IDEAS home Printed from https://ideas.repec.org/a/bla/inecol/v26y2022i1p164-182.html
   My bibliography  Save this article

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Author

Listed:
  • Mochen Liao
  • Kai Lan
  • Yuan Yao

Abstract

Artificial intelligence (AI) is an emerging technology that has great potential in reducing energy consumption, environmental burdens, and operational risks of chemical production. However, large‐scale applications of AI are still limited. One barrier is the lack of quantitative understandings of the potential benefits and risks of different AI applications. This study reviewed relevant AI literature and categorized those case studies by application types, impact categories, and application modes. Most studies assessed the energy, economic, and safety implications of AI applications, while few of them have evaluated the environmental impacts of AI, given the large data gaps and difficulties in choosing appropriate assessment methods. Based on the reviewed case studies in the chemical industry, we proposed a conceptual framework that encompasses approaches from industrial ecology, economics, and engineering to guide the selection of performance indicators and evaluation methods for a holistic assessment of AI's impacts. This framework could be a valuable tool to support the decision‐making related to AI in the fundamental research and practical production of chemicals. Although this study focuses on the chemical industry, the insights of the literature review and the proposed framework could be applied to AI applications in other industries and broad industrial ecology fields. In the end, this study highlights future research directions for addressing the data challenges in assessing AI's impacts and developing AI‐enhanced tools to support the sustainable development of the chemical industry.

