Carsharing’s life-cycle impacts on energy use and greenhouse gas emissions

This paper examines the life-cycle inventory impacts on energy use and greenhouse gas (GHG) emissions as a result of candidate travelers adopting carsharing in US settings. Here, households residing in relatively dense urban neighborhoods with good access to transit and traveling relatively few miles in private vehicles (roughly 10% of the U.S. population) are considered candidates for carsharing. This analysis recognizes cradle-to-grave impacts of carsharing on vehicle ownership levels, travel distances, fleet fuel economy (partly due to faster turnover), parking demand (and associated infrastructure), and alternative modes. Results suggest that current carsharing members reduce their average individual transportation energy use and GHG emissions by approximately 51% upon joining a carsharing organization. Collectively, these individual-level effects translate to roughly 5% savings in all household transport-related energy use and GHG emissions in the U.S. These energy and emissions savings can be primarily attributed to mode shifts and avoided travel, followed by savings in parking infrastructure demands and fuel consumption. When indirect rebound effects are accounted for (assuming travel-cost savings is then spent on other goods and services), net savings are expected to be 3% across all U.S. households.     

Cost-effectiveness of reductions in greenhouse gas emissions from High-Speed Rail and urban transportation projects in California

A rising trend in state and federal transportation finance is to invest capital dollars into projects which reduce greenhouse gas (GHG) emissions. However, a key metric for comparing projects, the cost-effectiveness of GHG emissions reductions, is highly dependent on the cost-benefit methodology employed in the analysis. Our analysis comparing California High-Speed Rail and three urban transportation projects shows how four different accounting framings bring wide variations in cost per metric tonne of GHG emissions reduced. In our analysis, life-cycle GHG emissions are joined with full cost accounting to better understand the benefits of cap-and-trade investments. Considering only public subsidy for capital, none of the projects appear to be a cost-effective means to reduce GHG emissions (i.e., relative to the current price of GHG emissions in California’s cap-and-trade program at $12.21 per tonne). However, after adjusting for the change in private costs users incur when switching from the counterfactual mode (automobile or aircraft) to the mode enabled by the project, all investments appear to reduce GHG emissions at a net savings to the public. Policy and decision-makers who consider only the capital cost of new transportation projects can be expected to incorrectly assess alternatives and indirect benefits (i.e., how travelers adapt to the new mass transit alternative) should be included in decision-making processes.     

Planning for economic and environmental resilience

Climate protection will require major reductions in GHG emissions from all sectors of the economy, including the transportation sector. Slowing growth in vehicle miles traveled (VMT) will be necessary for reducing transportation GHG emissions, even with major breakthroughs in vehicle technologies and low-carbon fuels (Winkelman et al., 2009). The Center for Clean Air Policy (CCAP) supports market-based policy approaches that minimize costs and maximize benefits. Our research indicates that significant GHG reductions can be achieved through smart growth and travel efficiency measures that increase accessibility, improve travel choices and make optimum use of existing infrastructure. Moreover, we find such measures can deliver compelling economic benefits, including avoided infrastructure costs, leveraged private investment, increased local tax revenues and consumer vehicle ownership and operating cost savings (Winkelman et al., 2009).As a society, what we build – where and how – has a tremendous impact on our carbon footprint, from building design to transportation infrastructure and land-use patterns. The empirical and modeling evidence is clear – people drive less in locations with efficient land use patterns, high quality travel choices and reinforcing policies and incentives (Ewing et al., 2008). It is also clear that there is growing and unmet market demand for walkable communities, reinforced by demographic shifts and higher fuel prices (Leinberger, 2006, Nelson, 2007). Transportation policy in the United States must rise to meet this demand for more travel choices and more livable communities.The academic, ideological and political debates about the level of GHG reductions and penetration rates that can or should be achieved via smart growth and pricing on the one hand, or measures such as ‘eco-driving’ and signal optimization on the other, have served their purpose: we know which policies are ‘directionally correct’ – policies that reduce GHG emissions even though we may not know the scope of those reductions. Now is the time to implement directionally correct policies, assess what works best where, and refine policy based on the results. It is a framework that CCAP calls “Do. Measure. Learn.”The Federal government is poised to spend $500 billion on transportation (Committee on Transportation and Infrastructure, 2009). CCAP encourages Congress to “Ask the Climate Question” – will our transportation investments help reduce GHG emissions or exacerbate the problem? Will they help increase our resilience to climate change impacts or increase our vulnerability? And, while we’re at it, will our investment foster energy security, livable communities and a vibrant economy? Federal transportation and climate policies should empower communities to implement locally-determined travel efficiency solutions by providing appropriate funding, tools and technical support.     

