SemaNet: Bridging words and numbers for predicting missing environmental data in life cycle assessment.

Chen, B., Chen, H., Quan, Z. et al. (4 more authors) (2025) SemaNet: Bridging words and numbers for predicting missing environmental data in life cycle assessment. Environmental Science & Technology, 59 (39). pp. 21131-21146. ISSN: 0013-936X

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Item Type: Article
Authors/Creators:
Copyright, Publisher and Additional Information:

© 2025 American Chemical Society. This is an author-produced version of a paper subsequently published in Environmental Science & Technology. Uploaded in accordance with the publisher's self-archiving policy.

Keywords: data efficiency; life cycle assessment; life cycle inventory; neural network method; semantic learning
Dates:
  • Submitted: 4 June 2025
  • Accepted: 11 September 2025
  • Published (online): 26 September 2025
  • Published: 7 October 2025
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematical and Physical Sciences
The University of Sheffield > University of Sheffield Research Centres and Institutes > AMRC with Boeing (Sheffield)
The University of Sheffield > Advanced Manufacturing Institute (Sheffield) > AMRC with Boeing (Sheffield)
Funding Information:
Funder
Grant number
MANCHESTER PRIZE
UNSPECIFIED
Date Deposited: 06 Oct 2025 08:05
Last Modified: 10 Oct 2025 11:10
Status: Published
Publisher: American Chemical Society (ACS)
Refereed: Yes
Identification Number: 10.1021/acs.est.5c07557
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Open Archives Initiative ID (OAI ID):

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Filename: SemaNet_FInal approved version before publication.pdf

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