The Trucking Sector Optimization Model: A tool for predicting carrier and shipper responses to policies aiming to reduce GHG emissions

In response to the growing Climate Change problem, governments around the world are seeking to reduce the greenhouse gas (GHG) emissions of trucking. The Trucking Sector Optimization (TSO) model is introduced as a tool for studying the decisions that shippers and carriers make throughout time (focusing on investments in Fuel Saving Technologies), and for evaluating their impact on life-cycle GHG emissions. A case study of fuel taxation in California is used to highlight the importance of (1) modeling the trucking sector comprehensively, (2) modeling the dynamics of the stock of vehicles, and (3) modeling different sources of emissions.     

An assessment of the free and secure trade (FAST) program along the Canada–US border

Several new programs were introduced under the Smart Border Action Plan of 2001 to ensure both secure and efficient trade across the Canada–US border. This article evaluates just how well one of the programs, Free and Secure Trade (FAST), has succeeded in expediting shipments and reducing delays at the border. The results of this study indicate that the FAST program has reduced the average border wait time at four of the five busiest crossing ports. However, the benefits associated with the FAST program are unevenly distributed among the ports, determined by the ability to accommodate infrastructure improvements, and firms, with larger trucking companies and exporters reaping the benefits and small and medium-sized trucking firms and exporters burdened by costs and often unable to capitalize on the program’s benefits. Recommendations for program improvement include: greater regulatory cooperation between Canada and the US to reduce costly duplication and paperwork, and providing tax incentives or subsidies to small and medium-size firms as a means to increase the participation rate in the program.     

Potential performance of urban land use and transport strategies in reducing greenhouse gas emissions: Khon Kaen case study,Thailand

ABSTRACT

Transport activities are a key contributor to greenhouse gas (GHG) emissions, global warming, and climate change. In Thailand, private cars are the second largest generator (after trucks) of GHG emissions from the transport sector. This article presents an analysis and evaluation of the implementation of land use and transport measures for reducing GHG emissions in the road network of the Khon Kaen University (KKU) area in Khon Kaen, Thailand. This research applied a bottom-up method to estimate the baseline GHG emissions for several scenarios by adopting the Clean Development Mechanism 2 (CDM2) and Pollution Control Department (PCD) methods over a 20-year planning horizon. The cleaner technology strategy clearly showed the greatest performance in reducing the GHG emission, followed by land use planning and restriction of private vehicle usage. The public transit improvement strategy illustrated the least GHG emissions reduction. Integrated scenarios clearly illustrated larger potential benefits, more effective than the individual scenarios. For both individual and integrated scenarios, the potential performances of the GHG emissions reduction estimated by the PCD method were greater than those assessed by the CDM2 method.     

Analysis of greenhouse gas emissions of freight transport sector in China

Due to the increasing commercial activities in China, the rapid growth of energy consumption and greenhouse gas (GHG) emissions in the freight transport sector has alarmed the Chinese central government. However, there is a lack of standard measure for evaluating GHG emissions generated from freight transport operations. To improve this situation, Chinese policy makers need to evaluate GHG emissions for energy saving and pollution reduction. This background leads us to examine the GHG emission trajectories and features of Chinese freight transport patterns in the last decade, i.e. between 2000 and 2011. In this study, we examine different regions’ freight turnover and energy consumption by various transport modes (i.e. railway, highway, waterway, aircraft, and oil pipeline) in China. Our results show that the total amount of GHG emissions caused by the Chinese freight transport sector reached 978 million tons in 2011, indicating an average annual growth of 74 million tons CO₂e for the last decade. Shandong, Anhui, and Henan are the main provinces producing GHG emissions, representing 11.7%, 10.3%, and 10% of total emissions generated from the freight transport sector in China, respectively. This study also compares the regional GHG emissions from different freight transport modes including railway, highway, waterway, air transport, and oil pipeline. Based on the findings, policy implications are provided on how to mitigate freight transport emissions among different Chinese regions.     

