Assessing the energy and greenhouse gas emissions mitigation effectiveness of potential US modal freight policies

This paper estimates the total embodied energy and emissions modal freight requirements across the supply chain for each of over 400 sectors using Bureau of Transportation Statistics Commodity Flow Survey data and Bureau of Economic Analysis economic input-output tables for 2002. Across all sectors, direct domestic truck and rail transportation are similar in magnitude for embodied freight transportation of goods and services in terms of ton-km. However, the sectors differ significantly in energy consumption, greenhouse gas emissions, and costs per ton-km. Recent pressure to reduce energy consumption and emissions has motivated a search for more efficient freight mode choices. One solution would be to shift freight transportation away from modes that require more energy and emit more (e.g., truck) to modes that consume and emit less (e.g., rail and water).Our results show there are no individual sectors for which targeting changes would significantly decrease the total freight transportation energy and emissions, therefore we have also looked at the prospect of policies encouraging many sectors to shift modes. There are four scenarios analyzed: (1) shifting all truck to rail, shifting top 20% sector mode choice, (2) based on their emissions, (3) based on a multi-attribute analysis, and (4) increasing truck efficiency (e.g., mpg). Increasing truck efficiency by 10% results in similar energy and emissions reductions (approximately 7% for energy and 6% for emissions) as targeting the top 20% of sectors when selected based on emissions, whereas selecting the top 20% based on availability to shift from truck results in slightly less reductions of energy and emissions. Implementing policies to encourage higher efficiency in freight trucks may be a sufficient short term goal while efforts to reduce truck freight transportation through sectoral policies are implemented in the long term.     

Projecting adoption of truck powertrain technologies and CO2 emissions in line-haul networks

In this paper we present a mixed-integer linear program to represent the decision-making process for heterogeneous fleets selecting vehicles and allocating them on freight delivery routes to minimize total cost of ownership. This formulation is implemented to project alternative powertrain technology adoption and utilization trends for a set of line-haul fleets operating on a regional network. Alternative powertrain technologies include compressed (CNG) and liquefied natural gas (LNG) engines, hybrid electric diesel, battery electric (BE), and hydrogen fuel cell (HFC). Future policies, economic factors, and availability of fueling and charging infrastructure are input assumptions to the proposed modeling framework. Powertrain technology adoption, vehicle utilization, and resulting CO₂ emissions predictions for a hypothetical, representative regional highway network are illustrated. A design of experiments (DOE) is used to quantify sensitivity of adoption outcomes to variation in vehicle performance parameters, fuel costs, economic incentives, and fueling and charging infrastructure considerations. Three mixed-adoption scenarios, including BE, HFC, and CNG vehicle market penetration, are identified by the DOE study that demonstrate the potential to reduce cumulative CO₂ emissions by more than 25% throughout the period of study.     

The potential mitigation of CO2 emissions via modal substitution of high-speed rail for short-haul air travel from a life cycle perspective – An Australian case study

The objective of this study is to provide a strategic evaluation of the mitigation of CO₂ emissions via modal substitution of high-speed rail for short-haul air travel on the Sydney–Melbourne, Australia city-pair from a life cycle perspective. It has been demonstrated that when considering CO₂ emissions from vehicle operations, the modal shift from air to high-speed rail on this city-pair has the potential to provide a means of CO₂ mitigation. However, uncertainty exists with regard to the level of mitigation potential when considering the whole-of-life performance of the systems. Given the significant difference in the infrastructure requirements between the air mode and the high-speed rail mode, this study quantifies the life cycle CO₂ load attributable to each system and examines the effect on CO₂ mitigation potential. The study concluded that while the inclusion of the linehaul infrastructure did increase the CO₂ load associated with high-speed rail mode, it did not equate to or exceed the CO₂ load per trip as experienced by the air mode. The avoided annual life cycle CO₂ emission in the target year 2056 was 0.37 Mt representing an 18% reduction when compared to the air mode only on the city pair. In fact, the scenario comparison indicated that the substitution of high-speed rail for short-haul air travel on the city pair resulted in CO₂ emissions avoidance throughout the longitudinal period.     

