Examining individuals preferences for hybrid electric and alternatively fuelled vehicles

This paper examines individuals motivations when purchasing vehicles, focusing upon what factors would encourage individuals to purchase hybrid electrical vehicle (HEV) or alternatively fuelled vehicle (AFV). AFVs in this paper refer to any cars run on alternatives to petrol and diesel. This research attempts to ascertain whether reductions in fuel costs, vehicle registration tax (VRT), or green house gas emissions would encourage individuals to purchase a HEV or an AFV instead of a conventional vehicle. VRT is an Irish tax that is levied on the purchase of new vehicles. One of the motivations to conduct this research was to examine a new car tax and VRT scheme introduced by the Irish government in 2008. This new policy rewards the purchase of environmentally friendly cars, with lower VRT and car tax rates. To understand individuals’ perceptions of these new taxes a survey was sent to recent customers of a car company in Ireland. The survey asked respondents about their recently purchased vehicle and how important they considered vehicle attributes such as environmental performance, fuel cost, and safety, before making their car purchase. The survey also contained a number of stated preference experiments that were designed to ascertain what factors influence individuals’ decisions when purchasing their new car. The results showed that respondents did not rate green house gas emissions or VRT as crucial attributes when purchasing a new vehicle. The vehicle attributes that respondents rated most highly were reliability, automobile safety, fuel costs, and the cost price. The majority of respondents agreed that HEVs and AFVs are better for the environment, cheaper to run than conventional vehicles and would be the vehicle of choice in ten years time.     

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.     

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.     

Critical aggressive acceleration values and models for fuel consumption when starting and driving a passenger car running on LPG

The models based on vehicle speed have been used to estimate fuel consumption and CO₂ emissions. However, these models could not properly estimate the change in fuel consumption and CO₂ emissions as the speed changes. As for the alternative method, people try to consider using acceleration instead of speed. Although acceleration has been seriously considered, determining critical aggressive acceleration value in relation to fuel consumption and CO₂ emissions is difficult to find. In this study, evaluation models of fuel consumption were developed using instantaneous acceleration, and we defined the critical aggressive acceleration values for different states of the vehicle from the viewpoints of fuel consumption and emissions. We used a mid-sized Liquefied Petroleum Gas (LPG) passenger car and obtained instantaneous data from a digital tachograph installed in the car while it accelerates. We developed two fuel consumption models and found critical aggressive accelerations, respectively: a model of starting vehicle that measures range of speed required to overcome the inertia during acceleration from stop state, and the other model for the driving state. We used Classification and Regression Tree (CART) analysis to find the critical aggressive accelerations at which the increments of fuel consumption change abruptly. As a result, the critical aggressive accelerations causing abrupt change in the increments of fuel consumption were found to be 2.598 m/s² for the starting of vehicles and 1.4705 m/s² when driving them. We also found that the increments of fuel consumption can be explained through quadratic and exponential functions with instantaneous acceleration.     

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.     

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.     

Automobile use,fuel economy and CO2 emissions in industrialized countries: Encouraging trends through 2008?

Car use and fuel economy are factors that determine oil demand and carbon dioxide (CO₂) emissions. Recent data on automobile utilization and fuel economy reveal surprising trends that point to changes in oil demand and CO₂ emissions. New vehicle and on-road fleet fuel economy have risen in Europe and Japan since the mid 1990s, and in the US since 2003. Combined with a plateau in per capita vehicle use in all countries analyzed, these trends indicate that per capita fuel use and resultant tail-pipe CO₂ emissions have stagnated or even declined.Fuel economy technology, while important, is not the only factor that explains changes in tested and on-road fuel economy, vehicle efficiency and transport emissions across countries. Vehicle size and performance choices by car producers and buyers, and driving distances have also played significant roles in total fuel consumption, and explain most of the differences among countries. Technology applied to new vehicles managed to drive down the fuel use per unit of horsepower or weight by 50%, yet most of the potential fuel savings were negated by overall increased power and weight, particularly in the US. Similarly, the promise of savings from dieselization of the fleet has revealed itself as a minor element of the overall improvement in new vehicle or on-road fuel economy. And the fact that diesels are driven so much more than gasoline cars, a difference that has increased since 1990, argues that those savings are minimal. This latter point is a reminder that car use, not just efficiency or fuel choice, is an important determinant of total fuel use and CO₂ emissions.We speculate that if the upward spiral of car weight and power slows or even reverses (as has been observed in Europe and Japan) and the now mandatory standards in many countries have the intended effect that fuel use will remain flat or only grow weakly for some time. If real fuel prices of 2008, which rivaled their peaks of the early 1980s, fell back somewhat but still remain well above their early 2000 values. If the prices remain high, this, combined with the strengthened fuel economy standards, may finally lead to new patterns of car ownership, use and fuel economy. However, if fuel prices continue their own stagnation or even decline after the peaks of 2008 and car use starts upward, fuel use will increase again, albeit more slowly.     

