School of Land Engineering, Chang'an University, Xi'an, 710064, Shaanxi, China.
Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an, 710064, Shaanxi, China.
Environ Sci Pollut Res Int. 2023 Aug;30(37):87535-87548. doi: 10.1007/s11356-023-28692-6. Epub 2023 Jul 10.
Global warming attributed to the emission of greenhouse gases has caused unprecedented extreme weather events, such as excessive heatwave and rainfall, posing enormous threats to human life and sustainable development. China, as the toppest CO emitter in the world, has promised to achieve carbon emission peak by 2030. However, it is difficult to estimate county-level carbon emissions in China because of the lack of statistical data. Previous studies have established relationship between carbon emission and nighttime light; however, using only nighttime light for carbon emission modeling ignores the impact of natural or other socioeconomic factors on emissions. In this paper, we adopted the back propagation neural network to estimate carbon emissions at county level in Shaanxi, China, using nighttime light, Normalized Difference Vegetation Index, precipitation, land surface temperature, elevation, and population density. Trend analysis, spatial autocorrelation, and standard deviation ellipse were employed to analyze the spatiotemporal distributions of carbon emission during 2012-2019. Three metrics (R, root mean square error, and mean absolute error) were adopted to validate the accuracy of the proposed model, with the values of 0.95, 1.30, and 0.58 million tons, respectively, demonstrating a comparable estimation performance. The results present that carbon emissions in Shaanxi Province rise from 256.73 in 2012 to 305.87 million tons in 2019, formatting two hotspots in Xi'an and Yulin city. The proposed model can estimate carbon emissions of Shaanxi Province at a finer scale with an acceptable accuracy, which can be efficiently applied in other spatial or temporal domains after being localized, providing technical supports for carbon reduction.
全球变暖归因于温室气体排放，导致了前所未有的极端天气事件，如热浪和暴雨过多，对人类生命和可持续发展构成了巨大威胁。中国作为世界上最大的 CO 排放国，承诺到 2030 年实现碳排放峰值。然而，由于缺乏统计数据，中国的县级碳排放很难估计。以前的研究已经建立了碳排放与夜间灯光之间的关系；然而，仅使用夜间灯光进行碳排放建模忽略了自然或其他社会经济因素对排放的影响。在本文中，我们采用反向传播神经网络，使用夜间灯光、归一化差值植被指数、降水、地表温度、海拔和人口密度来估算中国陕西省的县级碳排放。趋势分析、空间自相关和标准差椭圆被用来分析 2012-2019 年期间碳排放的时空分布。采用三个指标（R、均方根误差和平均绝对误差）来验证所提出模型的准确性，分别为 0.95、1.30 和 0.58 百万吨，表明具有相当的估计性能。结果表明，陕西省的碳排放从 2012 年的 256.73 百万吨增加到 2019 年的 305.87 百万吨，在西安市和榆林市形成了两个热点。所提出的模型可以以可接受的精度更精细地估计陕西省的碳排放，在本地化后可以有效地应用于其他空间或时间域，为减排提供技术支持。
