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Article

Environmental Impact of Electrification on Local Public Transport: Preliminary Study

by
Daniele Martini
*,
Pietro Bezzini
and
Michela Longo
Department of Energy, Politecnico di Milano, Via La Masa 34, 20156 Milan, Italy
*
Author to whom correspondence should be addressed.
Energies 2024, 17(23), 5886; https://doi.org/10.3390/en17235886
Submission received: 23 September 2024 / Revised: 18 November 2024 / Accepted: 21 November 2024 / Published: 23 November 2024
(This article belongs to the Special Issue Advancements in Smart Electric Mobility Systems: Integration of Renewable Energy and Energy Storage)
Browse Figures
Figure 1
<p>Emissions by sector in EU (2022).</p> "> Figure 2
<p>Components of the life cycle analysis of vehicle emissions.</p> "> Figure 3
<p>Longitudinal dynamics Simulator logic flowchart.</p> "> Figure 4
<p>The context of (<b>a</b>) Cecina in Italy; (<b>b</b>) Route 11 (blue line), connecting the city center with a sparsely populated area with schools in between; and (<b>c</b>) elevation profile [<a href="#B60-energies-17-05886" class="html-bibr">60</a>,<a href="#B61-energies-17-05886" class="html-bibr">61</a>].</p> "> Figure 5
<p>Speed and acceleration (<b>a</b>) and power and fuel consumption (<b>b</b>) profile of diesel bus on case study.</p> "> Figure 5 Cont.
<p>Speed and acceleration (<b>a</b>) and power and fuel consumption (<b>b</b>) profile of diesel bus on case study.</p> "> Figure 6
<p>Speed and acceleration (<b>a</b>) and power and fuel consumption (<b>b</b>) profile of electric bus on Route 11: navigating urban to extra-urban terrains.</p> "> Figure 7
<p>SEC values for different stages of energy transmission.</p> ">
Versions Notes

Abstract

:
The objective of this study is to provide a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions along a mixed urban–rural route in small urban settings. Utilizing a detailed simulation model, this research compares emissions from a diesel bus and an electric bus on a specific route in a small town in central Italy. Key findings reveal that electric buses significantly reduce local exhaust emissions but are not entirely emission-free, considering the full life cycle, including electricity generation. The Well-to-Wheel analysis shows lower CO 2 emissions for the electric bus compared with the diesel bus, with a substantial part of the emissions occurring at power generation facilities. Non-exhaust emissions, especially Total Suspended Particles, are similar for both bus types. This study highlights the advantages of adopting electric buses in urban areas to decrease local air pollution and greenhouse gas emissions. However, it also underscores the importance of cleaner electricity generation methods to fully leverage the environmental benefits of electric vehicles. The findings provide valuable insights for decision makers and urban planners in developing sustainable urban transportation systems.

1. Introduction

The increase in air pollution since the Industrial Revolution has significantly disrupted Earth’s climate equilibrium. Air pollution, comprising various elements that change atmospheric properties, is a key factor in climate change, ecosystem disruption, and health issues [1]. Post-1900 industrial activities have contributed to global temperature rise, sea level rise, ocean acidification, and increased atmospheric pollutants [2], highlighting the urgent need for human intervention in the climate crisis [3]. The 1952 Great London Smog, a severe air pollution event, led to the Clean Air Act of 1956, promoting smokeless zones and cleaner heating technologies [4]. The 1992 Earth Summit in Rio introduced Agenda 21 and the UNFCCC, paving the way for the creation of the Kyoto Protocol and the Paris Agreement, which focus on reducing greenhouse gas emissions and promoting sustainable development [5]. The Kyoto Protocol (2005) set emission limits for developed countries, while the Paris Agreement (2015) aimed to limit global warming to below 2 °C, preferably 1.5 °C [6]. However, current trends suggest a potential rise of 2.1 °C by 2100, even with net-zero targets, indicating that more rigorous actions are needed [7].
In 2021, the top CO 2 emitters were China, the US, EU27, India, Russia, and Japan, accounting for 67.8% of global emissions. Global CO 2 emissions from energy and industrial processes have been increasing since 1900, reaching 36.6 gigatons in 2022 [8]. Europe is transitioning from fossil fuels to renewables and nuclear energy, aiming for climate neutrality by 2050 and a 55% GHG reduction by 2030. However, Europe still heavily relies on imported fossil fuels. The region has seen a decline in GHG emissions across sectors, indicating the effectiveness of emission reduction measures [9].
The transport sector, heavily reliant on fossil fuels, significantly contributes to air pollution. The EU’s transport sector contributed over 43% of the total GHG emissions in 2020 [10], with the European Green Deal targeting a 90% reduction in transport-related emissions by 2050. Current measures predict only a 22% emission decrease by 2050 [11], with a projected 15% reduction in CO 2 emissions by 2030 and 36% by 2050 [12]. Transitioning to alternative fuels, especially for passenger cars, is crucial, as shown by the rise in electric and hybrid car sales [13]. Figure 1 highlights the share of CO 2 emission by sectors. The energy transition in transport aims to shift from fossil fuels to sustainable, low-carbon sources to mitigate climate change and improve air quality. This transition enhances energy security and aligns with sustainable development goals, fostering economic growth and innovation [11]. European emission standards regulate emissions from land vehicles [14], while CO 2 limits are defined on a European fleet-wide scale [14]. The EU27 has seen an increase in road vehicle stock, with fossil fuels remaining dominant. Buses, primarily diesel powered, are crucial for public transportation and social inclusion. In Italy, there is a pressing need to accelerate the transition to more environmentally friendly transportation options. Electric buses present benefits such as zero tailpipe emissions and reduced operational costs; however, their implementation must consider the specific requirements of the transit system. According to the Directive, an internal table (Table 4) outlines the minimum targets for public procurement of zero-emission buses for Italy, setting goals of 22.5% by 2025 and 32.5% by 2030 [15]. Gasoline and diesel, traditional carbon-based fuels, are environmentally impactful [16]. The European Green Deal targets a 90% reduction in transport emissions by 2050. Well-to-Wheel (WtW) analysis assesses the environmental impact of energy vectors in transportation, considering emissions from extraction to vehicle operation. It is crucial for informed decisions in energy and transportation strategies, differentiating between traditional fuels and alternatives like biofuels or electricity. Vehicle emissions depend on various factors, including engine technology, fuel type, and driving style [17].
This work categorizes relevant pollutants into Tank-to-Wheel (TtW) and Well-to-Tank (WtT) emissions. TtW emissions are further classified into exhaust and non-exhaust emissions. Exhaust emissions stem from the combustion process in the internal combustion engine (ICE), while non-exhaust emissions arise from the vehicle’s moving parts. WtT emissions, which vary based on the energy source, predominantly include pollutants found in the exhaust emission category, which have been studied here [18]. The focus of this work is on WtW. WtW analysis is a subcomponent of the life cycle assessment; the components are shown in Figure 2, where the red square is the WtW object of this study.
This work is organized as follows: The introduction covers the impact of vehicle emissions on climate and health, motivating this study. The literature review addresses the current state of the art of emission estimation modeling and the gaps. The Methodology explains the WtW framework, emission calculations, and simulation setup. The Results and Discussion section compares emissions and energy use between diesel and electric buses, analyzing both the TtW and WtT phases. Finally, the Conclusions summarizes the key findings and suggest directions for future research on sustainable urban transport.

