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Reverse logistics plays a critical role in sustainable development and circular economy by transforming waste into value. However, transporting large volumes of waste and recyclables generates significant carbon emissions. This paper explores an industry-led strategy for charging infrastructure development, which integrates the reverse logistics network designs with a network of charging stations to promote electric trucks (E-trucks) adoption and reduce carbon footprints in transportation. Our model optimizes operational costs while ensuring accessibility to charging infrastructure. A case study in Norway demonstrates that, despite higher operating costs due to additional charging stations, the adoption of E-trucks can decrease total reverse logistics costs by 0.18%—2.37% and cut carbon emissions by 16.3%—17.6%. These findings support the economic viability and environmental benefits of this strategy for advancing sustainable practices in transportation, and it further highlights the interdependencies between logistics network design and charging infrastructure planning. [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]

• First stated preference study on electric outboard engines adoption for water taxis. • Novel and replicable methodology, combining choice experiments with focus groups. • Taxi owners prefer small electric systems with similar power to combustion engines. • Collective preferences for charging infrastructure are specific to local context. • Study yields direct insights for policy design to decarbonize water taxi sector. This paper investigates individual and collective preferences of water taxi owners regarding electric outboard engines and batteries in the Galápagos Islands. The study addresses a gap in the literature on clean maritime transportation and deploys a novel mixed-method approach, using qualitative methods to complement discrete choice experiments. Results from mixed logit models and focus groups reveal a preference for smaller, lighter electric systems that can both provide similar speed and power to internal combustion counterparts while avoiding economic loss or excessive costs to service providers. Water taxi owners on different islands arrived at different collective preferences for charging infrastructure, thus illustrating the relevance of local context, and the importance of the participation of local actors in the successful design of systems and strategies facilitating maritime transport decarbonization. Additionally, identified instruments to reduce uncertainties and proposed subsidies offer practical considerations for accelerating the transition from internal combustion engines to electric propulsion. [ABSTRACT FROM AUTHOR]

• Framework for optimising sustainability, resilience, and cost (SRC) metrics is described and applied. • Traditional and low-carbon restoration strategies are compared on a bridge. • Fragility and restoration models are employed toward actionable SRC metrics. • Practical starting point for evaluating climate projections in adaptation strategies. Climate change exacerbates natural hazards and continuously challenges the performance of critical infrastructure. Thus, climate resilience and sustainable adaptation of infrastructure are of paramount importance. This paper puts forward a novel framework and metrics for optimising sustainability (Greenhouse Gas emissions - GHG), climate resilience (restoration time), and cost. The framework aims to facilitate decision-making by operators and stakeholders and communicate actionable trade-offs between these principles. It describes approaches for quantifying ex-ante adaptation and ex-post recovery from the lenses of sustainability and resilience using relevant metrics. This paper concludes with an application of the framework on a bridge, where normalised metrics are integrated into one unique index (I SRC), which can be used in the recovery prioritisation for portfolios of similar assets. The optimisation program includes a bridge recovery, while reducing GHG emissions. The impact of climate change on the sustainability and resilience indexes is examined and the results show how the optimum solutions are adversely affected by different climate projections. In all scenarios examined, more sustainable solutions leading to reduced GHG emissions (tCO2e) are the optimum solutions when weighing resilience and cost. Based on the case study analysed in this paper, the low carbon restoration strategy resulted in up to 50% higher I SRC , which can justify investments for low GHG adaptation strategies in transport assets. [ABSTRACT FROM AUTHOR]

While the trend towards building resilience in infrastructure systems for disaster risk reduction is accelerating, the application of infrastructure resilience is largely conceptual or an embellishment to established modelling techniques. In response, this paper sets out the theoretical foundations of infrastructure resilience, which broadly applies across critical infrastructure networks. This is followed by reviewing system-based and network-based approaches to resilience assessment with a focus on transport infrastructure, where there is an emerging body of studies. It spotlights critical issues in conflating resilience with other concepts of infrastructure safety management or merely tagging resilience on established methods, indicating a phenomenon of "old wine in new bottles". Also, oversimplifying disruption scenarios does not provide a sufficient basis for informed resilience planning. This paper reminds readers to look beyond resilience as a mere buzzword and offers guidance on how to do so by recognising the theoretical and methodological cornerstones of infrastructure resilience. [ABSTRACT FROM AUTHOR]

