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Article

Towards Sustainable Mobility: Assessing the Benefits and Implications of Internal Combustion Engine Vehicle Bans and Battery Electric Vehicle Uptake in Qatar

by
Abdulla Alishaq
* and
Daniel Mehlig
Centre for Environmental Policy, Imperial College London, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
Atmosphere 2024, 15(6), 677; https://doi.org/10.3390/atmos15060677
Submission received: 10 April 2024 / Revised: 25 May 2024 / Accepted: 29 May 2024 / Published: 31 May 2024
(This article belongs to the Section Air Pollution Control)
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Abstract

:
The global shift towards sustainable transportation, primarily through vehicle electrification, is critical in addressing climate change. Qatar presents a knowledge gap with specific challenges and opportunities in this transition. This study calculates the potential reduction in CO2-eq, NOx, and PM2.5 emissions resulting from substituting Internal Combustion Engine Vehicles (ICEVs) with Battery Electric Vehicles (BEVs) in Qatar, considering ICEV ban scenarios in 2030, 2035, and 2040, alongside five policy pathways. A Vehicle Stock Model (VSM) simulates Qatar’s fleet evolution from 2022 to 2050, focusing on the vehicle’s operational phase. An ICEV ban in 2030 would result in a 34% cumulative emission reduction in road transport between 2022 and 2050 compared with the Business-as-Usual (BAU) scenario. For NOx and PM2.5, cumulative emissions for the 2030 ICEV ban pathways are approximately 20% and 9% lower, respectively, compared with BAU. This study underscores the necessity of localising environmental strategies to meet Qatar’s specific needs and climate commitments, where results indicate significant emission reductions are possible through BEVs.

1. Introduction

Transportation is a significant contributor to greenhouse gas (GHG) emissions and environmental impacts; nevertheless, it is an essential part of the global economy and personal lives. New low-emission technologies are needed to meet the ever-growing demand for transport around the world. The electrification of vehicles and related adoption of Battery Electric Vehicles (BEVs) has emerged as a key technology for this sustainable transition. From a global standpoint, this strategy is not only pursued by the EU or USA, as many developing countries are likewise incentivised to adopt this overall goal of electrification in the context of climate goals and renewable energy promotion. Examples of specific targets involve nations such as Canada and the Netherlands, which aim to ban Internal Combustion Engine Vehicles (ICEVs) by 2030, while other countries, such as California or France, propose a timeline of 2035 and 2040, resulting in an uncertain and heterogenous timeframe for ICEVs [1].
One of the main objectives of fleet electrification is the potential to reduce emissions in the transportation sector by directly reducing the number of fossil fuels in vehicles while utilising renewable electricity production. Yet, the push towards a coupled renewable energy and electrification approach on the fleet level is seen to be crucial in achieving the international climate goals [2]. In Qatar, the transportation sector is predominantly reliant on ICEVs, which contribute substantially to the nation’s carbon footprint. As part of the Paris Agreement guidance, Qatar has committed to further reducing its GHG emissions, with the transportation sector being a focal area for these efforts. With its unique socio-economic and environmental conditions, Qatar’s future policies will incorporate economic diversification, climate adaptation, and mitigation. As part of their initial efforts, the Qatar General Electricity and Water Corporation (resp. Kahramaa) put forward the goal of electrifying 10% of the national fleet by 2030 [3]. For Kahramaa to achieve its goal, a clear understanding of the impact and feasibility of transitioning to BEVs becomes a crucial part of the overall development. This understanding is pivotal not only within the specific context of Kahramaa’s initiative but also in the broader scope of Qatar’s overarching goals to transition towards a more sustainable transport sector.
In the analysis of transportation emissions on the fleet level, road transport models are essential, as they provide the opportunity to simulate the use of alternative fuel technologies and their integration into the fleet [4]. While economic models mainly include changes on the fleet level based on vehicle cost, more comprehensive transport models integrate an array of decisions, covering the complete life cycle. In this regard, the Vehicle Stock Model (VSM) has been an integral part of shaping decisive policies; for example, VSMs in the UK have revealed the importance of ICEV bans and BEV uptake for reducing GHG emissions [5,6,7,8]. The VSM uses stock-flow methods to model the continuous inflow and outflow of units, specifically new sales and end-of-life vehicles, in a market influenced by individual and market actors. The integration of this model has improved the efficiency of policy selection, particularly through the incorporation of BEVs and their impact on achieving GHG emission reduction and air quality targets [9].
Studies have repeatedly shown that there are residual emissions from switching to BEVs. When targeting decarbonisation and supporting relevant policy decisions, Chiodi et al. (2013) implemented the TIMES model with the aim of finding an optimal solution for Ireland while minimising costs [10]. At the same time, a similar methodology was applied to Germany in order to evaluate the potential to decrease GHG emissions by introducing mobility services in passenger transport [11]. Other implementations were performed in the context of GHG emissions through added fuel surcharges in Italy while omitting other regulations, as well as literature on the interplay between reducing transport carbon emissions and its influence on mobility [12,13]. Nonetheless, an increase in CO2-eq emissions is still expected, illustrating an interconnected play between economic development, environmental impacts, emissions, transportation availability, and coupled energy consumption. Moreover, another study showed there are largely unchanged PM2.5 emissions since non-exhaust emissions remain with a fully BEV fleet [8]. These intricacies were also pointed out by the previous LCA study by the authors of this paper, showing that purely a change to BEVs does not provide a situation in which no pollutants are emitted. Therefore, the change on the fleet level has to encompass all relevant aspects of the sustainable future Qatar has put as a goal. This study reveals the limits of BEV adoption in the context of BEV uptake in Qatar.
Existing literature primarily focuses on the adoption of BEVs in Western and Asian countries, leaving a knowledge gap in the Middle Eastern context, especially in Qatar [5,14,15,16]. Regional disparities point out the need for context-specific yet globally relevant research. Supposing a unilateral pathway to sustainable transportation is insufficient, as varying regions face an array of location-specific issues. This study aims to address this gap by providing a comprehensive analysis of the potential environmental benefits and challenges of transitioning to BEVs in Qatar. Furthermore, it seeks to inform policy decisions that could facilitate and impact this transition by modelling a variety of road transport fleet scenarios, as it represents a strong policy lever to accelerate GHG reductions. This work aims to distinguish itself from the current literature by incorporating air pollutant emissions, which have been overlooked in many previous analyses, as the focus remained purely on the CO2-eq emission factor [7,17,18,19].
The remainder of this paper is structured as follows. The Section 2 presents the methodology employed in this study, including an overview of the Vehicle Stock Model (VSM), starting vehicle stock, vehicle kilometres travelled, vehicle parameters, electricity generation, and scenarios. The Section 3 discusses the findings obtained from the model, focusing on the yearly and cumulative emissions of CO2-eq, NOx, and PM2.5 for the 2030, 2035, and 2040 ICEV ban implementation scenarios. The Section 4 provides an analysis of the findings, their implications for policy decisions, and the limitations of the study. Finally, the Conclusion summarises the key insights and offers recommendations for future research and policy development.