2. Literature Review

This literature review explores existing studies on the environmental impact of electric buses and identifies critical areas that require further investigation to accurately understand their complete environmental impact. Transitioning to electric buses is widely seen as a strategy to reduce local emissions in urban settings. Studies have shown that electric buses significantly reduce petroleum dependence and urban air pollution and align with sustainability goals [19,20]. However, the overall emission reduction potential heavily depends on the cleanliness of the electricity used. One report notes that electric buses can reduce petroleum use by up to 87% and CO2 emissions by 24%, but these benefits are dependent on the power source [21]. Regions reliant on coal or other fossil fuels might see limited environmental benefits, as indicated by further analysis, which stresses the need for decarbonizing the power sector to achieve substantial GHG reductions [22]. Without integrating regional variability and seasonal fluctuations in grid sources, life cycle assessments of electric buses may underestimate true environmental costs.

2.1. The Challenge of Non-Exhaust Emissions in Urban Environments

Electric buses eliminate exhaust emissions, but they contribute to air pollution through non-exhaust emissions, including particulate matter (PM) from brake wear, tire wear, and road dust resuspension. Some researchers highlight that non-exhaust emissions represent a significant portion of urban particulate matter, especially as exhaust emissions decrease with electrification [23]. Non-exhaust emissions are substantial health risks, as particles from tire and brake wear can penetrate deeply into the respiratory system, posing risks to urban populations. Further studies emphasize the impact of non-exhaust emissions at bus stops and high-traffic areas, where exposure is concentrated. As electric buses are often heavier due to battery weight, they may produce more PM through tire and brake wear, raising questions about the adequacy of “zero-emission” labels without accounting for non-exhaust emissions [24]. Researchers have argued that legislation targeting non-exhaust emissions is necessary for meaningful air quality improvements [25,26].

2.2. The Role of Well-to-Wheel Analysis for a Complete Emission Profile and Research Gaps

Some studies suggest that WTW is essential to capture emissions of electric buses accurately [21,22]. However, many WTW studies lack integration of non-exhaust emissions or regional energy source variability. Standardized WTW methodologies should incorporate real-world usage patterns such as urban density, stop-and-go traffic, and variable occupancy, which influence the total emission profile of electric buses [27,28]. Despite the critical role of grid composition, some studies analyzing emissions from electric buses do not sufficiently account for the pollution produced by electricity generation apart from CO 2 . For instance, while [22,28] assess emissions based on energy consumption during operation, they omit details on the specific emission impact of varying grid sources, potentially underestimating environmental costs where electricity production is carbon intensive. Studies such as those by [19,20] largely focus on tailpipe and operational emissions while neglecting the non-exhaust emissions that electric buses contribute through tire and brake wear. This oversight can lead to an incomplete understanding of the air quality impact of electric buses in urban areas. Additionally, without accounting for these emissions, the “zero-emission” label attributed to the electric impact of electric buses on air quality still contributes to particulate pollution even without a tailpipe [24].
While electric buses can reduce emissions, current accounting methodologies fall short of capturing the complete pollution spectrum.

3. Vehicle Pollution

Pollution from vehicles is a major contributor to air pollution worldwide, leading to environmental degradation and health issues. Traditionally, the focus has been on exhaust emissions—the harmful gases released from the tailpipes of cars and trucks. These emissions include pollutants like carbon monoxide, nitrogen oxides, and hydrocarbons, which contribute to smog formation, acid rain, and respiratory illnesses in humans. However, non-exhaust emissions are also a significant source of pollution from vehicles. Unlike exhaust emissions, non-exhaust emissions originate from sources other than the vehicle’s engine combustion.