Transportation resilience is a multifaceted construct, including technical, socioeconomic, and geospatial factors. Developing effective plans demands evaluation of all resilience aspects across two major resilience dimensions: robustness and rapidity. Quantitative evaluation and monitoring of resilience aspects provides a thorough understanding of changes in transportation resilience over time. This paper proposes a resilience index framework to quantitively measure the road transportation systems' resilience to wind and water-related hazards in coastal areas. The framework streamlines abundant information derived from various resilience factors in a hierarchical structure and allows transportation planners to analyze changes in system resilience and identify corresponding root causes. Case study results indicate that the framework can effectively capture and track progress/deterioration in various resilience aspects and enhance our understanding of multidimensional transportation resilience. Transportation planners can utilize the developed index to set resilience goals, monitor progress toward goals, and prioritize projects to address the root causes of resilience deficiency. [ABSTRACT FROM AUTHOR]

Reliable assessment of the resilience of transportation networks to natural disasters is a prominent issue. This paper investigates how the spatial feature of natural disasters affects the resilience of transportation networks. Our research reveals that incorporating spatial feature data on disaster impacts is highly important for assessing network resilience. More precisely, the uncertainty linked to the estimation of the resilience index in the context of disaster-induced interruptions with spatial feature significantly exceeds that of conventional approaches. We present an illustrative case of a transportation network affected by a hurricane. The results reveal that the improved characteristics of disaster-induced outages greatly alter the manner in which the network recovers, including changes in emergency system characteristics. Our research indicates that extant methodologies employed for evaluating the resilience of critical infrastructure potentially inflate the confidence levels pertaining to network recovery predictions, primarily owing to insufficient attention given to the spatial configuration of disasters. [ABSTRACT FROM AUTHOR]

Climate change impacts such as extreme temperatures, snow and ice, flooding, and sea level rise posed significant threats to railway infrastructure networks. One of the important questions that infrastructure managers need to answer is, "How will maintenance costs be affected due to climate change in different climate change scenarios?" This paper proposes an approach to estimate the implication of climate change on the life cycle cost (LCC) of railways infrastructure assets. The proportional hazard model is employed to capture the dynamic effects of climate change on reliability parameters and LCC of railway assets. A use-case from a railway in North Sweden is analyzed to validate the proposed process using data collected over 18 years. The results have shown that precipitation, temperature, and humidity are significant weather factors in selected use-case. Furthermore, our analyses show that LCC under future climate scenarios will be about 11 % higher than LCC without climate impacts. [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]

To inform charger deployment decisions, this paper aims to understand the potential utilization and deployment priority for public charging infrastructure. A data-driven Cumulative Public Recharging (CPR) model is developed to explore the travel patterns by using 2017 National Household Travel Survey data. Given the daily trip sequence, trip distance, and dwell times, the study examines the daily expected driving range and BEV feasibility under different charging speeds, battery capacity, and charging behaviors. The results suggest that more advanced public chargers increase the daily expected driving range. Home charging is sufficient for most daily short-distance trips while public chargers are still needed for medium- and long-distance trips. Extreme fast charging (xFC) may not be necessary for people with home charging but could be more useful for people without home charging and for urgent charging. xFC becomes even less important to drivers with longer BEV ranges, a finding that contradicts conventional thinking. [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]

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]

