## 4 Results and Discussion
### Limitations and Future Perspectives  
While this analytical lens offers significant insights into the complexities of SUL and urban access management, acknowledging its limitations and areas for future refinement is important. One constraint lies in the inherent complexity of urban settings, involving numerous interacting factors that this perspective may not fully encompass. For instance, socio-economic factors, cultural nuances, and local regulations could affect the success of policies and strategies concerning fleet makeup and access management. Furthermore, this framework might not capture all potential feedback loops and nonlinear relationships within urban systems, potentially leading to some simplification of complex dynamics, particularly given the lack of distinction between deliveries to businesses and deliveries to end consumers (last-mile logistics). Additionally, its predictive power may be constrained by uncertainties in data availability and future trends, especially in rapidly evolving urban areas. For future advancement, efforts could focus on enhancing the accuracy and predictive capabilities of this approach by integrating more granular data. This could involve conducting empirical studies to validate its underlying assumptions and calibrate its parameters based on real-world observations. Moreover, a quantitative stock and flow model will be developed to assess the impacts of diverse measures. This model will be specifically engineered to overcome the limitations of the current analytical lens by providing a more rigorous and predictive tool for assessing the dynamic impacts of Smart Urban Logistics (SUL) and access management strategies. \begin{table}
\begin{tabular}{c c} \hline
**SDG Objective** & **Connection with UVAR and SUL** \\ \hline SDG 7 Affordable and & The Portion of alternative vehicles (shaped by UVAR, Incentives for the use of electric vehicles or HP or AV, and Availability of alternative vehicles) leads to \\ Clean Energy & lower Fossil consumption and fewer Transport emissions, supporting cleaner \\  & energy use. \\ SDG 9 Industry, Innovation & Road infrastructure utilization (affected by Road capacity) and Road safety are \\ and Infrastructure & improved through Dynamic access closing/opening and route optimization, \\  & fostering efficient and innovative urban logistics systems. \\  & Quality of urban life improves through lower Air pollution and Noise pollution \\ SDG 11 Sustainable Cities & due to the increased use of Alternative vehicles. Road safety also improves as a \\ and Communities & result of the Optimization of urban spaces enabled by better Parking area \\  & management and Space allocation. \\  & Reduced Air pollution and fewer Kilometers traveled by traditional vehicles \\ SDG 15 Life on Land & mitigate environmental degradation, supporting the protection of urban and \\  & natural ecosystems. \\ \hline \end{tabular}
\end{table} 
Table 2: Relationships between key variables of UVAR and SUL and selected SDGs. The proposed quantitative model will systematically translate the intricate interdependencies identified in the CLD into a robust system of quantifiable stocks and flows, structured around several interconnected sub-models. Initially, the model's development could strategically concentrate on a specific segment of the CLD, particularly the sub-model focused on optimizing fleet composition between traditional and alternative vehicles. This targeted approach would allow for an in-depth analysis of how restrictive measures and incentives can be effectively balanced to achieve desired fleet transformation. For fleet dynamics, the model will integrate stocks representing the number of traditional and alternative vehicles, with corresponding flows capturing vehicle acquisition, retirement, and conversion rates. These flows will be dynamically influenced by factors such as incentives for the adoption of electric, human-powered, or assisted vehicles.