2. Materials and Methods

2.1. Overview of the Vehicle Stock Model (VSM)

In order to address the Qatari road transportation fleet in the prospective 2022–2050 timeframe, the VSM serves as the backbone of this analytical approach. The model was selected as it is able to simulate the vehicle fleet dynamics from 2022 to 2050. This study specifically concentrates on the ban of ICEVs among LDVs, as they constitute the overwhelming majority of vehicles in Qatar, thereby presenting a significant opportunity for CO2-eq emission reductions in the road transport sector. Given the predominant reliance on ICEVs for both personal and commercial transport, transitioning to BEVs within this segment is pivotal for achieving Qatar’s ambitious environmental targets and aligning with global efforts towards sustainable mobility.
The VSM applies a well-to-wheel (WTW) CO2-eq system boundary, covering all emissions associated with the energy consumed by ICEVs and BEVs. This includes the fuels combusted in vehicles or in power stations and the upstream emissions from the fuel’s supply chain. The emission factors given in the Supplementary Information (Table S3) give the WTW CO2-eq emission factors for ICEVs and BEVs for each vehicle type in the VSM. For air pollutants NOx and PM2.5, only emissions from vehicles and power stations are considered to provide an indication of how air quality may change within Qatar. This excludes air pollutant emissions from the upstream fuel supply chain, which may not have a material impact on Qatari air quality due to their location. The inclusion of these upstream air pollutant sources could be a useful addition in future research.
Additionally, a distinction should be made between top-down and bottom-up calculation approaches in this context, as this makes the difference between a fuel consumption rate multiplied by a national emission factor (top-down) or a more detailed input model. Even though top-down models are useful in general descriptions, many detailed studies transition to a bottom-up approach in order to include parameters such as vehicle population, type of vehicles present, fuel type, and activity data. The latter methodology, furthermore, allows for a more complex and practical emission mitigation strategy and understanding, going beyond broad assumptions or national averages. The VSM employed in this study is a bottom-up approach, which enables a more granular analysis of the road transportation sector in Qatar.
For the Qatar VSM, multiple factors, such as vehicle technology adoption rates, fuel economy improvements, and vehicle lifespan, were integrated, providing a comprehensive view of the fleet’s evolution from 2022 to 2050. From this, the annual composition and characteristics of the fleet were included, for which new vehicles are added up to 2050, and the vehicle scrapping rate was used to determine the stock outflow by removal. This model included all common vehicle types, from LDVs to HDVs, buses and motorbikes, yet the electrification was focused on the LDV fraction of the overall fleet. The fuel economy parameter was fixed by use of the registration year, taking into account an annual technology and feature improvement. Additionally, the model included both ICEVs and BEVs, with gasoline and electricity, respectively, from natural gas as the main energy source. The emissions were calculated by the cumulative distance travelled, which results in a comprehensive emission profile of the complete vehicle fleet.

2.2. Starting Vehicle Stock

The starting conditions were based upon the available information of the Ministry of Interior of Qatar, with records from 2014–2018, as well as detailed information on vehicle type composition published by the Traffic Department [20]. This record shows the overall number of road vehicles categorised as private vehicles, governmental vehicles, police vehicles, taxis, public transport, and other classifications. To complement this data, vehicle characteristics were obtained from the National Vehicle Inspection Company (FAHES), a document containing information about the type of vehicle, manufacturer, model year, and other relevant details. The combination of these two sources provided an overview of the existing vehicle stock in Qatar, which served as the foundation for the VSM analysis. Future compositions were modelled using a coefficient of elasticity, which encompasses variables such as GDP growth, overall vehicle values, population composition, and relative changes thereof. Just as for other developed countries, Qatar went through an accelerated phase of 30–40 years, after which saturation was reached, and population density and economic parameters were essential [21]. Model projections for vehicle population and technology penetration rates were based on Qatar’s economic and demographic trends; therefore, key assumptions and inputs encompass the number of new vehicles with the respective technology level, the average lifespan, and the current fleet overview.

2.3. Vehicle Kilometres Travelled

In regard to the overall emission, the cumulative distance travelled by all vehicles is an essential input, defined as the vehicle kilometres travelled (VKT) [22]. This characteristic allows both to understand trends over alternative timeframes and locations while also providing a tool to identify impacts through policy decisions on, for example, infrastructure. In this study, the VKT parameter is considered vehicle-specific, varying between scenarios. To reflect a Qatar-relevant scenario, the VKT was based on FAHES, which inspects vehicles on an annual basis and provides odometer readings.

2.4. Vehicle Parameters

As the model is sensitive to technology changes and the inflow of newer vehicles, the vehicle technology parameters were included by use of the registration year. This establishes a concise list of characteristics, ranging from the Euro standard to the fuel consumption rate, the linked GHG emissions, and air pollutants. The Euro standard is a crucial part of the latter emission profile, with Qatar only adopting the Euro standard in 2002 and updating the law so that gasoline vehicles manufactured from 2018 onwards have to comply with the Euro 4 standard to pass inspection. Given the slow adoption of Euro standards in Qatar, this paper assumes that Euro 4 is used for vehicles manufactured prior to 2030, and then the adoption of the Euro 6 standard starts. This assumption was made after the jump from the Euro 2 to the Euro 4 standard in 2018. In comparison, diesel-fuel vehicles must comply with Euro 5 guidelines from 2023 onwards. The model of focus in this study adopts the mentioned timeline of Euro standard adoption for each type of vehicle. To couple the vehicles with their respective fuel economy and emissions, a Qatar-specific scenario was created.
First, all dynamic energy and fuel parameters had to be determined. ICEV model parameters are based on fuel economy, followed by the fuel type and its improvement throughout the years, as reported by the IEA and Global Fuel Institute with vehicle fuel consumption obtained from the U.S. Department of Energy [23]. An important aspect is that both direct (fuel CO2-content) and indirect emission sources are introduced. In other contexts, this indirect emission is often described as an impact related to the well-to-wheel life cycle. Yet, this terminology is not preferable, as other carbon sources can be used, independent of fossil fuel extraction practices or from renewable sources, especially in case BEVs are incorporated.
In the original defining of the model characteristics, it was clear that based on historical data of fuel economy improvements, reduction rates of 0.9 to 1.5% per year are still not enough to reach the proposed CO2-neutrality 2050 targets, nor the 2030 “50% reduction” targets, even with further improvement expectations [24,25]. On the contrary, it has been reported that emissions from ICEVs in the real world have not improved over the last 10 years, while larger vehicles are even using more fuel and resources [26,27]. This makes clear that variation between nations, vehicles, and locations will arise, proving the need for calculations on vehicle and fleet levels. The choice was made to adopt increasing environmental standards in Qatar with a linked increase in fuel efficiencies, similar to the data reported by the GFEI and IEA [25]. This resulted in a deducted starting point for ICEVs with the final fuel efficiencies for Qatar in 2050, with a 1.5% improvement over the 2022–2050 timeframe.
An additional aspect, outside of the BEV penetration of the fleet, is the transition of ICEV Euro standards within Qatar’s vehicle fleet, which is implemented to occur alongside the described policy pathways. As a later ICEV ban implementation would still result in a business-as-usual inflow and outflow of the fleet, the shares of vehicles with their respective Euro standard are given in Table 1. Initially dominated by Euro 2 vehicles, the fleet undergoes a significant transformation towards cleaner Euro 6 standards by 2050. This shift is critical in understanding the emission trends and the impact of ICEV bans. The reduction in the proportion of lower Euro-standard vehicles is a testament to the evolving vehicular landscape in Qatar, influenced by policy measures and technological advancements.

