Home Search Collections Journals About Contact us My IOPscience Characterizing the GHG emission impacts of carsharing: a case of Vancouver This content has been downloaded from IOPscience. Please scroll down to see the full text. 2015 Environ. Res. Lett. 10 124017 (http://iopscience.iop.org/1748-9326/10/12/124017) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 72.53.34.130 This content was downloaded on 18/02/2016 at 19:59 Please note that terms and conditions apply. Environ. Res. Lett. 10 (2015) 124017 doi:10.1088/1748-9326/10/12/124017 LETTER Characterizing the GHG emission impacts of carsharing: a case of OPEN ACCESS Vancouver RECEIVED 19 August 2015 Michiko Namazu and Hadi Dowlatabadi REVISED Institute for Resources, Environment and Sustainability, The University of British Columbia, Canada 13 November 2015 ACCEPTED FOR PUBLICATION E-mail: [email protected] 26 November 2015 Keywords: carsharing, greenhouse gas emissions (GHG), shared-use mobility, vehicle efficiency PUBLISHED 15 December 2015 Content from this work Abstract may be used under the Carsharing exemplifies a growing trend towards service provision displacing ownership of capital terms of the Creative Commons Attribution 3.0 goods. We developed a model to quantify the impact of carsharing on greenhouse gas (GHG) licence. emissions. The study took into account different types of households and their trip characteristics. Any further distribution of this work must maintain The analysis considers five factors by which carsharing can impact GHG emissions: transportation attribution to the fl fi author(s) and the title of mode change, eet vintage, vehicle optimization, more ef cient drive trains within each vehicle type, the work, journal citation and trip aggregation. Access to carsharing has already been shown to lead some users to relinquish and DOI. ownership of their personal vehicle. We find that even without a reduction in vehicle-kilometers traveled the change in characteristics of the vehicles used in carsharing fleets can reduce GHGs by more than 30%. Shifting some trips to public transit provides a further 10%–20% reduction in GHGs. 1. Introduction Several studies have already examined carsharing services as a GHG mitigation measure. Martin and Carsharing1 is growing in popularity [1, 2]. This paper Shaheen [6] and Transportation Ecology and Mobility intends to analyze the impacts of this transportation Foundation [7] surveyed members of multiple car- service on greenhouse gas (GHG) emissions. The sharing services and calculated emission reduction novelty of approach in this study is the attention paid resulted from carsharing participation. Firnkorn and to the factors and contexts through which carsharing Müller [8], and Cervero and Tsai [9] focused on one impacts are realized. specific carsharing organization each. While Firnkorn The concept of sharing vehicles among multiple and Müller [8] surveyed residents who were interested users has been practiced for more than three decades in carsharing, Cervero and Tsai [9] conducted multi- [3]; it was invented to provide more affordable access ple surveys throughout four years to analyze the to personal mobility [4]. The popularity of such ser- dynamic changes in user behavior by carsharing. vices has been accelerated by smartphones as plat- The focus of these earlier papers has been on how forms for software that facilitates the necessary car sharing leads to a reduction in vehicle-kilometers traveled (VKT) and hence GHG emissions. However, transaction elements such as: vehicle location, book- Lane [10] and Katzev [11] reported issues in VKT ing and payment for service [5]. Many carsharing plat- reporting; the responses they got were highly likely forms allow users the choice of vehicle type as well as inaccurate because few drivers actually knew their VKT. pick up locations. Some carsharing services even pro- In this paper, we model the impact of carsharing on vide one-way carsharing allowing users not to worry GHG emissions through five independent factors about returning the vehicle to the pickup location or beyond VKT reductions. The data used for the model is being responsible for pay-parking at their destination. derived from a travel diary survey in which travel dis- A closer examination of the environmental impact of tances were calculated based on trip start and end loca- such convenient personal mobility services is the focus tions. We use the survey data on trip distances and of this paper. characteristics of families who are not carsharing mem- bers to construct three clusters of household archetypes. 