Dynamic Low-Emission Zones for Urban Mobility: A Systematic Review

Abstract

Urban air pollution, particularly from vehicular emissions, poses a significant challenge to public health and environmental sustainability. Low-Emission Zones (LEZs) have emerged as a solution, reducing pollution in high-traffic areas by restricting access to high-emission vehicles. However, most LEZ implementations are static, failing to account for real-time changes in traffic and emissions. This review focuses on dynamic LEZ systems, which are adjusted based on real-time data to optimize emission reduction without disrupting traffic flow. By categorizing LEZ strategies into static, hybrid, and dynamic systems, this study highlights key case studies and technologies, such as traffic simulation tools and sensor networks, that enable these adaptive systems. The review also discusses the challenges and future opportunities in LEZ implementation, emphasizing the need for data-driven approaches to achieve both environmental and mobility goals. This study aims to provide insights for policymakers and researchers seeking to enhance urban air quality management through more flexible, efficient LEZ strategies.

1. Introduction

Air pollution in urban areas is one of the most significant challenges to public health and environmental sustainability in the 21st century. According to the World Health Organization (WHO) [1], more than 90% of the global population resides in areas where air quality is below recommended standards [1], with urban areas being disproportionately affected. Vehicular emissions are significant contributors to urban pollution, particularly from transportation, which releases high levels of nitrogen oxides (NOx), carbon dioxide (CO₂), and particulate matter (PM).

Urban air pollution has severe health impacts, leading to respiratory and cardiovascular diseases and contributing to premature mortality. The European Environment Agency (EEA) [2] reported that in 2019 alone, long-term exposure to air pollution was associated with nearly 400,000 premature deaths across Europe. Interestingly, road transport is a major source of these pollutants, responsible for over 30% urban NOx emissions and a substantial proportion of particulate matter.

As vehicle ownership increases worldwide [3], urban areas face growing traffic congestion and emissions. With more people moving to cities, reducing vehicular emissions has become critical to improving air quality and sustainable urban mobility. Consequently, a pressing question for urban policymakers is how to effectively mitigate these emissions without compromising mobility or economic growth.

Urban traffic congestion and emissions are influenced by a complex interplay of factors, including vehicular density, infrastructural limitations, traffic flow inefficiencies, and urban planning constraints. A high population density and increasing motorization rates contribute to elevated traffic volumes, exacerbating congestion, particularly in poorly designed road networks with inadequate public transportation alternatives. Traffic signal mismanagement, bottlenecks at intersections, and frequent stop-and-go conditions lead to suboptimal vehicle operation, increasing fuel consumption and emissions. Furthermore, the presence of high-emission vehicles, inefficient freight logistics, and outdated engine technologies significantly deteriorate air quality. Meteorological conditions, such as temperature inversions, can further exacerbate pollutant accumulation in urban canyons. Addressing these issues requires a multifaceted approach, integrating intelligent traffic management systems, investments in sustainable transportation infrastructure, and stringent emission regulations to mitigate the environmental and health impacts of urban congestion.

Low-Emission Zones (LEZs) affect different vehicle categories in distinct ways, depending on their emission levels, fuel type, and compliance with regulatory standards. LEZ policies aim to reduce urban air pollution by progressively restricting high-emission vehicles while promoting the adoption of cleaner alternatives, significantly impacting fleet composition and urban mobility strategies.

Older gasoline and diesel cars, particularly those not meeting Euro 4 or higher (depending on regional regulations), face access restrictions, daily charges, or outright bans. Electric and hybrid vehicles are typically unaffected and may benefit from incentives such as free access or preferential parking. Trucks and buses contribute significantly to urban emissions due to their high fuel consumption and long operating hours. Many LEZs impose stricter regulations on diesel trucks and buses, often requiring compliance with Euro VI standards [4]. Public transit fleets are increasingly switching to electric or low-emission alternatives to meet the LEZ criteria. Although many cities impose fewer restrictions on motorcycles due to their lower emissions per vehicle, older two-stroke engine models, which release higher levels of pollutants, may be subject to LEZ limitations.

Given their high mileage, taxis and ride-sharing vehicles are often required to meet stringent emission standards, with some cities mandating the adoption of hybrid or fully electric fleets. Delivery and Light Commercial Vehicles (LCVs) play a crucial role in urban logistics, and, thus, many LEZ regulations offer transition periods or exemptions for small businesses. However, cities increasingly incentivize the adoption of electric vans or alternative fuel vehicles. Ambulances, police vehicles, and other essential services often receive exemptions, although many municipalities encourage their gradual electrification or conversion to cleaner technologies

Stockholm pioneered Europe’s first Environmental Protection Zone (EPZ) in 1996 [5], targeting heavy-duty vehicles and buses powered by compression ignition engines (primarily diesel). London followed in 2008 [6], launching its LEZ as part of a broader “Transport for London” strategy aimed at improving air quality by restricting high-emission diesel trucks and buses. London’s LEZ now covers most of Greater London, enforcing strict emission standards and imposing tolls on noncompliant vehicles.

Today, there are more than 320 LEZs in the European Union, with projections that this number will exceed 500 by 2025 [7]. As cities increasingly adopt LEZs, understanding their effectiveness, especially when enhanced by dynamic real-time adjustments, is essential for refining these zones to better balance air quality and urban mobility.

In the classic literature on routing and dynamic traffic assignment, special emphasis is given to the concept of optimum in its various aspects: system optimum (SO), user equilibrium (UE), and its many variants [8,9]. Interestingly, when dealing with an LEZ, it seems that the system optimum criterion prevails considering the criteria of the environment, emissions, and pollution parameters, leaving user balancing criteria in the background. It seems reasonable to raise questions such as the following:

• What should be the optimal extent of an LEZ at each moment in time depending on the traffic condition and quality parameters, and what would be its impact?
• What is the penalty generated by an LEZ to the individual mobility of the citizen?
• Should the topology of such an LEZ be considered depending on its impact to mobility?
• Should an LEZ be active in a certain area if the thresholds are not met?
• Can better LEZ topologies be achieved for the quality thresholds raised with less impact to the citizen?

The establishment of an LEZ raises legal issues derived from the rights to use roads and also political problems facing promoters and detractors. A dynamic LEZ would be a reasonable proposal to help manage these concerns.

The aim of this study was to locate the previous work on dynamic LEZs and make comparisons between them. Another aim was also to identify mechanisms, policies, and strategies that would be feasible to develop such models. One of the great challenges we faced with the study was that there were no studies on real LEZ low-emission zones that applied dynamic criteria over time, considering the state of the roads or the actual air quality. At most, the LEZ was fully or partially activated or deactivated in cases exceeding emergency thresholds.

The main contributions of this paper are as follows:

• It provides an in-depth review of existing and current techniques proposed for implementing LEZs, focusing on the transition from static to dynamic systems.
• It categorizes LEZ strategies into static, hybrid, and dynamic systems, highlighting key case studies and enabling technologies.
• It discusses the challenges and future opportunities in LEZ implementation, emphasizing the need for data-driven and adaptive approaches.
• It offers insights and recommendations for policymakers and researchers aiming to enhance urban air quality management through more flexible and efficient LEZ strategies.

