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Artificial Intelligence and Sustainability Science: Lessons for Environment Magazine.

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Title: Artificial Intelligence and Sustainability Science: Lessons for Environment Magazine.
Authors: O'Riordan, Tim (AUTHOR)
Source: Environment. May/Jun2026, Vol. 68 Issue 3, p45-51. 7p.
Subject Terms: *Sustainability, *Sustainable urban development, Artificial intelligence, Decision making, Ethical problems, Social impact, Environmental databases
Abstract: This article examines four recent reports on the role of artificial intelligence (AI) in advancing sustainability science, focusing on environmental data analysis, earth system modeling, and decision-making. The World Resources Institute (WRI) and Stockholm Resilience Centre (SRC) reports highlight AI’s potential to enhance data integration, forecasting, and conservation efforts, while also acknowledging risks such as bias, misinformation ("hallucination"), and governance challenges. Philosopher Paul Kingsnorth and analyst Shirin Elahi provide critical perspectives on AI as part of a broader "Machine" that may undermine human freedom, cultural values, and socio-ecological diversity, emphasizing the need for resistance and alternative community models. The reports collectively underscore both the transformative possibilities and significant ethical, social, and regulatory concerns surrounding AI’s integration into sustainability science, especially in urban contexts and marginalized communities. [Extracted from the article]
Copyright of Environment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: This article examines four recent reports on the role of artificial intelligence (AI) in advancing sustainability science, focusing on environmental data analysis, earth system modeling, and decision-making. The World Resources Institute (WRI) and Stockholm Resilience Centre (SRC) reports highlight AI’s potential to enhance data integration, forecasting, and conservation efforts, while also acknowledging risks such as bias, misinformation ("hallucination"), and governance challenges. Philosopher Paul Kingsnorth and analyst Shirin Elahi provide critical perspectives on AI as part of a broader "Machine" that may undermine human freedom, cultural values, and socio-ecological diversity, emphasizing the need for resistance and alternative community models. The reports collectively underscore both the transformative possibilities and significant ethical, social, and regulatory concerns surrounding AI’s integration into sustainability science, especially in urban contexts and marginalized communities. [Extracted from the article]
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  Data: <i>Copyright of Environment is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/00139157.2026.2632570
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        Text: English
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      – SubjectFull: Sustainability
        Type: general
      – SubjectFull: Sustainable urban development
        Type: general
      – SubjectFull: Artificial intelligence
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      – SubjectFull: Decision making
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      – SubjectFull: Ethical problems
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      – SubjectFull: Social impact
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      – SubjectFull: Environmental databases
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              Text: May/Jun2026
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