System dynamics models for the simulation of sustainable urban development

Abstract

Purpose System dynamics is a whole-system modelling and learning approach, useful for tackling non-linear problems, such as sustainable urban development. The purpose of this paper is to review system dynamics applications in the simulation of sustainable urban development over a period from 2005 to 2017. Design/methodology/approach The analysis reveals that the number of applications of system dynamics modelling in the area of urban sustainable development increased in the analysed period. Research has changed its focus from the modelling of environmental problems to more complex models, portraying the multidimensional socio-economic processes that have an impact on the sustainability of urban development. Analysed case studies most often use the behaviour reproduction test for model validation, but without a unified approach. In most cases, modelling has been done in China, Germany and the USA, while urban development in the Eastern European countries, Africa and Latin America has not often been investigated. This paper indicates the knowledge gaps and suggests future research directions. Findings Papers that report the use of system dynamics modelling reveal a wide range of applications in urban sustainability. The analysis shows significant emphasis on environmental problems, while the interest for modelling social problems has been increasing during the last several years. Most of the modelled problems examine the sustainability of resources (land, water) and waste management, which are used for insights into the reasons for the system behaviour, forecasting future behaviour and policy testing. Originality/value The presented models were developed in most cases for the purpose of understanding the phenomena examined, as well as the future use of the models in policy planning. This brings us back to the need for greater stakeholder involvement, not only in the initial phase, but also during the whole modelling process, which could increase understanding, use and ownership of the models in the future, and thus increase their practical application.