A closer look at urban development under the emergence of autonomous vehicles: Traffic,land use and air quality impacts

Autonomous vehicles (AVs) hold great promise for increasing the capacity of existing roadways and intersections, providing more mobility to a wider range of people, and are likely to reduce vehicle crashes. However, AVs are also likely to increase travel demand which could diminish the potential for AVs to reduce congestion and cause emissions of greenhouse gases (GHG) and other air pollutants to increase. Therefore, understanding how AVs will affect travel demand is critical to understanding their potential benefits and impacts. We evaluate how adoption of AVs affects travel demand, congestion and vehicle emissions over several decades using an integrated travel demand, land-use and air quality modeling framework for the Albuquerque, New Mexico metropolitan area. We find that AVs are likely to increase demand and GHG emissions as development patterns shift to the region's periphery and trips become longer. Congestion declines along most roadways as expanded capacity from more efficient AV operation outpaces increasing demand. Most of the population can also expect a reduction in exposure to toxic vehicle emissions. Some locations will experience an increase in air pollution exposure and traffic congestion from changes in land-use and traffic patterns caused by the adoption of AVs.     

Assessing greenhouse gas emissions from electric vehicle operation in Australia using temporal vehicle charging and electricity emission characteristics

ABSTRACT

Significant interest exists in the potential for electric vehicles (EVs) to be a source of greenhouse gas (GHG) abatement. In order to establish the extent to which EVs will deliver abatement, however, a realistic understanding of the electricity and transport sector GHG emissions impacts arising from different approaches to integrating EVs into the power system is required. A key issue in this regard is the extent to which GHG emissions are a function of where and when EV charging will be enabled (or disabled) by the provision of recharging infrastructure and implementation of charging management strategies by the electricity industry. This article presents an investigation of the GHG emissions arising from electricity and gasoline consumption by plug-in hybrid EVs under a range of standard EV-power system integration scenarios. An assessment framework is presented, and GHG emissions from EV use are assessed for the New South Wales (NSW) and South Australian (SA) pools of the Australian National Electricity Market (NEM) using retrospective electricity system generation data for 2011. Results highlight that there is a range of possible outcomes depending on the integration scenario and emissions accounting approach used. This range illustrates value of a temporally explicit assessment approach in capturing the temporal alignment of electricity sector emission intensity and EV charging. Results also show the importance of a clean electricity generation mix in order for EVs to provide a GHG abatement benefit beyond what would be achieved by a hybrid (but non-plug-in) vehicle. The extent to which overnight charging in NSW is observed to produce higher emissions relative to unmanaged charging also illustrates a possible trade-off between GHG emissions and benefits for electricity industry from EV charging at times of low demand.     

A method for the estimation of greenhouse gas emissions based on road geometric design and its application to South Korea

South Korea has the tenth highest greenhouse gas (GHG) emissions worldwide, of which 16% originates from the road sector. Existing estimation methods of road GHG emissions have various limitations, such as low accuracy or the ability to only estimate GHG emissions within a limited area. Therefore, this study aimed to develop a methodology to estimate GHG emissions while considering various geometric designs of roads, including both vertical and horizontal alignment. The developed method is more objective and reliable than existing methodologies that consider only vertical alignment. First, Lamm's theory on travel speed profiles was applied to predict GHG emissions. Then, this study attempted to overcome the limited spatial estimation capacity of existing methods by considering upstream and downstream geometric design parameters simultaneously. Second, this study used the GHG operation mode extracted from the MOtor Vehicle Emission Simulator (MOVES), a modeling system that estimates emissions for mobile sources at the national, county, and project levels for criteria air pollutants, GHGs, and air toxicity. The operation mode includes vehicle type, fuel, and other factors, and is designed to estimate GHG emissions at 1-s intervals. Based on the results of the analysis, the effectiveness of the new method was compared to existing methods using an economic analysis (e.g., cost–benefits from the reduced emissions). This study presents a method for performing sensitive estimations of GHG emissions according to the geometric design of roads, which can be used to collect more accurate data on GHG emissions.     

Greenhouse gas impacts of different modality style classes using latent class travel behavior model

This study analyzed the interconnections between urban structure and socioeconomic, demographic and lifestyle variables and direct ground transport greenhouse gas (GHG) emissions in the Helsinki metropolitan region in Finland by using a latent class choice model (LCCM). The aim of the study was to identify and improve our understanding of the latent modality styles which guide people's everyday travel choices, and the resulting GHG implications. The GHG implications for different modality style groups are particularly interesting, since such analysis has not been included in previous studies. This study used the transport survey of the Helsinki Region Transport (HSL) from 2012, including over 17,000 trips, and consisted of two parts: first, the population was divided into classes based on latent class analysis, and secondly, the mode choices and GHG emissions were modelled for each class. Seven modality style groups were defined with strongly varying GHG impacts and travel profiles. According to the class specific choice modelling the probabilities of selecting different modes also varied significantly. The study offers new information for designing effective mitigation policies for different types of modality style groups. For example, car-oriented groups would benefit from more fuel-efficient vehicles and vehicles with alternative power sources, whereas the multimodal traveler could be more receptive to policies promoting cycling and public transport. Overall, the study depicts the potential of the latent class method to study the emissions caused by heterogeneous populations and to search for efficient GHG mitigation possibilities.     

