A system dynamics model for predicting the performance of green buildings

Purpose Construction companies typically look at important factors affecting building performance in order to make long-term improvements. In order to assess and forecast the performance of green buildings (GBs), this study considers the interrelationships among the performance measures and their dynamic behavior over time when assessing building performance. This consideration fills the gap in literature, where performance metrics were measured mostly individually and as steady values. Design/methodology/approach Following a comprehensive assessment of the literature and conversations with subject matter experts, 15 performance measures were identified. Five are qualitative, and 10 are quantitative. The analytic network process was used to calculate the priority weights for these performance indices. A system dynamics (SD) model was developed to simulate the interdependency among these indices and their dynamics over time. A case study of a commercial building was used to validate the model applicability. Findings The findings of the model simulation show the impact of the interrelationships among the studied key performance indicators (KPIs). For example, while sustainability indices like energy and water exhibited delayed effects, schedule and productivity served as triggering indications with rapid implications. Multiple KPIs were affected by early-stage interventions, especially innovation, as shown by scenario analysis (S1–S4). Innovation improvements, for instance, enhanced cost, schedule and environmental performance in Scenario S4. These findings confirm the model’s ability to capture both short-term and long-term dynamics in GB projects. Originality/value The developed SD model facilitates the early detection of performance gaps and offers practical advice to improve sustainability results. The model allows users to explore “what-if” scenarios and assess the potential impact of managerial decisions on the success and sustainability of GB projects which maximize the opportunities of success of GB projects.