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• Climate change concern can contribute to reduced car use and sustainable mobility. • Those worried about climate change were more willing to live without a car. • Relationship between climate change concern and car use differs based on type of car. • Worry about climate change was strongly related to lower use of a conventional car. • Worry about climate change was unrelated to the use of an electric car. Climate change concern has been linked to climate-friendly behavior, but little is known on whether and how it may promote sustainable mobility. This paper investigates how worry about climate change relates to attitudes and intentions towards the car as well as the actual use of conventional and electric cars in Oslo and Viken in Norway. Findings indicate a relationship between worry about climate change and reduced car use, mediated by mode preferences and willingness to live car-free. Those concerned about climate change were less likely to commute by a conventional car and used conventional cars less both on weekdays and weekends. Worry about climate change was also related to living in a denser neighborhood, preference for travel modes other than the car, and willingness to live car-free. Raising greater awareness of climate change could contribute to climate change mitigation by promoting car-reduced or car-free lifestyles and sustainable mobility. [ABSTRACT FROM AUTHOR]

• The failure of BEVs to provide long ranges in short charging times is a barrier to their adoption. • It can be overcome if consumers are willing to trade off range for charging infrastructure. • We use meta-regression to analyse the trade-offs individuals are willing to make. • Consumers are willing to accept compensations, but only if ranges are higher than 250–300 km. • This shows that an important technical barrier can be overcome with infrastructure investments. The failure of battery electric vehicles (BEVs) to provide sufficiently long ranges in a relatively short charging time (as compared with conventional vehicles) is one of the main technical barriers to consumers' adoption of these vehicles. However, this technical barrier could be mitigated if consumers were willing to trade-off lower ranges with better charging infrastructure. This paper analyses trade-offs individuals are willing to make between range and en-route charging station availability when considering the purchase of BEVs. By means of a meta-regression analysis of 33 stated preference studies, we find that consumers are willing to accept compensations for lower ranges in form of more dense charging infrastructure in their BEV purchase decision, but only when BEV ranges are longer than 250–300 km. This result is very relevant to promote the transition to BEVs because it shows that, in some circumstances, a significant technical barrier can be overcome with infrastructure investments. [ABSTRACT FROM AUTHOR]

[Display omitted] To overcome the challenge transport presents to net zero, the Scottish Government has proposed a series of interventions to significantly reduce transport emissions and car kilometres travelled. This paper develops, validates and applies a system dynamics model of the Scottish road passenger transport sector to interrogate key proposed interventions up to 2030: namely, modal shifting sub-10 km car journeys to active travel, modal shifting medium-length car journeys to buses, achieving a majority electrification of the bus fleet, and replacing 50 % of petrol/diesel cars with electric vehicles. Results indicate government targets can be met, but only as a result of multiple interventions. Modal shifting of medium-length car journeys and private car electrification are predicted to be the most effective interventions for emissions reduction, although these measures alone do not attain reduction targets. Results further indicate that realising the reduction in car kilometres does not guarantee the emissions target will also be achieved. [ABSTRACT FROM AUTHOR]

Most of the past research on public attitudes or choices on electric vehicles (EVs) have used user survey data (i.e., at a microscopic level). This paper adopts a multi-method approach to conduct a data-driven exploration of public sentiment evolution using a public tweet database spread across the last decade (2012–2022) (i.e., at a macroscopic level). Data mining from Twitter has enabled this research to obtain a rich alternative source of public sentiments on EVs. Natural Language Processing (NLP) techniques were then used to analyse the evolution in sentiments and reveal the underlying patterns in the discourse on EVs. Results and discussions in this paper categorise the shifts in public opinion and recognise critical linkages of economic summits or global events to sentiment changes. The research outcomes are expected to offer a strategic vantage point on the evolution of public discourse related to EV adoption and assist in designing better transportation electrification policies. [ABSTRACT FROM AUTHOR]

