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The Big Data, Artificial Intelligence, and Blockchain in True Cost Accounting for Energy Transition in Europe

Author

Listed:
  • Joanna Gusc

    (Faculty of Economics and Business, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands)

  • Peter Bosma

    (Deloitte, Groote Voort 291a, 8041 BL Zwolle, The Netherlands)

  • Sławomir Jarka

    (Institute of Management, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland)

  • Agnieszka Biernat-Jarka

    (Institute of Economics and Finance, Warsaw University of Life Sciences—SGGW, Nowoursynowska 166, 02-787 Warsaw, Poland)

Abstract

The current energy prices do not include the environmental, social, and economic short and long-term external effects. There is a gap in the literature on the decision-making model for the energy transition. True Cost Accounting (TCA) is an accounting management model supporting the decision-making process. This study investigates the challenges and explores how big data, AI, or blockchain could ease the TCA calculation and indirectly contribute to the transition towards more sustainable energy production. The research question addressed is: How can IT help TCA applications in the energy sector in Europe? The study uses qualitative interpretive methodology and is performed in the Netherlands, Germany, and Poland. The findings indicate the technical feasibilities of a big data infrastructure to cope with TCA challenges. The study contributes to the literature by identifying the challenges in TCA application for energy production, showing the readiness potential for big data, AI, and blockchain to tackle them, revealing the need for cooperation between accounting and technical disciplines to enable the energy transition.

Suggested Citation

  • Joanna Gusc & Peter Bosma & Sławomir Jarka & Agnieszka Biernat-Jarka, 2022. "The Big Data, Artificial Intelligence, and Blockchain in True Cost Accounting for Energy Transition in Europe," Energies, MDPI, vol. 15(3), pages 1-24, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:3:p:1089-:d:740230
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    Citations

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    Cited by:

    1. Radebe, Nomkhosi & Chipangamate, Nelson, 2024. "Mining industry risks, and future critical minerals and metals supply chain resilience in emerging markets," Resources Policy, Elsevier, vol. 91(C).
    2. Yang, Senmiao & Wang, Jianda & Dong, Kangyin & Dong, Xiucheng & Wang, Kun & Fu, Xiaowen, 2024. "Is artificial intelligence technology innovation a recipe for low-carbon energy transition? A global perspective," Energy, Elsevier, vol. 300(C).
    3. Wang, Jianda & Wang, Bo & Dong, Kangyin & Dong, Xiucheng, 2022. "How does the digital economy improve high-quality energy development? The case of China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    4. Matringe, Nadia & Power, Michael, 2024. "Memories lost: A history of accounting records as forms of projection," Accounting, Organizations and Society, Elsevier, vol. 112(C).
    5. Matringe, Nadia & Power, Michael, 2024. "Memories lost: a history of accounting records as forms of projection," LSE Research Online Documents on Economics 120410, London School of Economics and Political Science, LSE Library.
    6. Huang, Junbing & Wang, Yajun & Luan, Bingjiang & Zou, Hong & Wang, Jun, 2023. "The energy intensity reduction effect of developing digital economy: Theory and empirical evidence from China," Energy Economics, Elsevier, vol. 128(C).

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