The greenhouse gas mitigation potential of university commuting: A case study of the University of León (Spain)

This work analyzes the main drivers and critical aspects of mobility at the University of León (ULE) in Spain and its potential for mitigation of greenhouse gases (GHG). Through the implementation of a life cycle analysis (LCA) methodology (door-to-door and well-to-wheel approach), we examine the existing relationships between modal distribution, distances, travel times, urban structure, and direct and indirect carbon footprint and monetary costs of ULE users' commuting to its premises. Necessary primary information was collected by means of an online survey conducted on all ULE bodies and actors. Even though most distances traveled to the University are of <6 km (94.3% of the commutes), the use of the private car is second in importance (34%) after traveling by foot (41.5%), as well as the main hotspot in environmental terms (95% of GHG emissions). The results show how university policies focused on improving mobility habits (greater use of bus, bicycle and walking) can considerably reduce GHG emissions within a distance range of 0–6 km. For instance, the replacement of cars with bicycles (50% in <4 km distances) would reduce CO₂-eq emissions by 17.5% and increase monetary savings by 14.8%. However, 5.8% of the remaining travels (> 6 km) accumulate 49.4% of the emissions. Beyond that distance, behavior-oriented policies prove to be insufficient, and actions and measures at other levels encouraging technological change (use of electric cars and bicycles, etc.) and the improvement of infrastructures are also required. This article discusses the role of universities in promoting improvement of sustainable mobility within its premises.     

The impact of metropolitan,county, and local land use on driving emissions in US metropolitan areas: Mediator effects of vehicle travel characteristics

Many municipalities in the U.S. pursue compact development to reduce greenhouse gas (GHG) emissions from driving. Despite the efforts, however, recent studies suggest that some land use strategies such as densification and mixed-use development may result in slower vehicle movements, and consequently generate more driving emissions. Since vehicle miles of travel (VMT) is only a proxy and not an exact measure of emissions, reduction in VMT may not lead to a proportional reduction in transportation GHG emissions. Aside from local land use efforts, regional factors also influence vehicle travel and associated emissions.This study investigates the relationship between land use, vehicle travel, and driving emissions in the selected U.S. metropolitan areas at multiple geographic levels. The study employed structural equation modeling (SEM) techniques to examine how land use influences vehicle travel characteristics and associated emissions. The main data sources for the analyses include the 2009 National Household Travel Survey (NHTS) add-on samples and the Smart Location Database (SLD) from the U.S. Environmental Protection Agency (EPA). The study results show that VMT reduction and the associated environmental benefit do not show a one-on-one relationship due to the emissions penalty of lowered vehicle operating speed. Vehicle travel and associated emissions are not only influenced by local urban form factors but also affected by the greater geographical context.     

TRIBUTE: Trip-based urban transportation emissions model for municipalities

This research aims at developing modeling and scenario-comparison tools to explore the impacts of various transportation and land use planning policies on changing travel behavior and eventually greenhouse gas (GHG) emissions. A Trip-Based Urban Transportation Emissions (TRIBUTE) model is developed. Data required for TRIBUTE comes from household travel surveys and emissions inventories, which is a major advantage in cases where a detailed transportation network model is unavailable. TRIBUTE is composed of two main parts: a mode choice model and an emissions forecasting model. The mode choice model is responsible for estimating modal shares of alternative modes of travel in response to changes in personal, modal, and land use attributes. The emissions forecasting model translates the modal shares into vehicle kilometers traveled, and subsequently GHG emissions. TRIBUTE is a macroscopic model intended to assist municipalities evaluate alternative transportation and land use policy scenarios and eventually select the one(s) that help them meet their future GHG emission targets. This paper reports on the conceptual framework of the developed model and presents a case study.     