• • Cost efficiency analysis of vehicle technologies for semi-flexible transit. • • Total system cost estimated includes operator, user, and environmental costs. • • Diesel and battery-electric vehicle technology evaluated for three vehicle sizes. • • Optimal values of service headway and slack time are identified. • • Analysed sensitivity to charging speed, demand, and battery capacity. In low-demand areas, semi-flexible transit system (SFT) operated by battery electric vehicles (BEVs) can reduce operational costs and achieve zero emissions, allowing SFT to be used more widely, and transit agencies to benefit more significantly. This paper is aimed at analyzing the effect of the additional requirements of BEVs on the cost efficiency of SFT services while considering different headways and slack time to accommodate route-deviation. Analytical models are used for detailed estimation of the total cost, including operator, user, and environmental costs, allowing a comparison with internal combustion engine (ICEV) vehicle technology, and three vehicle sizes: minivans, standard vans, and minibuses. Study results can be used to evaluate budget requirements to upgrade an existing ICEV based standard bus service along an underperforming low demand route to a BEV based SFT service. The application of the proposed methodology is demonstrated for a low-demand bus route in Regina, Canada. [ABSTRACT FROM AUTHOR]

Replacing internal combustion engine vehicles with electric vehicles is the most direct pathway to decarbonize transportation. This paper investigates the environmental benefits of electric vehicle adoption in Shanghai—China's largest regional electric vehicle market. Using vehicle registration data and well-to-wheel analysis, this paper calculates the potential emissions reductions of carbon dioxide and air pollutants for each electric vehicle model sold in Shanghai in 2018. Our results indicate that electric vehicles have been over-subsidized: The average environmental benefit was less than one-fourth of the mean subsidy received. Also, the environmental benefits were not distributed uniformly: About 80% of electric vehicle models generated less than 20% of the aggregate environmental benefits. Finally, three policy recommendations are provided to improve the environmental benefits of electric vehicle adoption. [ABSTRACT FROM AUTHOR]

This paper examines individual preferences for a reduction of life-cycle CO 2 emissions in vehicle purchase decisions. The empirical analysis is based on data from a stated choice experiment among citizens in Germany that refers to decisions between three types of electric vehicles and one conventional vehicle that are characterized by several attributes. With respect to CO 2 emissions, we specifically examine emissions in vehicle production besides the commonly considered emissions in vehicle use. Our econometric analysis reveals a strong stated preference for the reduction of both components of life-cycle CO 2 emissions. Furthermore, we find that conventional vehicles are significantly preferred over electric vehicles. Surprisingly, environmental attitudes, i.e. environmental awareness and ecological policy identification, have no significant effects on the reduction of CO 2 emissions. Instead, citizens with a strong environmental identity rather tend to avoid the purchase of conventional vehicles. We discuss several practical policy implications. [ABSTRACT FROM AUTHOR]

Inequitable distribution of public electric vehicle (EV) charging facilities may result in disparities in charging accessibility, potentially impeding the uptake of EVs. In response, this paper proposes an analytical framework to assess the accessibility-based equity of public EV charging infrastructure systematically, using Hong Kong (HK) as the study area. The results demonstrated severe spatial inequity of charging facilities in central, north, and southwest HK. Meanwhile, the Gini index of all public EV charger types for the total population was 0.751, and the indices for low-income, low-education level, and government-funded housing subgroups were 0.791, 0.809, and 0.893, respectively. These indicated considerable horizontal and vertical inequity from a statistical perspective. Furthermore, age, education level, family structure, and housing type were identified as significant socio-demographic characteristics correlated to the accessibility-based equity of public EV charging infrastructure in HK. These findings are expected to be useful for future policymaking and infrastructure planning. [ABSTRACT FROM AUTHOR]

Electric vehicle (EV) adoption in the U.S. will be accelerated by the historic $7.5 billion public investments in EV charging infrastructure. Careful analysis of EV charging demands plays a vital role in understanding the energy requirements, power grid impact, and smart charging management opportunities of EVs. To this end, this paper develops a data-driven trip-chaining-based modeling framework including five steps: Trip data acquisition and preprocessing, EV adoption modeling, travel itinerary synthesis, EV charging demand simulation and EV load profile generation. The developed analysis framework was demonstrated using real-world data for one region in Virginia, U.S. The results show that the proposed modeling framework can work effectively. For the study region in 2040, the predicted number of plug-in EVs is 470,114, resulting in a weekly charging demand of 38,078,127 kWh (55 % home, 9 % work, and 36 % public) in September and 45,920,358 kWh (61 % home, 9 % work, and 30 % public) in February. [ABSTRACT FROM AUTHOR]