2.5. Electricity Generation

For BEVs, electricity generation emissions due to charging demand are required to calculate the emissions from the vehicle’s use phase. As all electricity generated in Qatar originates from natural gas, it is arguably the most challenging area to balance energy needs against environmental impacts. Currently, locally produced renewable energy in Qatar is negligible. For the improvement rate in BEVs, such as the technology learning effects that can affect the weight reduction potential of the vehicle itself (cfr. Battery pack energy density), the same rate as for ICEV was assumed (1.5%). This value was a fixed parameter in this study, as global institutes have not presented an agreed-upon value for this sector, and manufacturers are still in the early stages of their production lines. These fuel economy projections were chosen to be comparable to ICEV levels if no values were proposed by the manufacturer or any global institute, such as the EPA or GFEI, even if this results in a possibly lower improvement rate and thus would represent a worst-case scenario parameter. The full description and analysis of these fuel efficiencies, the improvements, and the intermediate and final energy consumption rates are given in the Supplementary Information. Nonetheless, a common critique, often brought forward with the large-scale introduction of BEVs, is the increased load on the electricity grid. For Qatar’s low-inertia distribution network, which already suffers from regular peaks due to high air conditioning loads, the additional load from charging BEVs was simulated based on several penetration and charging scenarios [28]. With 8.5 GW capacity and historical peak demand in 2018 of 7.8 GW, a 10% BEV fleet would already result in a 19.2% overshoot of the maximal production capacity in case the BEV fleet would not spread its charging cycles during summer. With increasing demand and future prospects of BEV shares of the fleet, it was clear from expert consultations with utility distributors (Kahramaa, Mr Thomas) that national capacity would be adapted to cover even the extreme scenarios from Safak Bayram [28].
In this study, the electricity generation mix for Qatar from 2022 to 2050 was assumed to be 100% natural gas, without the inclusion of solar energy or natural gas with CCS. This assumption is based on several factors. Firstly, despite Qatar’s significant solar energy potential, the current share of renewable energy in the country’s electricity mix is low, and the infrastructure for large-scale solar power generation is not yet in place. Secondly, while CCS technology could potentially reduce the carbon footprint of natural gas-based electricity generation, it is still in the early stages of development globally and has not been widely deployed locally in Qatar. Moreover, the additional costs and infrastructure requirements associated with CCS may hinder its implementation in the near future. Given these considerations and the fact that natural gas is abundant and plays a dominant role in Qatar’s energy sector, solar energy and natural gas with CCS are excluded from this study.

2.6. Scenarios

The final and crucial segment of the model and its characteristics is the effect of different policy scenarios on the fleet composition and its change over time. From this aspect, different scenarios of focus will influence the GHG emissions as well as air pollution markers with different speeds and accuracy, as the main divergence between scenarios is the adoption rate curve of BEVs. This study put forward the years 2030, 2035, and 2040 as points of interest to compare the following scenarios in Table 2. The adoption rates were obtained from the UK Climate Change Committee (CCC) and then adjusted to the Qatari context [29]. Noteworthy here is that the 100% BEV adoption of the Tailwinds and Widespread engagement scenario was achieved the earliest, yet the differences between BEV sales became smaller the longer Qatar postponed its ICEV ban. For a 2030 ICEV ban, the headwinds scenario does reach a 100% adoption but only from 2035 onwards; thus, a lag of 5 years is observed, while an ICEV ban in 2040 would result in a BEV adoption rate, which would result in only a limited shift between the separate scenarios. This rate of adoption, in its part, influences not only the sales rates of BEVs but all other interlinked characteristics of the inflow and outflow parameters of the VSM, such as GHG emission and air pollutant profiles.
The adoption of the UK CCC scenarios for the Qatari context is based on several factors. Firstly, the CCC is a reputable and independent advisory body that provides evidence-based recommendations for climate change mitigation and adaptation policies. The CCC’s scenarios are well-researched and have been widely used in policy planning and academic studies, making them a reliable source for BEV adoption projections. Secondly, the CCC scenarios follow a generic S-curve pattern, which is a common trajectory for technology adoption and diffusion. This suggests that the scenarios are plausible and can be adapted to different contexts. Thirdly, Qatar’s electricity grid mix is reliant on natural gas, which is somewhat similar to the UK’s current grid mix. This similarity in the energy landscape suggests that the adoption of BEVs in Qatar may follow a similar trajectory to that projected by the CCC for the UK. Lastly, due to the lack of Qatar-specific BEV adoption scenarios or equivalent projections, the use of the UK CCC scenarios provides a reasonable starting point for assessing the potential impact of BEV uptake in Qatar. While the specific adoption rates may differ due to local factors, the overall trends and patterns can serve as a valuable reference for policy planning and analysis in the Qatari context.