Estimation of cost and CO2 emissions with a sustainable cross-border supply chain in the automobile industry: A case study of Thailand and neighboring countries

CO₂ emissions are increasing because of the growth in the cross-border supply chain, which is leading the locations of assembly plants and suppliers to spread across a wider area. Given that one passenger vehicle needs more than 20,000 components and parts, the automobile industry exploits the cross-border supply chain. Recently, the free cross-border movement of people, goods, capital, and information has accelerated in Asia. Therefore, a sustainable cross-border supply chain is required to reduce both CO₂ emissions and cost. This study estimates total CO₂ emissions per vehicle including production and transportation processes in Thailand and neighboring countries and the change in CO₂ emissions based on future policy scenarios that consider the automobile market and locational conditions in 2030. The results show that locating production close to the place of consumption and the electricity emissions factors in each country should be considered.     

Integrated data-driven modeling to estimate PM2.5 pollution from heavy-duty truck transportation activity over metropolitan area

Based on the national emission inventory data from different countries, heavy-duty trucks are the highest on-road PM_2.5 emitters and their representation is estimated disproportionately using current modeling methods. This study expands current understanding of the impact of heavy-duty truck movement on the overall PM_2.5 pollution in urban areas through an integrated data-driven modeling methodology that could more closely represent the truck transportation activities. A detailed integrated modeling methodology is presented in the paper to estimate urban truck related PM_2.5 pollution by using a robust spatial regression-based truck activity model, the mobile source emission and Gaussian dispersion models. In this research, finely resolved spatial–temporal emissions were calculated using bottom-up approach, where hourly truck activity and detailed truck-class specific emissions rates are used as inputs. To validate the proposed methodology, the Cincinnati urban area was selected as a case study site and the proposed truck model was used with U.S. EPA’s MOVES and AERMOD models. The heavy-duty truck released PM_2.5 pollution is estimated using observed concentrations at the urban air quality monitoring stations. The monthly air quality trend estimated using our methodology matches very well with the observed trend at two different continuous monitoring stations with Spearman’s rank correlation coefficient of 0.885. Based on emission model results, it is found that 71 percent of the urban mobile-source PM_2.5 emissions are caused by trucks and also 21 percent of the urban overall ambient PM_2.5 concentrations can be attributed to trucks in Cincinnati urban area.     

Thermodynamic,environmental and economic analysis of absorption air conditioning unit for emissions reduction onboard passenger ships

In this paper, the waste heat of exhaust gases and jacket cooling water in marine diesel engines are analyzed to operate the absorption refrigeration unit (ARU). Thermo-economic and environmental analysis of the absorption refrigeration cycle operated with the two heat sources that use lithium bromide as an absorbent is carried out. The analysis is performed using Engineering Equation Solver (EES) software package where the thermodynamic properties of the steam and the LiBr-water mixtures are provided. The used EES code is verified by published experimental data. As a case study, high speed passenger vessel operating in the Red Sea area has been investigated. The results show that a considerable specific economic benefit could be achieved from ARU jacket cooling water operated over that gained from main engine exhaust gases. Environmentally, applying ARU machine during cruise will reduce the annual fuel consumption for the diesel generators by 156 ton with a reduction percentage of 23%. This will reduce the exhaust gas emissions by 6.3% from the applied main engine emissions. In addition, this will result in reducing NO_x, SO_x, and CO₂ emissions with cost-effectiveness of 4.99 $/kg, 13.18 $/kg, and 0.08 $/kg, respectively.     