• A data-driven approach to deploying charging facilities for electric buses (E-buses). • A real-world bus trajectory dataset used to quantify charging demand at the micro-scale. • Wireless charging lanes can greatly improve level of service with a slightly higher cost. • Parameters related to E-buses and charging facilities were influential to model outputs. This paper developed a data-driven micro-simulation optimization model to deploy charging infrastructure for a large-scale electric bus network, considering both traditional charging posts and wireless charging lanes (WCLs). The optimization model has two objectives: 1) minimize the total system cost and 2) maximize the level of service. We used New York City as the study area, and one-day GPS trajectories of 3,133 buses were analyzed to develop the micro-simulation approach, so as to represent the bus operation well. The results showed that the scenario with both charging posts and WCLs deployed had a significantly higher level of service (with its total delay time being 48.11% shorter), more energy saved and fewer emissions than the scenario with only charging posts deployed, though its total cost was 0.76% higher. Moreover, the sensitivity analysis results show that the parameters associated with electric buses and charging facilities could heavily influence the outputs of interest. [ABSTRACT FROM AUTHOR]

Sweden has set itself aspirational climate goals to reduce its fossil fuel dependence, and has the ambition of being a global climate transition leader. These goals have proven difficult to achieve in the transport system. In this paper we describe the development since 2016 of a broad and heterogeneous discourse coalition in favour of more strategic governance in Swedish transport planning, encompassing business, trade unions, environmental experts and green-left politicians. We first analyse their claims for a radical reorientation of Swedish transport planning, which are shown to focus on fiscal rules, a lack of political courage, and slow-moving environmental legislation processes, as three main reasons why the transport system is slow to transition. We then discuss whether these claims constitute a politicisation of the climate policy discourse, or rather a new iteration of the previously depoliticised discourse. [ABSTRACT FROM AUTHOR]

The innovations in battery technologies facilitate worldwide bus electrification, which can conserve energy and reduce emissions. This paper studies the long-term bus electrification that simultaneously optimizes the charging infrastructure deployment and bus fleet transition. The seasonal differences associated with battery capacities, bus lines, and bus timetables are considered. The studied problem is modeled as a mixed-integer linear programming formulation, in which the detailed charging schedules are determined to give the charging location and charging time. The objective minimizes both the cost and emissions. The proposed formulation is tested on a real-world case study. The computational results indicate that replacing DPBs with BEBs can save 17.8% of total cost and decrease 39.3% of carbon dioxide emission. Compared with designing a plan for each period separately, implementing long-term bus electrification can save 13.5% of total cost and 21.7% of operational cost. Moreover, the benefits of implementing long-term bus electrification are more remarkable when the planning horizon is longer. [ABSTRACT FROM AUTHOR]

This paper presents an integrated analysis of the costs and benefits of carefully constructed scenarios for the electrification of transport in Australia. Scenarios were constructed to responsively meet increased electricity demand, whilst maximising emission reduction, household finances and public health benefits. The scenarios bundle several technologies, including charging infrastructure, electric vehicles, home battery systems, and renewable energy. The fastest, mission-style, adoption scenario can reduce emissions by 9% of the 2030 carbon budget and reduce the loss of life by 24,000 by 2042. In all scenarios, government expenditure is dwarfed by the estimated public benefits, and households are nearly always better off in the medium to long term. Demonstrating the feasibility and advantages of surface transportation electrification in Australia, which presents cross-sections of rural and urban infrastructure, traffic and a large, socially and politically diverse population, provides broadly applicable evidence of the feasibility of electrification in other jurisdictions. [ABSTRACT FROM AUTHOR]

Road traffic has changed as an essential source of influential urban air pollution in China. This study developed the Spatial Durbin Model to analyze the spatial spillover effects on air pollution caused by urban transportation infrastructure. The results show that high-emission areas congregate in the eastern region of China, which reflects noticeable spatial spillover effects and agglomeration characteristics as high-emission "clubs." When the traffic construction 1 1 Traffic construction area refer to the acreage of street area within the city in this paper. area increases by 1%, the air pollution level will increase 3.2% as accompanied. The improvement of the transportation infrastructure will bring out economic development. The speed of air pollution generated by the resources consumed by economic development is higher than that of technological improvement in the economic transition period to reduce air pollution. In contrast, economic growth and air pollution exacerbation represent a U-shaped curve connection. Most provinces remain in a stage where air pollution worsened with economic growth. [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]