3.1. Exhaust Emissions

The combustion process in vehicles uses the oxygen from the air to efficiently use fuel to create mechanical energy. Atmospheric air, mainly composed of nitrogen (78%) and oxygen (21%), is utilized in combustion chambers. To ensure complete combustion and improve efficiency, excess air is introduced, increasing the available oxygen and nitrogen. This enhances the likelihood of oxygen reacting with fuel, reducing fuel waste and increasing combustion efficiency. The “air-to-fuel ratio”, a key process parameter, is the mass ratio of air to fuel entering the combustion chamber. Altering this ratio allows control over the combustion process and vehicle performance [29]. However, using carbon-based fuels, which may contain impurities and excess air, leads to the release of various chemical species into the atmosphere besides carbon dioxide, water, and unused nitrogen and oxygen. These emissions, known as “exhaust emissions”, vary depending on several factors, including the type of fuel, vehicle, onboard emission control systems, driving and road conditions, and the load carried. These emissions have significant environmental and health impacts. Electric buses, on the other hand, operate differently. Since EVs are powered by electricity stored in batteries, they do not burn any type of fuel during operation and, consequently, have no exhaust emissions. This means that, in terms of Tank-to-Wheel analysis, EVs do not produce the traditional exhaust pollutants associated with internal combustion engines. Therefore, while diesel vehicles emit a range of pollutants from their exhaust in addition to non-exhaust emissions, electric vehicles primarily have to consider non-exhaust emissions in their TtW emission profile. This difference marks a significant shift in the source and type of pollutants between these two types of vehicles.
  • Carbon dioxide ( CO 2 ) is a vital yet potent greenhouse gas, composed of one carbon atom double-bonded to two oxygen atoms. It is crucial for life on Earth, absorbing infrared radiation and retaining heat in the atmosphere. CO 2 dissolves in water, forming carbonate and bicarbonate, contributing to ocean acidification. Atmospheric CO 2 levels have risen significantly from pre-industrial levels, mainly due to fossil fuel combustion, contributing to climate change. While not toxic, high indoor concentrations can cause health issues like asphyxiation. CO 2 is also used in climatology to define “global warming potential” (GWP), a measure comparing the impact of different greenhouse gases. Other gases like methane and trifluoromethane have significantly higher GWP than CO 2 , emphasizing the need to monitor various emissions impacting climate change [30].
  • Carbon monoxide (CO) is a colorless, odorless, and poisonous gas, formed mainly from incomplete combustion of carbon-containing compounds. It varies in concentration across Earth’s atmosphere due to human activities and natural processes. CO levels increased over the 20th century but slightly declined in the 1990s with the adoption of catalytic converters in vehicles. Inhaled CO can replace oxygen in blood, leading to severe health risks or death, especially at high concentrations. Reducing CO emissions involves complete combustion or using catalytic converters to minimize its formation [31].
  • Nitrogen oxides ( NO x ) refer to nitrogen oxides, harmful pollutants formed mainly during combustion involving nitrogen. They are predominantly created by three mechanisms: thermal NO x (from atmospheric nitrogen at high temperatures), fuel bond NO x (from nitrogen in fuel), and prompt NO x (from early combustion stages). NO x formation is nearly inevitable with atmospheric oxygen and high temperatures, and its reduction is challenging, especially in diesel engines due to their oxygen-rich exhaust [32]. Compressed natural gas (CNG) vehicles show varied NO x emissions compared with diesel, with some cases even higher [33].
  • Volatile organic compounds (VOCs) are compounds that evaporate easily at room temperature, commonly found in fossil fuels and as contaminants in groundwater. They originate from various sources, including petroleum products and industrial chemicals, and can be harmful to both the environment and human health, with some being carcinogenic. VOCs contribute to global warming by affecting ozone levels. In vehicles, they are emitted as unburned hydrocarbons, with non-methane VOCs identified separately due to their environmental impact. Studies show that natural gas vehicles can emit more hydrocarbons than diesel ones. Catalytic converters are used to reduce VOC emissions from fossil fuel vehicles [16,34].
  • Particulate matter (PM) emissions from road transport, including tiny solid or liquid particles, come mainly from vehicle exhaust and non-exhaust sources like tire and brake wear. Classified as PM 10 and PM 2.5 based on size, PM poses significant health and environmental risks, contributing to respiratory and cardiovascular issues, pollution, and climate change [35]. These emissions are largely due to incomplete combustion of fossil fuels, with ultrafine particles being a notable concern due to their extremely small size and high health risk [36,37]. Solid particle number (SPN) is a different measurement used in portable emissions measurement systems (PEMS) to track the number of solid (non-volatile) particles emitted by vehicles. As modern technologies have reduced particulate mass emissions to very low levels. SPN measurements provide a more sensitive indicator, especially for vehicles equipped with particulate filters, where traditional mass-based measurements may not adequately capture all particulate emissions [38].
  • Sulfur oxide ( SO x ) emissions, primarily from fuel sulfur content, form sulfur dioxide ( SO 2 ) and sulfur trioxide ( SO 3 ), leading to acid rain and environmental harm. International regulations have significantly reduced sulfur in transport fuels, such as in ultra-low-sulfur diesel (ULSD) and gasoline (ULSG), making SO x emissions from road transport negligible [39,40].
  • Other pollutants: Ammonia emissions from road transport in Europe increased by 139% since 1990, worsening air quality, especially in urban areas [41]. These emissions contribute to particulate matter formation [39]. Meanwhile, emissions of toxic heavy metals from transport have decreased in the EU27 due to stricter regulations and cleaner fuels, reducing their environmental and health impacts [41,42].

3.2. Non-Exhaust Emissions

Non-exhaust emissions, comprising PM from vehicles, originate from sources other than exhaust gases—such as brake and tire wear, road abrasion, and road dust resuspension [43]. These emissions are influenced by vehicle component composition, weight, and speed, intensifying during acceleration and braking [44]. Contrary to the belief that electric vehicles are “zero-emission”, they, like internal combustion engine vehicles, produce non-exhaust emissions significant for environmental and health impacts [45]. Tyre and road abrasion are complex physio-chemical phenomena currently treated as separate sources due to limited data on their combined emission factors [46]. Notably, heavy-duty vehicles (HDVs) have higher tire wear factors than light-duty vehicles (LDVs) [46].
Europe’s diverse road surfaces, ranging from asphalt to concrete, significantly affect non-exhaust emissions, influenced by vehicle characteristics, driving behavior, road type, and moisture [46]. Brake systems, mainly disc and drum brakes, also contribute. Disc brakes, common in smaller vehicles and light-duty trucks’ front wheels, contrast with drum brakes, traditionally used in heavier vehicles. However, newer heavy-duty vehicles increasingly adopt disc brakes [46]. A study [46] in 2000 showed minimal gaseous emissions from mechanical brake abrasion, with no significant increases in CO, CO 2 , and HC levels.
Non-exhaust emissions are similar across vehicles of the same class (e.g., LDVs, HDVs, motorcycles), regardless of propulsion type, due to similar mass, braking systems, road abrasion, tire wear rate, and driving conditions [46]. For instance, a study comparing battery electric and diesel buses found only a slightly higher non-exhaust particle emission in electric buses, attributed to their heavier batteries [47].
The Tier 2 approach used for exhaust pollutants is also applicable to non-exhaust emissions. For urban buses (diesel and electric), the following non-exhaust emission factors (in Total Suspended Particles, TSP, per km) are assumed [46]:
  • Tire wear: 0.0299 gTSP/km;
  • Brake wear: 0.042 gTSP/km;
  • Road surface wear: 0.076 gTSP/km
The total non-exhaust TSP for buses is calculated as 0.1479 gTSP/km. To estimate the total non-exhaust TSP for a trip, the figure is multiplied by the trip length.

EEA Emission Factors for Urban Bus

The emission factors are the amount of pollutant emitted over a unit of measure, typically km. Table 1 presents a comprehensive overview of these factors for EURO 6 diesel buses, as compiled from the latest data provided by the European Environment Agency [48]. The table is structured to display emission factors (EFs) for different pollutants emitted by diesel buses. These factors are measured in grams per kilometer (g/km), number per kilometer (#/km), and number per kilowatt-hour (#/kWh), providing a multi-dimensional view of pollutant emissions. In particular, SPN 23 is the number of particles larger than 23 nm.

