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Peer-reviewedCC BY 4.0Environmental Research Letters

Strengthening and safeguarding artificial intelligence for a planet under pressure

  • Jieshu Wang1
  • J. Alberto Conejero2
  • Sergio Hoyas2
  • Jose Lobo3
  • Ricardo Vinuesa4
4 affiliations
  1. 1Stony Brook University
  2. 2Universitat Politecnica de Valencia
  3. 3Arizona State University
  4. 4University of Michigan-Ann Arbor
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Abstract

Artificial intelligence is rapidly reshaping sustainability science through advances in climate modeling, ecosystem monitoring, resource management, and evidence synthesis. Yet its use also raises concerns about reliability, equity, and environmental impacts. In this Prospective, we identify four priorities for responsible AI integration: ensuring AI-supported claims are causal, transparent, and auditable; treating model selection as an environmental decision; building sustainability-focused AI infrastructure; and engaging sustainability scientists and affected communities in AI design and governance. With these safeguards, AI can enhance sustainability science while promoting equitable and environmentally responsible decision-making.

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Cite

BibTeXRIS
BibTeX
@article{wang2026strengthening,
  title = {{Strengthening and safeguarding artificial intelligence for a planet under pressure}},
  author = {Jieshu Wang and J. Alberto Conejero and Sergio Hoyas and Jose Lobo and Ricardo Vinuesa},
  journal = {Environmental Research Letters},
  year = {2026},
  month = nov,
  doi = {10.1088/1748-9326/aebee4},
  url = {https://iopscience.iop.org/article/10.1088/1748-9326/aebee4},
  note = {CC BY 4.0},
}
RIS
TY  - JOUR
TI  - Strengthening and safeguarding artificial intelligence for a planet under pressure
AU  - Jieshu Wang
AU  - J. Alberto Conejero
AU  - Sergio Hoyas
AU  - Jose Lobo
AU  - Ricardo Vinuesa
PY  - 2026
DA  - 2026/11/06
JO  - Environmental Research Letters
DO  - 10.1088/1748-9326/aebee4
UR  - https://iopscience.iop.org/article/10.1088/1748-9326/aebee4
AB  - Artificial intelligence is rapidly reshaping sustainability science through advances in climate modeling, ecosystem monitoring, resource management, and evidence synthesis. Yet its use also raises concerns about reliability, equity, and environmental impacts. In this Prospective, we identify four priorities for responsible AI integration: ensuring AI-supported claims are causal, transparent, and auditable; treating model selection as an environmental decision; building sustainability-focused AI infrastructure; and engaging sustainability scientists and affected communities in AI design and governance. With these safeguards, AI can enhance sustainability science while promoting equitable and environmentally responsible decision-making.
SN  - 1748-9326
ER  -