检索规则说明:AND代表“并且”;OR代表“或者”;NOT代表“不包含”;(注意必须大写,运算符两边需空一格)
检 索 范 例 :范例一: (K=图书馆学 OR K=情报学) AND A=范并思 范例二:J=计算机应用与软件 AND (U=C++ OR U=Basic) NOT M=Visual
作 者:Hongye Cao Ling Han Ming Liu Liangzhi Li
机构地区:[1]China Jikan Research Institute of Engineering Investigations and Design,Co.,Ltd.,Xi’an 710043,China [2]College of Geological Engineering and Geomatics,Chang’an University,Xi’an 710061,China [3]School of Land Engineering,Chang’an University,Xi’an 710064,China [4]Xi’an Key Laboratory of Territorial Spatial Information,School of Land Engineering,Chang’an University,Xi’an 710064,China [5]Department of Geography and Environmental Management,University of Waterloo,Waterloo,Ontario N2L 3G1,Canada
出 处:《Journal of Environmental Sciences》2025年第3期358-373,共16页环境科学学报(英文版)
基 金:supported by the Key Research and Development Program in Shaanxi Province,China(No.2022ZDLSF07-05);the Fundamental Research Funds for the Central Universities,CHD(No.300102352901)。
摘 要: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^(2)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 emissionsmore 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.
关 键 词:Machine learning Energy carbon emissions Nighttime light Spatial distribution
分 类 号:X51[环境科学与工程—环境工程]
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在载入数据...
正在链接到云南高校图书馆文献保障联盟下载...
云南高校图书馆联盟文献共享服务平台 版权所有©
您的IP:216.73.216.124