3.3. Electricity Generation Pollution

Electricity production and consumption significantly contribute to environmental pollution, particularly CO 2 emissions [49]. Different technologies have been used for air pollution control, especially in reducing pollutants like NO, NO 2 , and SO 2 from emissions generated by electricity production. The efficiency and energy requirements of these technologies have been continuously developed and improved [50]. Around the world, the environmental quality of electricity varies by location, season, and time of day. This variation impacts the emission footprint, highlighting the importance of considering location and temporal effects in estimating emissions [51]. Increasing the efficiency of power stations and reducing electrical energy consumption are key strategies for reducing atmospheric pollution caused by electricity production. This approach focuses on minimizing the emission of pollutants like CO 2 , SO 2 , and NO x [52]. In China, increases in electricity generation, particularly thermal power output, have been positively correlated with increases in air pollutants like PM 2.5 and PM 10 [53]. Italy is transitioning from fossil fuels to renewable energy, reducing CO 2 emissions. By 2050, Italy could achieve 85.6% penetration of renewable energy sources (RESs) in electricity supply, leading to a significant reduction in CO 2 emissions compared with current levels [54].
Emissions from the 450 selected European Pollutant Emission Register (EPER) facilities were analyzed, with pollutants including NO x , CO, NMVOC, PM 10 , SO 2 , and CO 2 . The total emissions from these facilities were recorded as 1494 kt for NO x , 207 kt for CO, 6 kt for NMVOC, 91 kt for PM 10 , 2773 kt for SO 2 , and 1,006,598 kt for CO 2 .

4. Model Architecture: Logical Reasoning, Mechanical Design, and Parameterization

This section focuses on the architecture of the simulation, first on the logical structure, mechanical design, and pollution parameterization aspects. It is structured into three main subsections.
  • Model Logic: This subsection outlines the logical flow and computational steps of the simulator. It describes how the route is segmented, how vehicle parameters are initialized and updated, and how different vehicle states are managed throughout the simulation. The flowchart in Figure 3 serves as a visual guide to the simulator’s logic.
  • Mechanical Model: Here, the mathematical formulations and physical principles governing the vehicle’s longitudinal dynamics are presented. This includes the equations for acceleration, braking, resistive forces, and torque requirements. The subsection covers how these mechanical aspects are modeled for both ICE vehicles and BEVs; this is general for different kinds of vehicles.
  • WtT Analysis and Parameters: The final subsection focuses on the energy consumption and emissions from WtT. It presents the methodologies for calculating the WtT emissions for diesel buses and BEVs, detailing the parameters involved in fuel production, energy distribution, and the overall environmental impact. While the methodology is general, the parameters could be case specific.

4.1. Model Simulation Logic

The flowchart shown in Figure 3 represents the logic behind a Longitudinal Dynamics Simulator, a tool used for modeling the movement and behavior of a vehicle along a predefined route. The process begins with dividing the route into segments, each with unique characteristics like length and speed limits. The simulator initializes various indices and arrays to track the vehicle’s position, speed, acceleration, and power metrics. The main simulation loop updates these parameters based on the vehicle’s interaction with each segment, considering different speed states and calculating motor torque and resistance forces. For ICE vehicles, it computes engine power, thermal power, and fuel consumption, while for BEVs, it calculates motor power, regenerative power, and battery State of Charge. The vehicle’s state is continuously updated until it completes all segments. Finally, the data are concatenated into arrays, providing a comprehensive profile of the vehicle’s dynamics throughout the journey.
Energies 17 05886 g003
Figure 3. Longitudinal dynamics Simulator logic flowchart.
Figure 3. Longitudinal dynamics Simulator logic flowchart.
Energies 17 05886 g003
The methodology for quantifying the energy or fuel consumption of diesel vehicles and BEVs begins by identifying time intervals where the bus’s engine power is positive, indicating active fuel or electricity consumption to maintain necessary speed and acceleration. In contrast, intervals with negative or zero power involve no energy consumption for propulsion, though auxiliary systems like air conditioning and electronics still draw power.

4.1.1. Diesel Bus

For diesel buses, the energy analysis involves summing the motor power and auxiliary system consumption. The thermal power is then calculated based on the diesel engine’s thermal efficiency, which represents its ability to convert the chemical energy in diesel fuel into mechanical energy. The thermal power is used for estimating the amount of fuel consumed in each interval. This calculation uses the fuel’s lower heating value (LHV) and density ( ρ ), providing a measure in liters per second. In Equation (1), the fuel flow rate (F) in liters per second (L/s) is calculated. In Equation (2), the total power ( P tot ) requested from the driver and the auxiliaries is calculated, as follows:
F = P tot η th · LHV · ρ f
P tot = P k + P aux if P k > 0 P aux otherwise
where
  • F is the fuel flow rate in liters per second (L/s);
  • P aux is the auxiliary power consumption in kilowatts (kW);
  • η th is the thermal efficiency of the engine;
  • LHV is the lower heating value of the fuel (kJ/kg);
  • ρ f is the fuel density in kilograms per liter (kg/L).

4.1.2. Electric Bus

The approach differs significantly for BEVs. These vehicles draw energy from the battery. A notable feature of BEVs is regenerative braking, which allows the motor to act as a generator during braking, converting kinetic energy back into electrical energy to recharge the battery. This process is quantified through the State of Charge (SoC) parameter, reflecting the battery’s energy content as a ratio of its current energy content over total capacity. In periods where the motor consumes power, it operates as a standard motor without energy recovery. Conversely, when the motor generates power, it functions as a generator, with a certain percentage of this generated power (determined by the recovery rate) contributing to recharging the battery. The total power for each interval is calculated as the following Equation (4):
P tot = P k · η p if P k > 0 P k · η r if P k ≤ 0
SoC [ k ] = SoC [ k − 1 ] − P tot [ k ] · Δ t C batt
where
  • P tot is the total power at time interval k in kW;
  • P k is the motor power at time interval k in kW;
  • η p is the propulsion efficiency when the motor consumes power;
  • η r is the regeneration efficiency when the motor generates power;
  • SoC [ k ] is the State of Charge at time interval k;
  • Δ t is the duration of the time interval;
  • C batt is the total battery capacity.

