- Email: [email protected]
Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland
Research in Transportation Economics xxx (2016) 1e10
Contents lists available at ScienceDirect
Research in Transportation Economics journal homepage: www.elsevier.com/locate/retrec
Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland Niamh Rabbitt, Bidisha Ghosh* Civil, Structural and Environmental Engineering Department, School of Engineering, Trinity College, Dublin 2, Ireland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 November 2015 Received in revised form 22 September 2016 Accepted 3 October 2016 Available online xxx
To meet the binding annual Green House Gas (GHG) emission targets according to the European Union (EU) Effort Sharing Decision by 2020, transport related CO2 emissions are required to be reduced in Ireland. Internationally Car Sharing (CS) has been identified as a means of reducing car dependency and travel related CO2 emissions while still allowing users the benefits of car access. Rabbitt & Ghosh (2013) established that CSS adoption would be beneficial to Dublin & the benefits may extend to Ireland. This study extended the work by providing a detailed framework of evaluating economic and environmental impacts of joining CSS for both individuals and the collective society. The study also expanded the estimation of travel behaviour changes from the users in Dublin city to the potential users in the entire country of Ireland. The analysis identified that car owners who travel predominantly on alternative modes, could make significant travel cost and CO2 emission savings through joining CSS. The long-term benefits included a slower growth rate of car-ownership and in turn generating significantly high CO2 savings of 84 kt for Dublin and up to 229 kt for Ireland with some policy and financial support. © 2016 Published by Elsevier Ltd.
Classification codes: Transportation economics (R4) Keywords: Car sharing Car club CO2 emissions Sustainable travel Travel cost analysis Urban mobility
1. Introduction Ireland is a society in which car ownership and car travel are dominant transport choices. In 2011, the car mode share of commuter trips were 70% nationally and 61% in the Greater Dublin Area (GDA) which is the most populated area of the country (Central Statistics Office, 2011a, 2011b). 82.4% of Irish Households and 66.4% of Dublin Households owned one or more cars (Central Statistics Office, 2012a). This dependence on private car transport is reflected in Carbon Dioxide (CO2) emissions. Private cars were responsible for CO2 emissions of 12.6 MtCO2 and 43% of the total transport energy demand of 4326 Ktoe, both were the largest shares of any sector in the economy in 2013 (SEAI, 2014). Transport was responsible for 21% of all Green House Gas (GHG) emissions in Ireland in 2013 and it was projected that without additional policy measures, “Ireland is projected to cumulatively exceed its obligations by 4 MtCO2eq over the period 2013e2020” (EPA, 2015). It is apparent that ways of reducing both car travel itself and the impacts of car travel are crucial to meet the emission reduction targets
* Corresponding author. E-mail addresses: [email protected] (N. Rabbitt), [email protected] (B. Ghosh).
set down under the European Union (EU) Effort Sharing Decision of reducing GHG emissions by 2020 (Decision No 406/2009/EC). Sustainable strategies such as introduction of organised Car Sharing Services (CSS) can be considered as a way of reducing car dependence and transport related CO2 emissions (Martin & Shaheen, 2010; Rabbitt & Ghosh, 2013). In this paper the term ‘car sharing’ refers to the car rental schemes where members can rent cars from convenient points on a short term basis (typically hourly basis) for a monthly subscription fee, a per hour fee and/or a per km travelled fee. CSS provides members with access to a car on a short-term rental basis without the attendant capital costs. The term car club is also commonly used to describe the service providers, particularly in the UK. This service is distinct from carpooling or ride-sharing where journeys are shared with other people in the same car. At an individual level, change in personal travel behaviour is the most direct and observable short term impact of joining CSS. These individual changes in travel behaviour result in larger scale population level impacts on GHG emissions, travel mode shares of public transport and active modes, levels of private car ownership, distance travelled in private cars, times at which people travel (Cervero, 2003; Loose, 2010; Martin & Shaheen, 2010; Steininger, Vogl, & Zettl, 1996). Martin and Shaheen (2010) found CSS users
http://dx.doi.org/10.1016/j.retrec.2016.10.001 0739-8859/© 2016 Published by Elsevier Ltd.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
2
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
who own a private car before joining drove an average 21,250 km/ year prior to joining CSS; On joining CSS, 46% drove fewer than 800 km/yr and sold their car. Although the no car households would increase their travel in private cars and consequently their GHG emissions of travel on joining the CSS service, typically by 0e250 kg/year. However, the study concluded these no car households generally joined the CSS as a substitute for vehicle ownership, and that these households would have reported a higher emissions in the absence of a CSS. Across both member types in Europe, Loose (2010) found that the average private customer drove 737 km in a CS vehicle in 2008. A study on CSS Members’ (CSM) use of the services (Morency et al., 2007) revealed that the majority of members (over 90%) were occasional users, who used the service less than once per week, and make most of their journeys on weekends. The remaining users were frequent users using the service on weekdays. The collective impacts of CSS improve sustainability in transport networks through reduced car-dependency and travel related GHG emissions. The main attractiveness of CSS is that the members can have access to a car without the capital costs of owning it. However, there exist several barriers to the development of CSS, and foremost among these is a simple lack of understanding amongst national and local governments as to what a CSS is and how it might operate within an area (Enoch & Taylor, 2006). The study identified three areas of government policy that have the potential to promote CS. These are regulation, fiscal mechanisms and information. Regulation includes measures such as the provision of parking, or the waiving of the need to provide parking spaces in a development where a CSS is available. Fiscal mechanisms may include tax breaks for CSS in the early phases of development, or financial support in the purchasing of cars or the installation of on-street equipment and signage. Information support typically includes high level information from national governments disseminating information to local governments which supports the development of local level CSS and the usage of established transport information channels (e.g. links with public transport service providers) to disseminate information about car sharing. The potential market of introducing CSS in Ireland were examined by Rabbitt and Ghosh (2013). They identified the potential users through geographic analysis. This study complements the work by providing a detailed framework of evaluating the economic and environmental changes that can be experienced by the potential users. The study also expands the estimation of travel behaviour changes from the users of Dublin city to the potential users in the entire country of Ireland. This paper compares the existing and expected travel behaviours & changes in county Dublin & Ireland. The study demonstrates that the average travel costs and CO2 emissions by average individuals living outside Dublin is much higher than individuals living in Dublin. The collective economic and environmental benefits under all possible membership compositions and scenarios are illustrated in this study. The potential policy impacts of introduction of CSS are also discussed in detail. 2. Methodology This paper estimates the economic and environmental benefits of introduction of CSS in Ireland utilising a methodology described in Rabbitt and Ghosh (2013). As outlined in Fig. 1, the potential impacts of CSS in Ireland has been estimated in a three stage process. In the first stage, a geographic analysis was carried out to identify the population density of most likely CS adopters (termed as, Likely Users, LU) in unit geographic area based on socio-demographic data. Based on the population density of LU and the business requirements of CSS,
each unit area was then categorised into one of five CSS viability levels. In the second stage, the potential economic and environmental impacts of switching to car sharing was estimated for individual members assuming hypothetical travel behaviour change scenarios. In the final stage, the collective economic and environmental impacts of widespread adoption of car sharing was studied considering viability of CSS as determined in stage 1 and potential travel behaviour changes. These three stages are described briefly in the following subsections. For further details, please refer to Rabbitt and Ghosh (2013). 2.1. Identification of potential users of CSS CSS is most suitable for the lifestyles and travel patterns of certain groups of people, defined as LU in this paper. Following Rabbitt and Ghosh (2013), the most likely CS members or LU possess the following 5 traits: 1) residents of 1e2 person, no car households, 2) age between 25 and 49 years, 3) working or selfemployed, 4) educational qualification includes at least an ordinary degree, 5) non-car owners & 6) do not drive to work. The number of LU in each unit area can be identified using sociodemographic information. From a business perspective, a CS station with 2 CS cars is viable if there are at least 50 possible LU individuals in per km2 area. It is not expected that LU will be the only members of CSS, however they may provide the core membership enabling the CSS to reach a break-even point. Depending on population densities, four roll-out stages can be assumed for introducing CSS in any unit geographic area. 1. Early Rollout (Y1): Areas with LU population >1000/km2 are provided with 1 CS station with 5% of the LU population joining CSS. 2. Middle Rollout (Y2): Areas with LU population between 999 and 100/km2 are provided with 1 CS station with 50% of the LU population joining CSS. 3. Late Rollout (Y3): Areas with LU population Areas with LU population between 99 and 25/km2 and all (100%) are projected to join the CSS. 4. Maximum Limit (Y4): Areas with population density of 25 adults/km2, but fewer than 25 LU/km2 may join CSS if private car ownership is restricted.
