# Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review
## 6 Conclusions  
One of the pivotal strengths of dynamic LEZs may be their ability to adjust regulations and boundaries in response to fluctuating traffic conditions and emission levels. For instance, integrating sensor networks and real-time monitoring systems enables continuous assessment of air quality, allowing LEZs to respond promptly to spikes in pollution. This adaptability maximizes environmental benefits and minimizes disruptions to urban mobility, fostering a more balanced approach to sustainable transportation. Precise emission mapping combined with predictive mechanisms, and linked to management tools, enables strategic decision-making. Optimization algorithms and machine learning models, as evidenced in studies utilizing traffic simulation to implement urban digital twins, are a core enabler to implement dynamic LEZs. However, the implementation of dynamic LEZs is not without challenges. The primary obstacle is the complexity associated with integrating and managing vast amounts of real-time data. Ensuring these data's accuracy, reliability, and security is paramount, particularly when deploying decentralized systems that rely on blockchain technology. Privacy concerns also emerge as a significant barrier, as dynamic LEZs often require detailed tracking of vehicle movements and emission data. While cryptographic techniques and anonymization protocols offer promising solutions, achieving a balance between effective emission control and preserving individual privacy remains critical for further development. Public acceptance and stakeholder engagement are equally crucial for successfully adopting dynamic LEZs. The reviewed studies indicate that transparent communication about the benefits and functionalities of dynamic LEZs can enhance public trust and compliance. Moreover, integrating incentives for using low-emission vehicles and improving public transportation options can mitigate potential resistance and encourage broader participation. Policymakers must also consider the socio-economic implications of dynamic LEZs, ensuring that measures are equitable and do not disproportionately impact vulnerable populations. The design, operation, and management of dynamic Low-Emission Zones (LEZs) must integrate privacy-preserving mechanisms to ensure public acceptance, compliance, and regulatory effectiveness. As LEZs shift from static to adaptive models, real-time vehicle monitoring, dynamic pricing, and automated access control introduce significant privacy risks, including unwanted tracking, data misuse, and surveillance concerns. Ensuring secure, privacy-focused enforcement mechanisms is crucial to balancing environmental objectives with civil liberties. The analyzed works highlight how cryptographic protocols, decentralized architectures, and pseudonymous authentication methods can mitigate privacy risks. Privacy considerations must be embedded from the design phase--choosing privacy-preserving technologies--to the operation and management stages, ensuring transparent governance, minimal data retention, and robust anonymization techniques. By adoptingprivacy-first policies, dynamic LEZs can enhance public trust, promote fair enforcement, and achieve sustainable urban mobility goals without infringing on personal rights. Future research should focus on refining the technological frameworks that underpin dynamic LEZs, particularly in enhancing the interoperability of various data sources and improving the robustness of predictive models. Additionally, longitudinal studies are needed to assess the long-term impacts of dynamic LEZs on urban air quality, public health, and economic factors. Exploring the potential of emerging technologies such as artificial intelligence and the Internet of Things (IoT) to automate further and optimize LEZ operations could yield significant advancements. Collaborative efforts between urban planners, technologists, and policymakers will be essential in addressing the multifaceted challenges and unlocking the full potential of dynamic LEZs.