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.     

VMT, energy consumption, and GHG emissions forecasting for passenger transportation

Globalization, greenhouse gas emissions and energy concerns, emerging vehicle technologies, and improved statistical modeling capabilities make the present moment an opportune time to revisit aggregate vehicle miles traveled (VMT), energy consumption, and greenhouse gas (GHG) emissions forecasting for passenger transportation. Using panel data for the 48 continental states during the period 1998-2008, the authors develop simultaneous equation models for predicting VMT on different road functional classes and examine how different technological solutions and changes in fuel prices can affect passenger VMT. Moreover, a random coefficient panel data model is developed to estimate the influence of various factors (such as demographics, socioeconomic variables, fuel tax, and capacity) on the total amount of passenger VMT in the United States. To assess the influence of each significant factor on VMT, elasticities are estimated. Further, the authors investigate the effect of different policies governing fuel tax and population density on future energy consumption and GHG emissions. The presented methodology and estimation results can assist transportation planners and policy-makers in determining future energy and transportation infrastructure investment needs.     

Strategies to achieve deep reductions in metropolitan transportation GHG emissions: the case of Philadelphia

ABSTRACT

This paper investigates strategies that could achieve an 80% reduction in transportation emissions from current levels by 2050 in the City of Philadelphia. The baseline daily lifecycle emissions generated by road transportation in the Greater Philadelphia Region in 2012 were quantified using trip information from the 2012 Household Travel Survey (HTS). Emissions were projected to the year 2050 accounting for population growth and trends in vehicle technology for both the Greater Philadelphia Region and the City of Philadelphia. The impacts of vehicle technology and shifts in travel modes on greenhouse gas (GHG) emissions in 2050 were quantified using a scenario approach. The analysis of 12 different scenarios suggests that 80% reduction in emissions is technically feasible through a combination of active transportation, cleaner fuels for public transit vehicles, and a significant market penetration of battery-electric vehicles. The additional electricity demand associated with greater use of electric vehicles could amount to 10.8 TWh/year. The use of plug-in hybrid electric vehicles (PHEV) shows promising results due to high reductions in GHG emissions at a potentially manageable cost.     

Planning hierarchical urban transit systems for reductions in greenhouse gas emissions

Public transit systems with high occupancy can reduce greenhouse gas (GHG) emissions relative to low-occupancy transportation modes, but current transit systems have not been designed to reduce environmental impacts. This motivates the study of the benefits of design and operational approaches for reducing the environmental impacts of transit systems. For example, transit agencies may replace level-of-service (LOS) by vehicle miles traveled (VMT) as a criterion in evaluating design and operational changes. In previous work, we explored the unintended consequences of lowering transit LOS on emissions in a single-technology transit system. Herein, we extend the analysis to account for a more realistic case: a transit system with a hierarchical structure (trunk and feeder lines) providing service to a city where demand is elastic. By considering the interactions between the trunk and the feeder systems, we provide a quantitative basis for designing and operating integrated urban transit systems that can reduce GHG emissions and societal costs. We find that highly elastic transit demand may cancel emission reduction potentials resulting from lowering LOS, due to demand shifts to lower occupancy vehicles. However, for mass transit modes, these potentials are still significant. Transit networks with buses, bus rapid transit or light rail as trunk modes should be designed and operated near the cost-optimal point when the demand is highly elastic, while this is not required for metro. We find that the potential for unintended consequences increases with the size of the city. Our results are robust to uncertainties in the costs and emissions parameters.     

Encouraging environmentally sustainable holiday travel

Holiday travel behavior, individual characteristics of holiday travelers and strategies to change holiday travel behavior are the subjects of this article. From the environmental perspective, the journey to the destinations is the most critical aspect of traveling. Based on a 2003 survey of 1991 German inhabitants, the kilometers traveled and the choice of transportation mode for holiday purposes have been quantified. According to the number of trips and kilometers traveled, four travel groups have been identified. The groups vary according to socio-demographics, psychological factors, number of holiday trips, and travel mode choice. Persons who traveled to more distant destinations also traveled more often and used air travel for more than 60% of their trips. For the other groups, car travel was more important. Correlating the four travel groups with greenhouse gas emissions reveals that the smallest group—the long-haul travelers—was responsible for 80% of the emissions of the whole sample. Income, education, and openness to change were main indicators of individual greenhouse gas emissions. Target group oriented strategies to reduce the environmental impact of holiday mobility are discussed against the background of 84 in-depth interviews conducted with selected representatives of the first survey.     