Suggested Citation

  • Mochen Liao & Kai Lan & Yuan Yao, 2022. "Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework," Journal of Industrial Ecology, Yale University, vol. 26(1), pages 164-182, February.
    • Handle: RePEc:bla:inecol:v:26:y:2022:i:1:p:164-182
    DOI: 10.1111/jiec.13214
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jiec.13214
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jiec.13214?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. Aghbashlo, Mortaza & Mobli, Hossein & Rafiee, Shahin & Madadlou, Ashkan, 2013. "A review on exergy analysis of drying processes and systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 1-22.
    2. Rusul Abduljabbar & Hussein Dia & Sohani Liyanage & Saeed Asadi Bagloee, 2019. "Applications of Artificial Intelligence in Transport: An Overview," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
    3. Osuolale, Funmilayo N. & Zhang, Jie, 2016. "Energy efficiency optimisation for distillation column using artificial neural network models," Energy, Elsevier, vol. 106(C), pages 562-578.
    4. Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
    5. Gong, Hong-Fei & Chen, Zhong-Sheng & Zhu, Qun-Xiong & He, Yan-Lin, 2017. "A Monte Carlo and PSO based virtual sample generation method for enhancing the energy prediction and energy optimization on small data problem: An empirical study of petrochemical industries," Applied Energy, Elsevier, vol. 197(C), pages 405-415.
    6. Beamon, Benita M., 1998. "Supply chain design and analysis:: Models and methods," International Journal of Production Economics, Elsevier, vol. 55(3), pages 281-294, August.
    7. Wang, Lijun & Agyemang, Samuel A. & Amini, Hossein & Shahbazi, Abolghasem, 2015. "Mathematical modeling of production and biorefinery of energy crops," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 530-544.
    8. Petr Hájek & Jan Stejskal, 2018. "R&D Cooperation and Knowledge Spillover Effects for Sustainable Business Innovation in the Chemical Industry," Sustainability, MDPI, vol. 10(4), pages 1-20, April.
    9. Zhu, Qun-Xiong & Zhang, Chen & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling and saving potential analysis using a novel extreme learning fuzzy logic network: A case study of ethylene industry," Applied Energy, Elsevier, vol. 213(C), pages 322-333.
    10. Devrim Murat Yazan & Luca Fraccascia, 2020. "Sustainable operations of industrial symbiosis: an enterprise input-output model integrated by agent-based simulation," International Journal of Production Research, Taylor & Francis Journals, vol. 58(2), pages 392-414, January.
    11. Viet-Ngu Hoang & Mohammad Alauddin, 2012. "Input-Orientated Data Envelopment Analysis Framework for Measuring and Decomposing Economic, Environmental and Ecological Efficiency: An Application to OECD Agriculture," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 51(3), pages 431-452, March.
    12. Amin Mugera & Michael Langemeier & Allen Featherstone, 2012. "Labor productivity convergence in the Kansas farm sector: a three-stage procedure using data envelopment analysis and semiparametric regression analysis," Journal of Productivity Analysis, Springer, vol. 38(1), pages 63-79, August.
    13. Jahromi, Farid Sadeghian & Beheshti, Masoud & Rajabi, Razieh Fereydon, 2018. "Comparison between differential evolution algorithms and response surface methodology in ethylene plant optimization based on an extended combined energy - exergy analysis," Energy, Elsevier, vol. 164(C), pages 1114-1134.
    14. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    15. Yan Zhang & Lin An & Jie Xu & Bo Zhang & W. Jim Zheng & Ming Hu & Jijun Tang & Feng Yue, 2018. "Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    16. Vesnic-Alujevic, Lucia & Nascimento, Susana & Pólvora, Alexandre, 2020. "Societal and ethical impacts of artificial intelligence: Critical notes on European policy frameworks," Telecommunications Policy, Elsevier, vol. 44(6).
    17. Benjamin T. Hazen & Joseph B. Skipper & Christopher A. Boone & Raymond R. Hill, 2018. "Back in business: operations research in support of big data analytics for operations and supply chain management," Annals of Operations Research, Springer, vol. 270(1), pages 201-211, November.
    18. Ricardo Vinuesa & Hossein Azizpour & Iolanda Leite & Madeline Balaam & Virginia Dignum & Sami Domisch & Anna Felländer & Simone Daniela Langhans & Max Tegmark & Francesco Fuso Nerini, 2020. "The role of artificial intelligence in achieving the Sustainable Development Goals," Nature Communications, Nature, vol. 11(1), pages 1-10, December.
    19. Zhang, Bing & Bi, Jun & Fan, Ziying & Yuan, Zengwei & Ge, Junjie, 2008. "Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach," Ecological Economics, Elsevier, vol. 68(1-2), pages 306-316, December.
    20. Sharma, B. & Ingalls, R.G. & Jones, C.L. & Khanchi, A., 2013. "Biomass supply chain design and analysis: Basis, overview, modeling, challenges, and future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 24(C), pages 608-627.
    21. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    22. Choy, K.L. & Ho, G.T.S. & Lee, C.K.H. & Lam, H.Y. & Cheng, Stephen W.Y. & Siu, Paul K.Y. & Pang, G.K.H. & Tang, Valerie & Lee, Jason C.H. & Tsang, Y.P., 2016. "A recursive operations strategy model for managing sustainable chemical product development and production," International Journal of Production Economics, Elsevier, vol. 181(PB), pages 262-272.
    23. David F. Batten, 2009. "Fostering Industrial Symbiosis With Agent‐Based Simulation and Participatory Modeling," Journal of Industrial Ecology, Yale University, vol. 13(2), pages 197-213, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shahzad, Umer & Ghaemi Asl, Mahdi & Panait, Mirela & Sarker, Tapan & Apostu, Simona Andreea, 2023. "Emerging interaction of artificial intelligence with basic materials and oil & gas companies: A comparative look at the Islamic vs. conventional markets," Resources Policy, Elsevier, vol. 80(C).
    2. Ze Wei & Hui Liu & Xuewen Tao & Kai Pan & Rui Huang & Wenjing Ji & Jianhai Wang, 2023. "Insights into the Application of Machine Learning in Industrial Risk Assessment: A Bibliometric Mapping Analysis," Sustainability, MDPI, vol. 15(8), pages 1-29, April.

