文献类型：期刊文献

英文题名：How urban land-use intensity affected CO2 emissions at the county level: Influence and prediction

作者：Xia, Chuyu[1,8,9];Dong, Zhaoyingzi[2];Wu, Peng[3];Dong, Feng[4];Fang, Kai[2];Li, Qiang[1];Li, Xiaoshun[5];Shao, Zhuang[6];Yu, Zhenning[7]

机构：[1]Beijing Univ Technol, Fac Architecture Civil & Transportat Engn, Beijing 100124, Peoples R China;[2]Zhejiang Univ, Sch Econ, Hangzhou 310058, Zhejiang, Peoples R China;[3]Zhejiang Univ, Sch Publ Affairs, Hangzhou 310058, Zhejiang, Peoples R China;[4]China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China;[5]China Univ Min & Technol, Res Ctr Transit Dev & Rural Revitalizat Resource B, Xuzhou 221116, Peoples R China;[6]Beijing Forestry Univ, Sch Landscape Architecture, Beijing 100083, Peoples R China;[7]East China Univ Sci & Technol, Sch Social & Publ Adm, Shanghai 200237, Peoples R China;[8]MNR, Key Lab Ocean Space Resource Management Technol, Hangzhou 310007, Zhejiang, Peoples R China;[9]Beijing Normal Univ, Sch Environm, State Key Joint Lab Environm Simulat & Pollut Cont, Beijing 100875, Peoples R China

年份：2022

卷号：145

外文期刊名：ECOLOGICAL INDICATORS

收录：;EI(收录号：20224413052127);WOS:【SCI-EXPANDED(收录号:WOS:000917281200001)】；

基金：This work was supported by the National Natural Science Foundation of China (Grant No. 72004014, 71874192) .

语种：英文

外文关键词：Urban land -use intensity; CO 2 emissions; Spatial effect; Machine learning; Prediction

摘要：Currently, China will promote county towns' urbanization, and few studies have analyzed spatial effects of urban land-use intensity on CO2 emissions and predicted CO2 emissions at the county level accurately. Here, taking data from 2010 to 2015 at the county level of Zhejiang Province as an example, we analyzed the spatial effect of urban land-use intensity from three aspects of input, density and output on CO2 emissions by the spatial Durbin model (SDM). And then a machine learning method of Back Propagation Neural Network (BPNN) was proposed to predict CO2 emissions for 2035 nonlinearly under the different promotions of urban land-use intensity. The main result and conclusion showed that: (1) The spillover effects of urban land-use density were negatively related to CO2 emissions, and urban land-use output intensity showed positive spillover effects on CO2 emissions; (2) The prediction of BPNN showed that the improvement of urban land-use intensity would not be effective in CO2 emissions reductions for southwestern counties with both low levels of urbanization and urban land-use in-tensity; (3) It was effective in CO2 emissions reduction by slowing down the growth rate of urban land-use capital input intensity, especially for the northeast region which was developed by the port economy. Our study encouraged a regional differentiated urban land-use intensity improvement strategy for Zhejiang to achieve low carbon development.