2.2. Economic and environmental impacts to individual users The economic and environmental impacts to individuals on joining CSS are reduced travel costs and reduced travel related CO2 emissions. The estimated economic and environmental impacts to individuals were calculated following a four step process illustrated in Fig. 2(A). In the first step, a travel and activity diary based survey was undertaken among Irish residents to investigate their existing travel patterns. Further details of the survey is described in section 3.1. The second step calculated each individual respondent's travel related CO2 emissions and travel cost using information provided by respondents in conjunction with Department of Transport data. In the third step, respondents with LU attributes were identified and were categorised based on their dominant travel mode (travel style group). In the final step, three hypothetical behavioural change scenarios were assumed and applied to responses of individuals. The economic and environmental impacts were calculated based on their travel behaviour changes.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
3
Fig. 1. Schematic for estimating the potential impacts of CS adoption.
2.2.1. Calculation of travel costs The costs of travel were calculated on an annual basis. The direct costs of travel (fuel costs, maintenance costs, bus fares etc.) were obtained directly from survey responses. The indirect costs related to vehicle ownership were calculated separately. The methodology used to calculate the cost of travel in private vehicles and travel by public transport is described below. The cost of travel in a private car (Vauto) was calculated adapting a methodology used by Hensher and Chen (2011). In relation to calculation of this cost, three elements associated with vehicle ownership: 1) depreciation (vdep); 2) opportunity costs (vop); and 3) annual charges (vex) were considered. The annual estimated fuel costs (vfu) were calculated from survey responses.
The cost of travel by public transport (Vtransit) was calculated using two elements: commuter fares for travelling in bus/train, vcom and taxi & other short-term travel expenses, vshort.
vshort ¼Average weekly public transport fares ðincluding taxi faresÞ reported*52
(7)
Vauto ¼ vdep þ vop þ vex þ vfu
(1)
Vtransit ¼ vcom þ vshort
(8)
vop ¼ 0:05ðCar Valt Þ;
(2)
vdep ¼ Car Valtþ1 Car Valt ;
(3)
vex ¼ Road Tax þ Insurance þ Maintenance;
(4)
vfu ¼ Fuel costs per week*52
(5)
The current value of a private vehicle, Car_Valt, was calculated using information provided by respondents on the age and engine size of the cars they owned in conjunction with information provided by Irish Tax and Customs (2012a, 2012b). For each year group & engine size combination, the most common make & model combination was identified (Dept. of Transport Tourism and Sport, Ireland, 2011), and an Open Market Selling Price (OMSP), Depreciation Code (DC), and CO2/km figure was obtained from the Vehicle Registration Tax (VRT) database (Irish Tax and Customs, 2012a). Each respondent's car was assumed to be the most common make and model, for the year and engine size provided by the respondent. Using the OMSP, DC and current year for that make and model, each respondent's current car value was calculated (Irish Tax and Customs, 2012b).
vcom ¼ d*Annual Commuter Tickets þ d*ðMonthly Bus=Train=Tram=Combined Tickets*12Þ þ d*ðWeekly Bus=Train=Tram=Combined Tickets*52Þ (6)
d is a binary variable which is equal to 1 if a respondent is in possession of a certain type of ticket and zero otherwise. The cost of travel for each respondent was calculated by summing three components: Vauto, Vtransit and VCSS. The last element indicated the cost of using a CSS service such as GoCar in Ireland. 2.2.2. Travel related CO2 emission Calculation of CO2 emissions of travel was based on the travel distances in each mode of transport reported by survey respondents. The CO2 emissions of travel in private cars were calculated based on the year and engine size or tax band details provided by respondents. In case of insufficient details of the car in which they travelled, an average value of 147.3 g/km was used as obtained from the weighted average of all car details provided by respondents. CO2 emissions per km for other modes were taken the CMT emissions calculator (Environmental Protection Agency, 2009). The CMT emissions calculator also provided a value of 150 g/km travelled for an average private car in Ireland. This was therefore adopted as the emissions value for CSS vehicles. 2.2.3. Travel style categorisation The survey respondents possessing traits favourable for subscribing to CSS were identified and extracted for further analysis.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
4
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
Fig. 2. (A): Schematic for estimating individual economic and environmental benefits, (B) Schematic for estimating collective economic and environmental benefits.
These respondents were classified into three ‘travel style’ categories based on their dominant modes of travel. These were: 1) Active Traveller (AT) who took most trips by walking or cycling, 2) Public Transport Traveller (PT) where the respondent mostly used bus/tram/train and 3) Car Traveller (CT) where most trips were made on private car as either driver or passenger (Rabbitt & Ghosh, 2013). These travel style categories were utilised as a basis for simulating travel behaviour changes in chosen respondents. 2.2.4. Simulation of travel behaviour changes. In the final step, three travel behaviour change scenarios were simulated to estimate the extent of economic and environmental impacts of switching to CSS for the survey respondents. The justification behind the design of these scenarios were discussed in Rabbitt and Ghosh, 2013. This paper provides the detailed design of the scenarios. a) Best case scenario (BCS) In this scenario, respondents in all three travel styles joined CSS. They used a CS vehicle for making 10% of their reported journey distance travelled in a private car and the remaining distance were covered in either public transport or active modes in a proportion similar to the one originally reported. The following algorithm was used to simulate the scenario: Step 1. For each respondent, the costs of making 10% of his/her
car trips in a CS vehicle was calculated. This cost was a sum of yearly CSS subscription, booking fees, distance and hourly charges. Step 2. For each respondent, the CO2 emissions generated from his/her trips in CS vehicles were calculated based on the type of CS vehicle (conventional or electric). Step 3. The distance travelled in public transport (rBus, rTrain) and in active modes rAT were expressed as fractions of total non-private vehicle distance travelled by a respondent. In cases, where no travel was reported on any of these modes, a modal split of 25% bus, 25% train, 50% active modes was assumed.