    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. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    2. Espinoza Pérez, Andrea Teresa & Camargo, Mauricio & Narváez Rincón, Paulo César & Alfaro Marchant, Miguel, 2017. "Key challenges and requirements for sustainable and industrialized biorefinery supply chain design and management: A bibliographic analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 350-359.
    3. Joanna Domagała, 2021. "Economic and Environmental Aspects of Agriculture in the EU Countries," Energies, MDPI, vol. 14(22), pages 1-23, November.
    4. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "Energy modeling using an effective latent variable based functional link learning machine," Energy, Elsevier, vol. 162(C), pages 883-891.
    5. Zhang, Xiao-Han & Zhu, Qun-Xiong & He, Yan-Lin & Xu, Yuan, 2018. "A novel robust ensemble model integrated extreme learning machine with multi-activation functions for energy modeling and analysis: Application to petrochemical industry," Energy, Elsevier, vol. 162(C), pages 593-602.
    6. Fraccascia, Luca, 2020. "Quantifying the direct network effect for online platforms supporting industrial symbiosis: an agent-based simulation study," Ecological Economics, Elsevier, vol. 170(C).
    7. Erkip, Nesim Kohen, 2023. "Can accessing much data reshape the theory? Inventory theory under the challenge of data-driven systems," European Journal of Operational Research, Elsevier, vol. 308(3), pages 949-959.
    8. Asima Ihsan & Bilal Mehmood, 2016. "Relative Efficiencies of Public Parks in Three Cities of Punjab," International Journal of Economics and Empirical Research (IJEER), The Economics and Social Development Organization (TESDO), vol. 4(1), pages 9-24, January.
    9. Devrim Murat Yazan & Vahid Yazdanpanah & Luca Fraccascia, 2020. "Learning strategic cooperative behavior in industrial symbiosis: A game‐theoretic approach integrated with agent‐based simulation," Business Strategy and the Environment, Wiley Blackwell, vol. 29(5), pages 2078-2091, July.
    10. Kumar, Sourabh & Barua, Mukesh Kumar, 2022. "A modeling framework and analysis of challenges faced by the Indian petroleum supply chain," Energy, Elsevier, vol. 239(PE).
    11. Sueyoshi, Toshiyuki & Yuan, Yan & Goto, Mika, 2017. "A literature study for DEA applied to energy and environment," Energy Economics, Elsevier, vol. 62(C), pages 104-124.
    12. Mohammed M. Mabkhot & Pedro Ferreira & Antonio Maffei & Primož Podržaj & Maksymilian Mądziel & Dario Antonelli & Michele Lanzetta & Jose Barata & Eleonora Boffa & Miha Finžgar & Łukasz Paśko & Paolo M, 2021. "Mapping Industry 4.0 Enabling Technologies into United Nations Sustainability Development Goals," Sustainability, MDPI, vol. 13(5), pages 1-33, February.
    13. Rui Zhang & Lingling Zhang & Meijuan He & Zongzhi Wang, 2023. "Spatial Association Network and Driving Factors of Agricultural Eco-Efficiency in the Hanjiang River Basin, China," Agriculture, MDPI, vol. 13(6), pages 1-16, May.
    14. Pin, Lantos A. & Pennink, Bartjan J.W. & Balsters, Herman & Sianipar, Corinthias P.M., 2021. "Technological appropriateness of biomass production in rural settings: Addressing water hyacinths (E. crassipes) problem in Lake Tondano, Indonesia," Technology in Society, Elsevier, vol. 66(C).
    15. Aalto, Mika & KC, Raghu & Korpinen, Olli-Jussi & Karttunen, Kalle & Ranta, Tapio, 2019. "Modeling of biomass supply system by combining computational methods – A review article," Applied Energy, Elsevier, vol. 243(C), pages 145-154.
    16. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Khoshnoudi, Masoumeh, 2017. "A comprehensive review of data envelopment analysis (DEA) approach in energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 1298-1322.
    17. Xu, Yuan & Zhang, Mingqing & Ye, Liangliang & Zhu, Qunxiong & Geng, Zhiqiang & He, Yan-Lin & Han, Yongming, 2018. "A novel prediction intervals method integrating an error & self-feedback extreme learning machine with particle swarm optimization for energy consumption robust prediction," Energy, Elsevier, vol. 164(C), pages 137-146.
    18. Efthymios Rodias & Remigio Berruto & Dionysis Bochtis & Alessandro Sopegno & Patrizia Busato, 2019. "Green, Yellow, and Woody Biomass Supply-Chain Management: A Review," Energies, MDPI, vol. 12(15), pages 1-22, August.
    19. Haoran Yang & Qun Wu, 2019. "Land Use Eco-Efficiency and Its Convergence Characteristics Under the Constraint of Carbon Emissions in China," IJERPH, MDPI, vol. 16(17), pages 1-17, August.
    20. Henrik Skaug Sætra, 2021. "AI in Context and the Sustainable Development Goals: Factoring in the Unsustainability of the Sociotechnical System," Sustainability, MDPI, vol. 13(4), pages 1-19, February.

    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:1:p:164-182. 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.