The remainder of this paper is organized as follows. Section 1 introduces the context and significance of LEZs, while Section 2 focuses on the need for dynamic systems. Section 3 outlines the methodology used in this systematic review, including the search strategy, inclusion/exclusion criteria, data extraction, study quality assessment, and statistical analyses. Section 4 presents the search results and study characteristics, assesses the risk of bias, and provides detailed findings, including figures, tables, and relevant schemes. Finally, Section 5 examines the implications of the findings, critical challenges, and future directions for implementing dynamic LEZs in urban environments.

2. Dynamic Low-Emission Zones

LEZ policies may be classified into three categories:

• Static LEZs: Fixed boundaries and regulations that do not change in response to real-time conditions.
• Hybrid LEZs: A combination of static boundaries with some dynamic elements, such as time-based restrictions.
• Dynamic LEZs: Adaptive zones that adjust their boundaries and regulations based on real-time traffic and emission data.

2.1. Dynamic Low-Emission Zones

The concept of dynamism in traffic management implicitly entails the consideration of updating frequency, both in the monitoring of parameters and in the replanning of urban traffic control measures. In this sense, we must consider that emission measurements are a parameter that evolves continuously over time due to the propagation speed of gases and particles and the dependence and impact of climatological and meteorological factors. Similarly, traffic also exhibits a systemic hysteresis behavior since each vehicle travels a path between a certain origin and destination and shows segmentation in the routing of its paths. Once a vehicle enters a segment, it must travel through it before modifying its routing or trajectory. In this way, the monitoring schedule could be performed with minute criteria for routing reconfiguration (5–15 min) and with higher time aggregation criteria for a reconfiguration of LEZ zones that could be reconfigured on an hourly basis for a reasonable management for users. This temporal aggregation of information is also very useful for the generation of predictive models of traffic flows and, therefore, of future use of urban roads, allowing for the planning of LEZs with predictive models.

2.2. Challenges for Dynamic LEZ Implementations

Besides dynamic LEZ design, implementation models are also relevant. Several mechanisms may be considered, such as the following: (a) generation of different maps based on time parameters (timetables, daily) since LEZs are mainly based on mechanisms to delimit or restrict spaces; (b) use of road signage mechanisms, such as information panels; or (c) routing systems, either systemic or personal. In all cases, the communication of the correct information to the user is a central issue.

The implementation of dynamic Low-Emission Zones (LEZs) presents a fundamental trade-off between technological sophistication and practical feasibility. While more advanced LEZs offer increased efficiency through real-time monitoring, adaptive regulatory mechanisms, and data-driven decision-making, their deployment requires significant infrastructural investments, data management capabilities, and administrative oversight. Many urban areas, particularly those in the early stages of LEZ adoption, lack the necessary sensor networks, automated enforcement systems, and integrated data platforms to support dynamic regulation effectively. Consequently, municipalities must allocate substantial financial resources and time to develop these essential components, raising concerns regarding the economic viability and scalability of such systems.

Beyond the financial and infrastructural considerations, the complexity of dynamic LEZ enforcement introduces challenges related to reliability, cybersecurity, and system interoperability. More sophisticated systems necessitate the continuous collection, transmission, and analysis of high-resolution traffic and environmental data, increasing their susceptibility to technical failures, data inconsistencies, and potential security threats. Furthermore, dynamic enforcement mechanisms may lead to inconsistencies in regulatory application, potentially affecting public perception and compliance. A fundamental question arises regarding the balance between technological innovation and operational reliability, as municipalities must ensure that dynamic LEZs function transparently and equitably without introducing unnecessary administrative burdens.

From an environmental and societal perspective, the transition to dynamic LEZs is often justified by their potential to optimize emission reductions in response to real-time air quality fluctuations. However, this benefit must be weighed against the socioeconomic impact of such policies, particularly for businesses, logistics operators, and low-income populations who may struggle with the financial burden of compliance. Unlike static LEZs, which provide clear and predictable restrictions, dynamic LEZs introduce variability that may disrupt economic activities and require additional adaptation efforts from stakeholders. Public acceptance and policy legitimacy, thus, become central concerns, as resistance to change can hinder the effectiveness of even the most technologically advanced solutions.

Ultimately, the principal trade-off in the implementation of dynamic LEZs revolves around the tension between long-term sustainability goals and short-term practical constraints. Municipalities must decide whether to pursue incremental improvements—gradually enhancing existing LEZ frameworks—or commit to the rapid deployment of advanced, fully dynamic systems. This decision is contingent upon factors such as financial capacity, institutional governance, and public support. A comprehensive cost-benefit analysis, informed by empirical studies and case-based evaluations, is necessary to determine the most effective pathway for cities seeking to implement next-generation emission control measures while ensuring socioeconomic balance and operational viability.

2.3. LEZ and Green Urban Planning

Urban greening enhances Low-Emission Zones (LEZs) by mitigating air pollution, reducing urban heat islands, and improving environmental quality. Trees act as natural air filters, capturing pollutants like PM2.5 and NO_x, complementing emission reductions. Cities such as London, Madrid, and Milan have integrated extensive tree-planting initiatives into their LEZ policies [10].

Green spaces also regulate microclimates, lowering temperatures and reducing energy consumption for cooling, indirectly cutting emissions [11]. In Oslo and Paris, urban greening promotes active mobility, encouraging walking and cycling while improving urban aesthetics [12]. Additionally, trees absorb noise pollution, as seen in Berlin, where LEZ-adjacent vegetation improves urban livability [13].

Furthermore, urban trees serve as carbon sinks, supporting long-term decarbonization strategies, as exemplified by Stockholm [14]. However, improper tree placement can trap pollutants in urban canyons, and some species release volatile organic compounds (VOCs), requiring careful planning [15].

Integrating urban forestry with LEZ policies fosters sustainable, low-carbon cities, reinforcing emission reduction efforts while enhancing urban resilience and public health. Future urban policies should consider synergistic strategies that align LEZs, green infrastructure, and sustainable mobility.

3. Materials and Methods

Regarding the literature search strategy, the study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [16]. We systematically searched databases such as Springer, Scopus-Elsevier, IEEE, MDPI, and Sage for studies on dynamic LEZs, focusing on their impact on urban air quality and traffic management. The search covered studies published from 2014 up to 2024 in English. Studies were identified using terms related to “Low Emission Zones”, “Dynamic LEZs”, and “Optimized LEZs” (with indistinct search by LEZ or Low-Emission Zone). Reference lists of relevant articles were also reviewed to identify additional studies. Detailed search strategies are presented in the Supplementary Methods.

In IEEE, the “Low Emission Zones” search results yielded 690 results after applying the publication year filters from 2014 to 2024. In Scopus-Elsevier, the search was refined to include articles, conferences, and reviews within the same time frame and language, resulting in 5393 entries. Similarly, Springer provided 2136 results after filtering for articles, research articles, review articles, and conference papers from 2014 to 2024.

3.1. Inclusion and Exclusion Criteria

Studies were included if they proposed methods for transitioning from static LEZs to dynamic systems, utilizing observational approaches, case studies, or simulation-based designs. Additionally, eligible studies provided the data necessary to analyze the impact of these methods on emission reduction and/or traffic management. In cases with overlapping or identical data sources, the study with the largest or most comprehensive sample size was included.