rBus þ rTrain þ rAT ¼ 1
(9)
Step 4. For each respondent, the costs of making 90% of his/her car trips in alternative modes (rBus:rTrain:rAT) was calculated assuming an average cost/km for their travel on public transport based on reported data. In cases, where no travel or no costs were reported an average value of 13.1c/km was used. This 13.1c/km was based on the average reported cost for all respondents to the survey. Walking and cycling were assumed to attract no additional cost. Step 5. For each respondent, the CO2 emissions generated from his/her trips in step 4 were calculated. Step 6. For each respondent, the cost of travel in BCS was
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
calculated by adding the travel costs from step 1 & step 4 and subtracting any expenses related to vfu. Step 7. The total CO2 emissions generated in BCS was a sum of the same from step 2 & step 5. b) Most probable scenario (MPS) This scenario represented the most likely behavioural changes among CS members. In this scenario respondents were divided into three categories: (a) CT, car owners (b) AT/PT, car owners and (c) CT/ AT/PT, non-car owners. It was assumed that those in category (a) did not join the CSS, and consequently made no changes to their travel patterns. Those in category (b) joined the CSS and follow BCS, i.e. 10% of their reported journey distance travelled in a private car was covered in CS cars and the remaining 90% of reported journey distance was made using alternative modes. The methodology for calculating the impact of their behaviour change followed the description in BCS. Those in category (c) did join the CSS and their behavioural change was modelled as replacing all of their current car trips with CS trips subject to a minimum of 130 km/year. The travel costs were calculated accordingly. c) Worst case scenario (WCS) In this scenario minimal behavioural change occurred among CS members. Members in all three categories replaced all their current levels of car travel with travel in a CS vehicle as the sole occupant. The methodology otherwise followed the steps 1e2 described in BCS except the proportion 10% was replaced by 100%. No additional distance was travelled via public or active transport. 2.3. Collective economic and environmental impacts This analysis aims to establish the total economic and environmental impact of introduction of CS. The immediate changes and long-term projected behavioural changes, both, were estimated (Fig. 2(B)). The determinants of theses impacts are the levels of uptake of CSS and the change in travel behaviour expected in CSM. Following Rabbitt and Ghosh, 2013, three types of members subscribed to CSS: 1. LU members have AT, PT or CT style and do not own cars 2. Low Usage Car Owners (LUCO) members are car owners with AT or PT style 3. Radical Changers (RC) are car owners with CT style The estimating the immediate impacts, it was designed that 50%e80% of CSM were from the LU demographic and the remaining 20%e50% of users were comprised of LUCO and RC. The total travel cost and CO2 emissions were calculated assuming that the travel behaviour change in LU & LUCO members will follow the most probable scenario and in RC members will follow the best case scenario (section 2.2.4). In estimating the long-term projected impacts of introducing CSS, the absence of travel behaviour changes that could have happened if CSS was not available was taken into consideration. It was assumed that a certain percentage of AT & PT style LU, who were originally non-car owners would purchase cars in future in absence of CSS. The full impact of introducing CSS can be estimated by assuming a higher percentage of LUCO members (>50%) than LU members and a small percentage of RC members. This can be considered as an idealistic situation and would estimate the maximum travel cost and CO2 emission savings achievable through implementation of CSS. 3. Data The data used in this study were obtained from several sources. The data and the sources are described in this section.
5
3.1. Survey data A survey was undertaken to investigate the travel patterns and costs of Irish residents. The survey constituted a travel diary kept by the respondents over a 2 week period recording number of trips, costs, travel modes, trip time and distance, socio-demographic information etc., on a fixed response on-line questionnaire. The survey was conducted over a 6 week period between 10th February and 16th March 2012 and received 2639 responses from residents of Ireland. The survey responses were restricted to 1172 from the respondents who were employed or self-employed and within the age group 18e65. Out of this sample, there were 816 respondents who lived in the largest city in Ireland, Dublin. The details of these survey responses, classified by travel style and car ownership, are provided in Table 1. It can be observed that, on average, the respondents from Dublin made 27 less trips/year, travelling 3,912 km less and spending 30 h less than the overall average. These survey responses were in line with the National Travel Survey (NTS) data as was established in the study by Rabbitt and Ghosh (2013). Fig. 3 provides an overview of the number of journeys taken, the distance travelled and the time spent on each mode by respondents. The width of each box-plot indicates the number of respondents who used that mode in each case. The results show a large variation in the number of journeys reported by individuals on each mode of transport. 3.1.1. Travel costs & CO2 emissions Survey respondents were asked to provide some details of their travel costs. Their annual costs of travel calculated following the methodology described in section 2.2.1 are provided in Table 2. The average travel costs and average CO2 emissions for the chosen samples, calculated using the methodology described in section 2.2.1 & section 2.2.2 are provided in Table 1. 3.2. Census 2011 data To identify the potential CSS users (LU) the Small Area Population Statistics generated from the 2011 Irish census and published by the Central Statistics Office (2011b) were used. A small area is the smallest area unit in the census for which socio-demographics and all travel related information are published (Central Statistics Office, 2011b). The 2011 Irish census was carried out on the night of Sunday, April 10th, 2011 (Central Statistics Office, 2012a) and the results of the census are published as aggregate data, in small areas consisting of 264 people or 97 households on average. 3.3. Existing car sharing organisation in Ireland GoCar is a third party organisation operating a CSS in Dublin and Cork. In 2012, the company was operating 11 car bases. An anonymized breakdown of all customer bookings made from February 1st, 2012 to July 31st, 2012 was obtained. Members who joined or left CS during the study period were excluded from calculations. This data was used to establish the behaviour of new CS members, i.e. frequency of use, distances travelled etc. On average, the customers made 1 trip every 2 months, cars were booked for 2.3 h, travelled 21.67 km and were on the membership rate that charges V5/month, with V0.45/km and an additional V5.75/hr (GoCar Website, 2013). For reasons of commercial confidentiality it is not possible to give a full breakdown of this data set, however overall the dataset revealed low levels of usage among GoCar members and a large section of the members did not make a booking during the study period.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
6
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
Table 1 Summary of sample survey data. Active traveller
Car traveller
Public transport traveller
All respondents
Non-car owner
Car owner
Non-car owner
Car owner
Non-car owner
Car owner
Dublin Number of respondents % of Total % Female Average Age Average Household Size Average Km/Year Average No. of Trips/Yr Average Travel Time (Hrs) Average Annual Travel Cost Average CO2 Emissions(kg/yr)
189 23.16% 47.6% 33.0 2.20 7813 986 366.1 V1108 283.2
122 14.95% 47.5% 37.6 2.55 8164 944 336.3 V3750 496.5
45 5.51% 57.8% 32.4 2.64 12,809 900 348.3 V2570 1006.4
248 30.39% 57.7% 37.5 2.57 14,232 1070 391.5 V5559 1457.8
123 15.07% 52.8% 32.7 2.39 7139 933 387.8 V1784 448.9
89 10.91% 58.4% 37.9 2.54 10,057 1008 458.6 V5256 731.3
816 100.00% 53.2% 35.5 2.54 10,235 995 381.7 V3491 785.8
Ireland Number of Respondents % of Total Average Age Average Household Size Average Km/Year Average No. of Trips/Yr Average Travel Time (Hrs) Average Annual Travel Cost Average CO2 Emissions(kg/yr)
234 19.97% 33.1 2.3 10,946 960 356.9 V1190 483.2
149 12.71% 38.2 2.6 8243 945 325.3 V3827 541.6
63 5.38% 32.2 2.5 14,446 895 361.9 V2687 1197.2
422 36.01% 37.2 2.7 19,487 1133 440.6 V5944 2202.4
166 14.16% 32.9 2.5 10,058 926 440.1 V1974 614.3
138 11.77% 39.1 2.6 14,402 1049 497.9 V5652 986.3
1172 100.00% 35.9 2.6 14,147 1022 411.7 V3954 1225.9
Fig. 3. Per Fortnight, summary of (a) Number of Journeys Undertaken, (b) Distance Travelled and (c) Time Spent by Mode of Transport.
