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Artificial Intelligence for ecosystem services science

2026-08Artigo científico Acesso aberto
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dc.contributor.authorCarl, Thalia Ballell
dc.contributor.authorHoff, Vince van 't
dc.contributor.authorHaas, Jan
dc.contributor.authorCabral, Pedro
dc.contributor.authorAkinyemi, Felicia O.
dc.contributor.institutionInformation Management Research Center (MagIC) - NOVA Information Management School
dc.contributor.pblElsevier BV
dc.date.accessioned2026-05-28T13:40:04Z
dc.date.available2026-05-28T13:40:04Z
dc.date.issued2026-08
dc.descriptionCarl, T. B., Hoff, V. V. ., Haas, J., Cabral, P., & Akinyemi, F. O. (2026). Artificial Intelligence for ecosystem services science: The AI4ESS framework from a community perspective. Ecosystem Services, 80, Article 101871. https://doi.org/10.1016/j.ecoser.2026.101871
dc.description.abstractEcosystem services (ES) research draws on heterogeneous biophysical, socio-economic, and valuation data that are often collected at different scales and structured using incompatible classifications. This paper presents a community-based synthesis of how Artificial Intelligence (AI) can be responsibly embedded in Ecosystem Services Science (ESS) through the AI4ESS framework, developed from two structured expert dialogues organized under the Ecosystem Services Partnership (ESP) network. Drawing on interdisciplinary insights from researchers, practitioners, and policymakers, the AI4ESS framework is defined as a four-dimensional conceptual structure integrating challenges, ethics, data and models, and opportunities to guide transparent and equitable AI adoption in ES research and in subsequent decision support. The AI4ESS framework extends approaches such as GeoAI and AIXES by integrating ethical, governance, and knowledge-inclusion principles alongside technical considerations. It identifies key priorities for advancing responsible AI in ESS: improving data transparency, promoting explainable and open models, establishing community-driven ethics guidelines, and fostering collaboration between domain and AI experts. This synthesis provides a conceptual foundation for integrating AI into ES valuation and decision support. By articulating actionable pathways, the framework contributes to the development of trustworthy, inclusive, and scalable AI applications that strengthen sustainability governance and evidence-based ecosystem management.en
dc.description.versionpublishersversion
dc.description.versionepub_ahead_of_print
dc.format.extent9
dc.format.extent1332703
dc.identifier.doi10.1016/j.ecoser.2026.101871
dc.identifier.issn2212-0416
dc.identifier.otherPURE: 163808175
dc.identifier.otherPURE UUID: fd652e16-397c-4be7-bf5e-3e4027b38a3b
dc.identifier.otherScopus: 105040022017
dc.identifier.otherWOS: 001781449500001
dc.identifier.otherORCID: /0000-0001-8622-6008/work/216084699
dc.identifier.urihttp://hdl.handle.net/10362/203533
dc.identifier.urlhttps://www.scopus.com/pages/publications/105040022017
dc.identifier.urlhttps://www.webofscience.com/wos/woscc/full-record/WOS:001781449500001
dc.language.isoeng
dc.peerreviewedyes
dc.subjectGovernance
dc.subjectEcosystem Services (ES)
dc.subjectDecision Support
dc.subjectInterdisciplinary Collaboration
dc.subjectEnvironmental Data and Ethics
dc.subjectGlobal and Planetary Change
dc.subjectGeography, Planning and Development
dc.subjectEcology
dc.subjectAgricultural and Biological Sciences (miscellaneous)
dc.subjectNature and Landscape Conservation
dc.subjectManagement, Monitoring, Policy and Law
dc.subjectSDG 11 - Sustainable Cities and Communities
dc.subjectSDG 13 - Climate Action
dc.subjectSDG 15 - Life on Land
dc.subjectSDG 16 - Peace, Justice and Strong Institutions
dc.titleArtificial Intelligence for ecosystem services scienceen
dc.title.subtitleThe AI4ESS framework from a community perspectiveen
dc.typejournal article
degois.publication.titleEcosystem Services
degois.publication.volume80
dspace.entity.typePublication
rcaap.rightsopenAccess

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NIMS: MagIC - Artigos em revista internacional com arbitragem científica (Peer-Review articles in international journals)