4.2. Mechanical Model

The proposed model for describing the longitudinal dynamics of a vehicle, applicable to both ICE vehicles and BEVs, focuses on simulating vehicle movement and energy consumption under various conditions. This encompasses the vehicle’s ability to accelerate and decelerate, taking into account the forces involved, such as engine power, resistive forces, and inertia. The model also considers the RPM-Torque curve for ICE vehicles and the maximum torque for BEVs.
  • Dwell Time: A 30-s stop time is assumed for buses;
  • Maximum Speed: Set based on realistic conditions, varying by vehicle type and road segment (e.g., urban vs. extra-urban) [55];
  • Straight Path Assumption: Lateral dynamics are ignored, simplifying the calculation of route lengths and gradients using tools like Google Earth;
  • Technical Compliance: At each step (0.1 s), the vehicle must meet speed, acceleration, motor performance, and adhesion limits.
The model uses Equation (5) [56] to calculate braking distances and adjust vehicle speed to ensure it adheres to speed limits and comfort requirements. The braking distance ( d braking ) is influenced by the vehicle’s initial velocity ( V A ), desired final velocity ( V B ), and deceleration (a), which is the negative acceleration during braking.
Equation (7) is the resultant of the passive forces acting on the vehicle. Equation (8) calculates the gravitational force that applies to the vehicle. Equation (9) describes the rolling resistance F roll . Finally, Equation (10) determines the aerodynamic drag force F aero .
d braking = ( V B − V A ) 2 a 2
ω m = V R · τ g
F res = F weight + F roll + F aero
F weight = m · g · sin ( α )
F roll = f r · m · g · cos ( α )
F aero = 1 2 ρ C d V 2
where
  • d braking is the braking distance in m;
  • V A is the vehicle’s initial velocity in m/s;
  • V B is the desired final velocity in m/s;
  • a is the acceleration (negative during deceleration) in m/s2;
  • ω m is the motor’s angular velocity in rad/s;
  • V is the linear velocity (vehicle speed) in m/s;
  • R is the wheel radius in m;
  • τ g is the gear ratio;
  • T m is the motor torque in Nm;
  • F res is the total resistive force in N;
  • m is the vehicle mass in kg;
  • Δ m is the additional variable mass in kg;
  • F weight is the weight force in N;
  • F roll is the rolling resistance force in N;
  • F aero is the aerodynamic force in N;
  • g is the gravitational acceleration in m/s2;
  • α is the slope angle in rad;
  • f r is the rolling resistance coefficient;
  • ρ is the air density in kg/m3;
  • C d is the drag coefficient.
Torque requirements for ICE vehicles are based on the RPM-Torque curve, and for BEVs, they are based on the peak torque. Adjustments are made to maintain constant speeds or during coasting phases. Four scenarios are identified:
Acceleration: Urban buses are designed to not exceed an acceleration of 1.2 m/s2 to ensure passenger comfort, taking into consideration road conditions, bus design, seating arrangement, and suspension system efficiency [57]. Acceleration calculations, based on the available torque [56], account for resistance forces and vehicle mass. Equation (6) expresses the motor’s angular velocity ( ω m ). Equation (11) defines forward acceleration, a for , as follows:
a for = m i n η dir T m R · τ g − F res m · ( 1 + Δ m ) , a comfort
where
  • a for is the forward acceleration in m/s2;
  • η dir is the efficiency factor for direct drive;
  • F res is the total resistive force in N;
  • a comfort is the maximum acceleration for passenger comfort (typically 1.2   m / s 2 for urban buses).
Deceleration: A maximum deceleration limit is enforced to balance performance with safety and comfort.
Coasting: Equation (12) describes coasting acceleration ( a coast ). It calculates T coast (torque during coasting) adjusted by the radius (R) and time constant ( τ g ), subtracting the adjusted resistive force ( F res ) based on the efficiency of reverse operation ( η rev ), vehicle mass (m), and an additional mass factor ( Δ m ). Equation (13) details how T coast is derived by negating the product of reverse operation efficiency ( η rev ), resistive force ( F res ), radius (R), and time constant ( τ g ), as follows:
a coast = T coast R τ g − η rev F res η rev m ( 1 + Δ m )
T coast = − η rev F res R τ g
where
  • a coast is the coasting acceleration in m/s2;
  • T coast is the torque during coasting in Nm;
  • τ g is the gear ratio;
  • η rev is the efficiency of reverse operation.
This ensures that the torque does not exceed the maximum traction limit to prevent slipping. In Equation (14), T adh represents the maximum adhesion torque, as follows:
T adh = ± m g 2 cos ( α ) μ R τ g η dir
where
  • T adh is the maximum adhesion torque;
  • α is the slope of the segment in rad;
  • μ is the friction limit;
  • τ g is the gear ratio.
The model quantifies energy consumption and emissions by focusing on intervals where the engine generates power, using specific emission factors. This comprehensive approach allows for a detailed analysis of vehicle dynamics, energy consumption, and emissions, adaptable to different types of vehicles and road conditions.

4.3. WtT Analysis and Parameters

To accurately calculate WtT, it is essential to consider various inefficiencies and energy costs. Notably, diesel and electric buses exhibit distinct energy sources.

4.3.1. Diesel Bus WtT

The Well-to-Tank analysis quantifies the greenhouse gases emitted from crude oil extraction to the delivery of the fuel to the vehicle.
The emission from crude oil production (COP) amounts to 3.64 gCO 2 / MJ crude from CO 2 and 0.024 gCH 4 / MJ crude . Considering the global warming potential (GWP) of CH 4 results in a total of 4.25 gCO 2 , eq / MJ crude . The refining yield coefficient, representing the efficiency from crude oil to diesel ( η ref ), is 0.9091 MJdiesel/MJcrude.
Equation (16), the calculation to convert the emissions from crude oil production and refining processes to diesel equivalents, accounts also for the distribution process. This includes percentages of ship, rail, pipeline, and truck distributions. For example, for ship distribution, the total emissions are calculated by adding the emissions from shipping (0.439 gCO 2 / MJ crude ) and storage (0.009 gCO 2 / MJ crude ). The following Equation (15) is the final output of the CO 2 emission model:
CO 2 , WtW = CO 2 , WtT + CO 2 , TtW
CO 2 , WtT = CO 2 , COP + CO 2 , R η ref + ∑ k D k · CO 2 , D k
  • CO 2 , WtW is the total WtW CO 2 emissions;
  • CO 2 , WtT is the WtT CO 2 emissions;
  • CO 2 , TtW is the TtW CO 2 emissions;
  • CO 2 , COP is the CO 2 emissions from crude oil production;
  • CO 2 , R is the CO 2 emissions from the refining process;
  • η ref is the efficiency from crude oil to diesel (refining efficiency);
  • D k is the share of the distribution method k;
  • CO 2 , D k is the CO 2 emissions associated with the distribution method k.