Table 2 Breakdown of average annual costs and CO2 emissions of travel for survey respondents. Outside Dublin
Opportunity Costs of Car Ownership Depreciation Costs of Car Ownership Annual Contributions towards all private vehicle Fortnightly Costs for Travel in private vehicle Annual Train and Bus Tickets Cost Fortnightly costs for travel in buses and trains converted to yearly costs Annual Taxi Spend Total Travel Cost Carbon Dioxide Emissions (kg/yr)
Dublin
Total
No car
Car owner
No car
Car owner
416
372
1215
636
2639
VVV184.50 V498.44 V353.53 V823.40 V243.84 V2103.72 1192.1
V360.31 V1390.81 V1262.95 V2324.08 V289.48 V259.29 V142.81 V6029.74 3365.2
VVV134.20 V293.46 V286.62 V434.09 V248.26 V1396.63 794.1
V306.20 V1212.46 V1115.86 V1555.14 V162.20 V275.28 V259.19 V4886.33 1023.8
V124.58 V488.26 V537.82 V916.08 V267.59 V432.55 V235.33 V3002.21 1274.6
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
4. Analysis & results In this section the results of the analyses done following the methodology described in section 2 is provided. 4.1. Identification of LU The 2011 Irish census data was used to identify areas suitable for establishing car-sharing stations with sufficient densities of probable CSM. The results and analysis are same as that presented in Rabbitt & Ghosh, 2013. The results showed that the population of LU was concentrated in areas with higher population densities, principally in the four main cities: Dublin, Cork, Galway and Limerick. 97% of the LU population inhabited in these four cities in areas with the potential for early rollout of CS. For the entire country at the early rollout stage, 2.5% of the entire adult population were estimated as LU. In a late rollout, around 20% of the adult population could join CSS. For the early and middle rollout stages Dublin County provided the only continuous area with a high density of suitable locations. Hence, in the later subsections the overall impact of introduction of CSS has been studied in further detail for Dublin County along with the entire country. 4.2. Individual economic & environmental analysis This analysis was undertaken to establish the economic and environmental changes generated from the switch to CSS for individual users. The travel costs and CO2 emissions of individuals classified by travel style and car ownership are presented in Table 3. The reported costs & emissions are presented along with estimated costs & emissions as calculated considering travel behaviour changes as described in section 2.2.4 under three hypothetical scenarios. The calculations were carried out for Dublin county (N ¼ 816) and Ireland (N ¼ 1172) separately. In all cases, the average travel costs and emissions for the country was higher than that in Dublin. The average CO2 emissions were much higher for Ireland compared to Dublin.
7
Individuals of non-car based travel styles, AT & PT, who were car owners gained the most from joining CSS. The AT, car-owners saved around 74% and PT car-owners saved around 60% on joining CSS in both Dublin & Ireland under BCS & MPS. However, for non-car owners in AT & PT style, in most cases joining CSS might incur extra costs. However, it can be argued that this extra cost to non-car owners was reasonable, particularly when compared to the alternative option of a car purchase. The results for CO2 emissions were similar to the travel costs, i.e. car owner, could reduce their CO2 emission by joining CSS (AT 65% & PT 14e20%) and for non-car owners joining CSS would increase their CO2 emissions slightly. However, if the CSS vehicles were run by electric then both car owners and non-car owners would be able to reduce their CO2 emissions under all three scenarios especially in WCS. 4.3. Collective economic & environmental analysis This section illustrates the potential economic and environmental benefits that can be gained by introducing CSS extensively in Ireland. As indicated in 4.1, the early rollout and middle rollout of CSS is mainly viable in Dublin. The later rollout stages are possible only through policy and infrastructure support. The monetary saving to the users and travel related emission reductions were calculated following the methodology described in section 2.3. The immediate and the ideal impacts are presented for both Dublin and Ireland in four combinations of membership compositions in Table 4. The impacts for Dublin were calculated using average cost and emission savings as simulated for Dublin residents. The impacts for Ireland were calculated using average cost and emission savings as simulated from the entire sample size of 1172 survey respondents. It was assumed that the average value of cost and emission estimations from Table 3, can be applied to LU, LUCO and RC users in all small areas in Ireland. It can be argued that the introduction of CSS can be viable at a middle rollout stage with some policy support and a very likely membership composition of 70%LU, 25%LUCO & 5%RC can reduce CO2 emissions by 25.4 kt/yr in Dublin and 69.6 kt/yr in Ireland with electric CS vehicles.
Table 3 Individual Financial and Environmental Impacts of joining a Car Sharing Service. Dublin
Ireland
Active traveller
Car traveller
Non-car owner
Car owner
Non-car owner
V3750 V965 V965 V3111
Annual average travel costs in Euros Reported V1108 Best Case Scenario (BCS) V1055 Most Probable Scenario (MPS) V1286 Worst Case Scenario (WCS) V1744 Annual average travel related CO2 emission in kg Reported 283.2 496.5 Conventional Best Case Scenario 240.6 cars (BCS) Most Probable Scenario 293.4 (MPS) Worst Case Scenario 394.4 (WCS) Electric cars Best Case Scenario 148.5 (BCS) Most Probable Scenario 283.0 (MPS) Worst Case Scenario 179.8 (WCS)
Public transport traveller
Active traveller
Car traveller
Public transport traveller
Car owner
Car owner
Non-car owner
Non-car owner
Car owner
Non-car owner
Car owner
Car owner
Non-car owner
V2570 V3786 V2570 V6827
V5559 V3261 V5559 V8827
V1784 V1822 V1964 V2305
V5256 V2115 V2115 V3633
V1190 V1082 V1343 V1903
V3827 V979 V979 V3205
V2687 V3802 V2687 V7596
V5944 V3708 V5944 V11,471
V1974 V2001 V2123 V2639
V5652 V2170 V2170 V3984
1006.4 179.9
1457.8 669.2
448.9 598.0
731.3 428.0
483.2 585.6
541.6 427.6
1197.2 199.3
2202.4 792.0
614.3 888.6
986.3 591.4
850.6
179.9
1006.4
1457.8
458.4
585.6
493.1
199.3
1197.2
2202.4
623.6
850.6
689.4
1560.2
1943.7
448.9
731.3
605.8
723.4
1794.3
2677.8
702.3
1161.7
92.2
462.5
380.0
223.9
363.3
297.1
109.8
531.7
590.0
322.3
472.0
92.2
1006.4
1457.8
448.6
363.3
483.0
109.8
1197.2
2202.4
614.0
472.0
91.4
172.2
98.1
351.6
367.2
360.6
102.2
221.8
139.3
503.4
617.9
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
8
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
Table 4 Estimation of collective economic and environmental impacts of car sharing rollout in Dublin & Ireland. Dublin
Ireland
Immediate % Members per category
Projected number of Members
CO2 saved in Conventional Car CSS (kt/yr)
CO2 saved in Electric Car CSS (kt/yr)
Cost Saved by Members (Million Euros)