3. Results

3.1. 2030 ICEV Ban Implementation

The 2030 ICEV ban scenario is used as an initial view of the results and potential the policy pathway implementations reveal. For this 2030 ICEV ban, a decrease in CO2-eq emissions is the main observable trend, underscoring the effectiveness of this early policy intervention. In regards to the yearly CO2-eq emissions, except for the BAU scenario, all other scenarios show a similar pattern in reducing CO2-eq emissions with only limited differences between these policy pathways. The calculated yearly emissions in Figure 1 show that the slope of the BAU scenario (2020–2050 averaged value) exceeds the reduction rate of the alternative scenarios. Over the 2020–2050 timeframe, the BAU adds an average of 248 kt of CO2-eq every year to the modelled results. The alternative scenarios show the desired reversal of this trend with a reduction of the calculated CO2-eq emissions of, on average, 200 kt of CO2-eq per year (2022–2050), with little variance between the mentioned policy pathways. By calculating the rate of decrease from 2030 onwards, emissions from the ICEV ban would result in an average reduction of 320 kt of CO2-eq in the period 2030–2042. Nonetheless, with a similar rate of reduction and thus a similar endpoint, it is clear that all ICEV ban pathways in this scenario come to a CO2-eq emission in 2050, which is 23% lower than the initial starting point of 2022. The final calculated emissions of these pathways come to 7.42 Mt of CO2-eq. At the same time, the BAU scenario would result in a 61% increase in 2050 compared with 2022, coming to 17.81 Mt of CO2-eq; these emission per year results are given in Table 3 for model years 2023, 2030, 2040, and 2050.
With the closeness between policy pathways, the same overall trends can be observed in the cumulative CO2-eq emissions from the model. The 2030 ICEV ban scenario is how the implementation of these specific policies has the ability to curb emissions on a yearly basis as well as reduce the overall cumulative emissions from Qatar. Nonetheless, as emissions in this model have not reached a complete zero-carbon level, cumulative emissions cannot level off by 2050 (Figure 1). Introducing these ICEV bans, independent from the pathway, still has visible effects, showing the potential to reduce the cumulative emissions of Qatar. By 2050, this 2030 ICEV ban would lead to a 34% reduced cumulative emission compared with the BAU scenario, namely a cumulative BAU CO2-eq emission of 4.24 E8Mt compared with only 280 Mt of CO2-eq for the ICEV ban, with negligible differences between the four examined pathways. As noted, this ICEV ban does not result in a complete levelling off of the emissions but proves to be a significant part of the overall solution.
Separate from the CO2-eq emissions issue, the ICEV ban is seen to have a potential advantage in the case of NOx and PM2.5 emissions. For this facet of the study, both the yearly and cumulative emissions were modelled for the 2030 ICEV ban scenario and applicable policy pathways. While the effects on these pollutants are not as pronounced as those observed for the CO2-eq emissions, they nonetheless contribute to the needed understanding of the environmental impacts and large-scale implications of the BEV transition. Comparable to the described CO2-eq results, the different pathways show a parallel trajectory. Therefore, the results can be discussed as the comparison of the BAU pathway in relation to the ICEV ban case for which the Balanced Net Zero Pathway has been selected.
The analysis of the yearly NOx emissions revealed the strongest improvement trend from the pollutants of focus. The ICEV ban scenario would decrease emissions gradually, resulting in 2050 emissions that are 30% lower for all pathways compared with the 2050 BAU emissions. This gradual reduction in the NOx pollutants results in a cumulative emission that is around 20% lower for the ICEV ban pathways in 2050, as would be the case for the 2050 BAU emissions. Similarly, if the same pathways are modelled for the PM2.5 pollutants, the emissions would be reduced by 12% in 2050 compared with the 2050 BAU pathway. Meanwhile, the cumulative PM2.5 emissions for the ICEV ban pathways are reduced by 9% in relation to the 2050 BAU case.
As Qatar is heavily reliant on fossil fuels and the ICEV ban is not inherently linked to a complete switch to renewables, the latter results should be regarded from that perspective. This reiterates the need for a transition strategy that not only puts focus on the vehicle type but also on energy production in the case of centralised electricity from natural gas. Nonetheless, the consistent reduction of the parameters of focus does show that a general benefit is expected from the described ICEV ban.
The study focuses on the impact of an ICEV ban on LDV sales, as LDVs represent the vast majority of vehicles in Qatar. To assess the effectiveness of the ban, it is crucial to compare the emissions from new LDV sales under a BAU scenario to the emissions from new sales under the ban scenarios. The adaptation of the model to focus on LDVs enables a more targeted analysis of the ban’s effects on this dominant vehicle category. With the background of the previous descriptions and the fact that model characteristics remain equal, such as the reliance on fossil fuels and the change of Euro Standards shares, the impacts of the studied pollutants are influenced in the same regard. Nonetheless, some intricacies should be pointed out in order to give an idea of which share of the overall fleet should be the focus. For the year-to-year emissions over the years up to 2050, the CO2-eq and NOx charts show a comparable trend, shown for CO2-eq emissions in Figure 2. From the resulting calculations, it is obvious that the ICEV ban makes a significant impact on CO2-eq and NOx emissions, with again Widespread engagement and Tailwind pathways as the first scenarios to reduce the emissions, followed by the Balanced Net Zero Pathway and eventually the Headwind pathway. The time differences between the Tailwind and Headwind scenarios are similar to 5 years with the Balanced pathway in between (Figure 2). Compared with the BAU scenario, this focused ICEV ban results in a CO2-eq emission, which is almost 68% lower in 2050, while the NOx emissions would be impacted even more, as the 2050 levels would be reduced by just over 73%. The PM2.5 levels on a year-to-year basis up to 2050 would still improve, yet the final reduction in 2050 would only be 15%, with little to no difference between the different pathways compared with the 2050 BAU. For the cumulative emissions of the mentioned pollutants, the main improvements are seen for NOx, CO2-eq, and, on a small scale, also for PM2.5. Specifically, NOx, CO2-eq, and PM2.5 emissions will be reduced by 61%, 56%, and 15%, respectively, in 2050 compared with the BAU emissions for 2050.

3.2. 2035–2040 ICEV Ban Implementation

Likewise, adaptations were made to reflect the 2035 and 2040 ICEV bans. With the starting point and the final composition of the model remaining equal, the main differences between the ICEV ban scenarios are the adoption rate and the speed at which the system around it changes. For the complete fleet, this leads to the final 2050 emissions coming to 7.42 Mt of CO2-eq, with the values of NOx and PM2.5 emissions equal to the results from the 2030 ICEV ban section, namely 9.42 kt and 1.17 kt of NOx and PM2.5 emissions. Therefore, the 2030, 2035, and 2040 ICEV ban models should be focused on the slopes of the year-to-year emissions, as they represent the amount of change in that timeframe, as well as the impact of the ICEV ban timing itself on the cumulative results. For this comparison, the main differences of the calculated CO2-eq emissions on a year-to-year basis are presented in Figure 3, showing the slopes of the ICEV ban timings, giving an indication of both the effect on the cumulative CO2-eq emissions, as well as the expected impact on the surrounding system. From the model calculations, it can be concluded that, respectively, for the 2030, 2035, and 2040 ICEV ban, the slope and thus change in emissions is the most significant in the periods 2030–2043, 2032–2045, and 2035–2048. The slopes for the Balanced Net Zero pathways reduce the CO2-eq emissions by correspondingly 334 kt CO2-eq, 385 kt CO2-eq, and 478 kt CO2-eq for the 2030, 2035, and 2040 ICEV ban implementations. The final 2050 emissions still result in the previously reported 7.42 Mt of CO2-eq.
Nonetheless, the difference between the ICEV ban implementation years shows the need for the sudden implementation of BEVs to achieve the final Balanced Net Zero goal. As laid out before, the moment of this ICEV ban implementation will result in a sudden turnover of ICEVs, yet it will also influence the overall impact of the cumulative emissions of the pollutants of focus. Later implementation of ICEVs can leave time for a smooth BEV adoption curve yet reduce the overall potential to mitigate cumulative CO2-eq, NOx, and PM2.5 emissions. The effect of a late implementation is deducted from the cumulative CO2-eq results, from which it is clear that an early 2030 ICEV ban would reduce the cumulative CO2-eq emissions by 34% in 2050 compared with the BAU scenario. If only introduced in 2035, the potential would be reduced to 28% and only 23% lower compared with the BAU in 2050 if the ban was only enforced by 2040. The final cumulative CO2-eq emissions for the 2030, 2035, and 2040 ban scenarios are calculated to be 280 Mt, 304 Mt, and 329 Mt of CO2-eq, while the BAU comes to a cumulative CO2-eq emission of 425 Mt in 2050.