• Elaborate all the indicators influencing airport climate resilience. • Develop a climate resilience evaluation method using evidential reasoning. • Use big data to quantify the airport climate resilience. • Conduct an empirical study to analyse the climate resilience of the UK airports. Due to increased extreme weather events, climate adaptation has become an essential issue to be addressed by all transport infrastructures, including airports. This paper aims to develop a Climate Resilience Indicator (CRI) framework for assessing airport climate resilience, which for the first time, considers: climate exposure, vulnerability and adaptive capacity simultaneously and advances the development of climate risk analysis of airports to a point where their adaptation and resilience can be quantified under uncertainty in data. Climate-related data was collected from multiple sources to evaluate an airport's performance against each indicator. An evidential reasoning (ER) approach is used to evaluate each airport by integrating all the indicators to derive its final CRI score. The findings provide valuable insights into how urgently an airport needs to deal with climate change and reveal information to help with resource allocation for different airports nationally through proactive adaptation planning. [ABSTRACT FROM AUTHOR]

• An analytical framework is applied to identify barriers to adaptation. • A case study of the transport sector of Bogotá, Colombia is presented. • The analysis identified information, resources and incentives barriers. • The institutional environment is the most influential underlying factor. • There is potential for this study to be replicated in other parts of the world. The adaptation of transport systems to climate change is crucial for maintaining city functions in the future. Due to the complexity of this process, urban transport planners may not always be aware of all challenges they must face, leading to maladaptation or lack of adaptation of the transport systems of their cities. Recognising this complexity, this paper identifies barriers and opportunities for effective adaptation planning via a socio-technical perspective. An existing analytical framework, previously used in some cities of Latin America and Germany, is adopted and applied to a case study of Bogotá, Colombia. Interviews conducted with local professionals and a review of policy documents and technical reports provided insight into city governance processes and the extent to which climate change adaptation shapes transport sector decisions. The study reveals that incomplete or unusable information about climate change and its effects, reduced availability of economic and human resources, and the lack of adequate incentives are limiting adaptation within the transport planning processes of the city. It also demonstrates the critical influence of the institutional structure in determining which infrastructure options are considered feasible and preferable. The study elucidates what levers are available to transport planners and why some solutions are just not considered. There is potential for this study to be replicated in other parts of the world to help support the development of effective systemic intervention to support adaptation. [ABSTRACT FROM AUTHOR]

• Managing the organisational capabilities to ensure transport asset resilience is a challenge. • Need to determine systemic nature of organisable capabilities to develop a resilience strategy. • Use Social Network Analysis to examine seven transport projects impacted by adverse weather. • Major inter-related factors influencing transport asset resilience are identified. • A framework providing an insight into managing organisational capabilities is propagated. The management of the organisational capabilities needed to ensure the resilience of transport infrastructure assets is a challenge for governments worldwide. However, an absence of critical research in this area has exacerbated this challenge. The upshot, in this instance, has been the inability of governments to engender and enact an effective transport resilience strategy. This paper aims to fill this void and address the following research questions: (1) How do government organisational capabilities interact with one another to determine the resilience of transport projects? and (2) What is the best way to manage these organisational capabilities to aid a resilience strategy? Using Social Network Analysis, seven transport infrastructure case studies that were significantly impacted by natural hazards in the United Kingdom were used to answer the proposed research questions. The analysis revealed five inter-related factors are required to ensure the resilience of transport assets: (1) leadership; (2) reflexive (organisational) learning; (3) support from senior management; (4) a culture adept to resilience; and (5) continuous improvement (also investment) in asset absorbability, adaptability and vulnerability. The original contribution of this research is threefold: (1) a network providing a systematic visualisation of the interactions between organisational capabilities influencing asset resilience; (2) the prioritisation of governmental capabilities; and (3) the development of management framework providing a pathway that can accommodate environmental changes and asset resilience. [ABSTRACT FROM AUTHOR]