LU LUCO RC Early Adopters Middle Adopters Late Adopters Maximum Limit Early Adopters Middle Adopters Late Adopters Maximum Limit Early Adopters Middle Adopters Late Adopters Maximum Limit Early Adopters Middle Adopters Late Adopters Maximum Limit
80% 20% 0% 4676 174,883 416,137 450,151 0.20 7.34 17.47 18.89 0.28 10.54 25.09 27.14 V2.16 V80.78 V192.22 V207.93
70% 25% 5% 5344 199,866 475,586 514,459 0.52 19.60 46.63 50.44 0.68 25.48 60.64 65.60 V3.95 V147.74 V351.55 V380.28
55% 30% 15% 6801 254,375 605,291 654,766 1.34 50.25 119.58 129.35 1.68 62.81 149.46 161.68 V7.75 V289.77 V689.52 V745.88
Ideal
Immediate
10% 70% 20% 5344 199,866 475,586 514,459 1.83 68.41 162.78 176.08 2.24 83.79 199.38 215.68 V13.35 V499.39 V1188.31 V1285.44
80% 20% 0% 5373 306,632 849,112 1,206,189 0.23 12.86 35.61 50.58 0.51 28.97 80.22 113.96 V2.83 V161.27 V446.58 V634.38
Ideal 70% 25% 5% 6140 350,437 970,413 1,378,502 0.74 42.27 117.04 166.26 1.22 69.62 192.78 273.84 V4.96 V283.32 V784.56 V1114.49
55% 30% 15% 7815 446,010 1,235,072 1,754,457 2.07 118.37 327.79 465.63 3.00 171.02 473.57 672.72 V9.45 V539.40 V1493.67 V2121.81
10% 70% 20% 6140 350,437 970,413 1,378,502 2.31 86.35 205.47 222.27 4.01 228.68 633.26 899.56 V16.22 V925.57 V2563.04 V3640.88
Fig. 4. Collective economic and environmental impact for middle adapters in Dublin (A) collective economic impact (B) collective CO2 emission in conventional car CSS (C) Collective CO2 emission in electric car CSS.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
9
Fig. 5. Collective economic and environmental impact for middle adapters in Ireland (A) Collective economic impact (B) collective CO2 emission in conventional car CSS (C) Collective CO2 emission in electric car CSS.
For membership compositions varying between 20 and 80%LU, 0e20% RC, the costs and emission reductions in middle rollout stages are plotted for both Dublin (Fig. 4) and Ireland (Fig. 5). It is apparent that in ideal impact situations, i.e. percentage of LUCO > LU, the savings would be higher and also with higher percentage of RC members the savings would be even bigger. However, the changes in savings did not follow the same pattern for both Dublin and Ireland. For the entire country, a 50%LU users with high percentage of RC members generated maximum benefits. 5. Discussion The inferences drawn from the analyses in the previous section and their implications for policy changes are discussed in this section. The cost analysis indicated that owning a car significantly increases the annual travel related costs of an individual. Individuals
who are not dependant on a privately owned car to satisfy the majority of their travel needs would save significantly on joining CSS. The non-car owners can join CSS as an alternative to purchasing a car as CSS would provide access to a car without bearing the ownership and maintenance costs. The car owners would also benefit by saving ownership and maintenance costs; however CSS membership may not replace their total need for car travel if they do not intend to change a percentage of their trips to more sustainable modes. The financial benefits gained by the LUCO and noncar owners on joining CSS makes a definite business case for introducing public or private CSS to Ireland. The introduction of CSS would also provide significant CO2 savings at all rollout stages. The more car owners join CSS, the bigger the environmental benefit will be. If policy changes are made to support wide-spread introduction of CSS, it can contribute to the National Climate Change Strategy (Dept. of the Environment, Heritage, & Local, Government, Ireland, 2007), prove to be a
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
10
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
major step towards sustainability by promoting sustainable travel modes such as walking, cycling and public transport, by indirectly restricting car ownership levels and number of car trips. It would also reduce congestion levels, parking space demand and other externalities associated with increased car mode share. The environmental benefits of CSS can be best realised through provision of electric cars as CS vehicles. This will promote use of electric cars, further reduce CO2 levels and may bring in private investment from electric car manufacturers. The geographic analysis indicated that there exists a large group of individuals (denoted as LU in the paper) who are quite likely to join CSS. Accordingly, even without much policy support CSS is viable in areas with high population density within Dublin County. Outside Dublin, there is a lack of high density areas with suitable users. With governmental policy support such as tax incentive or work place mobility management schemes, a CSS could operate successfully in the major cities (Cork, Limerick, Galway) and other medium density areas in Ireland. The immediate benefits of introducing CSS may seem modest at early roll-out stages. However, the expected long term impacts are significant; the 30%LU/60%LUCO/10%RC spilt in the middle rollout areas in Dublin seems to be the most probable projection for introducing CSS which would save approximately V453million/ year for customers and 86 kt CO2 emissions per annum in Dublin with electric cars (Fig. 4). The savings will increase to V485million/ year and 104 kt/year when the entire country is considered (Fig. 5). This reduction in CO2 emissions per annum is important as transport is one of the largest contributing sectors in non-emission trading scheme (non-ETS) emissions and it is projected that Ireland may cumulatively exceed its GHG emission obligations by 4 Mt CO2eq over the period 2013e2035 (Environmental Protection Agency, 2015). Introduction of CSS may reduce the projected increase of 13% by 2020 in CO2 emissions in transport sector to a smaller figure. 5.1. Limitations Due to lack of available information, the study did not include the investment required for infrastructure development, operation and maintenance of CSS in Ireland, hence the cost savings are calculated on the basis of customer savings and not in terms of profits that could be obtained by CSS operators such as government or third parties. 6. Conclusions The study in this paper estimates individual and collective, economic and environmental impacts of introduction of CSS in Dublin and in Ireland. The study presents multiple alternative scenarios which examine the financial and environmental factors influencing CS adoption and use. The scenarios were developed using observations from existing CSS in other countries. Some observations from the small scale CSS, GoCar, operating in Ireland have also been used to compare the travel behaviour change among CSM in Ireland and elsewhere. These scenarios were applied to the available and collected travel information of the Irish population to estimate the potential impact of introducing CSS in Ireland. The travel information was collected through an activity diary based survey administered to over 2500 respondents. Information