Despite noticeable improvements in electric vehicle (EV) technology, EV charging time remains high, and the lack of fast-charging infrastructure extends the travelling time for long-distance trips even further. Charging strategies to coordinate EVs' charging plans dynamically will help reduce station waiting times. This paper aims to quantify and compare the performances of two decentralised strategies using regular information-sharing between EVs and stations to estimate future waiting time in charging stations accurately. The communication strategy relies only on the communication of optimal charging plan computed by EVs to stations without any reservation. In contrast, the reservation strategy enables additionally the booking of optimal charging time slots. In a dedicated multi-agent simulation framework, we tested both strategies on different levels of infrastructure saturation. The communication strategy shows the best performance especially when combined with more frequent charging stops. • Two charging strategies are compared: one using communication, the other reservation. • Communication reduces by up to 12.8% the time spent in charging stations. • Multiplying stops and allowing charges above 80% of SoC improve both strategies. • Communication alone has better performance than the reservation. [ABSTRACT FROM AUTHOR]

Due to the fundamental differences in motors and internal combustion engines, the real-time energy consumption profiles of internal combustion engine vehicles (ICEVs) and electric vehicles (EVs) are different, which motivates the need to identify the driving cycle for different types of vehicles. Existing EV driving cycle studies did not provide comparisons with ICEV driving cycles using the same geographical locations. This paper proposes a systematic method to develop the driving cycle cycles for EVs and ICEVs and combine the self-organizing map and support vector machine to eliminate the inconsistent clustering results in the driving segment classification, improving the clustering performance by 6.06–16.05%. Then, the driving cycles for the EV and ICEV are established under peak and off-peak traffic periods, and compared in the same traffic environment to illustrate the influence of driving cycle electrification. The emissions and energy consumption are also analyzed and compared for both types of vehicles. [ABSTRACT FROM AUTHOR]

• Research roadmap for pavement-EV interaction is proposed. • Three key pavement-EV interaction knowledge gaps are identified. • Eleven key urgent research questions are defined. The electrification of vehicle fleets will play a major role in reaching carbon neutrality by 2050. This paradigm shift for both passenger and heavy vehicles, coupled with changing mobility and freight trends, will require a change in current understanding of pavement design and management. This paper synthesizes the impacts of increasing electric vehicles (EVs) and mobility trends on pavements, in order to produce a 25-year research roadmap. From the literature review conducted, three principal pavement-EV interaction knowledge gaps were identified: 1) effect of EVs and upcoming mobility trends on pavement degradation, 2) pavement-EV energy loss mechanisms, and 3) effect of pavement on EV operating costs and battery life and associated environmental impacts. From the results of this study, a research roadmap is proposed to better understand these issues and develop solutions for the key challenges expected over the next 25 years. [ABSTRACT FROM AUTHOR]

With the rapid development of charging-while-driving technology, the deployments of wireless charging lanes will inevitably affect the route choice of Battery Electric Vehicle (BEV) drivers and the power supply of power grids. This paper builds a bi-level optimization problem to optimize the locations of charging lanes and charging stations given an integrated transportation-power network. The decision variables of the upper level include the locations of charging lanes and charging stations and the electricity generated in each busbar (an electricity generation node in the power network), while the lower level determines the charging strategy and the route choice of the drivers. Then we develop an effective solving algorithm for the problem through various reformulations. The results from the numerical study and sensitivity analysis show that charging infrastructures' deployment affects the route choices of BEV drivers significantly, and charging lanes can be a more economical and effective charging method than charging stations. • Integrated location optimization of wireless charging lanes and fast-charging stations. • A bi-level optimization framework considering traffic congestion for deploying charging infrastructure. • Mutual impacts between the power grid network and the transportation network are captured. • An effective algorithm is proposed to solve the non-convex bi-level optimization problem. [ABSTRACT FROM AUTHOR]