Combining technology development and behaviour change to meet CO2 cumulative emission budgets for road transport: Case studies for the USA and Europe

Global temperature rise over the long term will be proportional to the total amount of CO₂ emitted. Any given probability of exceeding a targeted maximum temperature rise implies a maximum limit on the cumulative total of CO₂ that can be emitted: a CO₂ “budget”. This paper describes an approach to modelling cumulative emissions from light and heavy duty road transport from the present to 2050, focussing on the USA and Europe, and comparing the potential impacts of a range of technological and behaviourally-based abatement measures with such cumulative emissions budgets.The model shows that abatement measures would have a lower effect on cumulative emissions from 2000 to 2050 than on annual emissions in 2050, so that focussing only on annual emissions could be misleading. It shows that technological developments would be insufficient on their own to enable Europe and the USA to meet CO₂ budgets for road transport. Behavioural changes, which potentially can be implemented much sooner, would be essential too. There is potential to keep European light duty emissions very close to CO₂ budgets, and US light duty emissions not far above the least restrictive budget, but the model predicts that heavy duty emissions in both regions are likely to exceed their CO₂ budgets. Deeper emissions reductions in other regions and sectors will be needed to compensate for this. Timing would be critical: for the greatest impact, behaviour change policies and interventions would need to be applied early and deeply.     

Characteristics of gaseous and particulate pollutants exhaust from logistics transportation vehicle on real-world conditions

On-road vehicle tests of three heavy duty diesel trucks were conducted by a portable emission measurement system (PEMS) in Chengdu, China. SEMTECH-ECOSTAR provided by Sensors Inc. was employed to detect gaseous emissions and MI2, an emissions measuring instrument powered by the Pegasor Particulate Sensor (PPS) was used to detect particulate emissions during the tests. The impacts of speed, acceleration and engine load on emissions were analyzed. The average nitrogen oxides (NOx) emission factors of the heavy duty diesel truck (HDDT), medium-duty diesel truck (MDDT), light duty diesel truck (LDDT) were 7.29, 5.29 and 5.53 g/km. The particulate emission factors were 0.60, 0.30 and 0.14 g/km respectively, higher than the similar reported in the previous studies. Both gaseous and particulate emission exhibit significant correlations with the change in vehicle speed, acceleration and power demand. The highest emission was generally in high VSPs and higher loads. High engine load caused by aggressive driving was the main factor of high emissions for the vehicles on real-world conditions.     

Comparing air pollution from ferry and landside commuting

Recent and anticipated growth in passenger ferry service has been complicated by concerns about air pollution from marine engines that are only starting to be regulated. While marine engines are known to be a significant and growing source category in some locations, sparse data and analytical difficulties have prevented rigorous comparisons of marine and on-land passenger travel. Using data gathered in the San Francisco Bay Area, we model emissions from three passenger ferries and the matching on-land travel that would be used by commuters if ferry service were not available. The results are analyzed parametrically for levels of ridership and induced travel demand, and for new technologies, including selective catalytic reduction and natural gas fuel. Results indicate that under some conditions, passenger ferries reduce some emissions (including particulate matter emissions) relative to the matching on-land service but increase others. Emissions of NO_X are particularly problematic––all the technologies examined lead to increased NO_X emissions due to ferry commuting. Some of the emissions comparisons are sensitive to mode split, ridership, or induced travel demand. However, NO_X emissions are not––ferry commuting always raises NO_X emissions, even with the most advanced technologies. Implications for local air quality regulators and for technology development are discussed.     

Economic valuation of Well-To-Wheel CO2 emissions from freight transport along the main transalpine corridors

CO₂ emissions are one of the main externalities related to freight transport. Their evaluation is extremely difficult, due to the presence of several scientific and economic uncertainties. This paper discusses the approaches currently adopted by literature to deal with CO₂, proposing a methodology based on a Well-To-Wheel quantification and an economic valuation deriving from a meta-regression. A freight transport analysis is then provided for one of the most critical areas of Europe, the Alps. Here, the different approaches adopted by the single nations determine divergent results in terms of modal shift towards rail and, consequently, CO₂ emissions. An integrated and transnational strategy could lead to better results, avoiding detoured traffic and increasing the share of railway traffic. To this aim, the carbon impacts of three specific alpine-wide measures are evaluated: namely, Alpine Crossing Exchange, Emissions Trading and Differentiated Toll System. In comparison with business-as-usual scenario, the case study reveals a potential CO₂ saving up to more than 600,000 tons and 38 M€ for the year 2030, thus providing policy makers with an integrative transnational tool able to evaluate the long-term carbon impact of their transport decisions.     

