved CO emissions index (ICEI) to calculate CO emissions. Compared with the total nighttime light (NTL), the average regression coefficient (R) can be improve from 0.73 to 0.78. We also used the coefficient of variation, spatial autocorrelation, and geographically weighted regression models to analyze the temporal and spatial variation mode of CO emissions, as well as the associated correlation and heterogeneity, at three different administrative unit scales during 2012-2019. Our experimental results demonstrate that: (1) the improved index (ICEI) is better than the traditional variable (NTL) in estimating CO emissions; (2) the highest CO emissions are primarily gathered in the developed coastal areas in eastern China; and (3) at the provincial level, the added value of the secondary industry is the most significant factor, whereas the added value of the tertiary industry is negatively correlated with CO emissions.
准确刻画 CO 排放的时空分布格局及其驱动因素十分重要，这对于科学合理地制定减排政策具有重要意义。然而，由于数据的可得性问题，大多数研究都局限于全球和国家尺度，所使用的模型也相对简单。在本文中，我们使用了 500m 可见光红外成像辐射仪套件日/夜带（VIIRS-DNB）数据和 250m 中等分辨率成像光谱仪归一化植被指数（MODIS NDVI），并提出了一种改进的 CO 排放指数（ICEI）来计算 CO 排放。与总夜间灯光（NTL）相比，平均回归系数（R）可以从 0.73 提高到 0.78。我们还使用变异系数、空间自相关和地理加权回归模型来分析 2012-2019 年三个不同行政单元尺度上 CO 排放的时空变化模式以及相关的相关性和异质性。我们的实验结果表明：（1）改进后的指数（ICEI）在估算 CO 排放方面优于传统变量（NTL）；（2）CO 排放的最高值主要集中在中国东部发达的沿海地区；（3）在省级水平上，第二产业的附加值是最重要的因素，而第三产业的附加值与 CO 排放呈负相关。