Dynamic wireless charging (DWC) technology has the potential to promote the uptake of electric freight vehicles (EFVs) by allowing charging on the move. This paper presented a joint DWC lane deployment and operation model based on the trajectory data of actual EFVs. The model was formulated as a multi-objective mixed-integer programming model, with objectives to maximize saved charging time, minimize charging cost, and minimize negative impact on traffic. The model considered various constraints related to investment, facility construction, and charging decisions of EFV drivers. It was first tested on a small network and was further applied into a large-scale scenario in Beijing. The results suggested that DWC could reduce charging time by approximately 0.08–0.11 h per EFV per day within an investment limit of 67 million dollars. The deployment of DWC can be long-term profitable, resulting in a net value of 140.83 million dollars over a 25-year period. [ABSTRACT FROM AUTHOR]

The development of BEVs (battery electric vehicles) can significantly mitigate the dependence on fossil fuels and reduce carbon emissions. To advance the development of BEVs, China is adjusting the subsidy and dual-credit policies. This paper addresses these two issues to help policymakers determine effective policies. Through the simulation with a system dynamics model that incorporates the government, automakers, infrastructure operators, and consumers, we find that (1) reducing the overall level of subsidies has a minor impact on the development of BEVs in the long term; (2) subsiding the construction of infrastructure, especially battery swapping stations, will produce better effects than others; (3) the proposed carbon quota mechanism, which is in line with the unified carbon market, is more conducive to the diffusion of BEVs and carbon reduction than the dual-credit policy. Our MLP-SD (multi-level perspective system dynamics) model broadens the analytical perspective of the technology transition in the automobile industry. [ABSTRACT FROM AUTHOR]

The massive incorporation of light electric freight vehicles (LEFV) in the last mile distribution has been identified as one of the alternatives that large Latin American cities shall include in their agenda for cleaner and more effective public policy in logistics operations. Thence, in this paper, a routing optimization model was used to evaluate the reduction in operational cost and GHG emissions when fueled cargo trucks or motorcycles are replaced by LEFV (i.e., e-trikes or e-bikes) in the last mile distribution in large Latin American cities. Results showed that for the case of a commercial area in Bogota, and two lines of business (i.e., parcel delivery and dry chain), the use of LEFV reduces more than 50% de operational cost and more than 95% the GHG emissions. These high reductions are due to the synergistic effect of switching from fuel to electricity and reducing the energy consumption of vehicles. [ABSTRACT FROM AUTHOR]

The development of electric vehicles (EVs) is expected to play an important role in achieving the emission reduction targets in the transportation sector by many countries and regions, including China's dual carbon goals. A bottleneck to the mass roll-out of EVs is the limited charging facilities. This paper considers the planning of charging facilities for a new developing area in a metropolis, and an optimization model for charging station planning based on the dynamic transportation system is proposed. The proposed model is developed using an objective framework that considers the spatio-temporal characteristics of EV charging demand in order to minimize the overall cost while the constraints from suppliers and drivers are met. The Voronoi diagram is used to determine the final service boundary of each charging station, and the effect is verified in the planning of charge stations for the Yizhuang new town in Beijing. The case study confirms that the proposed method can optimize the charging facilities that fit well with the traffic network conditions. Furthermore, it is shown that the charging demand varies in accordance with the population density and regional functionality in different areas. [ABSTRACT FROM AUTHOR]

While electric vehicle (EV) use has proliferated in developed countries, emerging nations have lagged behind because they lack the resources to establish effective EV charging networks. To address this issue, this paper has developed a framework through scenario analysis that aims to minimize the costs involved in establishing such networks and provide policy makers with insights on how to accomplish this. A given set of public institutions possessing EV fleets were first grouped into zones using clustering algorithms. Mathematical models were then developed to identify optimal EV charging station locations serving the EV fleets of these organizations, with an objective to minimize the total cost. Factors such as EV range anxiety and human walking distance were imbedded into the framework, which was examined using real world data from three major cities in Morocco: Rabat, Casablanca, and Fes. With the framework, policy makers can make better planning decisions on EV transitioning. [ABSTRACT FROM AUTHOR]

