Policy-Driven Network Modeling of Stormwater BMP Treatment Trains: A Decision-Support Framework for Municipal Planning

Urban stormwater management increasingly depends on policies that shape the long-term adoption and performance of best management practices (BMPs). This study presents a network-based analytical framework integrating agent-based decision dynamics, system economics, and BMP treatment-train connectivity to evaluate 11 green and gray practices under alternative regulatory and market conditions. Six policy scenarios representing varying inspection enforcement, maintenance behavior, land-value constraints, and technology mandates were tested over a 50-year horizon. Results showed that enforced inspection with minimum operations and maintenance (O&M) costs yields the most balanced outcomes, achieving approximately 90% of maximum pollutant-removal potential while generating more than four times the economic return of aggressive but uncoordinated deployment. Competition intensity strongly influences system structure: moderate levels (ρ≈4–5) promote efficient portfolios, whereas high competition eliminates secondary technologies and reduces environmental performance. Consistent technology hierarchies emerged across scenarios: Tier 1 manufactured treatment devices reach near-saturation; Tier 2 practices (infiltration, bioretention, and wet ponds) require moderate support; and Tier 3 practices (green roofs and constructed wetlands) remain constrained by cost–performance limitations. The framework supports municipal decision-making by linking policy mechanisms, economic incentives, and network interactions to long-term system performance, enabling planners to compare strategies, identify resilient BMP portfolios, and optimize stormwater investment in rapidly urbanizing watersheds.