4. Discussion

This study examined the environmental impacts and trends of implementing the ICEV bans in Qatar, with the bans starting in 2030, 2035, and 2040. This approach allowed for a nuanced understanding of the interplay between policy implementation and vehicle emissions on a year-to-year and cumulative basis. The modelled transition, moving away from ICEVs towards BEVs by use of an ICEV ban, shows that this shift in the nation’s fleet can lead to mitigating significant emissions and pollutants in Qatar. The findings from the VSM indicate the nuanced dynamics under varying ICEV ban scenarios for 2030, 2035, and 2040, depending further on the policy pathway that would be followed. The results show a clear trajectory towards reduced CO2-eq, NOx, and PM2.5 emissions, thus aligning with Qatar’s commitments as part of the international climate agreements. This reduction, as described in the results, should nevertheless be seen from the perspective of both the current dominance of ICEVs in the nation’s fleet and the still substantial environmental footprint associated with the operation of BEVs through natural gas electricity. The projections reveal that earlier policy interventions, independent of the specific policy pathway, will always accelerate the reduction of emissions, giving additional time for the surrounding system and infrastructure to change. Later implementations will not only result in lower reductions in emissions and pollutants but could stress the needed rate of change in Qatar, underscoring the critical role of timely and decisive policy action in achieving these environmental targets and the ICEV ban.
The transition to BEVs is not without its challenges and complexities. Several key considerations must be addressed as part of the model to ensure the successful implementation of the ICEV ban and optimise the desired reduction of both CO2-eq emissions and NOx and PM2.5 pollutants. Firstly, this adoption of BEVs is contingent upon the development of a nationwide robust and resilient charging infrastructure capable of supporting the anticipated increase in electric vehicle usage. This would necessitate both substantial investments in grid capacity and charging facilities, as well as the guarantee that peak demand could be met. Currently, the national grid is dependent on natural gas. Therefore, the integration of renewable energy sources would be the next step in mitigating the environmental impact of increased electricity demand.
It should be noted that the broader effects reach further than purely the yearly CO2-eq, NOx, and PM2.5 pollutant emissions. As the adoption of BEVs is not a standalone characteristic, the electricity grid, capacity, charging facilities, and production, all have to be integrated to achieve the final goal. These aspects are influenced by the moment of the ICEV ban implementation, and if one of these needs is not met, it could pose an obstacle. Therefore, with unexpected issues, the potential of the ICEV ban could result in an emission profile that aligns more with the BAU scenario.
To validate the accuracy of the model used, the study’s estimated CO2-eq emissions for road transport in Qatar for 2023 were compared to real-world data. For instance, the model’s estimation of 11.07 MT CO2-eq emissions for road transport in 2023 aligns closely with figures reported by Our World in Data (12.49 MT for 2020) and the IEA (12.2 MT for 2021), showing less than a 10% deviation from the transport emissions [30,31]. This proximity in values supports the model’s accuracy, especially given that the IEA data encompass the broader transport sector, not limited to road transport. Furthermore, the model’s basis on historical vehicle data, while being adjusted to reflect the latest available comprehensive datasets, some of which span back to 2014–2018, underscores the challenge of obtaining up-to-date, detailed inputs that reflect very recent changes in vehicle stock and usage patterns. Despite these limitations, the methodology employed in this study is consistent with established practices in the field and has been validated in other scientific investigations, reinforcing its suitability for projecting future emissions scenarios.
The findings of this study also show that for Qatar’s transition to a BEV-dominated fleet, there are identifiable emission floors for CO2-eq, NOx, and PM2.5. Even with a complete transition to BEVs, residual emissions persist due to the nation’s reliance on natural gas for electricity generation. This unique aspect of the study highlights the role of Qatar’s energy mix in determining the effectiveness of BEV adoption as a strategy for reducing transportation-related emissions.
The case study of Qatar is particularly relevant, as it sheds light on the challenges faced by countries heavily dependent on fossil fuels for electricity generation. The dominance of natural gas in Qatar’s energy mix directly impacts the environmental benefits of BEVs, as the emissions associated with charging these vehicles are tied to the carbon intensity of the electricity grid. This study emphasises the need for an approach to decarbonisation, recognising that the success of BEV adoption in reducing emissions is intrinsically linked to the transformation of the energy sector towards cleaner sources. One area of possible improvement is the incorporation of renewable sources, such as solar energy, yet without clear government plans for large-scale solar implementation, this was not taken up by this research. Similarly, as biofuels are currently not used as part of the fuel mix and no plans are available to integrate or increase the blended proportion to significant fractions, this was omitted from this study. This research is thus limited to the current Qatar situation; therefore, if major changes in renewable energy or biofuels occur, it is advised to revise the overall results in this study. Nonetheless, it should be noted that the current study setup provides an upper limit, as it agreed upon that both renewables and biofuels do lower the overall carbon footprint of the transportation sector. As Qatar and other nations with similar energy landscapes navigate the path to sustainable transportation, the insights from this study can inform policy decisions and highlight the importance of integrating renewable energy sources to maximise the environmental benefits of BEV adoption.
Moreover, the study acknowledges the limitations inherent in the VSM, particularly its reliance on current technological and economic trends, as well as the environmental, fuel, and energy characteristics. Further innovation in vehicle technology and the energy sector introduces a degree of uncertainty in the model’s projections. Additionally, the study boundary for GHGs was limited to WTW for CO2-eq and was more limited for air pollutants to those within Qatar’s border. Future research should broaden its scope to include a full vehicle lifecycle analysis, expanding the emission sources to include vehicle manufacture and end-of-life. While this study focuses on Qatar, the insights can provide broader applicability. The Middle East, with its unique socio-economic landscape and environmental conditions, has often been omitted from comparable studies yet is part of the countries seeking to navigate the complex terrain of sustainable transportation.