Paratransit, in particular the minibus taxi, is the mainstay of public transport in sub-Saharan Africa. These vehicles are often second-hand, ageing, fuel inefficient, and expensive to operate - issues that electrification can ameliorate. However, modeling and planning large-scale transitions to electric paratransit require reliable estimates of vehicle energy consumption. This paper provides such estimates by applying a vehicle kinetic model to per-second GPS data gathered on minibus taxis. Data include 62 trips across three routes with different driving conditions near Stellenbosch, South Africa. We find a range of energy consumption from 0.29 to 0.51 kWh/km (mean = 0.39 kWh/km). Past estimates in literature relied on per-minute GPS data, which we show leads to inaccurate energy consumption estimates. We recommend new kWh/km values for modeling vehicle operations and grid impact, and discuss how future work can utilize our analysis to advance the transition to electric paratransit sub-Saharan Africa. [Display omitted] • Highest fidelity kWh/km estimates for paratransit in sub-Saharan Africa to date. • Urban, hilly, and inter-city driving conditions are investigated. • 0.29–0.51 kWh/km (mean = 0.39 kWh/km), depending on driving condition. • Specific kWh/km recommendations for usage in operations planning and grid impact. • Per-minute data fails to capture effect of micro-mobility patterns on kWh/km. [ABSTRACT FROM AUTHOR]

Charging station planning is critical in the implementation of public transport electrification. However, in cities, there is insufficient experience and knowledge of the factors that influence the utilization of charging stations, particularly charging stations aimed at serving a fleet of electric taxis. Shenzhen is one of the pioneers in promoting electric taxis. In this paper, we collect large-scale datasets from Shenzhen and provide a data-driven space–time analysis of the relationship between charging station utilization and urban form and demand for taxi services. We use a Random Forest Regression model to explore these relationships and apply a Shapley value method to interpret the results. We find that demand for taxi services, measured as hourly pick-up and drop-off densities, have a non-linear relationship with utilization. Metro station density positively correlates with utilization, whereas the relationships between population density, land-use entropy, road density, and bus station density are more complicated. [ABSTRACT FROM AUTHOR]

This paper aims at studying the status of the current fleet of freight vehicles that travel through Uruguay, following international methodologies for calculating GHG gas emissions to determine the current inventory of gases caused by road freight transport. This inventory acts as a starting point to establish three future scenarios where different alternative powertrain technologies are implemented at different rates, such as battery electric vehicles (BEV) and fuel cell electric vehicles (FCEV). The transition from Euro III regulations into Euro V is also considered for these scenarios. Based on these future scenarios, it is possible to project the levels of future emissions that Uruguay will perceive in a time horizon that reaches the year 2050. This study aims to increase the level of information present in the region on the emissions generated in the road freight transport sector, providing new input to the development of future policies on these issues. [ABSTRACT FROM AUTHOR]

This paper provides insight into motivational reasons for consumers' preferences for Electric Vehicles (EVs) assuming equal and different prices between EVs and traditional vehicles. Referring to consumer behavior, it shows that reputation-driven consumers prefer EVs only when the purchase price is more expensive than that of other vehicles, thus suggesting that true environmental concern is attenuated by reputation motives; and that the desirability of EVs as sustainable products only increases if prices are more expensive. It provides insights into the influence of sociodemographic variables, car attributes and external environmental factors. The study offers an empirical approach with a sample set of more than 2.000 responses. Different logit models are estimated to explore the factors influencing the preference for an EV. It is found that age, being male, having children, education, living in urban areas, and previous experience positively influence EV adoption. Better infrastructure and information availability help to promote EVs. [ABSTRACT FROM AUTHOR]

