IDEAS home Printed from https://ideas.repec.org/a/bla/inecol/v26y2022i4p1175-1181.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jiec.13313
    Download Restriction: no

    File URL: https://libkey.io/10.1111/jiec.13313?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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).
    5. Ming Xu & Hua Cai & Sai Liang, 2015. "Big Data and Industrial Ecology," Journal of Industrial Ecology, Yale University, vol. 19(2), pages 205-210, April.
    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, February.
    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.
    10. Peter Bauer & Bjorn Stevens & Wilco Hazeleger, 2021. "A digital twin of Earth for the green transition," Nature Climate Change, Nature, vol. 11(2), pages 80-83, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Germán López Pérez & Isabel María García Sánchez & José Luis Zafra Gómez, 2024. "A systematic literature review and bibliometric analysis of eco‐innovation on financial performance: Identifying barriers and drivers," Business Strategy and the Environment, Wiley Blackwell, vol. 33(2), pages 1321-1340, February.
    2. Magdalena Rusch & Josef‐Peter Schöggl & Rupert J. Baumgartner, 2023. "Application of digital technologies for sustainable product management in a circular economy: A review," Business Strategy and the Environment, Wiley Blackwell, vol. 32(3), pages 1159-1174, March.
    3. Abdelhamid Zaidi & Samuel-Soma M. Ajibade & Majd Musa & Festus Victor Bekun, 2023. "New Insights into the Research Landscape on the Application of Artificial Intelligence in Sustainable Smart Cities: A Bibliometric Mapping and Network Analysis Approach," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 287-299, July.
    4. Kim, Myung Ja & Hall, C. Michael & Kwon, Ohbyung & Sohn, Kwonsang, 2024. "Space tourism: Value-attitude-behavior theory, artificial intelligence, and sustainability," Journal of Retailing and Consumer Services, Elsevier, vol. 77(C).
    5. Mohammadreza Akbari & John L. Hopkins, 2022. "Digital technologies as enablers of supply chain sustainability in an emerging economy," Operations Management Research, Springer, vol. 15(3), pages 689-710, December.
    6. Chițu Florentina & Mecu Andra-Nicoleta & Marin Georgiana-Ionela, 2024. "Exploring the Climate Change-AI Nexus: A Bibliometric and Scientometric Study," Proceedings of the International Conference on Business Excellence, Sciendo, vol. 18(1), pages 1658-1670.
    7. Noura Metawa & Rhada Boujlil & Saad Alsunbul, 2023. "Fraud-Free Green Finance: Using Deep Learning to Preserve the Integrity of Financial Statements for Enhanced Capital Market Sustainability," International Journal of Energy Economics and Policy, Econjournals, vol. 13(6), pages 610-617, November.
    8. Xaysackda Vilaysouk & Savath Saypadith & Seiji Hashimoto, 2022. "Semisupervised machine learning classification framework for material intensity parameters of residential buildings," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 72-87, February.
    9. Stefano Bianchini & Giacomo Damioli & Claudia Ghisetti, 2023. "The environmental effects of the “twin” green and digital transition in European regions," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 84(4), pages 877-918, April.
    10. Jaung, Wanggi, 2022. "Digital forest recreation in the metaverse: Opportunities and challenges," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    11. Chen, Peipei & Wu, Yi & Zhong, Honglin & Long, Yin & Meng, Jing, 2022. "Exploring household emission patterns and driving factors in Japan using machine learning methods," Applied Energy, Elsevier, vol. 307(C).
    12. Benedetti, Ilaria & Guarini, Giulio & Laureti, Tiziana, 2023. "Digitalization in Europe: A potential driver of energy efficiency for the twin transition policy strategy," Socio-Economic Planning Sciences, Elsevier, vol. 89(C).
    13. Pivetta, D. & Dall’Armi, C. & Sandrin, P. & Bogar, M. & Taccani, R., 2024. "The role of hydrogen as enabler of industrial port area decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    14. Yupeng Liu & Wei-Qiang Chen & Tao Lin & Lijie Gao, 2019. "How Spatial Analysis Can Help Enhance Material Stocks and Flows Analysis?," Resources, MDPI, vol. 8(1), pages 1-8, March.
    15. Chunyan Wang & Yi Liu & Wei‐Qiang Chen & Bing Zhu & Shen Qu & Ming Xu, 2021. "Critical review of global plastics stock and flow data," Journal of Industrial Ecology, Yale University, vol. 25(5), pages 1300-1317, October.
    16. Gupta, Brij B. & Gaurav, Akshat & Panigrahi, Prabin Kumar & Arya, Varsha, 2023. "Analysis of artificial intelligence-based technologies and approaches on sustainable entrepreneurship," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    17. Jonas Ardö, 2021. "A Sentinel-2 Dataset for Uganda," Data, MDPI, vol. 6(4), pages 1-16, March.
    18. Julien Walzberg & Jean‐Marc Frayret & Annika L. Eberle & Alberta Carpenter & Garvin Heath, 2023. "Agent‐based modeling and simulation for the circular economy: Lessons learned and path forward," Journal of Industrial Ecology, Yale University, vol. 27(5), pages 1227-1238, October.
    19. Dmitry A. Ruban, 2022. "Analytical Review of Conjugation of the Ethical Bases of Artificial Intelligence Implementation and Ecologization in Corporate Governance," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 21(2), pages 390-418.
    20. Purvis, Ben & Genovese, Andrea, 2023. "Better or different? A reflection on the suitability of indicator methods for a just transition to a circular economy," Ecological Economics, Elsevier, vol. 212(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bla:inecol:v:26:y:2022:i:4:p:1175-1181. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=1088-1980 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.