The relative contribution of transportation to supply chain greenhouse gas emissions: A case study of American wheat

This life cycle assessment case study puts the supply chain contribution of transportation to greenhouse gas emissions in context with other contributors using American wheat grain as a representative product. Multiple locations, species and routes to market are investigated. Transportation contributes 39–56% of the supply chain emissions, whereas there is a 101% intra-species and 62% inter-species variation in greenhouse gas emissions from production, demonstrating that transportation is both of smaller magnitude, and less sensitive than other factors, in particular, field sequestration.     

Impacts of freeway accidents on CO2 emissions: A case study for Orange County,California, US

Traffic congestion caused by traffic accidents contributes to CO₂ emissions. Generally, more efficient and prompt responses to accidents lead to reduced traffic congestion as well as CO₂ emissions. Here we assess the CO₂ emissions impacts of freeway accidents, applies an existing model to capture spatio-temporally congested regions caused by freeway accidents. A case study for the assessment of CO₂ emissions impacts of based on the results from the model is presented.     

Estimating the CO2 intensity of intermodal freight transportation

This paper looks at the environmental effects of shifting from road to rail freight transportation. Little data is available to shippers to calculate the potential CO₂ savings of an intermodal shift. In this paper we analyze a data set of more than 400,000 intermodal shipments to calculate the CO₂ intensity of intermodal transportation as a distinct mode. Our results indicate an average intensity of 67 g of CO₂ per ton-mile, but can vary between 29 and 220 g of CO₂ per ton-mile depending on the specific origin–destination lane. We apply the market area concept to explain the variance between individual lane intensities and demonstrate the complexity in predicting the potential carbon savings in a switch from truckload to intermodal.     

An epsilon-optimal algorithm considering greenhouse gas emissions for the management of a ship’s bunker fuel

This paper provides an algorithm to minimize the fixed ordering, purchase, and inventory-carrying costs associated with bunker fuel together with ship time costs; and environmental costs associated with greenhouse gas emissions. It determines the optimum ship speed, bunkering ports, and amounts of bunker fuel for a given ship’s route. To solve the problem, we use an epsilon-optimal algorithm by deriving a property. The algorithm is illustrated by applying it to typical sample data obtained and the effects of bunker prices, carbon taxes, and ship time costs on the ship speed are analyzed. The results indicate that the ship speed and CO₂ emissions are highly sensitive to the factors considered.     

Assessing greenhouse gas emissions from port vessel operations at the Port of Incheon

This paper measures greenhouse gas emissions from port vessel operations by considering the case of Korea’s Port of Incheon. It provides estimates of greenhouse gas emissions based on the type and the movement of a vessel from the moment of its arrival, to its docking, cargo handling, and departure. Taking a bottom-up approach based on individual vessels’ characteristics and using data on vessels processed by the port in 2012 estimate emissions. The results indicate that the level of emissions is five times higher than that estimated through the top-down approach. Among various types of vessels, international car ferries are the heaviest emitters, followed by full container vessels and car carriers. A vessel’s passage through lock gates and maneuver to approach the dock accounts for 96% of its emissions. Docking for cargo handling shows the lowest level of GHG emissions.     

A network-based dispatch model for evaluating the spatial and temporal effects of plug-in electric vehicle charging on GHG emissions

The well-to-wheel emissions associated with plug-in electric vehicles (PEVs) depend on the source of electricity and the current non-vehicle demand on the grid, thus must be evaluated via an integrated systems approach. We present a network-based dispatch model for the California electricity grid consisting of interconnected sub-regions to evaluate the impact of growing PEV demand on the existing power grid infrastructure system and energy resources. This model, built on a linear optimization framework, simultaneously considers spatiality and temporal dynamics of energy demand and supply. It was successfully benchmarked against historical data, and used to determine the regional impacts of several PEV charging profiles on the current electricity network. Average electricity carbon intensities for PEV charging range from 244 to 391 gCO₂e/kW h and marginal values range from 418 to 499 gCO₂e/kW h.     

The role of nano additives for biodiesel and diesel blended transportation fuels

The energy crisis is due to two reasons, one is the rapid increase in worldwide population and the other is changing living style of human beings. The fossil fuel is also a major contributor to add the harmful pollutants into the atmosphere. Fuel modifications play a major role in increasing engine efficiency and reducing emissions. In the present investigation focused on fuel modifications in diesel engine. Initially the single cylinder diesel engine was operated with 20MEOM, 40MEOM, 60MEOM, 8MEOM and 100MEOM without additives with diesel at different loads at constant rated speed. From the experimental study proved that 20MEOM is the best fuel ratio compared to other blends. In second phase based upon first phase results the engine was operated 20MEOM blended fuel with adding 50 ppm copper oxide nano additives with diesel using solgel process. From the results, the brake thermal efficiency was 2.19% improved compared than 20MEOM blend without additive at full load condition. Emissions of HC, CO and smoke were considerably reduced. The present analysis reveals that the biofuel from mahua oil with nano additives is quite suitable as an alternate fuel for diesel engine.     