5. Conclusions

This study contributes to the growing body of literature on the effects of implementing sustainable transportation policies, providing a country-specific examination of the potential impacts of ICEV bans in Qatar. It underscores the difference in and importance of policy pathways, the timing of policy introductions and the effects on year-to-year as well as cumulative emissions. This can further indicate the expected rate of change in infrastructure development. As Qatar moves towards a more sustainable future, similar studies will play a crucial role in shaping policies and approaches in regard to sustainable transportation.
This study focused on the potential reduction in environmental impacts, specifically CO2-eq emissions and NOx and PM2.5 pollutants, associated with the transition from ICEVs to BEVs within the Qatari context between 2022 and 2050. By using the VSM, three specific ICEV ban implementation scenarios (2030, 2035, and 2040) were modelled. For the main ICEV ban date of 2030, five policy pathways were combined to reflect the speed at which the ICEV ban would be integrated. For the 2035 and 2040 ban dates, the analysis focused on the BAU and Balanced Net Zero pathways to provide a comparative assessment of the potential outcomes. The findings demonstrate that the adoption of BEVs holds significant promise in reducing the focus parameters (CO2-eq, NOx, and PM2.5) of the study.
The timing of the ban was key for reducing cumulative CO2-eq emissions up to 2050, where an early 2030 ICEV ban would reduce the cumulative CO2-eq emissions by 34% in 2050 compared with the BAU scenario. Yet, if the ICEV ban was introduced later in 2035 or 2040, this reduction compared with the BAU would reduce to 28% or 23%, respectively. This shows the importance of timing strategic policies as part of wider actions to reduce emissions on a Qatar-wide scale.

Supplementary Materials

The following supporting information can be downloaded at https://www.mdpi.com/article/10.3390/atmos15060677/s1. Table S1, Share of LDVs based on Euro standard; Table S2, Mean air temperature in Qatar; Table S3, Full emission factors, Fuel efficiencies and energy consumptions for LDV, HDV and buses, 2022–2050; references [20,21,23,24,25,26,27,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53] are used within the Supplementary Material and are included in the reference list of this article.

Author Contributions

Conceptualisation, A.A.; methodology, A.A. and D.M.; software, A.A.; formal analysis, A.A.; data curation, A.A.; writing—original draft preparation, A.A.; writing—review and editing, D.M.; supervision, D.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Qatar Research Development and Innovation Council.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Data relevant to this research can be made available upon request to the corresponding author. The data are not publicly available due to privacy.