1 More accurately, very short term car rentals. We suspect that household characteristics determine © 2015 IOP Publishing Ltd Environ. Res. Lett. 10 (2015) 124017 Figure 1. Average weekday travel distance by trip purpose (baseline). flexibility in utilizing carsharing. Our scenario-based implications. In section 6 we explore the sensitivity of approach is designed to assess emission reductions as our results to modeling assumptions. We conclude different household types join carsharing programs. with a summary of findings in section 7. In this modeling study we consider three factors involving behavioral change: 2. Scope and data • Mode shifting—higher use of other modes of mobility (public transit, biking and walking); Since vehicle production only accounts for about 10% of the emissions in the lifecycle of a vehicle [12], and the • Right sizing—selecting the appropriate vehicle for majority of the vehicle exhaust gases consists of CO the task at hand; and, 2 [13], we focus on CO2 emissions due to vehicle • Trip planning—aggregation of a number of shorter operations. Vehicle operations are dependent on house- trips into longer trips. hold demands for mobility services. Thus, our metho- dology hinges on patterns of demand for mobility as And two factors arising from fleet composition: revealed in detailed travel diary surveys. For this study, we utilized data gathered by Metro Vancouver, the • — fl Newer cars carsharing eets are, on average, much regional authority for the Vancouver region, represent- newer than owned vehicles. This leads them to ing over 21 850 valid surveys from local households fi bene t from secular improvements in vehicle reporting their previous day’sweekdaytrips[14]. fi ef ciency; We rely on data from Transport Canada [15] for fi • Less macho—carsharing fleets, on average, include vehicle composition and fuel ef ciency. Unfortu- fi fl the more efficient drivetrains offered for each nately, fuel ef ciencies of the current eet are only vehicle type in their fleet. available at the aggregate national level, so our data is not specific to Metro Vancouver. We also used fleet We quantify the effects of each factor and propose composition and vintage from Modo, a local car coop- future steps to utilize carsharing as a GHG emission erative, and vehicle fuel economy statistics [16] that mitigation measure. use the more realistic fuel performance methodology In section 2 we describe the scope of and data used introduced in 2015 [17, 18]. in this study. In section 3 we explain the scenarios and The availability of data on household travel pat- their rationale. In section 4 a piecewise-linear emis- terns, and characteristics of the fleets of user-owned sion calculator is introduced to explore the impact of vehicles and those operated by carsharing services trip aggregation on engine temperature and fuel effi- were the reasons for choosing the Metro Vancouver ciency. In section 5 we present results and discuss their region. We suspect our findings are replicable 2 Environ. Res. Lett. 10 (2015) 124017 Figure 2. Mode of transportation by distance (baseline). wherever the characteristic differences between user- on car ownership and trip characteristics. For example, owned and carsharing fleets are present. 80% of people who join Modo4 sold or donated their cars [22]. More recently, Metro Vancouver’s carsharing [ ] 3. Scenario development survey 23 revealed that 28% of households who gained access to carsharing relinquished their privately For the purpose of this study, we propose three house- owned vehicle, and 70% of them became zero vehicle hold archetypes: (1) households with children and at households, meaning that access to carsharing fully [ ] least one person working away from home (hereafter substituted private car ownership 24 . referred to as household with children), (2) households without children and at least one person working away 3.1. Newer vehicle factor from home (referred to as household without children), Modo owns 340 vehicles [23], and the average age of and (3) households with neither children nor work their fleet is three years [25]. On the other hand, the away from home (referred to as retiree household).The average age of privately owned vehicles in British three households were chosen by following the classifi- Columbia is 11 years5 [26]. Given the secular trend to cation used in the Trip Diary Survey administered for higher fuel efficiency [13], this gap in vehicle vintage Metro Vancouver, Canada [14]. plays a significant positive role in reducing the GHG Trip distances and purposes by household arche- emissions from households who use a carsharing types are presented in figure 1. Mode shares are pre- vehicle instead of their own. This newer vehicle effect is sented in figure 2. These closely resemble2 data found present whenever carsharing is used. Thus the other in Metro Vancouver’s Trip Diary survey for families