Greenhouse gas emissions from road construction: An assessment of alternative staging approaches

The greenhouse gas (GHG) emissions associated with road construction activities are analyzed. The main focus of this analysis is on the vehicle emissions associated with alternative project staging approaches, specifically a full closure of the road during construction, versus an intermittent road closure. The analysis includes the direct and upstream emissions associated with materials, construction equipment, mobilization of resources to the work site, and maintenance activity associated with the project over its lifetime. The analysis is based on one case study of a road project in New Jersey. The assumptions underlying the staging analysis are based on hypothetical approaches. Results provide an assessment of the main sources of project related emissions and the ability to minimize total project emissions by minimizing traffic disruption. In the analysis with a full closure of the road, traffic disruption accounts for 26% of total emissions, while with an intermittent road closure, traffic disruption accounts for only 2% of total emissions. The other main sources are from materials and life-cycle maintenance. The analysis demonstrates the feasibility of minimizing project related GHG emissions during road construction activities.     

Spatio-temporal analysis of car distance,greenhouse gases and the effect of built environment: A latent class regression analysis

This work examines the temporal–spatial variations of daily automobile distance traveled and greenhouse gas emissions (GHGs) and their association with built environment attributes and household socio-demographics. A GHGs household inventory is determined using link-level average speeds for a large and representative sample of households in three origin–destination surveys (1998, 2003 and 2008) in Montreal, Canada. For the emission inventories, different sources of data are combined including link-level average speeds in the network, vehicle occupancy levels and fuel consumption characteristics of the vehicle fleet. Urban form indicators over time such as population density, land use mix and transit accessibility are generated for each household in each of the three waves. A latent class (LC) regression modeling framework is then implemented to investigate the association of built environment and socio-demographics with GHGs and automobile distance traveled. Among other results, it is found that population density, transit accessibility and land-use mix have small but statistically significant negative impact on GHGs and car usage. Despite that this is in accordance with past studies, the estimated elasticities are greater than those reported in the literature for North American cities. Moreover, different household subpopulations are identified in which the effect of built environment varies significantly. Also, a reduction of the average GHGs at the household level is observed over time. According to our estimates, households produced 15% and 10% more GHGs in 1998 and 2003 respectively, compared to 2008. This reduction can be associated to the improvement of the fuel economy of vehicle fleet and the decrease of motor-vehicle usage – e.g., a decrease of 4% is observed for fuel efficiency rates and 12% for distance according to the raw average estimates from 1998 with respect to 2008. A strong link is also observed between socio-demographics and the two travel outcomes. While number of workers is positively associated with car distance and GHGs, low and medium income households pollute less than high-income households.     

A carbon footprint analysis of railway sleepers in the United Kingdom

This paper provides an assessment of the lifecycle Greenhouse Gas (GHG) emissions associated with the four most common sleeper (railroad tie or cross-tie in North America) types present in the UK rail network. It estimates the embodied material, process and transport emissions linked with the lifecycle activities of construction, relay/renewal and end-of-life of these variants at low and high traffic tonnage. The analysis suggests that at low traffic loads, the softwood sleepers perform the best over the whole simulated-period. At high traffic loads, the concrete sleepers outperform all other variants in terms of lifecycle CO₂e emissions, followed by hardwood, softwood and steel. Regardless of the scenario examined, the steel sleepers perform the worst due to the carbon intensive nature of their manufacturing process. This performance gap is amplified at high traffic loads, as their service life is excessively compromised. The analysis reveals that the end-of-life pathway of timber is a critical determinant of its footprint. Results suggest that the impact of disposing of these sleepers results in their footprint being magnified. Nevertheless, if a minimum of 50% follows the combustion pathway with subsequent heat recuperation, then a GHG reduction potential of between 11% and 18% of their footprint is feasible. From a whole-lifecycle cost lens, for higher tonnage routes, the choice of concrete sleepers results in considerable financial savings. If the infrastructure manager was to install sleepers with stiff under sleeper pads (USPs), it may achieve additional economic and GHG savings, with potential for increasing the latter using recycled carbon-neutral USPs.     