4.3.2. Battery Electric Bus WtT

The BEV’s energy is sourced from the national electric infrastructure and transferred via an onboard charger at a charging station. The main contributor to the BEV’s carbon footprint is the production of this electricity, which has a significant environmental impact if generated from fossil fuels (excluding biomass, biogas, etc.). It is necessary to account for the energy inefficiency, in particular the charger efficiency, η st and the distribution efficiency η grid . Each power source (coal, gas, oil, renewables, etc.) contributes differently to the kWhe/km used by the vehicle based on the country’s energy mix. In the following Equations (17) and (18), the CO 2 emissions for both WtW and WtT are shown; of course, the BEVs only emit CO 2 in the WtT phase:
CO 2 , WtW = CO 2 , WtT
CO 2 , WtT = ∑ k E k · W E k η grid η st
where
  • η st is the charger (storage) efficiency, measuring how effectively energy taken from the grid is stored in the battery;
  • η grid is the distribution (grid) efficiency, indicating the efficiency of energy transfer from power plants to the grid;
  • E k is the weight percentage of the k-th power source in the country’s energy mix;
  • W E k is the carbon intensity of the k-th power source, measured in grams of CO 2 equivalent per kilowatt-hour ( gCO 2 eq / kWh e ).

5. WtW Analysis: Case Study

The WtW case study provides an example of how the model can help in understanding emissions and energy consumption associated with vehicles by considering all stages from fuel extraction to vehicle operation. This case study compares the environmental impact of a diesel-powered bus with that of an electric bus operating on the same route. By analyzing both TtW and WtT emissions, the analysis captures the full life cycle of energy usage and pollutant generation for both bus types. The study focuses on a representative route in a mid-sized city in Italy, examining energy consumption and the key emissions from both propulsion systems under real-world conditions.

5.1. Selected Representative Buses

A common diesel bus was selected to represent the state-of-art of diesel buses. This bus is powered by a 320-horsepower diesel engine, compliant with EURO 6 emission norms [58]. The synthetic technical data of the bus are presented in Table 2.
The electric bus sampled model [59] was selected for analysis under conditions mirroring those of an electric urban bus (specifics in Table 3). This choice was based on comparable factors such as full load mass, length, and passenger capacity, to maintain consistency in operating conditions. The bus was tested on route 11, initially focusing on evaluating its longitudinal dynamics and then measuring the emissions generated.
The electric bus exhibits higher acceleration compared with its diesel counterpart, despite its higher mass. This advantage is attributed to the electric bus’s ability to generate maximum torque instantaneously, unlike internal combustion engines, which rely on the RPM-Torque curve. This characteristic makes the electric bus more suited for urban environments with frequent stops and starts. A significant distinction between diesel and electric vehicles lies in energy recovery. Electric vehicles employ regenerative braking systems that convert kinetic energy back into electrical energy during coasting phases, thereby increasing the State of Charge (SoC) of the battery.

5.2. City and Route

The model is applied to a small city (around 28,000 inhabitants) in the center of Italy (Cecina, as shown in Figure 4a) to compare fully electric and diesel buses for a low-frequency extra-urban service. Figure 4b shows the path, while Figure 4c shows the altitude of the route. It evaluates CO 2 emissions via a WtW analysis and identifies key toxic pollutants, crucial in urban areas with limited airflow and high humidity. The model is first applied to a diesel bus, specifically a EURO 6 model currently in use in Cecina, and then on an electric bus. The electric bus is selected for its comparable mass and passenger capacity with the diesel model. Factors like torque, regenerative braking, and emission factors are considered. The analysis is conducted on the 16.5 km long bus line 11 “Casa-Scuole Medie Palazzi”, which operates during school terms, linking Cecina’s center to residential zones near the Palazzi district and “Scuole Marconi”. This route is representative of several others in the town, serving student transportation to various school districts.
Other simulation characteristics that have been considered are the same between the two cases, in order to ensure operating conditions as similar as possible, including the load conditions (full load), the conditions on the maximum allowed acceleration and deceleration for the comfort and safety of passengers, and the conditions on the speed to be respected on individual sections.

6. Analysis and Discussion of Results

This section provides a comparative analysis of the environmental performance of a diesel and an electric bus within the case study. The simulations consider a realistic driving style, with Figure 5 and Figure 6 illustrating the driving characteristics and energy demands for each bus type. The results provide a WtW evaluation of emissions, examining both local and global impacts; the non-exhaust emissions (both local and global) for the electric bus are considered.

6.1. Diesel Bus Simulation

The diesel bus simulation shows speed and acceleration performance along the case study route. Figure 5a demonstrates that speeds surpass 20 km/h predominantly in central segments, suggesting extra-urban travel, with peaks at 50 km/h. In contrast, speed drops to or below 20 km/h, indicating urban travel sections. Acceleration fluctuates between approximately −1.1 and 1 m/s2, with negative values indicating deceleration. The bus’s performance shows a repetitive pattern of acceleration and deceleration, which may correlate with stops or traffic conditions along the route (Figure 5a). In Figure 5b, it is shown that the diesel bus used 20.21 L of diesel at the end of Route 11. This consumption was calculated by cumulating the estimated flow for each time interval. Fuel consumption is directly linked to the engine’s power output, which in turn depends on the vehicle’s load, slope, and speed.
The vehicle’s emissions, focusing on both TtW and WtT emissions, are as follows:
  • TtW Average Fuel Consumption = 45.59 MJ Diesel km ;
  • CO 2 , TtW = 3390.37 gCO 2 km ;
  • CO 2 , WtT = 689.5 gCO 2 km ;
  • CO 2 , WtW = 4079.88 gCO 2 km .
Table 4 presents the pollutant emissions for each category considered in the single-route simulation, emphasizing the environmental impact of diesel operation.
Energies 17 05886 g005aEnergies 17 05886 g005b
Figure 5. Speed and acceleration (a) and power and fuel consumption (b) profile of diesel bus on case study.
Figure 5. Speed and acceleration (a) and power and fuel consumption (b) profile of diesel bus on case study.
Energies 17 05886 g005aEnergies 17 05886 g005b