High-speed rail's potential for the reduction of carbon dioxide emissions from short haul aviation: a longitudinal study of modal substitution from an energy generation and renewable energy perspective

Abstract

This paper quantifies and evaluates, utilising a ‘bottom-up’ approach, the effect on CO₂ emissions of a modal shift from short-haul air travel to high-speed rail (HSR), based on projected passenger movements, between Sydney and Melbourne, Australia during the period 2010–2030. To date, peer-reviewed studies assessing the CO₂ emissions from these competing modes of high-speed transportation have been restricted principally to a cross-sectional assessment, with a Eurocentric bias. This present comparative study seeks to address a gap in the literature by assessing, longitudinally, the CO₂ emissions associated with the proposed operation of HSR against the ‘business-as-usual’ air scenario between Sydney and Melbourne. Under the assumed 50/50 modal shift, and the Australian government's current renewable electricity target, an annual reduction in CO₂ emissions of approximately 14% could be achieved when compared with a ‘business-as-usual’ air scenario. This percentage reduction represents a 62 kt reduction in base year, 2010, and a 114 kt reduction in the final year, 2030. In total, the overall reduction achieved by such a modal shift, under the assumed conditions, during the period 2010–2030, equates to approximately 1.87 Mt of CO₂. Importantly, if the electrical energy supply for HSR operations was further ‘decarbonised’, then it follows that a greater emission reduction would be achieved.     

Hybrid- and battery-electric vehicles offer low-cost climate benefits in China

Car ownership in China is expected to grow dramatically in the coming decades. If growing personal vehicle demand is met with conventional cars, the increase in greenhouse gas emissions will be substantial. One way to mitigate carbon dioxide (CO₂) emissions from passenger travel is to meet growing demand for cars with alternative vehicles such as hybrid- and battery-electric vehicles (HEVs and BEVs). Our study examines the cost-effectiveness of transitioning from conventional cars to HEVs and BEVs, by calculating their marginal abatement cost (MAC) of carbon in the long-run. We find that transitioning from conventional to hybrid and battery electric light-duty, four-wheel vehicles can achieve carbon emissions reductions at a negative cost (i.e. at a net benefit) in China. In 2030, the average MAC is estimated to be about −$140/ton CO₂ for HEVs and −$515/ton CO₂-saved for BEVs, varying by key parameters. The total mitigation potential of each vehicle technology is estimated to be 1.38 million tons for HEVs and 0.75 million tons for BEVs.     

Exploring transport carbon futures using population microsimulation and travel diaries: Beijing to 2030

Evaluating transport policy for cities in developing countries is often constrained by data availability that limits the use of conventional appraisal models. Here, we present a new ‘bottom-up’ methodology to estimate transport CO₂ emission from daily urban passenger travel for Beijing, a megacity with relatively sparse data on travel behaviour. A spatial microsimulation, based on an activity diary survey and two sample population censuses, is used to simulate, for Beijing’s urban districts, a realistic synthetic population, and their daily travel and CO₂ emission over 2000–2010. This approach provides greater insight into the spatial variability of transport CO₂ emission than has previously been possible for Beijing, and further, enables an examination of the role of socio-demographics, urban form and transport developments in contributing to emissions over the modelled period.Using the 2000–2010 CO₂ emission estimates as a baseline, CO₂ emissions from passenger travel are then modelled to 2030 under scenarios exploring politically plausible strategies on transport (public transport infrastructure investment, and vehicle constraint), urban development (compaction) and vehicle technology (faster adoption of clean vehicle technology). The results showed that, compared to the trend scenario, employing both transport and urban development policies could reduce total passenger CO₂ emission to 2030 by 24%, and by 43% if all strategies were applied together. The study reveals the potential of microsimulation in emission estimation for large cities in developing countries where data availability may constrain more traditional approaches.     

