Examining threshold effects of built environment elements on travel-related carbon-dioxide emissions

Xinyi Wu, Tao Tao, Jason Cao, Yingling Fan, Anu Ramaswami

Research output: Contribution to journalArticlepeer-review

92 Scopus citations

Abstract

Understanding how built environment features are associated with travel-related carbon-dioxide (CO2) emissions is essential for planners to encourage environmentally sustainable travel through transportation and land use policies. Applying gradient boosting decision trees to the data from the Minneapolis-St. Paul metropolitan area, this study addresses two gaps in the literature by identifying critical built environment determinants of CO2 emissions, and more importantly, illustrating threshold effects of built environment elements. The results show that three neighborhood-level built environment factors have the strongest influences on CO2 emissions: distance to the nearest transit stop, job density, and land use diversity. The distance to downtowns also has a substantial impact. This study further confirms that built environment variables are effective only within a certain range. These threshold effects offer valuable implications for planners to achieve desirable environmental benefits efficiently.

Original languageEnglish (US)
Pages (from-to)1-12
Number of pages12
JournalTransportation Research Part D: Transport and Environment
Volume75
DOIs
StatePublished - Oct 2019

Bibliographical note

Funding Information:
This study was funded by the Sustainable Research Network project of the National Science Foundation of USA: Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy and Livable Cities. (Award #: 1444745). Mr. Kirti Das led the survey administration.

Funding Information:
This study was funded by the Sustainable Research Network project of the National Science Foundation of USA : Integrated Urban Infrastructure Solutions for Environmentally Sustainable, Healthy and Livable Cities . (Award #: 1444745 ). Mr. Kirti Das led the survey administration.

Publisher Copyright:
© 2019 Elsevier Ltd

Keywords

  • Climate change
  • Greenhouse gas emissions
  • Machine learning
  • Travel behavior
  • Urban form

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