The screening process for the obtained search results discarded those articles related to medical processes, health care, and chemistry topics.

3.2. Data Extraction

The information extracted from the eligible studies is shown in

Table 1. Overview of data extracted from eligible studies for analysis.

Title	The title of the study.
Authors	The authors who conducted the study.
Identified Problems or needs	The specific issues or challenges related to LEZs.
System Proposal	The proposed system or framework introduced by the study to tackle the identified problems.
Proposed Measures	The strategies or measures suggested for implementing the proposed system, such as variable pricing, emission-based access controls, or integration with public transport.
Key Results	The main findings and outcomes of the study include reductions in emissions (e.g., NOx, CO2, PM), improvements in traffic flow, and improvements in levels of public acceptance.
Year of Publication	The year the study was published.
Dataset	The dataset used for case studies and practical experiments. Where available, the city, country, traffic volumes, time bands, and the status of real or synthetic data are shown.

:

3.3. Study Quality Assessment

The quality of eligible studies was assessed using an adapted version of the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies [17]. This checklist comprises nine items with four possible responses (yes, no, unclear, or not applicable). Given the methodological diversity of studies on LEZs, certain items were interpreted or deemed inappropriate to better align with the nature of the studies. For instance, items related to population representativeness were adapted to assess the relevance and robustness of the simulation models or datasets used.

Each study was assigned an overall quality rating (high, moderate, or low) based on a qualitative evaluation of its performance across the nine items, with particular emphasis placed on (a) the appropriateness of the data sources or simulation frameworks for modeling LEZ impacts, (b) the robustness and transparency of methods for transitioning from static to dynamic LEZ configurations, and (c) the validity and reliability of emissions and traffic data analysis.

This adapted approach ensured that the quality assessment accounted for the specific characteristics of LEZ studies, including the frequent use of theoretical frameworks, simulation-based methodologies, and urban-level policy evaluations.

4. Results

4.1. Search Results and Study Characteristics

The literature search across IEEE, Scopus, Springer, and MDPI databases yielded a total of 5393 records. After applying filters for publication years (2014–2024) and English language, abstracts and full texts were screened. While IEEE, Scopus, and Springer provided relevant studies for review, no eligible studies were identified from the MDPI database.

After a screening process, 14 eligible studies focused on methods for transitioning static LEZs to dynamic systems were included in this systematic review. Details of the literature screening process are displayed in the PRISMA flowchart in Figure 1.

For dynamic LEZs, four studies used tolls, five implemented access-control systems, two utilized dynamic pricing mechanisms, and three employed additional strategies such as optimization and data-driven approaches, as detailed in

Table 2. Overview of dynamic LEZs methods.