available through the census and National Travel Survey data of Ireland were also utilised. The study showed CSS holds immense potential in terms of cost savings to members, reduction of travel related CO2 emission and increased share of sustainable modes of travel. In almost all the scenarios designed in the study, financial savings were estimated for members of CSS. This is expected to be the most influential factor in attracting individuals to join CSS. The study in this paper was based on two-way CSS where cars are required to be returned to the same base from which they were rented. The one way CS, similar to a bike sharing scheme, where cars may be returned to a different station from the collection point needs to be investigated as well. The increased flexibility of oneway CS may lead to increased scope of CSS in Ireland. Acknowledgements The authors wish to thank the Irish Research Council for Science, Engineering and Technology for providing a grant to support this research. References Central Statistics Office, Ireland. (2011a). National travel survey 2009 (Prn A11/1239). Dublin, Ireland: Dublin: Stationery Office. Central Statistics Office, Ireland. (2011b). Census of population 2011: Preliminary results. Dublin, Ireland: Dublin: Stationery Office. Central Statistics Office, Ireland. (2012a). This is Ireland: Highlights from census 2011 Part 1. Dublin, Ireland: Dublin: Stationery Office. Cervero, R. (2003). City car share: First-year travel demand impacts. Transportation Research Record: Journal of the Transportation Research Board, 1839(1), 159e166. Dept. of the Environment, Heritage, & Local, Government, Ireland. (2007). Ireland : National climate change Strategy 2007-2012. Dublin: Dept. of the Environment, Heritage and Local Government. Dept. of Transport Tourism and Sport, Ireland. (2011). Irish bulletin of vehicle and driver statistics 2011. Enoch, M. P., & Taylor, J. (2006). A worldwide review of support mechanisms for car clubs. Transport Policy, 13(5), 434e443. Environmental Protection Agency Ireland. (2009). Change CMT calculator emission factor sources. EPA. Environmental Protection Agency Ireland. (2015). Ireland's greenhouse gas emission projections 2014-2035. EPA. GoCar Website. (2013). GoCar official website. Retrieved 14th April 2013, 2013, from http://www.gocar.ie/. Hensher, D. A., & Chen, X. (2011). What does it cost to travel in Sydney? Spatial and equity contrasts across the metropolitan region. Road and Transport Research, 20(2). Irish Tax and Customs. (2012a). Online vehicle registration tax (VRT) valuation enquiry system. Retrieved 1st May 2012, 2012, from https://www.ros.ie/evrt-enquiry/ vrtenquiry.html?execution¼e2s1. Irish Tax and Customs. (2012b). Vehicle registration tax, section 8, valuation system for new and used vehicles. Dublin: Revenue.ie Retrieved from http://www.revenue. ie/en/about/foi/s16/vehicle-registration-tax/vrt-manual-section-08.pdf. Loose, W. (2010). “The state of European car-sharing”, project momo final report d 2.4 work package 2. Martin, E. W., & Shaheen, S. A. (2010). Greenhouse gas emission impacts of carsharing in North America. United States., California: Mineta Transportation Institute (San State Jose, CA: Mineta Transportation Institute, College of Business, San Jose University). panier, M., Agard, B., Martin, B., & Quashie, J. (2007, September). Car Morency, C., Tre sharing system: What transaction datasets reveal on users’ behaviors. In 2007 IEEE Intelligent Transportation Systems Conference (pp. 284e289). IEEE. Rabbitt, N., & Ghosh, B. (2013). A study of feasibility and potential benefits of organised car sharing in Ireland. Transportation Research Part D: Transport and Environment, 25, 49e58. SEAI. (2014). Energy in transport 2014 report (SEAI, Trans.) energy policy statistical support unit. Dublin: Sustainable Energy Authority Ireland. Steininger, K., Vogl, C., & Zettl, R. (1996). Car-sharing organizations: The size of the market segment and revealed change in mobility behavior. Transport Policy, 3(4), 177e185.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
Contents lists available at ScienceDirect
Research in Transportation Economics journal homepage: www.elsevier.com/locate/retrec
Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland Niamh Rabbitt, Bidisha Ghosh* Civil, Structural and Environmental Engineering Department, School of Engineering, Trinity College, Dublin 2, Ireland
a r t i c l e i n f o
a b s t r a c t
Article history: Received 21 November 2015 Received in revised form 22 September 2016 Accepted 3 October 2016 Available online xxx
To meet the binding annual Green House Gas (GHG) emission targets according to the European Union (EU) Effort Sharing Decision by 2020, transport related CO2 emissions are required to be reduced in Ireland. Internationally Car Sharing (CS) has been identified as a means of reducing car dependency and travel related CO2 emissions while still allowing users the benefits of car access. Rabbitt & Ghosh (2013) established that CSS adoption would be beneficial to Dublin & the benefits may extend to Ireland. This study extended the work by providing a detailed framework of evaluating economic and environmental impacts of joining CSS for both individuals and the collective society. The study also expanded the estimation of travel behaviour changes from the users in Dublin city to the potential users in the entire country of Ireland. The analysis identified that car owners who travel predominantly on alternative modes, could make significant travel cost and CO2 emission savings through joining CSS. The long-term benefits included a slower growth rate of car-ownership and in turn generating significantly high CO2 savings of 84 kt for Dublin and up to 229 kt for Ireland with some policy and financial support. © 2016 Published by Elsevier Ltd.
Classification codes: Transportation economics (R4) Keywords: Car sharing Car club CO2 emissions Sustainable travel Travel cost analysis Urban mobility
1. Introduction Ireland is a society in which car ownership and car travel are dominant transport choices. In 2011, the car mode share of commuter trips were 70% nationally and 61% in the Greater Dublin Area (GDA) which is the most populated area of the country (Central Statistics Office, 2011a, 2011b). 82.4% of Irish Households and 66.4% of Dublin Households owned one or more cars (Central Statistics Office, 2012a). This dependence on private car transport is reflected in Carbon Dioxide (CO2) emissions. Private cars were responsible for CO2 emissions of 12.6 MtCO2 and 43% of the total transport energy demand of 4326 Ktoe, both were the largest shares of any sector in the economy in 2013 (SEAI, 2014). Transport was responsible for 21% of all Green House Gas (GHG) emissions in Ireland in 2013 and it was projected that without additional policy measures, “Ireland is projected to cumulatively exceed its obligations by 4 MtCO2eq over the period 2013e2020” (EPA, 2015). It is apparent that ways of reducing both car travel itself and the impacts of car travel are crucial to meet the emission reduction targets
* Corresponding author. E-mail addresses: [email protected] (N. Rabbitt), [email protected] (B. Ghosh).