Electric vehicles for greenhouse gas reduction in China: A cost-effectiveness analysis

There have been ongoing debates over whether battery electric vehicles contribute to reducing greenhouse gas emissions in China’s context, and if yes, whether the greenhouse gas emissions reduction compensates the cost increment. This study informs such debate by examining the life-cycle cost and greenhouse gas emissions of conventional vehicles, hybrid electric vehicles and battery electric vehicles, and comparing their cost-effectiveness for reducing greenhouse gas emissions. The results indicate that under a wide range of vehicle and driving configurations (range capacity, vehicle use intensity, etc.), battery electric vehicles contribute to reducing greenhouse gas emissions compared with conventional vehicles, although their current cost-effectiveness is not comparable with hybrid electric vehicles. Driven by grid mix optimization, power generation efficiency improvement, and battery cost reduction, the cost-effectiveness of battery electric vehicles is expected to improve significantly over the coming decade and surpass hybrid electric vehicles. However, considerable uncertainty exists due to the potential impacts from factors such as gasoline price. Based on the analysis, it is recommended that the deployment of battery electric vehicles should be prioritized in intensively-used fleets such as taxis to realize high cost-effectiveness. Technology improvements both in terms of power generation and vehicle electrification are essential in improving the cost-effectiveness of battery electric vehicles.     

Assessing greenhouse gas and related air pollutant emissions from road traffic counts: A case study for Mauritius

The road transport sector is one of the major contributors of greenhouse gases and other air pollutants emissions. Regional emissions levels from road vehicles were investigated, in Mauritius, by applying a fuel-based approach. We estimated fuel consumption and air emissions based on traffic counts on the various types of classified roads at three different regional set ups, namely urban, semi urban and rural. The Relative Development Index (RDI), a composite index calculated from socio-economic and environmental indicators was used to classify regions. Our results show that the urban motorways were the most polluting due to heavy traffic. Some rural areas had important pollution levels as well. Our analysis of variance (ANOVA), however, showed little difference in emissions among road types and regions. The study can provide a simple tool for researchers in countries where data are very scarce, as is the case for many developing countries.     

Using GPS-data to determine optimum electric vehicle ranges: A Michigan case study

Fuel-switching personal transportation from gasoline to electricity offers many advantages, including lower noise, zero local air pollution, and petroleum-independence. But alleviations of greenhouse gas (GHG) emissions are more nuanced, due to many factors, including the car’s battery range. We use GPS-based trip data to determine use type-specific, GHG-optimized ranges. The dataset comprises 412 cars and 384,869 individual trips in Ann Arbor, Michigan, USA. We use previously developed algorithms to determine driver types, such as using the car to commute or not. Calibrating an existing life cycle GHG model to a forecast, low-carbon grid for Ann Arbor, we find that the optimum range varies not only with the drive train architecture (plugin-hybrid versus battery-only) and charging technology (fast versus slow) but also with the driver type. Across the 108 scenarios we investigated, the range that yields lowest GHG varies from 65 km (55+ year old drivers, ultrafast charging, plugin-hybrid) to 158 km (16–34 year old drivers, overnight charging, battery-only). The optimum GHG reduction that electric cars offer – here conservatively measured versus gasoline-only hybrid cars – is fairly stable, between 29% (16–34 year old drivers, overnight charging, battery-only) and 46% (commuters, ultrafast charging, plugin-hybrid). The electrification of total distances is between 66% and 86%. However, if cars do not have the optimum range, these metrics drop substantially. We conclude that matching the range to drivers’ typical trip distances, charging technology, and drivetrain is a crucial pre-requisite for electric vehicles to achieve their highest potential to reduce GHG emissions in personal transportation.     

Benefit of speed reduction for ships in different weather conditions

Currently, the shipping industry is facing a great challenge of reducing emissions. Reducing ship speeds will reduce the emissions in the immediate future with no additional infrastructure. However, a detailed investigation is required to verify the claim that a 10% speed reduction would lead to 19% fuel savings (Faber et al., 2012).This paper investigates fuel savings due to speed reduction using detailed modeling of ship performance. Three container ships, two bulk carriers, and one tanker, representative of the shipping fleet, have been designed. Voyages have been simulated by modeling calm water resistance, wave resistance, propulsion efficiency, and engine limits. Six ships have been simulated in various weather conditions at different speeds. Potential fuel savings have been estimated for a range of speed reductions in realistic weather.It is concluded that the common assumption of cubic speed-power relation can cause a significant error in the estimation of bunker consumption. Simulations in different seasons have revealed that fuel savings due to speed reduction are highly weather dependent. Therefore, a simple way to include the effect of weather in shipping transport models has been proposed.Speed reduction can lead to an increase in the number of ships to fulfill the transport demand. Therefore, the emission reduction potential of speed reduction strategy, after accounting for the additional ships, has been studied. Surprisingly, when the speed is reduced by 30%, fuel savings vary from 2% to 45% depending on ship type, size and weather conditions. Fuel savings further reduce when the auxiliary engines are considered.     

