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浏览/检索结果: 共5条，第1-5条 帮助 限定条件 发表日期：2021     已选(0)清除 条数/页：5101520253035404550556065707580859095100   排序方式：请选择作者升序作者降序题名升序题名降序发表日期升序发表日期降序提交时间升序提交时间降序 Spatial interaction between urbanization and ecosystem services in Chinese urban agglomerations 期刊论文 LAND USE POLICY, 2021, 卷号: 109, 页码: 10 作者:  Ouyang, Xiao;  Tang, Lisha;  Wei, Xiao;  Li, Yonghui 收藏  |  浏览/下载：48/0  |  提交时间：2021/11/05 Urban agglomeration  Urbanization  Ecosystem services  Spatial interaction   How much did China's emergence as "the world's factory" contribute to its national income 期刊论文 CHINA ECONOMIC REVIEW, 2021, 卷号: 69, 页码: 21 作者:  Duan, Yuwan;  Dietzenbacher, Erik;  Los, Bart;  Yang, Cuihong 收藏  |  浏览/下载：11/0  |  提交时间：2022/04/02 China  Exports  National income  Input-output tables   County-level estimates of population and economic scenarios under the shared socioeconomic pathways: A case study in Inner Mongolia, China 期刊论文 PHYSICS AND CHEMISTRY OF THE EARTH, 2021, 卷号: 122, 页码: 13 作者:  Bai, Yuping;  Wang, Wenxuan;  Hu, Yecui;  Wang, Zehao 收藏  |  浏览/下载：40/0  |  提交时间：2021/08/19 Population projection  GDP Projection  Shared socioeconomic pathways  County-level  Inner Mongolia   Exploring regional land use dynamics under shared socioeconomic pathways: A case study in Inner Mongolia, China 期刊论文 TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 2021, 卷号: 166, 页码: 15 作者:  Bai, Yuping;  Deng, Xiangzheng;  Cheng, Yunfei;  Hu, Yecui;  Zhang, Lijin 收藏  |  浏览/下载：22/0  |  提交时间：2021/06/10 Land use change  Shared Socio-economic pathways  CGELUC model  DLS  Inner Mongolia   Research on the Spatio-Temporal Dynamic Evolution Characteristics and Influencing Factors of Electrical Power Consumption in Three Urban Agglomerations of Yangtze River Economic Belt, China Based on DMSP/OLS Night Light Data 期刊论文 REMOTE SENSING, 2021, 卷号: 13, 期号: 6, 页码: 33 作者:  Zhong, Yang;  Lin, Aiwen;  Xiao, Chiwei;  Zhou, Zhigao 收藏  |  浏览/下载：24/0  |  提交时间：2021/07/09 YREB  three urban agglomerations  DMSP  OLS night light data  EPC  spatiotemporal dynamics  Geographically weighted regression (GWR) model  random forest algorithm