set down under the European Union (EU) Effort Sharing Decision of reducing GHG emissions by 2020 (Decision No 406/2009/EC). Sustainable strategies such as introduction of organised Car Sharing Services (CSS) can be considered as a way of reducing car dependence and transport related CO2 emissions (Martin & Shaheen, 2010; Rabbitt & Ghosh, 2013). In this paper the term ‘car sharing’ refers to the car rental schemes where members can rent cars from convenient points on a short term basis (typically hourly basis) for a monthly subscription fee, a per hour fee and/or a per km travelled fee. CSS provides members with access to a car on a short-term rental basis without the attendant capital costs. The term car club is also commonly used to describe the service providers, particularly in the UK. This service is distinct from carpooling or ride-sharing where journeys are shared with other people in the same car. At an individual level, change in personal travel behaviour is the most direct and observable short term impact of joining CSS. These individual changes in travel behaviour result in larger scale population level impacts on GHG emissions, travel mode shares of public transport and active modes, levels of private car ownership, distance travelled in private cars, times at which people travel (Cervero, 2003; Loose, 2010; Martin & Shaheen, 2010; Steininger, Vogl, & Zettl, 1996). Martin and Shaheen (2010) found CSS users
http://dx.doi.org/10.1016/j.retrec.2016.10.001 0739-8859/© 2016 Published by Elsevier Ltd.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
2
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
who own a private car before joining drove an average 21,250 km/ year prior to joining CSS; On joining CSS, 46% drove fewer than 800 km/yr and sold their car. Although the no car households would increase their travel in private cars and consequently their GHG emissions of travel on joining the CSS service, typically by 0e250 kg/year. However, the study concluded these no car households generally joined the CSS as a substitute for vehicle ownership, and that these households would have reported a higher emissions in the absence of a CSS. Across both member types in Europe, Loose (2010) found that the average private customer drove 737 km in a CS vehicle in 2008. A study on CSS Members’ (CSM) use of the services (Morency et al., 2007) revealed that the majority of members (over 90%) were occasional users, who used the service less than once per week, and make most of their journeys on weekends. The remaining users were frequent users using the service on weekdays. The collective impacts of CSS improve sustainability in transport networks through reduced car-dependency and travel related GHG emissions. The main attractiveness of CSS is that the members can have access to a car without the capital costs of owning it. However, there exist several barriers to the development of CSS, and foremost among these is a simple lack of understanding amongst national and local governments as to what a CSS is and how it might operate within an area (Enoch & Taylor, 2006). The study identified three areas of government policy that have the potential to promote CS. These are regulation, fiscal mechanisms and information. Regulation includes measures such as the provision of parking, or the waiving of the need to provide parking spaces in a development where a CSS is available. Fiscal mechanisms may include tax breaks for CSS in the early phases of development, or financial support in the purchasing of cars or the installation of on-street equipment and signage. Information support typically includes high level information from national governments disseminating information to local governments which supports the development of local level CSS and the usage of established transport information channels (e.g. links with public transport service providers) to disseminate information about car sharing. The potential market of introducing CSS in Ireland were examined by Rabbitt and Ghosh (2013). They identified the potential users through geographic analysis. This study complements the work by providing a detailed framework of evaluating the economic and environmental changes that can be experienced by the potential users. The study also expands the estimation of travel behaviour changes from the users of Dublin city to the potential users in the entire country of Ireland. This paper compares the existing and expected travel behaviours & changes in county Dublin & Ireland. The study demonstrates that the average travel costs and CO2 emissions by average individuals living outside Dublin is much higher than individuals living in Dublin. The collective economic and environmental benefits under all possible membership compositions and scenarios are illustrated in this study. The potential policy impacts of introduction of CSS are also discussed in detail. 2. Methodology This paper estimates the economic and environmental benefits of introduction of CSS in Ireland utilising a methodology described in Rabbitt and Ghosh (2013). As outlined in Fig. 1, the potential impacts of CSS in Ireland has been estimated in a three stage process. In the first stage, a geographic analysis was carried out to identify the population density of most likely CS adopters (termed as, Likely Users, LU) in unit geographic area based on socio-demographic data. Based on the population density of LU and the business requirements of CSS,
each unit area was then categorised into one of five CSS viability levels. In the second stage, the potential economic and environmental impacts of switching to car sharing was estimated for individual members assuming hypothetical travel behaviour change scenarios. In the final stage, the collective economic and environmental impacts of widespread adoption of car sharing was studied considering viability of CSS as determined in stage 1 and potential travel behaviour changes. These three stages are described briefly in the following subsections. For further details, please refer to Rabbitt and Ghosh (2013). 2.1. Identification of potential users of CSS CSS is most suitable for the lifestyles and travel patterns of certain groups of people, defined as LU in this paper. Following Rabbitt and Ghosh (2013), the most likely CS members or LU possess the following 5 traits: 1) residents of 1e2 person, no car households, 2) age between 25 and 49 years, 3) working or selfemployed, 4) educational qualification includes at least an ordinary degree, 5) non-car owners & 6) do not drive to work. The number of LU in each unit area can be identified using sociodemographic information. From a business perspective, a CS station with 2 CS cars is viable if there are at least 50 possible LU individuals in per km2 area. It is not expected that LU will be the only members of CSS, however they may provide the core membership enabling the CSS to reach a break-even point. Depending on population densities, four roll-out stages can be assumed for introducing CSS in any unit geographic area. 1. Early Rollout (Y1): Areas with LU population >1000/km2 are provided with 1 CS station with 5% of the LU population joining CSS. 2. Middle Rollout (Y2): Areas with LU population between 999 and 100/km2 are provided with 1 CS station with 50% of the LU population joining CSS. 3. Late Rollout (Y3): Areas with LU population Areas with LU population between 99 and 25/km2 and all (100%) are projected to join the CSS. 4. Maximum Limit (Y4): Areas with population density of 25 adults/km2, but fewer than 25 LU/km2 may join CSS if private car ownership is restricted.
2.2. Economic and environmental impacts to individual users The economic and environmental impacts to individuals on joining CSS are reduced travel costs and reduced travel related CO2 emissions. The estimated economic and environmental impacts to individuals were calculated following a four step process illustrated in Fig. 2(A). In the first step, a travel and activity diary based survey was undertaken among Irish residents to investigate their existing travel patterns. Further details of the survey is described in section 3.1. The second step calculated each individual respondent's travel related CO2 emissions and travel cost using information provided by respondents in conjunction with Department of Transport data. In the third step, respondents with LU attributes were identified and were categorised based on their dominant travel mode (travel style group). In the final step, three hypothetical behavioural change scenarios were assumed and applied to responses of individuals. The economic and environmental impacts were calculated based on their travel behaviour changes.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
3
Fig. 1. Schematic for estimating the potential impacts of CS adoption.
2.2.1. Calculation of travel costs The costs of travel were calculated on an annual basis. The direct costs of travel (fuel costs, maintenance costs, bus fares etc.) were obtained directly from survey responses. The indirect costs related to vehicle ownership were calculated separately. The methodology used to calculate the cost of travel in private vehicles and travel by public transport is described below. The cost of travel in a private car (Vauto) was calculated adapting a methodology used by Hensher and Chen (2011). In relation to calculation of this cost, three elements associated with vehicle ownership: 1) depreciation (vdep); 2) opportunity costs (vop); and 3) annual charges (vex) were considered. The annual estimated fuel costs (vfu) were calculated from survey responses.
The cost of travel by public transport (Vtransit) was calculated using two elements: commuter fares for travelling in bus/train, vcom and taxi & other short-term travel expenses, vshort.
vshort ¼Average weekly public transport fares ðincluding taxi faresÞ reported*52
(7)
Vauto ¼ vdep þ vop þ vex þ vfu
(1)
Vtransit ¼ vcom þ vshort
(8)
vop ¼ 0:05ðCar Valt Þ;
(2)
vdep ¼ Car Valtþ1 Car Valt ;
(3)
vex ¼ Road Tax þ Insurance þ Maintenance;
(4)
vfu ¼ Fuel costs per week*52
(5)
The current value of a private vehicle, Car_Valt, was calculated using information provided by respondents on the age and engine size of the cars they owned in conjunction with information provided by Irish Tax and Customs (2012a, 2012b). For each year group & engine size combination, the most common make & model combination was identified (Dept. of Transport Tourism and Sport, Ireland, 2011), and an Open Market Selling Price (OMSP), Depreciation Code (DC), and CO2/km figure was obtained from the Vehicle Registration Tax (VRT) database (Irish Tax and Customs, 2012a). Each respondent's car was assumed to be the most common make and model, for the year and engine size provided by the respondent. Using the OMSP, DC and current year for that make and model, each respondent's current car value was calculated (Irish Tax and Customs, 2012b).