A study of feasibility and potential benefits of organised car sharing in Ireland

In this study, the market potential of car sharing has been evaluated using multiple alternative scenarios which examine the geographic, financial and environmental factors influencing car sharing adoption. The scenarios are applied to the available and collected travel information of the Irish population to estimate the potential impact of introducing car sharing in Ireland. The analysis identified that car owners who travel predominantly on alternative modes, could make significant cost and CO₂ savings through car sharing. A reduction of yearly CO₂ emissions of 86 kt is readily achievable through car sharing, with reductions up to 895 kt possible with appropriate policy and financial support. These figures are comparable to other measures proposed under the Irish National Climate Change Strategy.     

In-use measurement of the activity, fuel use, and emissions of eight cement mixer trucks operated on each of petroleum diesel and soy-based B20 biodiesel

In-use micro-scale fuel use and emission rates were measured for eight cement mixer trucks using a portable emission measurement system. Each vehicle was tested on petroleum diesel and B20 biodiesel. Average fuel use and emission rates increase monotonically versus engine manifold absolute pressure. A typical duty cycle includes loading at a cement plant, transit while loaded from the cement plant to work site, creeping in a queue of vehicles at the worksite, unloading, and transit without load from the site to the plant. For B20 versus petroleum diesel, there is no significant change in the rate of fuel use, CO₂ emissions, and NO emissions, and significant decreases in emissions for CO, hydrocarbons, and particulate matter. For loaded versus unloaded onroad travel, fuel use and CO₂ emissions rates are approximately 60% higher and the rates for other pollutants are approximately 30–50% higher. A substantial portion of cycle emissions occurred at the work site. Inter-vehicle and intra-cycle variability are also quantified using the micro-scale methodology.     

Modeling the relationships among urban passenger travel carbon dioxide emissions,transportation demand and supply,population density,and proxy policy variables

To support the development of policies that reduce greenhouse gas (GHG) emissions by encouraging reduced travel and increased use of efficient transportation modes, it is necessary to better understand the explanatory effects that transportation, population density, and policy variables have on passenger travel related CO₂ emissions. This study presents the development of a model of CO₂ emissions per capita as a function of various explanatory variables using data on 146 urbanized areas in the United States. The model takes into account selectivity bias resulting from the fact that adopting policies aimed at reducing emissions in an urbanized area may be partly driven by the presence of environmental concerns in that area. The results indicate that population density, transit share, freeway lane-miles per capita, private vehicle occupancy, and average travel time have a statistically significant explanatory effect on passenger travel related CO₂ emissions. In addition, the presence of automobile emissions inspection programs, which serves as a proxy indicator of other policies addressing environmental concerns and which could influence travelers in making environmentally favorable travel choices, markedly changes the manner in which transportation variables explain CO₂ emission levels.     

Development of an emissions inventory model for mobile sources

Traffic represents one of the largest sources of primary air pollutants in urban areas. As a consequence, numerous abatement strategies are being pursued to decrease the ambient concentrations of a wide range of pollutants. A mutual characteristic of most of these strategies is a requirement for accurate data on both the quantity and spatial distribution of emissions to air in the form of an atmospheric emissions inventory database. In the case of traffic pollution, such an inventory must be compiled using activity statistics and emission factors for a wide range of vehicle types. The majority of inventories are compiled using ‘passive’ data from either surveys or transportation models and by their very nature tend to be out-of-date by the time they are compiled. Current trends are towards integrating urban traffic control systems and assessments of the environmental effects of motor vehicles. In this paper, a methodology for estimating emissions from mobile sources using real-time data is described. This methodology is used to calculate emissions of sulphur dioxide (SO₂), oxides of nitrogen (NO_x), carbon monoxide (CO), volatile organic compounds (VOC), particulate matter less than 10 μm aerodynamic diameter (PM₁₀), 1,3-butadiene (C₄H₆) and benzene (C₆H₆) at a test junction in Dublin. Traffic data, which are required on a street-by-street basis, is obtained from induction loops and closed circuit televisions (CCTV) as well as statistical data. The observed traffic data are compared to simulated data from a travel demand model. As a test case, an emissions inventory is compiled for a heavily trafficked signalized junction in an urban environment using the measured data. In order that the model may be validated, the predicted emissions are employed in a dispersion model along with local meteorological conditions and site geometry. The resultant pollutant concentrations are compared to average ambient kerbside conditions measured simultaneously with on-line air quality monitoring equipment.     

