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PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector

Author

Listed:
  • Judit Oláh

    (Faculty of Economics and Business, University of Debrecen, 4032 Debrecen, Hungary
    TRADE Research Entity, North-West University, Vanderbijlpark 1900, South Africa)

  • Eszter Krisán

    (Faculty of Economics and Social Sciences, Szent István University, 2100 Gödöllő, Hungary)

  • Anna Kiss

    (Faculty of Food Science, Szent István University, 1118 Budapest, Hungary)

  • Zoltán Lakner

    (Department of Food Economics, Faculty of Food Science, Szent István University, 2100 Gödöllő, Hungary)

  • József Popp

    (TRADE Research Entity, North-West University, Vanderbijlpark 1900, South Africa
    Faculty of Economics and Social Sciences, Szent István University, 2100 Gödöllő, Hungary)

Abstract
The bioethanol sector is an extremely complex set of actors, technologies and market structures, influenced simultaneously by different natural, economic, social and political processes. That is why it lends itself to the application of system dynamics modelling. In last five years a relatively high level of experience and knowledge has accumulated related to the application of computer-aided system modelling for the analysis and forecasting of the bioethanol sector. The goal of the current paper is to offer a systematic review of the application of system dynamics models in order to better understand the structure, conduct and performance of the bioethanol sector. Our method has been the preferred reporting items for systematic reviews and meta-analyses (PRISMA), based on English-language materials published between 2015 and 2020. The results highlight that system dynamic models have become more and more complex, but as a consequence of the improvement in information technology and statistical systems, as well as the increasing experience gained they offer an efficient tool for decision makers in the business and political spheres. In the future, the combination of traditional system dynamics modelling and agent-based models will offer new perspectives for the preparation of more sophisticated description and forecasting.

Suggested Citation

  • Judit Oláh & Eszter Krisán & Anna Kiss & Zoltán Lakner & József Popp, 2020. "PRISMA Statement for Reporting Literature Searches in Systematic Reviews of the Bioethanol Sector," Energies, MDPI, vol. 13(9), pages 1-35, May.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:9:p:2323-:d:354947
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    References listed on IDEAS

    as
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    Cited by:

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    7. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," MetaArXiv haf2v, Center for Open Science.
    8. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," SocArXiv 9vdwf, Center for Open Science.
    9. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
    10. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," EdArXiv 5dwrt, Center for Open Science.
    11. Fernando ALMEIDA & Samantha LOW-CHOY, 2021. "Exploring The Relationship Between Big Data And Firm Performance," Management Research and Practice, Research Centre in Public Administration and Public Services, Bucharest, Romania, vol. 13(3), pages 43-57, September.
    12. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," LawArXiv kczj5, Center for Open Science.
    13. Saeed Nosratabadi & Amirhosein Mosavi & Puhong Duan & Pedram Ghamisi & Ferdinand Filip & Shahab S. Band & Uwe Reuter & Joao Gama & Amir H. Gandomi, 2020. "Data Science in Economics: Comprehensive Review of Advanced Machine Learning and Deep Learning Methods," Mathematics, MDPI, vol. 8(10), pages 1-25, October.
    14. Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," Thesis Commons auyvc, Center for Open Science.

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