Challenges in the green building supply chain: multi-objective planning for prefabricated factory site selection.

Saved in:
Bibliographic Details
Title: Challenges in the green building supply chain: multi-objective planning for prefabricated factory site selection.
Authors: Guo, Guihuan1,2 (AUTHOR) guoguihuan@whut.edu.cn, Wang, Junwu1,2 (AUTHOR) 267544@whut.edu.cn, Wanjin, Yingjun2 (AUTHOR) 300395@whut.edu.cn, Song, Yinghui2,3 (AUTHOR) songyinghui@whut.edu.cn
Source: Environment, Development & Sustainability. Jul2026, Vol. 28 Issue 7, p16037-16079. 43p.
Subject Terms: *Multi-objective optimization, *Carbon emissions, *Optimization algorithms, *Geoinformatics, *Sustainable development, *Supply chains, *Sustainable buildings
Geographic Terms: China
Abstract: In the context of the global trend towards promoting green development, the strategic selection of construction sites for prefabricated building component factories is crucial for the sustainable growth of businesses and aligns with environmental stewardship efforts, contributing to China's pursuit of "dual carbon" policy objectives. However, current research on the site selection of prefabricated component factories has several typical shortcomings. These include the absence of a comprehensive site selection index evaluation system, subjective qualitative analyses, excessive emphasis on economic benefits at the expense of environmental importance, and the limited accuracy of quantitative analysis tools. To address these issues, this study proposes a site selection evaluation system that considers both economic and environmental factors. Utilizing GIS analysis, the optimal candidate areas for prefabricated component factory site selection can be identified. Additionally, the improved GM (1,1) model is employed to forecast the demand for prefabricated components in the target area. Based on this forecast, a site selection objective function model is established to minimize total construction and transportation costs and carbon emissions. This study also introduces an improved NSGA-III algorithm (MBPSO-NSGA-III) for solving the site selection model. The optimal candidate sites for prefabricated component factories are then selected based on the site selection index evaluation system. The results of real case analyses show that employing this site selection model can lead to cost savings of about 15% or more and lower carbon dioxide emissions, support green development in the construction industry, boost enterprises' capacity for sustainable development, and achieve environmental responsibility objectives. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
Text:
  Availability: 1
Header DbId: enr
DbLabel: Energy & Power Source
An: 194936931
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Challenges in the green building supply chain: multi-objective planning for prefabricated factory site selection.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Guo%2C+Guihuan%22">Guo, Guihuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> guoguihuan@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Junwu%22">Wang, Junwu</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> 267544@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Wanjin%2C+Yingjun%22">Wanjin, Yingjun</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> 300395@whut.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Song%2C+Yinghui%22">Song, Yinghui</searchLink><relatesTo>2,3</relatesTo> (AUTHOR)<i> songyinghui@whut.edu.cn</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Environment%2C+Development+%26+Sustainability%22">Environment, Development & Sustainability</searchLink>. Jul2026, Vol. 28 Issue 7, p16037-16079. 43p.
– Name: Subject
  Label: Subject Terms
  Group: Su
  Data: *<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink><br />*<searchLink fieldCode="DE" term="%22Optimization+algorithms%22">Optimization algorithms</searchLink><br />*<searchLink fieldCode="DE" term="%22Geoinformatics%22">Geoinformatics</searchLink><br />*<searchLink fieldCode="DE" term="%22Sustainable+development%22">Sustainable development</searchLink><br />*<searchLink fieldCode="DE" term="%22Supply+chains%22">Supply chains</searchLink><br />*<searchLink fieldCode="DE" term="%22Sustainable+buildings%22">Sustainable buildings</searchLink>
– Name: SubjectGeographic
  Label: Geographic Terms
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22China%22">China</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In the context of the global trend towards promoting green development, the strategic selection of construction sites for prefabricated building component factories is crucial for the sustainable growth of businesses and aligns with environmental stewardship efforts, contributing to China's pursuit of "dual carbon" policy objectives. However, current research on the site selection of prefabricated component factories has several typical shortcomings. These include the absence of a comprehensive site selection index evaluation system, subjective qualitative analyses, excessive emphasis on economic benefits at the expense of environmental importance, and the limited accuracy of quantitative analysis tools. To address these issues, this study proposes a site selection evaluation system that considers both economic and environmental factors. Utilizing GIS analysis, the optimal candidate areas for prefabricated component factory site selection can be identified. Additionally, the improved GM (1,1) model is employed to forecast the demand for prefabricated components in the target area. Based on this forecast, a site selection objective function model is established to minimize total construction and transportation costs and carbon emissions. This study also introduces an improved NSGA-III algorithm (MBPSO-NSGA-III) for solving the site selection model. The optimal candidate sites for prefabricated component factories are then selected based on the site selection index evaluation system. The results of real case analyses show that employing this site selection model can lead to cost savings of about 15% or more and lower carbon dioxide emissions, support green development in the construction industry, boost enterprises' capacity for sustainable development, and achieve environmental responsibility objectives. [ABSTRACT FROM AUTHOR]
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=194936931
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1007/s10668-024-05491-8
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 43
        StartPage: 16037
    Subjects:
      – SubjectFull: Multi-objective optimization
        Type: general
      – SubjectFull: Carbon emissions
        Type: general
      – SubjectFull: Optimization algorithms
        Type: general
      – SubjectFull: Geoinformatics
        Type: general
      – SubjectFull: Sustainable development
        Type: general
      – SubjectFull: Supply chains
        Type: general
      – SubjectFull: Sustainable buildings
        Type: general
      – SubjectFull: China
        Type: general
    Titles:
      – TitleFull: Challenges in the green building supply chain: multi-objective planning for prefabricated factory site selection.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Guo, Guihuan
      – PersonEntity:
          Name:
            NameFull: Wang, Junwu
      – PersonEntity:
          Name:
            NameFull: Wanjin, Yingjun
      – PersonEntity:
          Name:
            NameFull: Song, Yinghui
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 1387585X
          Numbering:
            – Type: volume
              Value: 28
            – Type: issue
              Value: 7
          Titles:
            – TitleFull: Environment, Development & Sustainability
              Type: main
ResultId 1