6.2. Battery Electric Bus Simulation

In assessing emissions, it is important to consider two factors for electric buses:
  • Emissions from electrical energy generation needed for charging the bus;
  • Non-exhaust emissions like particulate matter from brake, tire, and other mechanical wear and road abrasion.
The results of the electric bus simulation are shown in Figure 6, which illustrates speed and acceleration patterns in (a) and the energy consumption profile in (b). This figure demonstrates the performance and charging demands of the electric bus across varied urban and extra-urban terrain, providing a basis for calculating energy consumption.
This study assumes a charging efficiency η st equal to 90% and a grid efficiency η grid equal to 94.1% for the Italian electric distribution grid (details in Appendix A) [62]. In Figure 7, the the different Specific Energy Consumptions (SECs) are shown for each step of energy transmission. The energy drawn from the battery is 2.130 kWh/km, from the charger it is 2.364 kWh/km, and finally, from the grid it is 2.513 kWh e / km . These values are very useful in future research and can be assumed for other lines with similar geographical patterns.
Energies 17 05886 g006
Figure 6. Speed and acceleration (a) and power and fuel consumption (b) profile of electric bus on Route 11: navigating urban to extra-urban terrains.
Figure 6. Speed and acceleration (a) and power and fuel consumption (b) profile of electric bus on Route 11: navigating urban to extra-urban terrains.
Energies 17 05886 g006
For the first emission category, a WtW analysis is conducted. The SEC of the electric bus is calculated to be 2.13 kWh/km. Further calculations determine the energy required per kilometer from the charging station and the energy supplied to the grid per kilometer traveled. Based on electricity production data, the estimated CO 2 equivalent emissions for the electric bus are 949.52 gCO 2 / km , resulting in a total of 15.69 kg of CO 2 emissions for the entire route.

BEV Non-Exhaust Emissions: Local and WtT

In the literature, it was not found how to differentiate non-exhaust emissions by weight or other variables. This is the reason the result is the same for Total Suspended Particles (TSP) equivalent to 2.45 g.
It is worth noticing that the scope of WtW analysis is typically the CO 2 emissions. However, as demonstrated in Table 4 the electric vehicle has the benefit of not emitting pollution locally except TSP. However, it is not true that the electricity production itself does not emit air pollution similar to the emissions seen in Table 4. For example, electricity generation is one of the main contributors to PM production also due to the heavy pollution of coal combustion.
Energies 17 05886 g007
Figure 7. SEC values for different stages of energy transmission.
Figure 7. SEC values for different stages of energy transmission.
Energies 17 05886 g007
In fact, in Table A1 are shown the values of the main pollution agent released into the atmosphere from the combustion of fossil fuel due to the power generation. In a similar calculation, the emissions are calculated based on the same concept of Equation (17), assuming zero emissions for renewable energy and excluding biomass contribution. Table 4 shows the synthetic values for all the computed pollutant emission estimation, emitted mainly where the electricity is produced, except for TSP.

6.3. Discussion of Results

The absence of local exhaust emissions from electric buses is a significant advantage over diesel buses. This finding aligns with the global push for electrification in transportation to mitigate urban air pollution. However, electric buses are not entirely emission-free, as their environmental impact extends beyond local emissions to include electricity generation.
The WtW analysis provides a more holistic view of emissions, accounting for the entire energy life cycle. The lower CO 2 emissions from the electric bus underscore the potential of electric vehicles (EVs) in reducing greenhouse gases. However, this study highlights the crucial role of the electricity generation mix in determining the overall environmental impact of EVs. The same levels of TSP emissions from both bus types are notable. This emphasizes that while EVs address exhaust emissions, non-exhaust emissions remain a challenge for all vehicle types. The methodology found in the literature to estimate them is not enough elaborate to capture the differences.
This study’s findings are in line with the existing literature that emphasizes the importance of cleaner energy sources for electric vehicles to maximize their environmental benefits. Previous studies have also highlighted the significance of the electricity generation mix in determining the net environmental impact of EVs. Additionally, the issue of non-exhaust emissions from vehicles, a relatively less explored area, is corroborated by recent research emphasizing its impact on urban air quality.
The results can be valuable for policymakers and urban planners. They suggest that while transitioning to electric buses is beneficial for reducing local pollution and greenhouse gas emissions, a simultaneous focus on greening the electricity grid is crucial. Urban transport policies should incorporate strategies for both vehicle electrification and renewable energy integration.
This study indicates a need for technological improvements in EVs, particularly in reducing non-exhaust emissions. Future research could focus on developing materials and technologies to minimize tire and brake wear, contributing significantly to urban particulate matter.

7. Conclusions

This work aims to present a comprehensive analysis of the environmental impact of diesel and electric buses, with a focus on pollutant emissions in small urban settings (mixed urban–rural route). Through a detailed simulation model applied to a specific route in Cecina, Italy, this research quantitatively compares the emissions of a diesel bus (EURO 6) and an electric bus. The findings indicate that while electric buses do not produce local exhaust emissions, they are not entirely emission-free when considering the full life cycle, including electricity generation. The WtW analysis demonstrates that for the chosen route, the electric bus results in significantly lower CO 2 emissions (15.69 kg) compared with the diesel bus (67.44 kg). However, the electric bus’s environmental impact is influenced by the electricity generation mix, with emissions occurring primarily at the power plants.
Non-exhaust emissions, particularly Total Suspended Particles (TSP), are comparable for both bus types, highlighting the importance of addressing these emissions irrespective of the propulsion technology. This study underscores the benefits of transitioning to electric buses in urban areas for reducing local air pollution and greenhouse gas emissions. However, it also emphasizes the need for cleaner energy sources for electricity generation to maximize the environmental benefits of electric vehicles. The results contribute valuable insights for policymakers and urban planners in designing sustainable urban transportation strategies.
Two gaps emerge from the findings. First, non-local pollution from electricity generation plays a critical role in determining the true environmental benefits of electric buses. In regions where fossil fuels dominate the energy mix, emissions from electricity production can offset the benefits of transitioning to electric vehicles. Second, local non-exhaust emissions—particularly particulate matter from tire and brake wear—remain a persistent source of urban pollution that affects both electric and diesel buses. Given the additional battery weight, electric buses may even generate higher levels of non-exhaust emissions than lighter vehicles. In this analysis, Total Suspended Particle (TSP) emissions are comparable for both bus types, underscoring the importance of including non-exhaust sources in pollution assessments.
Future studies could focus on examining the role of renewable energy and battery storage in further reducing emissions from electric buses. For example, integrating photovoltaic energy generation with battery storage systems could enable overnight recharging using renewable energy sources, which would minimize dependence on grid electricity and reduce WtW emissions associated with electric bus operation. Analyzing the feasibility and environmental impact of such a renewable-based charging infrastructure would provide a more sustainable model for electric bus deployment, especially in cities with high solar potential.
Additionally, research could investigate materials and design modifications aimed at reducing non-exhaust emissions, such as brake and tire wear, which contribute to particulate matter pollution in urban areas. Studying the environmental performance of electric buses across diverse urban settings with varying traffic conditions, topographies, and climates could provide tailored insights, helping to refine urban transportation strategies to meet specific local needs and conditions. Expanding these areas of research will support a more holistic understanding of electric bus systems and their potential for sustainable urban mobility.

