Modeling, assessment, and optimization of urban sustainability using probabilistic system dynamics

Abstract

Urban sustainable development of cities could ameliorate many aspects such as population expansion, housing price increase, and ecological environment. For a better understanding of urban sustainability, many scholars have conducted different frameworks to make an accurate evaluation for urban planning. However, a myriad of existing research analyzes sustainable development based on separate and static indicts, which inevitably miss some important information related to interconnects between different indicators and timelines. This study, thus, proposes a comprehensive method that integrates the system dynamic and Monte Carlo sensitivity analysis to examine the interrelationships between different subsystems with a time series, in order to predict the feasibility of new policies for future development. A probabilistic system dynamics approach for policy optimization of sustainable urbanization is proposed with the consideration of dynamic changes and time flows. Several scenarios are simulated to perform and validate the proposed framework, where the comparisons from different results provide optimal strategies for urban sustainability. Some interesting findings can be drawn: (1) the social system is more sensitive to policy change, typically for transport connections and traffic saturation flow respects; (2) policies A1, B1, and B2 are encouraged to implement, as these regulations will boost the urban development; (3) the proposed hybrid method can be used to analyze the variables with a dynamic and long-term urban system.