Title	Authors	Identified Problems	System Proposal	Proposed Measures	Key Results	Year	Dataset
Electronic Road Pricing System for Low Emission Zones to Preserve Driver Privacy [18]	Roger Jardí-Cedó et al.	Privacy concerns in LEZs due to tracking and potential misuse of driver data and inadequate fraud detection mechanisms in existing systems	An ERP system incorporating cryptographic proofs and revocable anonymity, ensuring honest driver privacy while enabling identification of fraudulent drivers	Use of tamper-proof secure elements (SEs), dynamic fee calculation based on emissions and time, and cryptographic checkpoints for authentication and fraud detection	A robust privacy-preserving system that dynamically adjusts fees ensures fairness and identifies dishonest drivers without tracking itineraries	2014	No experimental data used
Privacy-Preserving Electronic Toll System with Dynamic Pricing for Low Emission Zones [19]	Roger Jardí-Cedó et al.	Privacy issues and fraud detection inefficiencies in traditional ERP systems for LEZs	A dynamic pricing ERP system leveraging cryptographic techniques and prepayment mechanisms to enhance privacy and fraud detection	Dynamic pricing based on real-time traffic, revocable anonymity protocols, secure tamper-proof modules, and cryptographically signed records	Robust fraud control, ensured privacy for honest drivers, scalability for urban environments, and operational feasibility	2015	Theoretical. No experimental data used
Pollution and congestion in urban areas: The effects of low emission zones [20]	Bernardo et al.	Ineffectiveness of LEZs in mitigating congestion and trade-offs between the objectives of pollution reduction and congestion alleviation	Analyzes the impact of LEZs on pollution and congestion in European cities	Use of panel data to evaluate PM2.5 pollution and congestion effects and a comparison of LEZs’ effectiveness with urban tolls	LEZs effectively reduce PM2.5 emissions, with a reduction of up to 33% in areas such as Rome; minimal impact on congestion reduction due to latent demand and substitution of vehicles	2021	EU CLARS (Charging, Low Emission Zones, other Access Regulation Schemes) covering 33 cities from 5 countries [21]
Time-based low emission zones preserving drivers’ privacy [22]	Roger Jardí-Cedó et al.	Privacy concerns in LEZs due to vehicle tracking, inadequate fraud detection, and trade-offs between fraud control and driver privacy	A user-centric ERP system for LEZs, prioritizing privacy for honest drivers while maintaining fraud detection efficiency	Implementation of cryptographic techniques for anonymity, revocation for fraud detection, and dynamic proof generation to prevent traceability	Successfully balances privacy and fraud detection, enabling practical deployment with mobile checkpoints and dynamic conditions	2018	Theoretical. No experimental data used
Decentralized Privacy-preserving Access for Low Emission Zones [23]	Carles Anglès-Tafalla et al.	Privacy concerns and reliance on centralized entities in LEZ systems, leading to potential misuse of user data and lack of transparency	A decentralized access-control system using blockchain and group signatures to ensure privacy and fraud detection	Blockchain integration for access and payment processes, group signature schemes for anonymity and unlinkability, and revocable anonymity for fraud control	Achieved efficient and privacy-preserving access control with reduced reliance on centralized entities, demonstrating scalability and lightweight implementation	2019	Theoretical. No experimental data used
Optimized Design of Low Emission Zones in SUMO: A Dual Focus on Emissions Reduction and Travel Time Improvement [24]	Paricio-Garcia et al.	Inefficiencies in static LEZ configurations that fail to adapt to real-time traffic and emission data	An optimization model for LEZs using the SUMO framework and genetic algorithms to adjust boundaries based on real-time traffic conditions dynamically	Use of genetic algorithms to select optimal LEZ boundaries, integration of real-time data, and validation of the approach through simulations in Guadalajara, Spain	Achieved a 35% reduction in CO2 emissions and a 15% improvement in travel times compared to traditional static LEZs	2024	Synthetic dataset from Guadalajara, Spain
A data-driven method of traffic emission mapping with land use random forest models [25]	Wen et al.	Lack of precision in estimating traffic emissions and the inability of traditional LEZ models to integrate real-time, link-level emission data	Introduction of the land use random forest (LURF) approach to dynamically map real-time vehicle emissions using high-density traffic and land use data	Integration of 272 predictors, dynamic temporal and spatial analyses of emissions, scenario evaluation for LEZ policies, and a focus on scalability for real-time applications	Achieved $R^2>0.8$ accuracy in traffic volume and speed simulations, identified significant emission reductions under traffic demand management (up to 66% CO2 reduction) and fleet electrification (31% CO2 reduction)	2022	Chengdu, China
Security and Privacy in a Blockchain-Powered Access Control System for Low Emission Zones [26]	Carles Anglés-Tafalla et al.	Privacy threats in centralized Low-Emission Zone (LEZ) management systems due to single points of failure and indiscriminate user data collection	A decentralized LEZ access-control system using blockchain and smart contracts for privacy preservation and enhanced security	Integration of blockchain transactions for vehicle access, implementation of smart contracts for automatic fee calculation, and revocable anonymity for fraud detection	Demonstrated feasibility in real-world scenarios, ensuring driver privacy and scalable fraud detection, validated through extensive experiments	2023	No experimental data used for LEZ. Individual vehicle estimations
Collaborative road pricing strategy for heterogeneous vehicles considering emission constraints [27]	Rui Feng et al.	Urban congestion and emissions from heterogeneous vehicle types, lack of integrated congestion and emission pricing strategies	A bi-level collaborative road pricing model incorporating congestion and emission tolls for conventional vehicles (CVs) and new energy vehicles (NEVs)	Bi-level optimization model, distinct tolling mechanisms for VCs and NEVs, real case study in Beijing, dynamic analysis of NEV penetration and emission constraints	Achieved a 5.04% reduction in total travel time, a 10.0% reduction in LEZ emissions, and a 14.35% increase in bus participation rate; demonstrated significant environmental and efficiency benefits	2023	Beijing, China
Privacy-Preserving and Secure Decentralized Access Control System for LEZ [28]	Roger Jardí-Cedó et al.	Privacy concerns and limitations of centralized access-control systems in LEZs	A decentralized access-control system using blockchain technology to preserve privacy and prevent fraud	Implementation of blockchain-based transactions, pseudonyms for user anonymity, cryptographic proofs for fraud prevention, and scalability with cryptocurrency mixing services	Provides privacy preservation, non-repudiation, and fraud control, ensuring scalable and adaptable LEZ management	2016	Theoretical. No experimental data used
An Optimization-based Approach for Designing Urban Low Emission Zones [29]	Pili et al.	Limitations of trial-and-error approaches in designing LEZs, such as inefficiency in determining optimal boundaries and locations for infrastructure	A Mixed Integer Linear Programming (MILP)-based optimization framework to design LEZs by balancing coverage area, user travel costs, and network link saturation	Integrated optimization model, hexagonal zoning system for precise configuration, multi-criteria objective function, and case study validation	Achieved significant reductions in travel costs and network congestion, with efficient computational performance, solving scenarios in under 20 s	2023	Synthetic graph data. Small scenario
Advancing Sustainability Impact Assessment: A Comprehensive Tool for Low Emission Zone Management [30]	Fernández et al.	Lack of integrated, real-time data-driven tools for effective LEZ management and impact assessment	A comprehensive tool that combines real-time monitoring, air quality modeling, and data analytics to enhance urban sustainability in LEZs	Use of IoT sensors, cameras, advanced air quality models, and real-time data integration for monitoring and evaluating LEZ effectiveness	Achieved up to 86% CO2 equivalent reductions in Helsinki, demonstrating the potential of adaptive LEZ management strategies	2023	Simulation in several EU cities: Tallinn, Amsterdam, Helsinki, Paris, Stavanger
On DICE-free Smart Cities, Particulate Matter, and Feedback-Enabled Access Control [31]	Katsikouli et al.	Lack of regulation on non-exhaust particulate matter (PM) emissions, mainly from tire and brake wear, exacerbated by increased use of electric vehicles	A feedback-enabled access-control system that regulates vehicle access based on real-time PM emission levels and encourages ride-sharing	Introduction of a feedback-based regulation mechanism, ride-sharing promotion through a probabilistic selection method, and a digital token compliance system to enforce ride-sharing agreements	Simulations showed that the system effectively maintains PM levels within safe thresholds while promoting fair access for drivers and passengers	2020	Simulation over Dublin, Ireland
Security and Privacy in a Blockchain-Powered Access Control System for Low Emission Zones [32]	Carles Anglés-Tafalla et al.	High infrastructure costs and privacy concerns in traditional LEZ access-control systems, particularly with vehicle-installed devices	A lightweight, privacy-preserving access-control system utilizing smartphones for driver identification and access validation	Use of low-cost cryptographic protocols, smartphone integration for access control, and real-time data collection to optimize LEZ management	Laboratory and field tests validated the system’s effectiveness, confirming its scalability, low-cost implementation, and adaptability to various traffic and environmental conditions	2018	Synthetic generic simulation scenario

.

4.2. Pricing Strategies

Pricing strategies provide economic incentives and regulatory frameworks that influence driver behavior and environmental outcomes. Thus, they may be fundamental to the design and effectiveness of dynamic LEZs; applying dynamic pricing, which adjusts tolls based on real-time traffic, emissions, and vehicle type; and ensuring flexibility and responsiveness to urban mobility needs. However, challenges arise in balancing fairness, social acceptance, and enforcement, as excessive fees may disproportionately impact lower-income drivers while insufficient pricing may fail to reduce congestion.

4.2.1. Electronic Road Pricing System for Low-Emission Zones to Preserve Driver Privacy [18]

This study introduces an innovative electronic road pricing (ERP) system designed explicitly for LEZs to balance the need for fraud control and driver privacy. The proposed system utilizes cryptographic mechanisms and revocable anonymity to ensure that only fraudulent drivers lose their privacy while honest drivers remain anonymous. Its key contributions are follows:

• Revocable Anonymity: Honest drivers retain complete anonymity, while fraudulent drivers are identified through revocation mechanisms when they fail authentication or proper payment.
• Tamper-Proof Secure Elements (SEs): Each vehicle is equipped with a secure element to perform sensitive operations, ensuring the integrity of entrance and exit proofs.
• Dynamic Fee Calculation: Prices are calculated based on vehicle category, emission levels, and time spent within the LEZ, promoting equitable and environmentally sensitive tolling.
• Fraud Control Mechanisms: Checkpoints use cryptographic proofs and take photographs only in cases of authentication failure, ensuring fairness and accuracy in fraud detection.
• Privacy Preservation: The system ensures that vehicle itineraries cannot be reconstructed, and each entrance and exit process generates unique encrypted credentials, preventing traceability.
• Scalability and Efficiency: The protocol supports real-time interaction between vehicles and checkpoints, making it practical for deployment in busy urban environments.

This ERP system is directly applicable to dynamic LEZ implementations, where real-time data and privacy preservation are critical. Its combination of fraud detection, privacy, and dynamic pricing offers a robust framework for adaptive LEZ strategies, enabling efficient traffic management and emission reduction.

