The bioethanol sector is an extremely complex set of actors, technologies and market
structures, influenced simultaneously by dierent 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 oer 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 oer
an ecient 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 oer new perspectives for the
preparation of more sophisticated description and forecasting.
The bioethanol sector is an extremely complex set of actors, technologies and market structures, influenced simultaneously by dierent 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 oer 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 oer an ecient 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 oer new perspectives for the preparation of more sophisticated description and forecasting.