A novel machine-learning approach for evaluating rebounds-associated environmental footprint of households and application to cooperative housing.

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作者： Rhythima,Shinde ^[1] ; Andreas,Froemelt ^[2] ; Aleksandra,Kim ^[3] ; Stefanie,Hellweg ^[4]

作者单位： Swiss Federal Institute of Technology, ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Ecological Systems Design, John-von-Neumann-Weg 9, 8093, Zürich, Switzerland. Electronic address: shinde@ifu.baug.ethz.ch. ^[1] Swiss Federal Institute of Technology, ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Ecological Systems Design, John-von-Neumann-Weg 9, 8093, Zürich, Switzerland; Future Cities Lab Global, Zurich Hub, Singapore-ETH Centre at CREATE, Switzerland. Electronic address: froemelt@ifu.baug.ethz.ch. ^[2] Swiss Federal Institute of Technology, ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Ecological Systems Design, John-von-Neumann-Weg 9, 8093, Zürich, Switzerland; Paul Scherrer Institute, Technology Assessment Group, Forschungsstrasse 111, 5232, Villigen PSI, Switzerland. Electronic address: aleksandra.kim@psi.ch. ^[3] Swiss Federal Institute of Technology, ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Environmental Engineering, Ecological Systems Design, John-von-Neumann-Weg 9, 8093, Zürich, Switzerland. Electronic address: stefanie.hellweg@ifu.baug.ethz.ch. ^[4]

关键词 Environmental footprint Housing sector Machine learning Rebounds Regression

主题词环境(Environment);家庭特征(Family Characteristics);住房(Housing);人类(Humans);收入(Income)

DOI 10.1016/j.jenvman.2021.114205

PMID 34891055

发布时间 2022-01-12

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