Interplay between ethanol and electric vehicles as low carbon mobility options for passengers in the municipality of São Paulo

The Brazilian cities as well as many of the large urban centers in the world continue to expand, increasing the demand for mobility and transport, while, at the same time, the same cities are investing in greenhouse gas (GHG) mitigation to avoid climate change. Brazil's urbanization rate increased from 26% in 1940 to almost 70% in 1980. During this period, the Brazilian population tripled and the urban population multiplied by seven. In 2010, the transport sector in São Paulo accounted for 71% of the total emissions released by the energy sector. Ethanol has been considered a fuel with less greenhouse gas emissions, when compared with fossil fuels. However, ethanol production would have to double to meet the expected demand. Electric vehicles (EVs) market is expanding around the world, and is also an option to reduce the transport emissions, if powered by clean electricity. To assess whether the adoption of EVs might bring more benefits than the current ethanol, we develop prospective scenarios supported by the Long-range Energy Alternatives Planning (LEAP) simulation tool, taking a bottom-up tank-to-wheel approach to consider the CO₂ emissions of car in São Paulo. The scenario considering a substitution of 25% of gasoline-powered cars by EV in 2030 showed a reduction in energy consumption and CO₂ emissions, around 15% and 26% respectively in that year in comparison with 2015. We discuss the interplay between ethanol and EV, also considering emission coefficients from life cycle analysis conducted in Brazil, and concluded EV will have higher positive impact on climate change mitigation than ethanol.     

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 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.     

A GHG reduction obligation for sustainable aviation fuels (SAF) in the EU and in Germany

Despite various legal drivers already in place, a large-scale use of sustainable aviation fuels (SAF) is still missing. Thus, bringing SAF for GHG reduction reasons into the day-to-day business more effective market injection mechanisms are needed. In this context, direct and indirect effects of a greenhouse gas (GHG) reduction obligation in the EU and in Germany are analysed, considering an obligation level of 2% in 2023 increasing stepwise to 10% by 2030 following the goals derived from transport-related RED II targets. For such GHG reduction obligations referred to the entire EU 27 and the entire German aviation fuel uplifts, obligation induced SAF demands possibly exceed future SAF production quantities by a factor of about two without effective capacity expansion exceeding the published expansion plans. The considered SAF options lead to an aviation fuel price increases of 5–45% referring to a conventional aviation fuel price of 550 €/t. Aviation related EU ETS and German aviation tax revenues are mostly insufficient to significantly cover obligation-induced SAF costs or additional fuel costs. Fostering a broad spectrum of SAF technologies will thus require obligation related sub-mechanisms, due to (significantly) different SAF compliance costs. Thereby, sub-mandates represent the most effective approach. In terms of obligation avoidance measures, tankering – in contrast to three re-routing options considered – is likely to be economically feasible for airlines and requires appropriate countermeasures.     

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.     

Modeling fuel saving investments and fleet management in the trucking industry: The impact of shipment performance on GHG emissions

Much has been written about the potential of technologies to reduce the greenhouse gas (GHG) emissions of trucking, but much less on the determinants of these investments. The Trucking Sector Trip Segmentation Model (TSTS) predicts how firms make these investments in the context of operating heterogeneous truck fleets to service the spatially dispersed demand of shippers. This analysis suggests that improving the performance of trucking (speeding up shipments) could reduce significantly GHG emissions: investments in technologies are incentivized by fuel savings accruing sooner. This effect could be potentially large in the US as trucking firms often discount the future heavily.     

Green accessibility: Estimating the environmental costs of network-time prisms for sustainable transportation planning

Accessibility, or the ease to participate in activities and obtain resources in a given environment, is crucial for evaluating transportation systems. Greater accessibility is often achieved by increasing individuals' potential mobility. However, potential mobility, if realized by motorized modes, can also generate negative environmental impacts such as fossil fuel consumption and greenhouse gas (GHG) emissions. While the negative environmental impacts of greater mobility are acknowledged, there has been a lack of research to validate those impacts using empirical data, especially considering variations in individuals' mobility levels. This paper presents a method for estimating the expected environmental costs of accessibility represented by a network-time prism (NTP). A NTP delimits all accessible locations within a network and the available time for an individual to present at each location given a scheduled trip origin and destination, a time budget and the maximum achievable speeds along network edges. Estimating the expected environmental costs of a NTP involves three steps: (1) semi-Markov techniques to simulate the probabilities to move along network edges at given times; (2) the speed profiles for reachable edges, and (3) a cost function that translates speeds into environmental impacts. We focus on air quality and employ the motor vehicle emission simulator MOVESLite to estimate the CO₂ emissions at both the edge and prism levels. We calibrate and validate the methods for experimental NTPs defined within the Phoenix, AZ, USA road and highway network using vehicles instrumented with GPS-enabled onboard diagnostic devices (OBD). We demonstrate the effectiveness of our method through two scenarios and investigate the impact of changes in mobility levels on the expected CO₂ emissions associated with the experimental NTPs.     