• Non-linear cost optimization model to allocate dynamic wireless charging facility. • Sensitivity analysis of parameters while maintaining minimum state-of-charge level. • Case study of Auckland motorway network to design wireless charging lane assignment. • DWC lane utilization based on initial SOC level and lower threshold SOC level of EV. It is widely recognized that electric vehicle (EV) users get frustrated by the limited range of EVs, slow charging, and limited charging facilities available in the vicinity of their journey route. These factors contribute to slowing down the uptake of EVs. Dynamic wireless charging (DWC) system has the potential to offer a cost-effective solution to these issues. This paper investigates the DWC allocation problem from the decision makers' perspective to minimize individual users' costs as well as the investment cost for DWC facilities. We propose a mixed-integer optimization model to achieve a cost-effective solution for the optimal placement of DWC facilities on the large road network while maintaining an acceptable state-of-charge level. Sensitivity analysis is conducted on the optimization model parameters to check the feasibility and significance of model solutions for DWC placements. The results provide useful insights into the optimal settings of DWC facilities under multiple route environments. [ABSTRACT FROM AUTHOR]

When designing new electric vehicles for urban transport, both vehicle producers and operators need to establish and verify requirements that the vehicle has to fulfil regarding, e.g., energy storage capacity, driving range, or battery wear. These requirements are typically verified using simulation tools that concentrate mostly on electrical quantities, but disregard the influence of road infrastructure, surrounding traffic and detours. At the same time, an expansion of the electric vehicle fleet may have a negative impact on the existing power grid. Sufficient dimensioning of the grid elements and their verification are therefore necessary to keep the electric transport service reliable. This paper demonstrates a tool suitable for verification of battery-assisted trolleybus fleet and power infrastructure parameters, based on extensions to the SUMO traffic simulator. The tool carries out a joint simulation of electric and traffic-related quantities. It is demonstrated on seven use-cases inspired by real-life problems. • A tool to verify battery-assisted trolleybus fleet configuration is presented. • Includes fast DC power grid solver for electric grid analysis. • Combines traffic and electric circuit simulation to study mutual interactions. • The effect of surrounding traffic on vehicle power demands is considered. • Study of different parameters of a sample trolleybus network is presented. [ABSTRACT FROM AUTHOR]

• Transition to fossil-free road transport by adopting EFVs. • Internal, external and governmental drivers for and barriers to EFV adoption. • Political ambivalence results in uncertainty over the future development of EFVs. • Developing a brand image as being a sustainable firm acts as a major driver for EFV adoption. • Grid uncertainties concerning capacity and ensuring sustainable energy production. Freight transport in urban areas needs to become more sustainable. There is much potential to reduce emissions by transitioning to electric freight vehicles (EFVs). The context of this study is Sweden, a country with ambitious goals for transitioning to fossil-free road transport that incentivizes the adoption of EFVs through regulations, subsidies and environmental zones. However, it seems that few companies are adopting EFVs. This paper aims to explore why that is by investigating drivers for and barriers to the adoption of EFVs. An embedded case study of five firms in Stockholm that are exploring the possibilities of using EFVs is conducted. The findings reveal a number of drivers and barriers, which are then categorized as internal, external or governmental. The results show uncertainties at the micro, meso and macro levels. Uncertainties relate to political and legal uncertainties, technological and infrastructure-related uncertainties, customer expectations and willingness to pay, and operational uncertainties. [ABSTRACT FROM AUTHOR]

This paper performs a novel data-driven approach to optimize electric vehicle (EV) public charging. We translate the study area into a directed graph by partitioning it into discrete grids. A modified geographical PageRank (MGPR) model is developed to estimate EV charging demand, built upon trip origin–destination (OD) and social dimension features, and validated against real-world charging data. The results are fed into the capacitated maximal coverage location problem (CMCLP) model to optimize the spatial layout of public charging stations by maximizing their utilization. It is shown that MGPR can effectively quantify the EV charging demand with satisfactory accuracy. Optimized EV charging stations based on the CMCLP model can remedy the spatial mismatch between the EV demand and the existing charging station allocations. The developed methodological framework is highly generalizable and can be extended to other regions for EV charging demand estimation and optimal charging infrastructure siting. [ABSTRACT FROM AUTHOR]