Acknowledgments

The authors thank Jeremy Woods from the Centre for Environmental Policy at Imperial College London for his guidance and support throughout the research process.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Milman, O. Car Firms Agree at Cop26 to end Sale of Fossil Fuel Vehicles by 2040 |Cop26| The Guardian. Available online: https://www.theguardian.com/environment/2021/nov/10/cop26-car-firms-agree-to-end-sale-of-fossil-fuel-vehicles-by-2040 (accessed on 9 April 2024).
  2. United Nations UNFCCC. Outcome of the First Global Stocktake; UNFCCC: Bonn, Germany, 2023. [Google Scholar]
  3. Qatar General Electricity & Water Corporation (KAHRAMAA). KAHRAMAA Launches “Electric Car Charging Stations Project–Phase 1”. Available online: https://www.km.qa/MediaCenter/Pages/NewsDetails.aspx?ItemID=266 (accessed on 9 April 2024).
  4. Krause, J.; Thiel, C.; Tsokolis, D.; Samaras, Z.; Rota, C.; Ward, A.; Prenninger, P.; Coosemans, T.; Neugebauer, S.; Verhoeve, W. EU road vehicle energy consumption and CO2 emissions by 2050—Expert-based scenarios. Energy Policy 2020, 138, 111224. [Google Scholar] [CrossRef]
  5. Brand, C.; Anable, J.; Ketsopoulou, I.; Watson, J. Road to zero or road to nowhere? Disrupting transport and energy in a zero carbon world. Energy Policy 2020, 139, 111334. [Google Scholar] [CrossRef]
  6. Chatterton, T.; Barnes, J.; Wilson, R.E.; Anable, J.; Cairns, S. Use of a novel dataset to explore spatial and social variations in car type, size, usage and emissions. Transp. Res. D Transp. Environ. 2015, 39, 151–164. [Google Scholar] [CrossRef]
  7. Craglia, M.; Cullen, J. Modelling transport emissions in an uncertain future: What actions make a difference? Transp. Res. D Transp. Environ. 2020, 89, 102614. [Google Scholar] [CrossRef]
  8. Mehlig, D.; Staffell, I.; Stettler, M.; ApSimon, H. Accelerating electric vehicle uptake favours greenhouse gas over air pollutant emissions. Transp. Res. D Transp. Environ. 2023, 124, 103954. [Google Scholar] [CrossRef]
  9. Wang, H.; Ou, X.; Zhang, X. Mode, technology, energy consumption, and resulting CO2 emissions in China’s transport sector up to 2050. Energy Policy 2017, 109, 719–733. [Google Scholar] [CrossRef]
  10. Chiodi, A.; Gargiulo, M.; Deane, J.; Lavigne, D.; Rout, U.K.; Gallachóir, B.P. Modelling the impacts of challenging 2020 non-ETS GHG emissions reduction targets on Ireland′s energy system. Energy Policy 2013, 62, 1438–1452. [Google Scholar] [CrossRef]
  11. Grischkat, S.; Hunecke, M.; Böhler, S.; Haustein, S. Potential for the reduction of greenhouse gas emissions through the use of mobility services. Transp. Policy 2014, 35, 295–303. [Google Scholar] [CrossRef]
  12. Gallo, M. A fuel surcharge policy for reducing road traffic greenhouse gas emissions. Transp. Policy 2011, 18, 413–424. [Google Scholar] [CrossRef]
  13. Girod, B.; van Vuuren, D.P.; Deetman, S. Global travel within the 2 °C climate target. Energy Policy 2012, 45, 152–166. [Google Scholar] [CrossRef]
  14. Gebrehiwot, M.; Van Den Bossche, A. Driving electric vehicles: As green as the grid. In Proceedings of the IEEE AFRICON Conference, Addis Ababa, Ethiopia, 14–17 September 2015. [Google Scholar] [CrossRef]
  15. Kamal, A.; Al-Ghamdi, S.G.; Koç, M. Modeling and understanding the impacts of efficiency measures on fleet fuel consumption in vehicle importing countries: A case study of Qatar. J. Clean. Prod. 2020, 259, 120619. [Google Scholar] [CrossRef]
  16. Verma, S.; Dwivedi, G.; Verma, P. Life cycle assessment of electric vehicles in comparison to combustion engine vehicles: A review. Mater. Today Proc. 2021, 49, 217–222. [Google Scholar] [CrossRef]
  17. Baptista, P.C.; Silva, C.M.; Farias, T.L.; Heywood, J.B. Energy and environmental impacts of alternative pathways for the Portuguese road transportation sector. Energy Policy 2012, 51, 802–815. [Google Scholar] [CrossRef]
  18. Hill, G.; Heidrich, O.; Creutzig, F.; Blythe, P. The role of electric vehicles in near-term mitigation pathways and achieving the UK’s carbon budget. Appl. Energy 2019, 251, 113111. [Google Scholar] [CrossRef]
  19. Trofimenko, Y.; Komkov, V.; Trofimenko, K. Forecast of energy consumption and greenhouse gas emissions by road transport in Russia up to 2050. Transp. Res. Procedia 2020, 50, 698–707. [Google Scholar] [CrossRef]
  20. Ministry of Interior. Transport and Communications Statistics; Ministry of Interior: Budapest, Hungary, 2019. [Google Scholar]
  21. Wang, Y.; Teter, J.; Sperling, D. China’s soaring vehicle population: Even greater than forecasted? Energy Policy 2011, 39, 3296–3306. [Google Scholar] [CrossRef]
  22. Williams, T.A.; Chigoy, B.; Borowiec, J.; Glover, B. Methodologies Used to Estimate and Forecast Vehicle Miles Traveled (VMT); Texas A&M Transportation Institute: Austin, TX, USA, 2016. [Google Scholar]
  23. U.S. Department of Energy, EPA. The Official U.S. Government Source for Fuel Economy Information. Available online: https://www.fueleconomy.gov/ (accessed on 9 April 2024).
  24. Cox, B.; Bauer, C.; Beltran, A.M.; van Vuuren, D.P.; Mutel, C.L. Life cycle environmental and cost comparison of current and future passenger cars under different energy scenarios. Appl. Energy 2020, 269, 115021. [Google Scholar] [CrossRef]
  25. Bandivadekar, A.; Miller, J.; Kodjak, D.; Muncrief, R.; Yang, Z.; Jong, R.; De Fabian, B. Vehicle Efficiency and Electrification: A Global Status Report; Global Fuel Economy Initiative: London, UK, 2020. [Google Scholar]
  26. Craglia, M.; Cullen, J. Do technical improvements lead to real efficiency gains? Disaggregating changes in transport energy intensity. Energy Policy 2019, 134, 110991. [Google Scholar] [CrossRef]
  27. Dornoff, J.; Tietge, U.; Mock, P. On The Way to “Real-World” CO2 Values: The European Passenger Car Market in Its First Year after Introducing the WLTP; ICCT: Beijing, China, 2020. [Google Scholar]
  28. Safak Bayram, I. A Stochastic Simulation Model to Assess the Impacts of Electric Vehicle Charging on Power Generation: A Case Study for Qatar. In Proceedings of the ITEC 2019–2019 IEEE Transportation Electrification Conference and Expo, Detroit, MI, USA, 19–21 June 2019. [Google Scholar] [CrossRef]
  29. Committee on Climate Change. The Sixth Carbon Budget The UK’s Path to Net Zero; Committee on Climate Change: London, UK, 2020. [Google Scholar]
  30. IEA. Qatar—Countries & Regions. Available online: https://www.iea.org/countries/qatar/emissions (accessed on 9 April 2024).
  31. Our World in Data: Greenhouse gas emissions by sector, Qatar, 1990 to 2020. Available online: https://ourworldindata.org/grapher/ghg-emissions-by-sector-stacked?country=~QAT (accessed on 9 April 2024).
  32. Standardization Organization for G.C.C. Motor Vehicles Allowable Limits of Gaseous Pollutants Emitted to the Atmosphere from Unleaded Gasoline Engined Vehicles; Standardization Organization for G.C.C.: Riyadh, Saudi Arabia, 2003. [Google Scholar]
  33. Qatar Civil Aviation Authority. Meteorological Conditions. Available online: https://www.caa.gov.qa/en-us/Pages/Metrogical.aspx (accessed on 9 April 2024).
  34. European Environment Agency. Greenhouse Gas Emissions from Transport in Europe. Available online: https://www.eea.europa.eu/data-and-maps/indicators/transport-emissions-of-greenhouse-gases/transport-emissions-of-greenhouse-gases-12 (accessed on 9 April 2024).
  35. Winkler, S.L.; Anderson, J.E.; Garza, L.; Ruona, W.C.; Vogt, R.; Wallington, T.J. Vehicle criteria pollutant (PM, NOx, CO, HCs) emissions: How low should we go? npj Clim. Atmospheric Sci. 2018, 1, 26. [Google Scholar] [CrossRef]
  36. Li, C.; Managi, S. Contribution of on-road transportation to PM2.5. Sci. Rep. 2021, 11, 1–12. [Google Scholar] [CrossRef]
  37. McDuffie, E.E.; Martin, R.V.; Spadaro, J.V.; Burnett, R.; Smith, S.J.; O’rourke, P.; Hammer, M.S.; van Donkelaar, A.; Bindle, L.; Shah, V.; et al. Source sector and fuel contributions to ambient PM2.5 and attributable mortality across multiple spatial scales. Nat. Commun. 2021, 12, 3594. [Google Scholar] [CrossRef]