4.2.2. Collaborative Road Pricing Strategy for Heterogeneous Vehicles Considering Emission Constraints [27]

This study presents a bi-level collaborative road pricing strategy for heterogeneous traffic, incorporating congestion and emission tolls. The approach targets two vehicle types: conventional vehicles (CVs) and new energy vehicles (NEVs), treating them as distinct categories with specific tolling mechanisms. The strategy integrates traffic management and environmental considerations, focusing on mitigating congestion and reducing emissions in Low-Emission Zones (LEZs). Its key contributions are as follows:

• Bi-Level Optimization Model: A bi-level programming framework simultaneously optimizes travel efficiency and emission reduction. The upper-level model minimizes total travel time and emissions, while the lower-level model ensures equilibrium in multi-modal transportation networks.
• Distinct Tolling Mechanisms: CVs pay both congestion and emission tolls, while NEVs only pay congestion tolls, encouraging the adoption of cleaner vehicle technologies.
• Real-World Case Study: Conducted within the Second Ring Road of Beijing, the strategy achieved a 5.04% reduction in total travel time and a 10% reduction in LEZ emissions, with overall network emissions reduced by 2.04%.
• Dynamic Impact Analysis: Explored the effects of varying NEV penetration rates and emission constraints, demonstrating that a balanced NEV penetration rate of 20–30% maximizes network efficiency while meeting emission targets.
• Collaborative Tolling Benefits: Compared to standalone congestion or emission pricing, the collaborative approach significantly improves traffic flow distribution and environmental outcomes, highlighting the synergistic benefits of integrating both pricing strategies.

A comprehensive framework for dynamic LEZ management is described, incorporating real-time data to adjust tolls based on traffic and emission metrics. The bi-level optimization and focus on heterogeneous traffic types align closely with the objectives of adaptive LEZ systems.

4.2.3. Pollution and Congestion in Urban Areas: The Effects of Low-Emission Zones [20]

This study examines the effectiveness of Low-Emission Zones (LEZs) in mitigating pollution and compares their impact with urban tolls, focusing on large European urban areas from 2008 to 2016. It utilizes a unique panel dataset to evaluate congestion using data from TomTom and pollution (PM2.5) from environmental sciences. The key contributions are as follows:

• Impact on Pollution: LEZs are particularly effective in reducing PM2.5 emissions, especially in cities with high initial pollution levels, broad coverage areas, and stringent restrictions on vehicle types. For instance, previous studies indicate average reductions in PM10 emissions of up to 13% in German cities and 33% for PM10 in Rome’s restricted areas.
• Ineffectiveness on Congestion: LEZs have a minimal impact on congestion mitigation. The renewal of car fleets with cleaner vehicles compliant with LEZ regulations may initially reduce congestion but is counterbalanced by latent demand and car substitution effects in multi-car households.
• Trade-offs in Policy Goals: The study highlights the differing goals of LEZs (curbing pollution) and urban tolls (reducing congestion), showing that while both can address externalities simultaneously, their primary objectives result in varying effectiveness.
• Support for LEZs: LEZs are generally more socially acceptable than tolls, primarily affecting older, more polluting vehicles while leaving compliant drivers unaffected. This acceptability contributes to their widespread adoption across European cities.

The importance of tailoring LEZ policies based on local pollution severity is described, together with their coverage areas and regulation stringency. It suggests that dynamic LEZs could optimize their impact by integrating real-time data to address congestion spillovers and latent demand effects while maintaining public acceptance.

4.3. Technological Innovations

Technological innovations are pivotal in defining and implementing dynamic Low-Emission Zones (LEZs), ensuring adaptability, efficiency, and public acceptance. The referred studies show the transformative role of blockchain technology, cryptographic security, dynamic pricing, and real-time data analytics in enhancing LEZ operations. Blockchain-based access-control systems improve privacy preservation and fraud detection, while electronic road pricing (ERP) with dynamic tolling optimizes traffic flow and emission management. Advanced sustainability assessment tools integrate real-time monitoring and predictive modeling, enabling data-driven policy adjustments.

4.3.1. Security and Privacy in a Blockchain-Powered Access Control System for Low-Emission Zones [26]

This study proposes a decentralized Low-Emission Zone (LEZ) management system that addresses the limitations of traditional access-control schemes reliant on centralized entities. The proposed system ensures enhanced privacy, security, and system availability by utilizing blockchain technology and smart contracts. The main contributions proposed are as follows:

• Decentralization of Access Control: The system replaces centralized entities with a blockchain-based distributed network, eliminating single points of failure and improving transparency in managing vehicle accesses and fee payments.
• Privacy Preservation: Unlike conventional camera-based systems, which capture indiscriminate license plate data, this approach guarantees user anonymity unless fraudulent behavior is detected. Honest drivers retain their privacy throughout the process.
• Smart Contract Implementation: Vehicle entries and payments are processed as blockchain transactions through smart contracts. This ensures automated, tamper-proof fee calculations and payments using digital currencies.
• Secure Fraud Detection: Fraudulent users can be identified and penalized without compromising the privacy of compliant users. The system provides revocable anonymity and integrity for all recorded transactions.
• Performance Validation: Extensive laboratory and real-world low-traffic street-environment testing confirmed the system’s feasibility. The smart contract’s gas consumption was analyzed, demonstrating the approach’s cost-effectiveness.

This blockchain-powered access-control system addresses critical challenges in dynamic LEZ implementations, such as scalability, user trust, and efficient fee collection. Its decentralized and privacy-preserving features make it a valuable foundation for future dynamic LEZ strategies.

4.3.2. Time-Based Low-Emission Zones Preserving Drivers’ Privacy [22]

This study presents a novel electronic road pricing (ERP) system designed explicitly for LEZs that addresses two significant challenges: preserving the privacy of honest drivers and improving fraud detection. Unlike traditional ERP systems, which compromise privacy by tracking vehicle paths and recording data at checkpoints, this proposal uses a user-centric approach. It ensures that only dishonest drivers are identified, maintaining the anonymity of compliant users. Its key contributions are as follows:

• User-Centric Design: The system protects the privacy of honest drivers while maintaining a robust mechanism for detecting fraud. Only vehicles suspected of fraudulent behavior are photographed at checkpoints, preserving privacy for compliant users.
• Anonymity Revocation for Fraud Detection: Dishonest users lose their anonymity when fraudulent activity is detected. The system utilizes cryptographic techniques to revoke anonymity while safeguarding honest users’ data.
• Advanced Cryptographic Protocols: The ERP system incorporates secure proof generation and verification processes. Vehicles interact with checkpoints using encrypted proofs, ensuring data integrity, non-repudiation, and limited traceability.
• Feasibility and Efficiency: Empirical evaluations demonstrate the system’s practicality, highlighting its ability to operate seamlessly in real-world scenarios with mobile checkpoints and dynamic traffic conditions.

This work highlights a critical trade-off between privacy and fraud detection in ERP systems and provides a balanced solution. Its contributions align with the need for privacy-preserving technologies in LEZs, ensuring public acceptance while enhancing system reliability.

The integration of privacy-preserving technologies into dynamic LEZ systems is essential for fostering user trust and compliance. This study showcases how such technologies can be implemented without compromising fraud detection or system efficiency, offering valuable insights for next-generation LEZ strategies.