A fuel surcharge policy for reducing road traffic greenhouse gas emissions

This paper proposes a car pricing policy based on fuel surcharges in substitution of car ownership taxes for reducing greenhouse gas emissions by cars. The aim of the proposed policy is to change some (fixed) costs of car use that are not perceived at each trip into (variable) costs. The amount of fuel surcharges and the effects of their application on fuel consumption and on GHG emissions are estimated by a model that is able to relate gasoline and diesel consumption with fuel prices. The effects of the proposed policy on fuel consumption and on GHG emissions are estimated for Italy. The results show that car users prefer to shift towards more efficient fuel vehicles than to public transport, producing a significant, but less than expected, reduction of GHG emissions.     

Greenhouse gas emissions from flying can offset the gain from reduced driving in dense urban areas

Numerous studies have illustrated how denser urban forms lead to smaller greenhouse gas (GHG) emissions from passenger transport. Many of these studies have excluded aviation since the association between urban structure and air travel is not as intuitive as it is the case of ground travel. However, several recent studies have concluded that air travel is a significant contributor to the GHGs from passenger transport. Furthermore, even air travel habits depend heavily on lifestyles and socio-economic factors that are related to the urban form. Here we analyse the interactions between urban structure and different transportation modes and their GHG impacts in Finland. The study utilises the data from the Finnish Transportation Agency’s passenger traffic survey from May 2010 to May 2011, which includes over 12 000 people and over 35 000 trips. The survey is based on one-day travel diaries and also includes additional data on long-distance trips from a longer period. Methodologically, the study takes a traveller’s perspective to assess the GHG emissions from passenger transport. We found that (1) air travel breaks the pattern where GHG emissions decrease with increasing density of urban structures, and (2) in the metropolitan region there is a clear trade-off between car-ownership and air travel in the middle income class. The main policy implication of our study is that air travel must be included in GHG assessments and mitigation strategies targeting travel behaviour. In dense urban regions, the emissions of air travel have the potential to offset the gain from reduced private driving.     

Modeling green vehicle adoption: An integrated approach for policy evaluation

This article employs an integrated discrete-continuous car ownership model to jointly forecast households’ future preferences on vehicle type, quantity and use, and to estimate greenhouse gas (GHG) emissions. The model system is estimated on a dataset collected from a web-based stated preference survey conducted in Maryland in 2014. The data contain vehicle purchase decisions and sociodemographic information of 456 households who were requested to state their future preferences over a 9-year period (2014–2022). In each time period, a respondent is faced to four alternatives that include the current vehicle, a new gasoline vehicle, a new hybrid electric vehicle, and a new battery electric vehicle. Intertemporal choices between conventional and “green” vehicles such as hybrid and electric cars capture dynamics in vehicle purchase decisions. Short run and medium-long run situations were predicted and compared based on the first 4-year data and the entire 9-year data of the dynamic panel. Vehicle GHG emissions were calculated correspondingly. We find the introduction of “green” vehicles makes a positive impact on car ownership and use, especially in a medium-long run. Two “green” taxation policies, gasoline tax and ownership tax, were proposed and their impact on vehicle use and emission reductions was evaluated. Results indicate that: (a) gasoline tax is a more effective way to reduce vehicle miles traveled and GHG emissions and (b) gasoline tax makes a higher impact on car use and emission reductions in the medium-long run, while ownership tax makes a higher impact in the short run.     

The capability of Motorways of the Sea of being competitive against road transport. The case of the Italian mainland and Sicily

The paper deals with a quantitative analysis on the competitiveness of intermodal transport, based on Motorways of the Sea (MoS), in comparison with all-road transport. This analysis is applied to MoS routes connecting the Italian mainland with Sicily. The study involves: a detailed intermodal network model which compares monetary costs and travel times from all relevant origins in the mainland to all relevant destinations in Sicily; and a survey carried out at some representative trucking firms operating to/from Sicily. The aim of the interviews has been: on one side to make a comparison between the theoretical and actual mode of transport and routes taken, and in case they are different, to understand the reasons of the discrepancy; on the other side to determine what are the main aspects taken into account by trucking companies in their modal choice, in order to understand how the competiveness of MoS against road transport can be improved. The results of the analysis show that the modal choice is affected by several elements: monetary costs and travel times; reliability of MoS routes; availability of MoS routes; MoS routes frequencies; but it resulted from the interviews that monetary costs and travel times are the most important factors considered by trucking companies in the modal and route choice. This study has also shown that an improvement of MoS routes on the Italian Adriatic side is necessary: actually, a strong reason for which in Italy MoS traffic is still a small percentage of road traffic is the low number of MoS routes currently in operation and their low frequency. This analysis could help decision makers, and maritime operators, to efficiently invest in the improvement of MoS routes. Moreover, this analysis, developed for an Italian case study, can be applied to other European and Mediterranean scenarios.     