  38. Michiels, H.; Mayeres, I.; Panis, L.I.; De Nocker, L.; Deutsch, F.; Lefebvre, W. PM2.5 and NOx from traffic: Human health impacts, external costs and policy implications from the Belgian perspective. Transp. Res. D Transp. Environ. 2012, 17, 569–577. [Google Scholar] [CrossRef]
  39. IEA. Trajectories of Rated Fuel Economy of New Light-Duty Vehicles against Global Fuel Economy Initiative targets and IEA Scenarios, 2005–2050. Available online: https://www.iea.org/data-and-statistics/charts/trajectories-of-rated-fuel-economy-of-new-light-duty-vehicles-against-global-fuel-economy-initiative-targets-and-iea-scenarios-2005-2050 (accessed on 9 April 2024).
  40. Global Fuel Economy Initiative. Vehicle Fuel Economy in Major Markets 2005–2019; Global Fuel Economy Initiative: London, UK, 2021. [Google Scholar]
  41. Bouoiyour, J.; Selmi, R. The Qatar-Gulf Crisis and Risk Management in Oil and Gas Markets. Hal Arch.-Ouvert. 2019, 1–26. [Google Scholar] [CrossRef]
  42. ICCT. CO2 Emission Standards for Passenger Cars and Light-Commercial Vehicles in the European Union; ICCT: Beijing, China, 2019. [Google Scholar]
  43. Baldino, C.; Tietge, U.; Muncrief, R.; Bernard, Y.; Mock, P. Road Tested: Comparative Overview of Real-World versus Type-Approval NO X and CO2 Emissions from Diesel Cars in Europe; ICCT: Beijing, China, 2017. [Google Scholar]
  44. Plötz, P.; Link, S.; Ringelschwendner, H.; Keller, M.; Moll, C.; Bieker, G.; Dornoff, J.; Mock, P. Real-World Usage of Plug-in Hybrid Vehicles in Europe A 2022 Update on Fuel Consumption, Electric Driving, and CO2 Emissions; ICCT: Beijing, China, 2022. [Google Scholar]
  45. Ecoinvent: Impact Assessment. Available online: https://ecoinvent.org/the-ecoinvent-database/impact-assessment/#1661420435296-03a17d13-bc0e16614291937081661429832917 (accessed on 9 April 2024).
  46. Office of Energy Efficiency and Renewable Energy. Fuel Economy in Hot Weather. Available online: https://www.fueleconomy.gov/feg/hotweather.shtml (accessed on 9 April 2024).
  47. Huff, S.; West, B.; Thomas, J. Effects of Air Conditioner Use on Real-World Fuel Economy; SAE Technical Paper 2013-01-0551; SAE: Warrendale, PA, USA, 2013. [Google Scholar] [CrossRef]
  48. Lohse-Busch, H.; Duoba, M.; Rask, E.; Stutenberg, K.; Gowri, V.; Slezak, L.; Anderson, D. Ambient Temperature (20°F, 72°F and 95°F) Impact on Fuel and Energy Consumption for Several Conventional Vehicles, Hybrid and Plug-in Hybrid Electric Vehicles and Battery Electric Vehicle; SAE Technical Paper 2013-01-1462; SAE: Warrendale, PA, USA, 2013. [Google Scholar] [CrossRef]
  49. IHS Markit. Average Age of Cars and Light Trucks in the U.S. Approaches 12 Years, According to IHS Markit. 2020. Available online: https://news.ihsmarkit.com/prviewer/release_only/slug/bizwire-2020-7-28-average-age-of-cars-and-light-trucks-in-the-us-approaches-12-years-according-to-ihs-markit (accessed on 9 April 2024).
  50. Spangher, L.; Gorman, W.; Bauer, G.; Xu, Y.; Atkinson, C. Quantifying the impact of U.S. electric vehicle sales on light-duty vehicle fleet CO2 emissions using a novel agent-based simulation. Transp. Res. D Transp. Environ. 2019, 72, 358–377. [Google Scholar] [CrossRef]
  51. Greene, D.L.; Leard, B. Trends in scrappage and survival of U.S. light-duty vehicles. Transp. Res. Part A Policy Pract. 2024, 180, 103982. [Google Scholar] [CrossRef]
  52. Global Petrol Prices. Gasoline Prices around the World, 28-Mar-2022|GlobalPetrolPrices.com. Available online: https://www.globalpetrolprices.com/gasoline_prices/ (accessed on 9 April 2024).
  53. The World Bank. GDP per Capita, PPP (Constant 2017 International $). Available online: https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD?most_recent_value_desc=true&year_high_desc=true (accessed on 9 April 2024).
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Figure 1. Left panel, yearly CO2-eq emissions corresponding to the alternative scenarios (2030 ICEV ban scenario). Right panel, cumulative CO2-eq emissions corresponding to the BAU and Balanced Net Zero pathway (2030 ICEV ban scenario).
Figure 1. Left panel, yearly CO2-eq emissions corresponding to the alternative scenarios (2030 ICEV ban scenario). Right panel, cumulative CO2-eq emissions corresponding to the BAU and Balanced Net Zero pathway (2030 ICEV ban scenario).
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Figure 2. Left panel, yearly CO2-eq emissions, new LDV sales only using 2023 as a starting point, corresponding to the BAU and alternative scenarios. Right panel, cumulative CO2-eq emissions, new LDV sales only using 2023 as a starting point, corresponding to the BAU and alternative scenarios.
Figure 2. Left panel, yearly CO2-eq emissions, new LDV sales only using 2023 as a starting point, corresponding to the BAU and alternative scenarios. Right panel, cumulative CO2-eq emissions, new LDV sales only using 2023 as a starting point, corresponding to the BAU and alternative scenarios.
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Figure 3. Left panel, yearly CO2-eq emissions, comparison of the BAU and Balanced Net Zero—ICEV ban scenarios. Right panel, cumulative CO2-eq emissions, comparison of the BAU and Balanced Net Zero—ICEV ban scenarios.
Figure 3. Left panel, yearly CO2-eq emissions, comparison of the BAU and Balanced Net Zero—ICEV ban scenarios. Right panel, cumulative CO2-eq emissions, comparison of the BAU and Balanced Net Zero—ICEV ban scenarios.
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Table 1. ICEV fleet composition—Euro standards.
Table 1. ICEV fleet composition—Euro standards.
YearEuro 2Euro 4Euro 6
202272.5%27.5%0.0%
203039.1%60.9%0.0%
203525.9%40.3%33.8%
204017.1%26.6%56.2%
204511.3%17.6%71.0%
20507.5%11.7%80.8%
Table 2. ICEV ban scenarios (based on UK CCC, adjusted for Qatar).
Table 2. ICEV ban scenarios (based on UK CCC, adjusted for Qatar).
PathwayDescription
Balanced Net Zero PathwayDevelopments occur to drive improvements in a balanced manner.
Widespread EngagementHigher levels of behavioural and societal shifts.
Individuals and businesses are assumed to be ready to implement more drastic modifications to their lifestyles/operations.
TailwindsConsiders significant improvements in behavioural and societal actions as effort go further than the balanced pathway.
HeadwindsConsiders that changes only in behavioural, societal actions occur on a lesser than anticipated scale.
Business-as-Usual (BAU)Reflects the current local trend of no electrification.
Table 3. Yearly emissions in 2022, 2030, 2040, and 2050 for the BAU and Balanced Net Zero Pathways (BNZP) CO2-eq, NOx, and PM2.5.
Table 3. Yearly emissions in 2022, 2030, 2040, and 2050 for the BAU and Balanced Net Zero Pathways (BNZP) CO2-eq, NOx, and PM2.5.
CO2-eq (Mt)NOx (kt)PM2.5 (kt)
YearBAUBNZPBAUBNZPBAUBNZP
202311.0711.0711.4911.490.640.64
203013.4211.8410.749.610.780.75
204015.638.6511.468.001.300.91
205017.817.4213.549.421.331.17
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Alishaq, A.; Mehlig, D. Towards Sustainable Mobility: Assessing the Benefits and Implications of Internal Combustion Engine Vehicle Bans and Battery Electric Vehicle Uptake in Qatar. Atmosphere 2024, 15, 677. https://doi.org/10.3390/atmos15060677

AMA Style

Alishaq A, Mehlig D. Towards Sustainable Mobility: Assessing the Benefits and Implications of Internal Combustion Engine Vehicle Bans and Battery Electric Vehicle Uptake in Qatar. Atmosphere. 2024; 15(6):677. https://doi.org/10.3390/atmos15060677

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Alishaq, Abdulla, and Daniel Mehlig. 2024. "Towards Sustainable Mobility: Assessing the Benefits and Implications of Internal Combustion Engine Vehicle Bans and Battery Electric Vehicle Uptake in Qatar" Atmosphere 15, no. 6: 677. https://doi.org/10.3390/atmos15060677

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