A comparative life-cycle energy and emissions analysis for intercity passenger transportation in the U.S. by aviation,intercity bus,and automobile

Intercity passenger trips constitute a significant source of energy consumption, greenhouse gas emissions, and criteria pollutant emissions. The most commonly used city-to-city modes in the United States include aircraft, intercity bus, and automobile. This study applies state-of-the-practice models to assess life-cycle fuel consumption and pollutant emissions for intercity trips via aircraft, intercity bus, and automobile. The analyses compare the fuel and emissions impacts of different travel mode scenarios for intercity trips ranging from 200 to 1600 km. Because these modes operate differently with respect to engine technology, fuel type, and vehicle capacity, the modeling techniques and modeling boundaries vary significantly across modes. For aviation systems, much of the energy and emissions are associated with auxiliary equipment activities, infrastructure power supply, and terminal activities, in addition to the vehicle operations between origin/destination. Furthermore, one should not ignore the embodied energy and initial emissions from the manufacturing of the vehicles, and the construction of airports, bus stations, highways and parking lots. Passenger loading factors and travel distances also significantly influence fuel and emissions results on a per-traveler basis. The results show intercity bus is generally the most fuel-efficient mode and produced the lowest per-passenger-trip emissions for the entire range of trip distances examined. Aviation is not a fuel-efficient mode for short trips (<500 km), primarily due to the large energy impacts associated with takeoff and landing, and to some extent from the emissions of ground support equipment associated with any trip distance. However, aviation is more energy efficient and produces less emissions per-passenger-trip than low-occupancy automobiles for trip distances longer than 700–800 km. This study will help inform policy makers and transportation system operators about how differently each intercity system perform across all activities, and provides a basis for future policies designed to encourage mode shifts by range of service. The estimation procedures used in this study can serve as a reference for future analyses of transportation scenarios.     

Well-to-wake energy and greenhouse gas analysis of SOX abatement options for the marine industry

This paper investigates the well-to-wake energy consumption and greenhouse gas emissions of several key SO_X abatement options in marine transportation, ranging from the manufacture of low sulfur fuels to equipping the vessel with suitable scrubber solutions. The findings suggest that a scrubber system, used with current heavy fuel oils, has the potential to reduce SO_X emissions with lower well-to-wake energy consumption and greenhouse gas emissions than switching to production of low sulfur fuels at the refinery. A sensitivity analysis covering a series of system parameters shows that variations in the well-to-tank greenhouse gas emissions intensity and the energy efficiency of the main engine have the highest impacts in terms of well-to-wake emissions.     

车载LNG储罐BOG回收研究

本文提出了在液化天然气客车上增设BOG储罐以回收利用蒸发损失的方案。通过计算百分百回收BOG时的理想蒸发率值可以知道,现有蒸发率仍有较大改善空间;通过维持时间的计算发现,由于新增的储罐对整车的维持时间增加少于一天,BOG回收效果并不显著,未来提高保温性降低蒸发损失或采用ANG吸附回收,是更有价值的研究方向。     

Mathematical properties and constraints representation for bottom-up approaches to the evaluation of GHG mitigation policies

Bottom-up models, including MARKAL, MESSAGE and AIM, are widely used when analyzing the effect of greenhouse gas (GHG) abatement policies. These bottom-up models are mostly formulated as a linear programming (LP) optimization model to find both the minimal cost combination of abatement technologies and energy flows while satisfying demands. It is not unusual that the bottom-up modeling involves a great number of technical, industrial, socioeconomic and environmental constraints. Investigating representative constraints needed for analyzing GHG abatement policies, this study proposes how to implement these constraints in bottom-up modeling.     

Evaluating policies to reduce greenhouse gas emissions from private transportation

This paper proposes a model system to forecast household greenhouse gas emissions (GHGEs) from private transportation. The proposed model combines an integrated discrete-continuous car ownership model with MOVES 2014. Four modeling components are calibrated and applied to the calculation of GHGEs: vehicle quantity, vehicle type and vintage, miles traveled, and rates of GHGEs. The model is applied to the Washington D.C. Metropolitan Area. Three tax schemes are evaluated: vehicle ownership tax, purchase tax and fuel tax. We calculate that the average GHGEs per vehicle is 5.15 tons of carbon dioxide-equivalent (CO₂E) gases. Our results show that: (a) a fuel tax is the most effective way to reduce vehicle GHGEs, especially for households with fewer vehicles; (b) a purchase tax reduces vehicle GHGEs mainly by decreasing vehicle quantity for households with more vehicles; and (c) an ownership tax reduces vehicle GHGEs by decreasing both vehicle quantity and miles traveled.