China Jikan Research Institute of Engineering Investigations and Design, Co., Ltd., Xi'an 710043, China; College of Geological Engineering and Geomatics, Chang'an University, Xi'an 710061, China.
School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China.
J Environ Sci (China). 2025 Mar;149:358-373. doi: 10.1016/j.jes.2023.08.007. Epub 2023 Aug 22.
Carbon emissions resulting from energy consumption have become a pressing issue for governments worldwide. Accurate estimation of carbon emissions using satellite remote sensing data has become a crucial research problem. Previous studies relied on statistical regression models that failed to capture the complex nonlinear relationships between carbon emissions and characteristic variables. In this study, we propose a machine learning algorithm for carbon emissions, a Bayesian optimized XGboost regression model, using multi-year energy carbon emission data and nighttime lights (NTL) remote sensing data from Shaanxi Province, China. Our results demonstrate that the XGboost algorithm outperforms linear regression and four other machine learning models, with an R of 0.906 and RMSE of 5.687. We observe an annual increase in carbon emissions, with high-emission counties primarily concentrated in northern and central Shaanxi Province, displaying a shift from discrete, sporadic points to contiguous, extended spatial distribution. Spatial autocorrelation clustering reveals predominantly high-high and low-low clustering patterns, with economically developed counties showing high-emission clustering and economically relatively backward counties displaying low-emission clustering. Our findings show that the use of NTL data and the XGboost algorithm can estimate and predict carbon emissions more accurately and provide a complementary reference for satellite remote sensing image data to serve carbon emission monitoring and assessment. This research provides an important theoretical basis for formulating practical carbon emission reduction policies and contributes to the development of techniques for accurate carbon emission estimation using remote sensing data.
能源消耗所产生的碳排放已成为全球各国政府亟待解决的问题。利用卫星遥感数据准确估算碳排放已成为一个至关重要的研究课题。先前的研究依赖于统计回归模型，这些模型无法捕捉碳排放与特征变量之间复杂的非线性关系。在本研究中，我们提出了一种基于机器学习的碳排放量估算方法，即贝叶斯优化 XGboost 回归模型，该模型使用了来自中国陕西省多年的能源碳排放数据和夜间灯光（NTL）遥感数据。研究结果表明，XGboost 算法在 R 为 0.906 和 RMSE 为 5.687 的情况下，优于线性回归和其他四种机器学习模型。我们观察到碳排放量呈逐年增长趋势，高排放县主要集中在陕西省北部和中部，呈现出从离散、零星点到连续、扩展的空间分布的转变。空间自相关聚类显示出主要的高-高和低-低聚类模式，经济发达的县呈现出高排放聚类，经济相对落后的县呈现出低排放聚类。我们的研究结果表明，利用 NTL 数据和 XGboost 算法可以更准确地估算和预测碳排放量，并为卫星遥感图像数据在碳排放量监测和评估方面提供补充参考。这项研究为制定切实可行的减排政策提供了重要的理论依据，并为利用遥感数据进行精确碳排放量估算技术的发展做出了贡献。
