## 3 Materials and Methods
### Conceptual Framework: Defining System Boundaries and Variable Classes for Dynamic Analysis  
Based on insights drawn from the existing body of literature, a model boundary chart was created to define the key parameters under consideration [34]. A total of 51 parameters were identified as relevant to the exploration of SUL and urban access management. These parameters were subsequently categorized into four groups: endogenous variables, which both influence and are influenced by other variables within the system; exogenous variables, which affect endogenous variables without being affected in return; excluded variables, which are not currently integrated into the system dynamics model; and 2030 Agenda outcomes, which represent long-term sustainability objectives aligned with the Sustainable Development Goals (SDGs), as summarized in Table 1. The parameters classification was carried out through a comprehensive literature review and expert consultation, constituting the initial step of the classification process, while further validation steps are planned for future work. Once the model boundaries were established, the causal loop diagram (CLD) was developed. The CLD highlights the primary feedback loops and provides a simplified visualization of the system's structure. The initial phase of the analysis involved mapping the interactions within the system using a systems dynamics perspective and building the corresponding causal loop diagram. The following section will explore the CLD in greater depth, moving from a general overview to a more detailed examination. The CLD (Figure 1) \begin{table}
\begin{tabular}{c c} \hline \hline
**Category** & **Variables** \\ \hline \multirow{7}{*}{Endogenous variables} & Portion of alternative vehicles, Use of alternative vehicles, Use of traditional trucks, Number of deliveries, Delivery pressure, Traffic and congestion, Road infrastructure utilization, Road capacity, Number of accesses, Time to circulate, Time to park, Level of consumption of parking areas, Optimization of urban spaces, Road safety, Air pollution, Fossil fuel consumption, Noise pollution, Quality of urban life, Total shipments, Number of alternative/traditional shipments, Number of kilometers traveled by traditional/alternative vehicles, Alternative/Traditional transport costs, \\  & \multirow{2}{*}{UVAR, Incentives for the use of electric vehicles, Incentives for the use of human-powered or assisted vehicles, Restrictions on parking time, Dynamic access closing/opening, Dynamic management of parking areas, Dynamic closing/opening of parking areas, Level of route optimization. Logistic concept, Outsourcing, Delocalization, Delivery failure rate, Difference between online and in-store orders, E-commerce trends, Consumer behavior, Labor regulations, Fuel policies, Technological innovation rate, Logistical service quality, Weather conditions. \\  & SDG 7: Affordable and Clean Energy, SDG 9: Industry, Innovation and Infrastructure, SDG 11: Sustainable Cities and Communities, SDG 15: Life on Land. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Model boundary chart. is structured into three distinct areas, each delineating crucial aspects of the study's focus. The variables highlighted in red in Figure 1 represent enabling technologies that play a crucial role in shaping the dynamics of urban logistics and access management. Their inclusion emphasizes the impact of technological advancements on the system's behavior, facilitating more adaptive and efficient logistics processes. At the center lies the box titled \"Company and urban interactions\" which serves as the nexus for understanding the dynamics between corporate entities and the urban environment. This main area encapsulates the intricate interplay between businesses, logistics operations, and the broader urban landscape, shedding light on how these interactions influence the flow of goods and services within the city.