Resource recovery reshapes adaptation-sustainability trade-offs in urban drainage renewal: From high-throughput exploration to generative AI-enabled decision support.
- DOI
- 10.1016/j.watres.2026.126563
- Published
- 2026 Jul 24
- Container
- Water research
- Publisher
- Not recorded
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.watres.2026.126563,
title = {Resource recovery reshapes adaptation-sustainability trade-offs in urban drainage renewal: From high-throughput exploration to generative AI-enabled decision support.},
author = {Dong Q and Li L and Sha A and Xu Y and Zhao X and Bai S and Yang T and Ren N},
year = {2026},
journal = {Water research},
doi = {10.1016/j.watres.2026.126563},
url = {https://doi.org/10.1016/j.watres.2026.126563}
}RIS
TY - JOUR TI - Resource recovery reshapes adaptation-sustainability trade-offs in urban drainage renewal: From high-throughput exploration to generative AI-enabled decision support. AU - Dong Q AU - Li L AU - Sha A AU - Xu Y AU - Zhao X AU - Bai S AU - Yang T AU - Ren N PY - 2026 JO - Water research DO - 10.1016/j.watres.2026.126563 UR - https://doi.org/10.1016/j.watres.2026.126563 ER -
APA
Q, D., L, L., A, S., Y, X., X, Z., S, B., T, Y., & N, R. (2026). Resource recovery reshapes adaptation-sustainability trade-offs in urban drainage renewal: From high-throughput exploration to generative AI-enabled decision support.. Water research. https://doi.org/10.1016/j.watres.2026.126563
Source records
- pubmed · retrieved 2026-09-25T11:49:42.602Z