Flying into the future: A scenario-based analysis of carbon emissions from China's civil aviation

Aviation emissions will significantly affect climate change with the continued growth of the air transport. This study decomposes the drivers influencing China's civil aviation carbon emissions change in the period from 1985 to 2015. Emission trends are predicted using a Monte Carlo simulation. The results show a peak and reduction in the period between 2016 and 2030. Results also show that air transportation revenue growth is associated with increasing historical carbon emissions, while rising industry energy intensity significantly hampers carbon emissions reduction. The carbon intensity (CI) reduction goals were achieved in the different scenarios before 2020, but the carbon emissions peak target before 2030 can only be achieved under the technological breakthrough scenario (TBS). The reduction of air transport growth rate and promotion of new energy technologies are two essential strategies to reduce carbon emissions in the foreseeing future.     

Assessment of out-of-state truck activity in California

The continued growth of interstate freight trucking brings with it the potential for inaccuracies in official government statistics on trucks’ road use and pollutant emission contributions. State government agency emission estimates for states within the US commonly rely on in-state truck and fuel use records. Consequently, these records can incompletely reflect overall truck activity and emissions by neglecting the trucks that are registered and/or are fueled out of state. An in-person interview survey of 433 heavy-duty truck drivers was conducted at select points of entry into California to better understand the contribution of out-of-state trucks, their fuel use, and their emissions in California. The results indicate that non-California-registered trucks and non-California-diesel fuel make up approximately 30% of Class 8 heavy-duty truck mileage on California roads. Non-California-registered-truck mileage is disproportionately concentrated in four California air basins that have significant air quality issues.     

Multiobjective optimization of asphalt pavement design and maintenance decisions based on sustainability principles and mechanistic-empirical pavement analysis

Highway pavement as an important component of transport infrastructure has significant impacts on economy, society, and environment. The management of highway pavement has been traditionally focused on economy. In this study, the impacts of management decisions are examined in three dimensions, including life-cycle cost (LCC), energy consumption, and greenhouse gas (GHG) emissions. Quantitative models to predict the three dimensions are developed from mechanistic-empirical pavement analysis results. Two decision variables, pavement thickness and threshold roughness level for pavement resurfacing, are found to be significant in affecting the three dimensions. These two variables are subsequently used as decision variables in multiobjective optimization. The ranges of decisions that result in minimum LCC, energy consumption, and GHG emissions are identified through multiobjective optimization. Although the analysis is illustrated in the context of pavement design and management in Hong Kong, the analysis techniques and procedures can be easily applied in other regions.     

Emissions,performance, and design of UK passenger vehicles

Consumer, legal, and technological factors influence the design, performance, and emissions of light-duty vehicles (LDVs). This work examines how design choices made by manufacturers for the UK market result in emissions and performance of vehicles throughout the past decade (2001–2011). LDV fuel consumption, CO₂ emissions, and performance are compared across different combinations of air and fuel delivery system using vehicle performance metrics of power density and time to accelerate from rest to 100 km/h (62 mph, t_z-62). Increased adoption of direct injection and turbocharging technologies helped reduce spark ignition (SI, gasoline vehicles) and compression ignition (CI, diesel vehicles) fuel consumption by 22% and 19%, respectively, over the decade. These improvements were largely achieved by increasing compression ratios in SI vehicles (3.6%), turbocharging CI vehicles, and engine downsizing by 5.7–6.5% across all technologies. Simultaneously, vehicle performance improved, through increased engine power density resulting in greater acceleration. Across the decade, t_z-62 fell 9.4% and engine power density increased 17% for SI vehicles. For CI vehicles, t_z-62 fell 18% while engine power density rose 28%. Greater fuel consumption reductions could have been achieved if vehicle acceleration was maintained at 2001 levels, applying drive train improvements to improved fuel economy and reduced CO₂ emissions. Fuel consumption and CO₂ emissions declined at faster rates once the European emissions standards were introduced with SI CO₂ emissions improving by 3.4 g/km/year for 2001–2007 to 7.8 g/km/year thereafter. Similarly, CI LDVs declined by 2.0 g/km/year for 2001–2007 and 6.1 g/km/year after.