Author Contributions

Conceptualization, P.B. and D.M.; methodology, D.M.; formal analysis, D.M.; investigation, D.M.; resources, M.L.; writing—original draft preparation, P.B. and D.M.; writing—review and editing, M.L.; supervision, M.L.; funding acquisition, M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This study was carried out within the MOST—Sustainable Mobility Center and received funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR)—MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4—D.D. 1033 17/06/2022, CN00000023). This manuscript reflects only the authors’ views and opinions; neither the European Union nor the European Commission can be considered responsible for them.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A. Grid Mix Parameters

Table A1 presents a breakdown of the main power sources used in Italy’s national electricity production, along with their respective contributions to greenhouse gas emissions, measured in CO 2 equivalents. Additionally, it includes the emissions associated with imported electricity from neighboring countries, reflecting the varying environmental impacts of different energy mixes. These data provide a comprehensive overview of both domestic and imported energy sources, highlighting their environmental footprint in terms of CO 2 and other key pollutants [63,64,65].
Table A1. Power sources in national electricity production and their CO 2 equivalent emissions, including imported electricity and its main origins.
Table A1. Power sources in national electricity production and their CO 2 equivalent emissions, including imported electricity and its main origins.
Power SourceShareGHGCO NO x NMVOCParticulate SO 2
Unit% gCO 2 , eq kWh gCO MWh gNO x MWh gNMVOC MWh gPM MWh gSO 2 MWh
Hydroelectric913
Wind713
Solar926
Geothermal238
Biomass4230*****
Natural Gas4754852.2335.885.6883.962.448
Oil3115656.5270213.3257.64860
Coal51083320.7685522.868022.63826.8
Imported Electricity14121.6
  France30.439
  Switzerland42.878
  Austria3.226
  Slovenia13.1250
  Greece3.7352
  Montenegro6.8435
* Data assumed as 0 [65].

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Figure 1. Emissions by sector in EU (2022).
Figure 1. Emissions by sector in EU (2022).
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Figure 2. Components of the life cycle analysis of vehicle emissions.
Figure 2. Components of the life cycle analysis of vehicle emissions.
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Figure 4. The context of (a) Cecina in Italy; (b) Route 11 (blue line), connecting the city center with a sparsely populated area with schools in between; and (c) elevation profile [60,61].
Figure 4. The context of (a) Cecina in Italy; (b) Route 11 (blue line), connecting the city center with a sparsely populated area with schools in between; and (c) elevation profile [60,61].
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Table 1. Emission factors of pollutants for EURO 6 bus.
Table 1. Emission factors of pollutants for EURO 6 bus.
PollutantEF (g/km)EF (#/km)EF (#/kWh)
CO0.223
NO x 0.597
NMVOC0.220
CH 4 , urban 0.175
CH 4 , rural 0.08
NH 3 0.009
PM0.0023
Pb0.0000154
PN tot , urban 1.73 × 10 13
PN tot , rural 1.09 × 10 13
SPN 23 , urban 1.79 × 10 11
SPN 23 , rural 6.09 × 10 11
Tyre wear TSP *0.0299
Brake wear TSP *0.042
Road surface wear TSP *0.076
* applies also to battery electric buses.
Table 2. Sample 12 m diesel bus specifications.
Table 2. Sample 12 m diesel bus specifications.
SpecificationValue
Length10,757 mm
Width2550 mm
Height3460 mm
Wheelbase5300 mm
Gross vehicle weight18,000 kg
EngineTector 7 EURO VI
Max power235 kW
Max torque1100 Nm @1250 RPM
Passenger capacity47
Table 3. Sample 12 m electric bus specifics [59].
Table 3. Sample 12 m electric bus specifics [59].
SpecificationValue
Length12,068 mm
Width2566 mm
Height3291 mm
Wheelbase5925 mm
Gross Vehicle Weight20,000 kg
EngineSync. permanent magnets motor
Max Power160 kW
Max Torque3000 Nm
Passenger CapacityDepending on the configuration
Table 4. Amounts of pollutants emitted by electric and diesel buses.
Table 4. Amounts of pollutants emitted by electric and diesel buses.
PollutantElectric BusDiesel Bus
CO 2 * 15.69 kg** 67.44 kg
CO* 1.83 g3.69 g
NO x * 9.57 g9.87 g
NMVOC* 0.18 g3.64 g
PM* 17.48 g0.038 g
SO 2 * 14.61 g-
TSP2.45 g2.45 g
CH 4 -1.97 g
Pb-0.00025 g
NH 3 -0.149 g
PN-2.31 × 10 14
SPN 23 -2.28 × 10 13
* The pollutant is not emitted at the same location as the bus. ** include all the WtW emissions.
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Martini, D.; Bezzini, P.; Longo, M. Environmental Impact of Electrification on Local Public Transport: Preliminary Study. Energies 2024, 17, 5886. https://doi.org/10.3390/en17235886

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Martini D, Bezzini P, Longo M. Environmental Impact of Electrification on Local Public Transport: Preliminary Study. Energies. 2024; 17(23):5886. https://doi.org/10.3390/en17235886

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Martini, Daniele, Pietro Bezzini, and Michela Longo. 2024. "Environmental Impact of Electrification on Local Public Transport: Preliminary Study" Energies 17, no. 23: 5886. https://doi.org/10.3390/en17235886

APA Style

Martini, D., Bezzini, P., & Longo, M. (2024). Environmental Impact of Electrification on Local Public Transport: Preliminary Study. Energies, 17(23), 5886. https://doi.org/10.3390/en17235886

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