How can we alter our carbon footprint? Estimating GHG emissions based on travel survey information

The city of Montreal has taken recent initiatives to significantly reduce overall greenhouse (GHG) emissions from the transport sector and has made large investments in alternative transportation. In particular, the city has called upon the participation of all businesses and institutions to further these goals. In light of these recent plans, this study identifies with two objectives: first, to develop a methodology for estimating GHG emissions generated by commuters to McGill University’s downtown campus; and secondly, to better understand who, how, and when each commuter to McGill generates travel-related GHG. Mode split, travel distance, age, gender and job category were uncovered by a 2011 travel survey that we conducted across the University, from which daily individual GHG emissions are estimated. Details about these trips not only reveal who the largest polluters are and where they are coming from, but also the seasonality of their emissions. These associations are then used to narrate scenarios which present alternatives to the structure of individuals’ commutes by examining the outcomes of selected shifts in travel behavior on total GHG emissions.     

Commuters’ behavior towards upgraded bus services in Greater Beirut: Implications for greenhouse gas emissions,social welfare and transport policy

Climate change is one of the most critical environmental challenges faced in the world today. The transportation sector alone contributes to 22% of carbon emissions, of which 80% are contributed by road transportation. In this paper we investigate the potential private car greenhouse gas (GHG) emissions reduction and social welfare gains resulting from upgrading the bus service in the Greater Beirut Area. To this end, a stated preference (SP) survey on mode switching from private car to bus was conducted in this area and analyzed by means of a mixed logit model. We then used the model outputs to simulate aggregate switching behavior in the study area and the attendant welfare and environmental gains and private car GHG emissions reductions under various alternative scenarios of bus service upgrade. We recommend a bundle of realistic bus service improvements in the short term that will result in a reasonable shift to buses and measurable reduction in private car emissions. We argue that such improvements will need to be comprehensive in scope and include both improvements in bus level of service attributes (access/egress time, headway, in-vehicle travel time, and number of transfers) and the provision of amenities, including air-conditioning and Wi-Fi. Moreover, such a service needs to be cheaply priced to achieve reasonably high levels of switching behavior. With a comprehensively overhauled bus service, one would expect that bus ridership would increase for commuting purposes at first, and once the habit for it is formed, for travel purposes other than commuting, hence dramatically broadening the scope of private car GHG emissions reduction. This said, this study demonstrates the limits of focused sectorial policies in targeting and reducing private car GHG emissions, and highlights the need for combining behavioral interventions with other measures, most notably technological innovations, in order for the contribution of this sector to GHG emissions mitigation to be sizable.     

Challenges and opportunities for transportation: Implementation of the clean air act amendments of 1990 and the intermodal surface transportation efficiency act of 1991

The Federal Clean Air Act Amendments of 1990 (CAAA) may be the most powerful of all environmental laws affecting transportation. They are intended to significantly affect transportation decision-making, not only to achieve air quality goals but also to affect broader environmental goals related to land use, travel mode choice, and reductions in vehicle miles traveled. The CAAA require greater integration of transportation and air quality planning, and assign a greater responsibility to transportation plans and programs for reducing mobile source emissions. By expanding the requirements for determining the conformity of transportation plans, programs, and projects with State Implementation Plans for air quality, and by expanding the use of highway funding sanctions to enforce those requirements, the CAAA ensure a continuing linkage between transportation and environmental goals.While the CAAA give transportation and air quality decision-makers the mandate to better coordinate their respective planning processes, the Intermodal Surface Transportation Efficiency Act of 1991 offers the tools to help carry out that mandate. Consequently, this paper summarizes the transportation and air quality provisions of both of these Acts and their relationships.     

Achieving environmental sustainability beyond technological improvements: Potential role of high-speed rail in the United States of America

In order to reduce energy use and cut emissions that contribute to climate change, countries need to radically reinvent their fossil-fuel intensive transportation systems. As a major consumer of energy and contributor to greenhouse gas (GHG) emissions, the U.S. transportation sector faces extraordinary challenges in the twenty-first century. Transportation in the U.S. depends heavily on fossil-fuel dependent cars and planes to the near exclusion of more energy-efficient electric trains. In order to address this concern, some policy makers refer to “technological optimism” which seeks no systemic change but instead focuses on employing technology to reduce the energy demand and environmental impact of the status quo. On the other hand, some researchers suggest a systematic paradigm shift away from cars and planes to intermodal systems that improve the sustainability of the system as a whole. High-speed rail (HSR) is arguably such an investment that can further this shift and help to achieve a more diversified and balanced transportation system. In this respect, by largely examining the role of the U.S. cars and planes “culture” in the economy, this paper elaborates on how building a HSR system may help U.S. advance towards environmental sustainability in transportation, make a break from the status quo, and create a more balanced, multimodal transportation system that will improve the quality and efficiency of travel.     

