Modeling and spatio-temporal analysis of city-level carbon emissions based on nighttime light satellite imagery
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DOI: 10.1016/j.apenergy.2020.114696
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- Hu, Ting & Wang, Ting & Yan, Qingyun & Chen, Tiexi & Jin, Shuanggen & Hu, Jun, 2022. "Modeling the spatiotemporal dynamics of global electric power consumption (1992–2019) by utilizing consistent nighttime light data from DMSP-OLS and NPP-VIIRS," Applied Energy, Elsevier, vol. 322(C).
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- Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
- Du, Mengbing & Zhang, Xiaoling & Xia, Lang & Cao, Libin & Zhang, Zhe & Zhang, Li & Zheng, Heran & Cai, Bofeng, 2022. "The China Carbon Watch (CCW) system: A rapid accounting of household carbon emissions in China at the provincial level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
- Fang, Guochang & Gao, Zhengye & Tian, Lixin & Fu, Min, 2022. "What drives urban carbon emission efficiency? – Spatial analysis based on nighttime light data," Applied Energy, Elsevier, vol. 312(C).
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- Chao Wang & Jianing Wang & Le Ma & Mingming Jia & Jiaying Chen & Zhenfeng Shao & Nengcheng Chen, 2024. "Prediction Modeling and Driving Factor Analysis of Spatial Distribution of CO 2 Emissions from Urban Land in the Yangtze River Economic Belt, China," Land, MDPI, vol. 13(9), pages 1-21, September.
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- Huo, Weidong & Qi, Jie & Yang, Tong & Liu, Jialu & Liu, Miaomiao & Zhou, Ziqi, 2022. "Effects of China's pilot low-carbon city policy on carbon emission reduction: A quasi-natural experiment based on satellite data," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
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Keywords
Spatio-temporal; DMSP-OLS; Nighttime light data; Ensemble- learning structure; Greenhouse gas;All these keywords.
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