• We evaluate decarbonization of passenger transportation in Rio de Janeiro, Brazil. • Public transport and carpooled shared mobility rise system capacity and reduces CO 2. • Biofuels reduce CO 2 emissions at no increased system cost. • CO 2 price is the most effective decarbonization policy, but it is costlier. • Increased renewable electricity and electrification of the fleet reduce the most CO 2. This paper applies an energy systems model to evaluate how shared mobility (ride-hailing and shared autonomous vehicles), public buses, alternative energy sources (electricity and biofuels) and carbon pricing contribute to reduce passenger vehicles CO 2 emissions in Rio de Janeiro state, Brazil, from 2016 to 2050. Public buses and carpooled shared mobility increase system capacity, resulting in lower vehicle ownership, energy demand and CO 2 emissions, as well as savings per ton of CO 2 abated (from $3 to $4186). Biofuels reduce CO 2 emissions at no increased system cost, while carbon pricing is the most effective policy to reduce CO 2 emissions, but it is costlier than the alternatives and results in greater private vehicle use (up to 260%) if the expansion of zero-carbon public transportation remains limited due to technology adoption. The policy that reduces the most emissions (by 84%) combines the expansion of renewable electricity generation and implementation of a CO 2 price. [ABSTRACT FROM AUTHOR]

• Minimising well-to-wheels GHG emissions subject to battery supply constraint in EU. • Optimising cars' electrification level to best allocate limited battery resources. • PHEVs as the key component of optimal sales mix in a battery-constrained future. • Long-range PHEVs require high utility factor to play a role in optimal sales mix. • PHEVs with high utility factors lead to the best utilisation of battery resources. Recent forecasts for the rapid electrification of the road transport sector towards 2030 have given rise to considerable uncertainty associated with battery production capacity and whether it will be able to meet the growing demand in Europe. In view of this uncertainty and the potential implications for greenhouse gas (GHG) emissions, this paper explores the optimal electrification level of passenger cars for minimising well-to-wheels (WTW) GHG emissions as a function of battery production capacity. The findings indicate that plug-in hybrid electric vehicles (PHEVs) could be the key components of the optimal sales mix in a battery-constrained future; especially the lower-range PHEVs with smaller batteries would be preferable over hybrid electric vehicles (HEVs) and battery electric vehicles (BEVs). To ensure the best utilisation of the available battery resources, the longer-range PHEVs require far higher levels of utility factor to be able to play a role in the optimal sales mix. [ABSTRACT FROM AUTHOR]

Transportation Research Part D , launched in 1996, has succeeded in its mission of becoming a key outlet for interdisciplinary and policy-relevant research on transport and the environment. Using a bibliographic analysis of 2,521 papers published in this journal from 1996 to 2020, we identify key topics, top contributors, the most productive universities, and the leading research trends within the field. Emissions and air quality, climate change, energy, electric vehicles, noise and the built environment (land use and travel behavior) are dominant topics since the journal's founding. The journal has had an impact on policy, especially regarding land use and travel behavior. Citation levels seem to mirror these impacts. Electric vehicle policy and environmental impacts of shipping and ports are recent topical areas with large citation counts. All these issues will continue to resonate providing rich opportunities for innovative manuscripts that analyze problems and provide solutions. [ABSTRACT FROM AUTHOR]

• Preferences for PEVs are estimated for a representative national sample. • Vehicle choice is analysed taking both fuel type and car segment into account. • A mixed logit model with random effects reveals many substitution patterns. • Twenty WTP measures are found, e.g. running costs, emissions and charging aspects. • Elasticities for price and range are derived based on the model after calibration. The electrification of transport systems requires a change in the composition of the vehicle fleet towards higher shares of electric vehicles. A successful transition, however, depends on many factors of which some relate to purchase prices and vehicle features, while others relate to technology and charging infrastructure. This paper analyses the transition towards plug-in electric vehicles. We use data from a large representative Danish stated choice survey. Based on these data, we estimate a mixed logit model that allows for correlated random effects across fuel types and car segments as well as systematic heterogeneity. The results show that correlation and substitution indeed goes across these dimensions. Willingness-to-pay (WTP) measures are estimated for a variety of attributes. These suggest that the WTP for range varies with fuel types, that the possibility for home charging is highly valued, and that CO 2 is a significant concern among individuals in the sample. [ABSTRACT FROM AUTHOR]