The revenue and environmental benefits of new off-peak commuter rail service: the case of the Pascack Valley line in New Jersey

Although researchers have long argued in favor of off-peak transit service, studies that have empirically estimated its benefits regarding revenue generation, trip diversions, and greenhouse gas (GHG) emission are rare. This study provides important evidence about the benefits of off-peak commuter rail service by focusing on the Pascack Valley line in New Jersey, where off-peak service was introduced in October 2007. The research involved two focus groups and an onboard survey of passengers. Benefits were estimated regarding additional revenue generation and reduction in vehicle miles traveled (VMT) and GHG emission. The research shows that the new off-peak service potentially reduced VMT by more than 12 million annually due to diversions from other modes. Although diversions from other modes resulted in a substantial reduction in GHG emissions, due to the additional diesel fuel used by the new trains, the net GHG savings were in the range of 28–49 %. The research further shows that both peak period and off-peak riders benefited from the new off-peak service. Evidence is found about an increase in new transit riders and a modest increase peak period usage because of the off-peak service.     

Investigation of the use of smartphone applications for trip planning and travel outcomes

This paper explores the use of smartphone applications for trip planning and travel outcomes using data derived from a survey conducted in Halifax, Nova Scotia, in 2015. The study provides empirical evidence of relationships of smartphone use for trip planning (e.g. departure time, destination, mode choice, coordinating trips and performing tasks online) and resulting travel outcomes (e.g. vehicle kilometers traveled, social gathering, new place visits, and group trips) and associated factors. Several sets of factors such as socio-economic characteristics and travel characteristics are tested and interpreted. Results suggest that smartphone applications mostly influence younger individuals’ trip planning decisions. Transit pass owners are the frequent users of smartphone applications for trip planning. Findings suggest that transit pass owners commonly use smartphone applications for deciding departure times and mode choices. The study also identifies the limited impact of smartphone application use on reducing travel outcomes, such as vehicle kilometers traveled. The highest impact is in visiting new places (a 48.8% increase). The study essentially offers an original in-depth understanding of how smartphone applications are affecting everyday travel.     

The implications of school choice on travel behavior and environmental emissions

We examine the implications of school choice on walkability, school travel mode and overall environmental emissions. In developing this proof-of-concept model we show—and quantify—differences between city-wide schools and their neighborhood school counterpart. Our analysis demonstrates how children attending city-wide schools may have heightened travel distance, greenhouse gas emissions, and exposure to bus fumes. Using available data along with a series of informed assumptions we figure the city-wide school had six times fewer children walking, 4.5 times as many miles traveled, 4.5 times the system cost, and 3–4.5 times the amount of criteria air pollutants and greenhouse gas emissions. By providing bus service, the overall miles traveled (and resulting emissions) decreased 30–40% compared to the scenario without bus service, however system costs were higher for both the neighborhood and city-wide school (no pollution externality costs were factored in).     

A bi-level model for GHG emission charge based on a continuous distribution of travelers’ value of time (VOT)

Transportation is an important source of greenhouse gas (GHG) emissions. In this paper, we develop a bi-level model for GHG emission charge based on continuous distribution of the value of time (VOT) for travelers. In the bi-level model framework, a policy maker (as the leader) seeks an optimal emission charge scheme, with tolls differentiated across travel modes (e.g., bus, motorcycles, and cars), to achieve a given GHG reduction target by shifting the proportions of travelers taking different modes. In response, travelers (as followers) will adjust their travel modes to minimize their total travel cost. The resulting mode shift, hence the outcome of the emission charge policy, depends on travelers’ VOT distribution. For the solution of the bi-level model, we integrate a differential evolution algorithm for the upper level and the “all or nothing” traffic assignment for the lower level. Numerical results from our analysis suggest important policy implications: (1) in setting the optimal GHG emission charge scheme for the design of transportation GHG emission reduction targets, policy makers need to be equipped with rigorous understanding of travelers’ VOT distribution and the tradeoffs between emission reduction and system efficiency; and (2) the optimal emission charge scheme in a city depends significantly on the average value of travelers’ VOT distribution—the optimal emission charge can be designed and implemented in consistency with rational travel flows. Further sensitivity analysis considering various GHG reduction targets and different VOT distributions indicate that plausible emission toll schemes that encourage travelers to choose greener transportation modes can be explored as an efficient policy instrument for both transportation network performance improvement and GHG reduction.     

