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The future of artificial intelligence in the context of industrial ecology

Author

Listed:
  • Franco Donati
  • Sébastien M. R. Dente
  • Chen Li
  • Xaysackda Vilaysouk
  • Andreas Froemelt
  • Rohit Nishant
  • Gang Liu
  • Arnold Tukker
  • Seiji Hashimoto

Abstract

Artificial intelligence (AI) applications and digital technologies (DTs) are increasingly present in the daily lives of citizens, in cities and in industries. These developments generate large amounts of data and enhance analytical capabilities that could benefit the industrial ecology (IE) community and sustainability research in general. With this communication, we would like to address some of the opportunities, challenges, and next steps that could be undertaken by the industrial ecology community in this realm. This article is an adapted summary of the discussion held by experts in industrial ecology, AI, and sustainability during the 2021 Industrial Ecology Day conference session titled “The Future of Artificial Intelligence in the Context of Industrial Ecology.” In brief, building on previous studies and communications, we advise the industrial ecology community to: (1) create internal committees and working groups to monitor and coordinate AI applications within and outside the community; (2) promote and ensure transdisciplinary efforts; (3) determine optimal infrastructure and governance of AI for IE to minimize undesired effects; and (4) act on effective representation and on reduction of digital divides.

Suggested Citation

  • Franco Donati & Sébastien M. R. Dente & Chen Li & Xaysackda Vilaysouk & Andreas Froemelt & Rohit Nishant & Gang Liu & Arnold Tukker & Seiji Hashimoto, 2022. "The future of artificial intelligence in the context of industrial ecology," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1175-1181, August.
    • Handle: RePEc:bla:inecol:v:26:y:2022:i:4:p:1175-1181
    DOI: 10.1111/jiec.13313
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    References listed on IDEAS

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    1. Rupa Mahanti, 2021. "Data Governance and Compliance," Springer Books, in: Data Governance and Compliance, chapter 0, pages 109-153, Springer.
    2. Carlos Mesta & Ramzy Kahhat & Sandra Santa‐Cruz, 2019. "Geospatial Characterization of Material Stock in the Residential Sector of a Latin‐American City," Journal of Industrial Ecology, Yale University, vol. 23(1), pages 280-291, February.
    3. Hadi Arbabi & Maud Lanau & Xinyi Li & Gregory Meyers & Menglin Dai & Martin Mayfield & Danielle Densley Tingley, 2022. "A scalable data collection, characterization, and accounting framework for urban material stocks," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 58-71, February.
    4. Nishant, Rohit & Kennedy, Mike & Corbett, Jacqueline, 2020. "Artificial intelligence for sustainability: Challenges, opportunities, and a research agenda," International Journal of Information Management, Elsevier, vol. 53(C).
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    6. Rupa Mahanti, 2021. "Data Governance and Compliance: Concluding Thoughts and the Way Ahead," Springer Books, in: Data Governance and Compliance, chapter 0, pages 155-157, Springer.
    7. Rupa Mahanti, 2021. "Data Governance and Compliance," Springer Books, Springer, number 978-981-33-6877-4, October.
    8. Andreas Froemelt & René Buffat & Stefanie Hellweg, 2020. "Machine learning based modeling of households: A regionalized bottom‐up approach to investigate consumption‐induced environmental impacts," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 639-652, June.
    9. Guillaume Majeau‐Bettez & Jean‐Marc Frayret & Anu Ramaswami & Yang Li & Niko Heeren, 2022. "Data innovation in industrial ecology," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 6-11, February.
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    Cited by:

    1. Luke Cullen & Andrea Marinoni & Jonathan Cullen, 2024. "Machine learning for gap‐filling in greenhouse gas emissions databases," Journal of Industrial Ecology, Yale University, vol. 28(4), pages 636-647, August.
    2. Da HUO & Tianying SUN & Wenjia GU & Aidi TANG, 2025. "AI and ESG - New Quality Productivity in Digital Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-23, July.
    3. John Ryter & Karan Bhuwalka & Michelena O'Rourke & Luca Montanelli & David Cohen‐Tanugi & Richard Roth & Elsa Olivetti, 2024. "Understanding key mineral supply chain dynamics using economics‐informed material flow analysis and Bayesian optimization," Journal of Industrial Ecology, Yale University, vol. 28(4), pages 709-726, August.
    4. Yongyue Gong & Fengmei Ma & Heming Wang & Asaf Tzachor & Wenju Sun & Junming Zhu & Gang Liu & Heinz Schandl, 2025. "The evolution of research at the intersection of industrial ecology and artificial intelligence," Journal of Industrial Ecology, Yale University, vol. 29(2), pages 440-457, April.
    5. Silalahi, Andri Dayarana K., 2025. "Can generative artificial intelligence drive sustainable behavior? A consumer-adoption model for AI-driven sustainability recommendations," Technology in Society, Elsevier, vol. 83(C).

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