Carbon dioxide emissions from port container distribution: Spatial characteristics and driving factors

Port carbon dioxide (CO₂) emissions in China have become an ever-increasing public concern due to their significant impacts on human health and the environment. However, existing studies focus mainly on CO₂ emissions from vessels calling at the ports and cargo handling within the ports, paying little attention to the inland distribution networks. To fill this gap, this paper proposes an easily implemented method for calculating CO₂ emissions from port container distribution (PCD) and investigates their spatial characteristics and driving factors. By analyzing 30 container ports in China, the main findings are as follows. First, road transportation is the major contributor of CO₂ emissions from PCD due to the lack of rail and inland water transportation. Second, PCD carbon emissions exhibit significant local spatial clustering. That is, ports with similar geographical locations tend to present a similar pattern of PCD carbon emissions. Third, as suggested by the spatial Durbin model, PCD carbon emissions are negatively determined by local gross domestic product, number of port berths, but are positively determined by local tertiary industry value and highway freight volume, and waterway freight volume in both local and neighboring ports. These results provide empirical insights into cross-port collaboration in reducing PCD carbon emissions.     

Light duty gasoline vehicle emission factors at high transient and constant speeds for short road segments

Vehicle emissions estimates are needed at high spatial and temporal resolution to estimate near-roadway air quality and human exposures. The MOBILE6 emission factor model is based on transient test cycles of less than 65 mph. Correction factors for high speed and constant speed are developed based on vehicle-specific power-based modal models for light duty gasoline vehicles, using data from portable emission measurement systems. At 80 mph versus 65 mph, the estimated average emission rates are greater by 30%, 20%, 80%, and 10% for NO_x, HC, CO, and CO₂. The ratio of constant to average of transient speed emission rates range from 0.49 to 0.94 for NO_x at speeds of 20 mph and 80 mph. The high speed and constant speed correction factors are applied to estimate vehicle emissions for a freeway segment that includes vehicle cruising speeds between 65 and 80 mph. The potential error for not accounting for constant speed operation on a short segment of highway could be 49% at moderate speed and 24% at high speed.     

Estimating changes in transport CO2 emissions due to changes in weather and climate in Sweden

There is a considerable body of studies on the relationship between daily transport activities and CO₂ emissions. However, how these emissions vary in different weather conditions within and between the seasons of the year is largely unknown. Because individual activity–travel patterns are not static but vary in different weather conditions, it is immensely important to understand how CO₂ emissions vary due to the change of weather. Using Swedish National Travel Survey data, with emission factors calculated through the European emission factor model ARTEMIS, this study is a first attempt to derive the amount of CO₂ emission changes subject to the change of weather conditions. A series of econometric models was used to model travel behaviour variables that are crucial for influencing individual CO₂ emissions. The marginal effects of weather variables on travel behaviour variables were derived. The results show an increase of individual CO₂ emissions in a warmer climate and in more extreme temperature conditions, whereas increasing precipitation amounts and snow depths show limited effects on individual CO₂ emissions. It is worth noting that the change in CO₂ emissions in the scenario of a warmer climate and a more extreme temperature tends to be greater than the sum of changes in CO₂ emissions in each individual scenario. Given that a warmer climate and more extreme weather could co-occur more frequently in the future, this result suggests even greater individual CO₂ emissions than expected in such a future climate.