Embodied greenhouse gas emissions of buildings—Machine learning approach for early stage prediction-Reference-Cited by-同舟云学术

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Embodied greenhouse gas emissions of buildings—Machine learning approach for early stage prediction

Published:2024-06 Issue: Volume:257 Page:111523
ISSN:0360-1323
Container-title:Building and Environment
language:en
Short-container-title:Building and Environment

Author:

Fenton Sandie Kate^ORCID,Munteanu Adrian^ORCID,De Rycke Klaas^ORCID,De Laet Lars^ORCID

Publisher

Elsevier BV

Reference92 articles.

1. Industrial Ecology and Sustainable Engineering;Graedel,2010

2. UN Environment Programme, 2020 Global Status Report for Buildings and Construction, Global Status Report, 2020, pp. 20–24, URL.

3. Carbon emissions of infrastructure development;Müller;Environ. Sci. Technol.,2013

4. Compendium on greenhouse gas baselines and monitoring;Eichhorst,2017

5. Embodied greenhouse gas emissions in structural materials for the german residential building stock—Quantification and mitigation scenarios;Hingorani;Build. Environ.,2023

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Spatio-temporal distribution and peak prediction of energy consumption and carbon emissions of residential buildings in China;Applied Energy;2024-12

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