Drivers and barriers to the adoption and diffusion of Sustainable Jet Fuel (SJF) in the U.S. Pacific Northwest

Sustainable Jet Fuel (SJF) represents an important component of the airline industry's strategy to simultaneously reduce GHG emissions while meeting a growing demand for international air travel. SJFs also have the potential to provide fuel supply diversification and security, enhance fuel price stability and provide regional/rural economic development benefits. This paper measures and ranks perceived drivers and barriers to an economically viable SJF industry in a unique U.S. region, the U.S. Pacific Northwest (PNW), through personal interviews with key aviation fuel supply chain stakeholders conducted from June to September 2015. In addition to providing a fertile arena for this first effort to systematically assess these drivers and barriers, the U.S. PNW is unique due to the region's long strategic focus on aviation innovation and its importance to the regional economy, the seminal efforts in the region to outline a path forward on SJF beginning in 2010, and the relatively small population spread over a large geographic area with a limited number of “hub” airport nodes which geographically concentrates aviation fuel demand and distribution. Nineteen stakeholder interviewees acknowledge that, in order for regional SJF adoption-diffusion to occur, airline jet fuel buyers must drive the process, particularly as they deal with greenhouse gas (GHG) emission issues and related policy considerations. Important perceived barriers to SJF industry scale-up in the U.S. PNW include the high production costs of SJF and related issues, such as fuel logistics and quality control in the transport, storage, and blending of SJFs. Perceptions around chain-of-custody issues, such as blending, tracking, and crediting of SJFs and future SJF market share projections for the year 2030 were also examined. Incorporating stakeholder input into discussions about adding blended SJF into the U.S. aviation fuel supply provides needed insight for the biofuels industry, policymakers, and researchers.     

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.     

Berth allocation considering fuel consumption and vessel emissions

We propose a more elaborate model on berth allocation considering fuel consumption than before, and overcome the nonlinear complexity by casting it as a mixed integer second order cone programming model. Furthermore, we conduct the vessel emission (in sailing periods) calculation with the widely-used emission factors. Besides, vessel emissions in mooring periods are also analyzed through a post-optimization phase on waiting time. Experimental results demonstrate that the new berth allocation strategy, reflected by the proposed model, is competent to significantly reduce fuel consumption and vessel emissions, while simultaneously retaining the service level of the terminal.     

Modeling resilience of the ATC (Air Traffic Control) sectors

This paper develops a theoretical framework containing the methodology for assessing resilience of the ATC (Air Traffic Control) sectors affected by the impact of a given disruptive event. The resilience is considered as ability of these sectors to retain a certain level of the regular/nominal performance during the impact and fully recover relatively fast afterwards. The actually rear disruptive event is considered to be the large-scale failure of a component of the ATC facilities and equipment supporting safe, efficient, and effective air traffic. Under such conditions, different mitigating contingency measures are generally applied resulting in deteriorating the operational, economic, and environmental performance of the affected sectors while maintaining the required level of safety. This performance is represented by the indicators such as demand, capacity, traffic complexity, the ATC controller workload, aircraft/flight delays and their costs, and additional fuel consumption and related emissions of GHG (Green House Gases). The proposed methodology consists of the generic model of resilience, the analytical models for estimating the indictors of ATC sectors’ performance, and the analytical models of resilience based the indicators as figures-of-merit for assessing resilience. These models are based on the practice-close mitigating contingency measures applied to the ATC sectors affected by a given disruptive event. The possible application of the proposed methodology is also elaborated.     

Emissions and exposure costs of electric versus conventional vehicles: A case study in Texas

The emissions and human exposure impacts of electric vehicle (EV) adoption, especially in comparison to conventional gasoline- or diesel-powered engines, depend on numerous factors including geography, electricity generation, and fuel mix. Results of any analysis also vary depending on the nature of data collected and their level of aggregation by time or location. This paper combines several approaches to develop a robust estimate of these impacts specific to the state of Texas by considering marginal emissions by time of day, as well as location of vehicle and power plant emissions. The authors estimate health and other external costs of operating an EV in the state at approximately $62 per year, compared with an average of $136 for a passenger car powered by gasoline.     

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.