4.3.3. Privacy-Preserving and Secure Decentralized Access-Control System for Low-Emission Zones [33]

This study introduces a novel decentralized access-control system for LEZs that combines privacy-preserving mechanisms with blockchain technology to overcome the limitations of centralized access-control schemes. It emphasizes user privacy, non-repudiation, and fraud prevention, aligning with the evolving needs of modern LEZ implementations. Its key contributions are as follows:

• Decentralized Control: The system replaces centralized entities with a blockchain-supported network for managing vehicle accesses and payments, enhancing transparency and reducing the risk of privacy violations.
• Privacy Preservation: User anonymity is preserved using pseudonyms that can be renewed regularly, ensuring the non-traceability of users’ actions unless fraudulent behavior is detected.
• Blockchain and Smart Contracts: Access events are processed as blockchain transactions using smart contracts, enabling automated fee calculation and payment while ensuring data integrity.
• Non-Repudiation and Fraud Control: The system incorporates cryptographic proofs to prevent unauthorized access and ensures that dishonest users can be identified without compromising the privacy of compliant drivers.
• Scalable and Adaptable Design: Integrating onboard units (OBUs) and cryptocurrency mixing services ensures scalability, making the system adaptable for urban environments with varying traffic densities.

This system provides a foundation for implementing dynamic LEZs that require real-time data processing and user trust by decentralizing access control and enhancing privacy. Its blockchain-powered approach ensures reliable, equitable, and privacy-focused management of urban emission zones.

4.3.4. Privacy-Preserving Electronic Toll System with Dynamic Pricing for Low-Emission Zones [19]

This study presents a novel electronic road pricing (ERP) system for Low-Emission Zones (LEZs) that enhances fraud detection and driver privacy through a robust dynamic pricing model. By leveraging cryptographic techniques and a prepayment mechanism, the system addresses key challenges in traditional ERP methods, including privacy concerns and fraud detection inefficiencies. Its key contributions are as follows:

• Dynamic Pricing: Prices for LEZ stretches are dynamically adjusted based on real-time traffic volume, ensuring efficient flow management and fair pricing for drivers based on their emission category.
• Privacy Preservation: The system guarantees the anonymity of honest drivers using revocable anonymity protocols. Vehicle itineraries are encrypted and cannot be linked to specific users unless fraud is detected.
• Fraud Detection: Fraudulent drivers are identified through digital certificates and on-board unit (OBU) verification at checkpoints. Honest drivers are protected from false accusations by cryptographically signed records.
• Scalability and Feasibility: The system is designed to function in urban environments with dynamic traffic conditions, providing robust fraud detection and privacy preservation without introducing excessive computational or infrastructural costs.
• Integration of Security Mechanisms: It incorporates tamper-proof modules and secure vehicle elements to ensure data integrity and authenticity throughout the tolling process.

This ERP system exemplifies how advanced security and pricing mechanisms can enhance the operational efficiency and public acceptance of LEZs. By dynamically adjusting prices and ensuring privacy preservation, it aligns with the goals of dynamic LEZs to optimize emission reduction while maintaining equitable access to urban areas.

4.3.5. Advancing Sustainability Impact Assessment: A Comprehensive Tool for Low-Emission Zone Management [30]

A comprehensive tool for managing Low-Emission Zones (LEZs) is presented. It integrates real-time monitoring, air quality modeling, and advanced data analytics to support decision-making for urban sustainability. It has been validated in five European cities—Helsinki, Stavanger, Paris, Amsterdam, and Tallinn—and demonstrates significant potential for reducing emissions and improving urban air quality. Key contributions are as follows:

• Real-Time Monitoring and Data Integration: Utilizes IoT sensors, cameras, and communication networks to collect hyperlocal data on vehicle emissions, traffic patterns, and compliance levels within LEZs.
• Advanced Air Quality Modeling: Incorporates CHIMERE-WRF chemical models and bilinear interpolation techniques to predict pollutant dispersion at a street-level resolution, enabling targeted interventions.
• Sustainability Impact Assessment: Provides key performance indicators (KPIs) and visualization tools to evaluate the environmental and traffic impacts of LEZ scenarios in real time.
• Validated Emission Reductions: Achieved up to 86% CO₂ equivalent reductions in Helsinki under optimal scenarios, demonstrating the efficacy of adaptive LEZ management strategies.
• Interactive User Interface: Includes simulation tools, heatmaps, and dashboards for defining and monitoring LEZ boundaries and optimizing policies based on contextual intelligence and real-time data.

The tool exemplifies how advanced technologies and predictive analytics can enhance the adaptability and effectiveness of LEZ systems. By integrating real-time data and scenario simulations, it supports dynamic LEZ implementations, enabling policymakers to balance environmental goals with urban mobility needs.

4.4. Data and Evidence Mapping

Integrating data-driven models and decentralized systems enhances the effectiveness, scalability, and public acceptance of LEZ policies, ensuring that emission reduction strategies remain targeted, efficient, and equitable. Data and evidence mapping enable real-time policy adjustments and precision-based interventions. High-resolution emission mapping using Land Use Random Forest (LURF) models allows policymakers to identify pollution hotspots, optimize LEZ boundaries, and adapt strategies dynamically. Similarly, blockchain-powered access control ensures secure, transparent, and decentralized data management for adaptive LEZ operations.

4.4.1. Decentralized Privacy-Preserving Access for Low-Emission Zones [23]

A decentralized access-control system for LEZ is introduced. It ensures privacy preservation and fraud detection through group signature schemes and blockchain technology. The proposed system addresses the limitations of centralized LEZ schemes by decentralizing access verification and payment processes. Its key contributions are as follows:

• Decentralized Architecture: Utilizes blockchain to eliminate centralized third-party control over LEZ access and payment processes, transferring trust to a decentralized network of nodes.
• Privacy Preservation through Group Signatures: Ensures anonymity and unlinkability for honest users by leveraging group signatures, where access data are signed on behalf of vehicle emission category groups without disclosing individual identities.
• Revocable Anonymity for Fraud Control: Honest users retain complete anonymity, but fraudulent users who violate protocol can have their identities disclosed through group signature revocation.
• Integration with Smart Contracts: Automates payment and verification processes using Ethereum smart contracts, which calculate and transfer access fees in LEZ tokens, ensuring a transparent and tamper-proof system.
• Efficiency and Scalability: Designed to operate efficiently with minimal computational overhead, leveraging lightweight cryptographic operations suitable for real-time LEZ scenarios.

Blockchain and privacy-enhancing technologies may be integrated into dynamic LEZs to adapt to real-time traffic and emission conditions. By ensuring scalability, transparency, and privacy, it aligns closely with the goals of adaptive LEZ management.

