What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China.
Material intensity coefficient (MIC) is vital for material stock accounting in the field of industrial ecology. However, the categorization of MIC varies across regions especially for buildings that diverge greatly along the history and space aspect, and acquisition of MIC data and building informat...
| Published in: | Journal of Industrial Ecology Vol. 26; no. 5; pp. 1809 - 1824 |
|---|---|
| Main Authors: | , , , , , |
| Format: | Article |
| Published: |
Springer Nature
Oct2022
|
| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=159764419&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 159764419 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10881980 FL1 jtl: Journal of Industrial Ecology issn: 10881980 maglogo: Y pubinfo: dt: Oct2022 vid: 26 iid: 5 pid: 237 pub: Springer Nature artinfo: ui: 159764419 10.1111/jiec.13332 ppf: 1809 ppct: 15 formats: tig: atl: What matters most to the material intensity coefficient of buildings? Random forest‐based evidence from China. aug: au: Zhang, Ruirui Guo, Jing Yang, Dong Shirakawa, Hiroaki Shi, Feng Tanikawa, Hiroki affil: Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, China Qilu University of Technology (Shandong Academy of Sciences), Institute of Science and Technology for Development of Shandong, Jinan, China su: China Industrial ecology Random forest algorithms Circular economy Acquisition of data Materials analysis sug: subj: Industrial ecology China Random forest algorithms Circular economy Acquisition of data Materials analysis keyword: bottom‐up material stock analysis building material intensity Chinese buildings feature importance industrial ecology random forest bottom‐up material stock analysis building material intensity Chinese buildings feature importance industrial ecology random forest ab: Material intensity coefficient (MIC) is vital for material stock accounting in the field of industrial ecology. However, the categorization of MIC varies across regions especially for buildings that diverge greatly along the history and space aspect, and acquisition of MIC data and building information have always been a challenge in related studies. In this study, the state‐of‐art ensemble model "Random Forest" was developed on Chinese buildings to identify the impact of four building attributes (building structure, construction year, use type, and region) on MIC, and these features' importance was further assessed by considering variable correlations. The features' importance and their individual effects on MIC were intuitively revealed by depicting the partial dependence plots. Finally, a set of hierarchical MIC values was estimated by integrating the order of four variables' importance and a quick MIC calculator was provided. Results showed that building structure is the most influential attribute for MIC, followed by the construction year, use type, and region, successively. The RF‐based MIC values allow researchers to apply it to material stock and flow analysis by choosing a specific building feature(s) in the MIC calculator, which is (are) available in building physical inventory data. This study provides a method that could help researchers locate key influencing variables and give insights into the comparability of MIC research across regions and play an important role in developing urban mining and circular economy strategies. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|