The analysis of travel and emission impacts of travel demand management strategies using activity-based models

This paper demonstrates, tests and shows the value of activity-based travel demand models and household sample enumeration forecasting techniques in evaluating the transportation and air quality impacts of travel demand management strategies. Using data from the Portland, Oregon metropolitan area, three transportation policies were evaluated both individually and in combination: transit improvements, pricing, and telecommunications. The activity-based models used in this testing represents a significant improvement to today's "four-step" sequential model systems by providing a deeper insight into the individual decision making process in response to transportation policies. A wider range of impacts is predicted, and indirect effects as well as synergistic effects of such policies are taken into consideration. These models are capable of providing the information needed to improve the linkage of transportation models with emissions and air quality analysis methodologies by improving the prediction of variables that are important to accurately estimating emissions and air quality impacts of transportation actions.     

Methodologies to monetize the variations in load factor and GHG emissions per passenger-mile of airlines

Global GHG emissions from air travel are currently at 3% and it could increase to 15% of the total GHG emissions by 2050. To curb the growth of GHG emissions from air travel, the U.S. Federal Aviation Administration (FAA) has created a policy to achieve carbon neutral growth by 2020 relative to the 2005 baseline. If the airline industry is to both grow and meet the objectives set by this policy, new and innovative aircraft designs, operational efficiencies, and widespread use of alternate fuels are required. To accomplish this would require large research and development investment. The federal government and state governments have passed legislations that provide tax breaks and other incentives to encourage investments in new technologies. One such tax policies is cap and trade system. This had partial success in reducing GHG emissions in certain industries but was not successful in the airline industry. This paper presents alternate methods to raise capital to invest in GHG emissions reduction projects in the airline sector. The four methodologies presented here monetizes the GHG emissions resulting from differences in load factor (ratio of number of passengers to number of seats) and GHG emissions per passenger-mile among different airlines, among different flight sectors, etc. to raise the capital. Based on 2012 air travel data, these methodologies could raise more than $300 million dollars annually to invest in GHG emissions reduction projects.     

Estimating transboundary economic damages from climate change and air pollution for subnational incentives for green on-road freight

Subnational incentives to adopt zero emission vehicles (ZEVs) are critical for reducing the external economic damages posed by transportation to air quality and the climate. Few studies estimate these damages for on-road freight, especially at scales relevant for subnational policies requiring cross-border cooperation. Here, we assess the damages to US receptors from emissions of air pollutants (PM_2.5, NO_x, SO₂, NH₃), and greenhouse gases (CO₂, CH₄, N₂O) from medium and heavy duty freight trucking, and the benefits of ZEV adoption by census division in the Province of Ontario. We develop an integrated modelling framework connecting a travel demand model, a mobile emissions simulator, and a regression based marginal damages model of air pollutants and climate change. We estimate $1.9 billion (2010 USD) in annual cross-border damages, or $0.16/VKT, resulting from scaled up atmospheric emissions from a ‘typical day’ of medium and heavy duty truck traffic volume for Ontario in 2012. This implies approximately $8000 per truck per year in damages, which could inform an economic incentive for emission reduction. The provincial goal of 5% ZEV adoption would reduce GHG emissions in 2012 by 800 ktCO₂e, yielding $89 Million (2010 USD) in cross-border benefits annually, with air quality co-benefits of $83/tCO₂e. This result varies between −19% and 22% based on sensitivity analysis for travel and emissions models, though economic damages are likely the largest uncertainty source. Such advances in subnational scale integrated modeling of the environmental impacts of freight can offer insights into the sustainable design of clean freight policy and programs.     

The net effects of the built environment on household vehicle emissions: A case study of Austin,TX

Growing concerns over climate change have led to an increasing interest in the role of the built environment to reduce transportation greenhouse gas (GHG) emissions. Many studies have reported that compact, mixed-use, and well-connected developments reduce vehicle miles traveled (VMT). Others, however, argue that densification and mixture of land uses can slow down vehicle movements, and consequently generate more driving emissions. Methodologically, VMT is only a proxy, not an exact measure of emissions. This study quantifies the net effects of the built environment on household vehicle emissions through a case study of Austin, TX. The study employed structural equation modeling (SEM) techniques and estimated path models to improve understanding of the relationship between the built environment and vehicle emissions. The results show a rather complex picture of the relationship. Densification can reduce regional vehicle emissions despite its secondary effect of reduced vehicle travel speed. A 1% increase in density was found to reduce household vehicle emissions by 0.1%. However, intensification of the design feature of the built environment in developed areas may work in the opposite direction; the modeling results showed a 1% increase in grid-like network being associated with 0.8% increase in household vehicle emissions. Based on the results, the study addressed the potential of and the challenges to reducing vehicle emissions through modifying the built environment in local areas.