4.4.2. A Data-Driven Method of Traffic Emissions Mapping with Land Use Random Forest Models [25]

This study introduces a novel land use random forest (LURF) approach to mapping real-time, link-level vehicle emissions. The method integrates high-density traffic monitoring data with land use information to estimate emissions dynamically, providing a foundation for adaptive Low-Emission Zones (LEZs). Its key contributions are as follows:

• High-Resolution Emission Mapping: Leveraging 272 predictors, including road features, population density, and land use variables, the model achieves a spatial generalization accuracy $R^2>0.8$ for traffic volume and speed simulations.
• Dynamic Temporal and Spatial Analysis: Evaluated hourly and daily CO, HC, NOx, PM2.5, and CO₂ emission patterns, revealing significant variations during peak and off-peak traffic periods.
• Drivers of Spatial Heterogeneity: Nonlinear relationships were identified between emissions and urban features, such as population density and proximity to logistical centers. For instance, freight activity predominantly influences NOx emissions, while CO₂ emissions are higher near Central Business Districts (CBDs).
• Scenario Evaluation for LEZ Policies: It conducted fine-grained assessments of emission reductions under two scenarios: (i) traffic demand management, exemplified by COVID-19 restrictions, achieving up to 66% CO₂ reductions; and (ii) fleet electrification, resulting in a 31% CO₂ reduction and significant reductions in NOx and PM2.5 emissions.
• Scalability and Policy Applications:The LURF model’s computational efficiency and adaptability make it suitable for real-time traffic data integration, supporting LEZ strategies in global megacities.

The study highlights how LURF-based mapping tools enable targeted LEZ policies by identifying emission hotspots and tailoring strategies to specific urban dynamics. The approach aligns with adaptive LEZ objectives, emphasizing precision and real-time adaptability for emission reduction.

4.5. Optimization and Simulation

Optimization must be integrated with simulation tools in designing dynamic Low-Emission Zones (LEZs) to ensure real-world feasibility and adaptability. Optimization models, such as Mixed Integer Linear Programming (MILP) or genetic algorithms, define optimal LEZ boundaries based on predefined objectives like emission reduction and traffic efficiency. However, these models rely on assumptions that may not fully capture complex urban dynamics.

Simulation tools, like SUMO, provide high-fidelity traffic and emission modeling, allowing researchers to test, validate, and refine optimized LEZ designs under real-time conditions. They account for nonlinear traffic behaviors, congestion spillovers, and dynamic mobility patterns, ensuring optimized policies remain practical, scalable, and effective.

4.5.1. An Optimization-Based Approach to Designing Urban Low-Emission Zones [29]

Optimization-based methodologies for designing LEZs are a key factor. This work studied how to maximize the area covered while minimizing user travel costs and network link saturation. The approach addresses the limitations of existing trial-and-error LEZ design methods by providing a systematic framework based on Mixed Integer Linear Programming (MILP). The main contributions of the study are as follows:

• Integrated Optimization Framework: The model simultaneously determines optimal LEZ boundaries and parking facility locations for modal shifts, balancing user costs and traffic network performance.
• Hexagonal Zoning System: The city is divided into small, identical hexagonal zones, enabling precise configuration and evaluation of potential LEZ layouts.
• Multi-Criteria Objective Function: It optimizes generalized user travel costs and link saturation while ensuring accessibility for all Origin–Destination (OD) pairs.
• Case Study Validation: Tested on a simplified urban network with four feasible configurations, the model demonstrated significant potential for balancing LEZ area expansion with minimal travel cost increases and reduced network congestion.
• Efficient Computational Performance: The MILP approach solved each scenario in under 20 s, showcasing its practicality for real-world applications.

The optimization framework provides a robust foundation for dynamic LEZ design, enabling real-time adjustments to LEZ boundaries and infrastructure based on traffic and emission data. This approach aligns with adaptive strategies for sustainable urban mobility by incorporating network-wide impacts and user costs.

4.5.2. Optimized Design of Low-Emission Zones in SUMO: A Dual Focus on Emissions Reduction and Travel Time Improvement [24]

This study explores optimizing Low-Emission Zones (LEZs) through a novel integration of the Simulation of Urban Mobility (SUMO) framework and genetic algorithms. The approach balances emission reduction and urban mobility, proposing dynamic configurations to address real-time traffic conditions. The key contributions are as follows:

• Dynamic Configuration: Introduces an optimization model for LEZs that adjusts boundaries dynamically based on real-time data, including emission levels and traffic congestion.
• Genetic Algorithm for Optimization: Employs genetic algorithms to select optimal LEZ edges by minimizing emission and travel times, ensuring topological connectivity, and maintaining geographical constraints.
• Experimental Validation: Simulations were conducted in Guadalajara, Spain, to validate the approach, demonstrating a 35% reduction in CO₂ emissions and a 15% improvement in travel times compared to traditional static LEZs.
• Comprehensive Data Collection: Utilizes SUMO’s traffic simulation capabilities to collect and process extensive datasets, enabling precise modeling of traffic and emission dynamics.
• Policy Implications: Highlights the feasibility of integrating optimized LEZ designs into urban planning to achieve sustainable mobility goals.

It showcases how real-time adjustments can significantly enhance both environmental and traffic outcomes. The results suggest that optimized LEZs offer a viable pathway for cities to reduce emissions while improving mobility efficiency.

4.6. Access-Control Systems

Access-control systems ensure that only compliant vehicles gain entry, enabling authorities to dynamically adjust restrictions based on real-time traffic, pollution levels, and policy goals. Without robust access control, LEZs risk inefficiency, enforcement challenges, and public resistance.

4.6.1. Secure and Privacy-Preserving Lightweight Access-Control System for Low-Emission Zones [32]

This study introduces a lightweight and privacy-preserving access-control system for LEZs that prioritizes deployability in real-world scenarios while addressing the limitations of existing methods. The proposed system requires only low-cost infrastructure and leverages common smartphones as the primary interface, eliminating the need for expensive roadside units or vehicle-installed devices. The key contributions are as follows:

• Privacy Preservation: The system ensures that honest drivers maintain their anonymity. Cryptographic protocols guarantee that only non-compliant drivers are identified, addressing privacy concerns raised by current intrusive LEZ systems.
• Low-Cost Infrastructure: Using lightweight cryptographic protocols, the system operates effectively on single-board computers, reducing implementation and maintenance costs.
• Smartphone Integration: Drivers interact with the system using their smartphones, which serve as both the identification tool and access validator, removing the need for dedicated vehicle hardware.
• Real-Time Data Collection: The system collects metadata on vehicle entries and exits, which can be analyzed to optimize LEZ management and policies without compromising user privacy.
• Feasibility Validation: Laboratory and field tests confirmed the system’s effectiveness under real-world conditions, demonstrating its ability to handle varying traffic speeds and environmental constraints.

Widely available technologies such as Bluetooth and Wi-Fi may leverage dynamic LEZ implementation, providing scalability, cost efficiency, and public acceptance. They ensure seamless integration and adaptability across diverse urban environments, enhancing deployment feasibility and operational flexibility.

4.6.2. On DICE-Free Smart Cities, Particulate Matter, and Feedback-Enabled Access Control [31]

This study introduces a feedback-enabled access-control mechanism to mitigate particulate matter (PM) emissions in urban areas by regulating vehicle access and promoting ride-sharing schemes. Unlike traditional LEZ policies, which often focus on tailpipe emissions, this system also addresses non-exhaust emissions such as those from tire and brake wear, a growing concern with the increased use of heavier electric vehicles. Key contributions are as follows:

• PM Emissions from Non-Exhaust Sources: The study highlights that tire-related PM emissions in cities like Dublin might already exceed levels deemed safe by the World Health Organization (WHO), even without internal combustion engine (ICE) vehicles.
• Feedback-Based Regulation: It introduces an adaptive control mechanism to regulate vehicle access based on real-time PM emission levels, ensuring emissions remain below safe thresholds while maximizing city access for vehicles and passengers.
• Ride-Sharing Scheme: It encourages carpooling by integrating a probabilistic selection method for vehicles based on occupancy, prioritizing fully occupied cars for city access.
• Digital Token Compliance System: A digital token system is used as a compliance mechanism to ensure adherence to ride-sharing agreements. Tokens are forfeited for non-compliance, such as failing to pick up passengers or promoting cooperative behavior.
• Simulation Results: It demonstrated through simulations that the system can maintain PM levels within safe boundaries while ensuring fair access for drivers and passengers over extended periods.

The feedback mechanism and compliance-driven ride-sharing model provide an innovative pathway for reducing urban PM emissions while balancing mobility needs and environmental safety.

4.7. Policy Impact and Integration

Can Low-Emission Zones Be Managed More Dynamically and Effectively? [34]

This study introduces the concept of Green Activity Zones (GAZs), a novel dynamic pricing model designed to improve the efficiency of LEZs by leveraging real-time emission data. The GAZ system dynamically adjusts charges based on continuous emission measurements from vehicles operating within the zone, providing a more accurate and equitable framework compared to traditional static LEZ policies. The main contributions are as follows:

• Dynamic Pricing Based on Real Emissions: Charges are calculated using a differentiated pricing model for pollutants such as NOx, PM, HC, and CO. The system accounts for spatial and temporal variations in environmental sensitivity, allowing for adaptive tolling during periods of high pollution.
• Incentives for Behavioral Change: By implementing real-time data collection and feedback mechanisms, the GAZ model encourages carriers to adopt eco-friendly practices, such as optimizing routes, increasing load factors, and adopting cleaner vehicles.
• Fairness and Transparency: The system adheres to the Polluter Pays Principle, ensuring that charges are proportionate to the environmental impact of each vehicle. Stakeholders can also document their environmental performance, creating incentives for sustainable competition.
• Stakeholder Engagement and Acceptability: Surveys conducted among retailers, wholesalers, and carriers highlight variations in acceptance levels. Wholesalers are more willing to adopt the GAZ framework than carriers, who express concerns over cost implications and limited perceived effectiveness.
• Technological Feasibility: The study emphasizes the readiness of current technologies, such as onboard sensors and real-time monitoring systems, to support the implementation of GAZ.

The GAZ model exemplifies a next-generation approach to LEZ management by integrating dynamic pricing mechanisms with real-time data. Its focus on fairness, behavioral incentives, and stakeholder engagement aligns with the goals of dynamic LEZs to enhance environmental and operational outcomes.

5. Discussion

This systematic review comprehensively analyzes the transition from static to dynamic LEZs, underscoring the significant advancements and ongoing challenges in optimizing urban air quality and traffic management.

One of the main findings obtained from the study is the lack of systematic studies that holistically address the problem associated with urban models that implement dynamic low-emission zones. In this sense, we can see how existing studies address various implementation strategies that could support the development of dynamic low-emission zones.

The findings reveal that dynamic LEZs, which leverage real-time data and adaptive strategies, offer substantial improvements over their static and hybrid counterparts in reducing vehicular emissions without adversely affecting traffic flow. Notably, studies incorporating advanced technologies such as blockchain for secure and privacy-preserving access control, as well as sophisticated traffic simulation tools, demonstrate enhanced efficiency and scalability in LEZ implementations.

The review encompasses 14 studies on transitioning from static to dynamic LEZs. The key findings can be summarized as follows:

• Technological Integration: A significant number of studies emphasized the adoption of advanced technologies such as blockchain and smart contracts to enhance the security and privacy of access-control systems in dynamic LEZs [19,26].
• Pricing Strategies: Dynamic pricing mechanisms, including electronic road pricing (ERP) systems with real-time fee adjustments based on emissions and traffic conditions, were identified as practical tools for reducing vehicular emissions without disrupting traffic flow [18,19].
• Emission Reduction Efficacy: Studies demonstrated that dynamic LEZs can achieve substantial reductions in CO₂ emissions (up to 66%) and significant decreases in other pollutants such as NOx and PM2.5 [24,25].
• Traffic Management Improvements: Optimized LEZ designs using simulation tools like SUMO and genetic algorithms resulted in improved travel times (up to 15%) and a better traffic distribution, indicating enhanced urban mobility [24,29].
• Privacy Preservation: Several studies addressed privacy concerns by implementing anonymization protocols and decentralized access-control systems, ensuring that only non-compliant drivers are identified without tracking honest drivers [22,23].
• Public Acceptance and Compliance: The review highlights the importance of transparent communication and stakeholder engagement in fostering public trust and compliance with dynamic LEZ regulations [20,34].

These findings collectively indicate that dynamic LEZs, when effectively implemented with supportive technologies and strategic planning, may significantly contribute to urban sustainability by reducing emissions and improving traffic management while maintaining public trust and compliance.

Assessment of Risk of Bias

The study quality of six papers is rated as high, while eight other papers are rated to be of moderate quality, and zero are rated as low-quality as shown in

Table A1. Risk of bias assessment for studies on dynamic low-emission zones (LEZs).

First Author (Year)	1. Sample Frame Appropriate?	2. Sample Size Adequate?	3. Study Setting Described in Detail?	4. Data Analysis Coverage?	5. Valid Methods for Emissions?	6. Emissions Measured Reliably?	7. Statistical Analysis Appropriate?	Overall Quality Rating
Roger Jardí-Cedó (2014) [18]	No	No	Yes	Yes	Unclear	Unclear	Yes	Moderate
Roger Jardí-Cedó (2015) [19]	No	No	Yes	Yes	Unclear	Unclear	Yes	Moderate
Bernardo et al. (2021) [20]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Roger Jardí-Cedó (2018) [22]	No	No	Yes	Yes	Unclear	Unclear	Yes	Moderate
Anglès-Tafalla (2019) [23]	No	No	Yes	Yes	Unclear	No	Yes	Moderate
Paricio-Garcia (2024) [24]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Wen et al. (2022) [25]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Anglès-Tafalla (2023) [26]	No	No	Yes	Yes	Unclear	No	Yes	Moderate
Rui Feng (2023) [27]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Jardí-Cedó (2016) [28]	No	No	Yes	Yes	Unclear	Unclear	Yes	Moderate
Pili et al. (2023) [29]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Fernández et al. (2023) [30]	Yes	Yes	Yes	Yes	Yes	Yes	Yes	High
Katsikouli et al. (2020) [31]	No	No	Yes	Yes	Unclear	No	Yes	Moderate
Anglestafalla (2018) [32]	Yes	Yes	Yes	Yes	Unclear	Unclear	Yes	Moderate

. Sources of potential bias among these studies are a lack of representativeness of the study sample (e.g., reliance on simulations or theoretical proposals in 10 studies), small sample size (e.g., a limited number of scenarios analyzed in some simulation-based studies), low coverage of emission measurement methods (5 studies used unvalidated or unclear methods), and a lack of standardized and reliable methodologies for follow-up evaluations (particularly in studies that focus on conceptual systems without real-world testing).

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 adopting privacy-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.