vcom ¼ d*Annual Commuter Tickets þ d*ðMonthly Bus=Train=Tram=Combined Tickets*12Þ þ d*ðWeekly Bus=Train=Tram=Combined Tickets*52Þ (6)
d is a binary variable which is equal to 1 if a respondent is in possession of a certain type of ticket and zero otherwise. The cost of travel for each respondent was calculated by summing three components: Vauto, Vtransit and VCSS. The last element indicated the cost of using a CSS service such as GoCar in Ireland. 2.2.2. Travel related CO2 emission Calculation of CO2 emissions of travel was based on the travel distances in each mode of transport reported by survey respondents. The CO2 emissions of travel in private cars were calculated based on the year and engine size or tax band details provided by respondents. In case of insufficient details of the car in which they travelled, an average value of 147.3 g/km was used as obtained from the weighted average of all car details provided by respondents. CO2 emissions per km for other modes were taken the CMT emissions calculator (Environmental Protection Agency, 2009). The CMT emissions calculator also provided a value of 150 g/km travelled for an average private car in Ireland. This was therefore adopted as the emissions value for CSS vehicles. 2.2.3. Travel style categorisation The survey respondents possessing traits favourable for subscribing to CSS were identified and extracted for further analysis.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
4
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
Fig. 2. (A): Schematic for estimating individual economic and environmental benefits, (B) Schematic for estimating collective economic and environmental benefits.
These respondents were classified into three ‘travel style’ categories based on their dominant modes of travel. These were: 1) Active Traveller (AT) who took most trips by walking or cycling, 2) Public Transport Traveller (PT) where the respondent mostly used bus/tram/train and 3) Car Traveller (CT) where most trips were made on private car as either driver or passenger (Rabbitt & Ghosh, 2013). These travel style categories were utilised as a basis for simulating travel behaviour changes in chosen respondents. 2.2.4. Simulation of travel behaviour changes. In the final step, three travel behaviour change scenarios were simulated to estimate the extent of economic and environmental impacts of switching to CSS for the survey respondents. The justification behind the design of these scenarios were discussed in Rabbitt and Ghosh, 2013. This paper provides the detailed design of the scenarios. a) Best case scenario (BCS) In this scenario, respondents in all three travel styles joined CSS. They used a CS vehicle for making 10% of their reported journey distance travelled in a private car and the remaining distance were covered in either public transport or active modes in a proportion similar to the one originally reported. The following algorithm was used to simulate the scenario: Step 1. For each respondent, the costs of making 10% of his/her
car trips in a CS vehicle was calculated. This cost was a sum of yearly CSS subscription, booking fees, distance and hourly charges. Step 2. For each respondent, the CO2 emissions generated from his/her trips in CS vehicles were calculated based on the type of CS vehicle (conventional or electric). Step 3. The distance travelled in public transport (rBus, rTrain) and in active modes rAT were expressed as fractions of total non-private vehicle distance travelled by a respondent. In cases, where no travel was reported on any of these modes, a modal split of 25% bus, 25% train, 50% active modes was assumed.
rBus þ rTrain þ rAT ¼ 1
(9)
Step 4. For each respondent, the costs of making 90% of his/her car trips in alternative modes (rBus:rTrain:rAT) was calculated assuming an average cost/km for their travel on public transport based on reported data. In cases, where no travel or no costs were reported an average value of 13.1c/km was used. This 13.1c/km was based on the average reported cost for all respondents to the survey. Walking and cycling were assumed to attract no additional cost. Step 5. For each respondent, the CO2 emissions generated from his/her trips in step 4 were calculated. Step 6. For each respondent, the cost of travel in BCS was
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
calculated by adding the travel costs from step 1 & step 4 and subtracting any expenses related to vfu. Step 7. The total CO2 emissions generated in BCS was a sum of the same from step 2 & step 5. b) Most probable scenario (MPS) This scenario represented the most likely behavioural changes among CS members. In this scenario respondents were divided into three categories: (a) CT, car owners (b) AT/PT, car owners and (c) CT/ AT/PT, non-car owners. It was assumed that those in category (a) did not join the CSS, and consequently made no changes to their travel patterns. Those in category (b) joined the CSS and follow BCS, i.e. 10% of their reported journey distance travelled in a private car was covered in CS cars and the remaining 90% of reported journey distance was made using alternative modes. The methodology for calculating the impact of their behaviour change followed the description in BCS. Those in category (c) did join the CSS and their behavioural change was modelled as replacing all of their current car trips with CS trips subject to a minimum of 130 km/year. The travel costs were calculated accordingly. c) Worst case scenario (WCS) In this scenario minimal behavioural change occurred among CS members. Members in all three categories replaced all their current levels of car travel with travel in a CS vehicle as the sole occupant. The methodology otherwise followed the steps 1e2 described in BCS except the proportion 10% was replaced by 100%. No additional distance was travelled via public or active transport. 2.3. Collective economic and environmental impacts This analysis aims to establish the total economic and environmental impact of introduction of CS. The immediate changes and long-term projected behavioural changes, both, were estimated (Fig. 2(B)). The determinants of theses impacts are the levels of uptake of CSS and the change in travel behaviour expected in CSM. Following Rabbitt and Ghosh, 2013, three types of members subscribed to CSS: 1. LU members have AT, PT or CT style and do not own cars 2. Low Usage Car Owners (LUCO) members are car owners with AT or PT style 3. Radical Changers (RC) are car owners with CT style The estimating the immediate impacts, it was designed that 50%e80% of CSM were from the LU demographic and the remaining 20%e50% of users were comprised of LUCO and RC. The total travel cost and CO2 emissions were calculated assuming that the travel behaviour change in LU & LUCO members will follow the most probable scenario and in RC members will follow the best case scenario (section 2.2.4). In estimating the long-term projected impacts of introducing CSS, the absence of travel behaviour changes that could have happened if CSS was not available was taken into consideration. It was assumed that a certain percentage of AT & PT style LU, who were originally non-car owners would purchase cars in future in absence of CSS. The full impact of introducing CSS can be estimated by assuming a higher percentage of LUCO members (>50%) than LU members and a small percentage of RC members. This can be considered as an idealistic situation and would estimate the maximum travel cost and CO2 emission savings achievable through implementation of CSS. 3. Data The data used in this study were obtained from several sources. The data and the sources are described in this section.
5
3.1. Survey data A survey was undertaken to investigate the travel patterns and costs of Irish residents. The survey constituted a travel diary kept by the respondents over a 2 week period recording number of trips, costs, travel modes, trip time and distance, socio-demographic information etc., on a fixed response on-line questionnaire. The survey was conducted over a 6 week period between 10th February and 16th March 2012 and received 2639 responses from residents of Ireland. The survey responses were restricted to 1172 from the respondents who were employed or self-employed and within the age group 18e65. Out of this sample, there were 816 respondents who lived in the largest city in Ireland, Dublin. The details of these survey responses, classified by travel style and car ownership, are provided in Table 1. It can be observed that, on average, the respondents from Dublin made 27 less trips/year, travelling 3,912 km less and spending 30 h less than the overall average. These survey responses were in line with the National Travel Survey (NTS) data as was established in the study by Rabbitt and Ghosh (2013). Fig. 3 provides an overview of the number of journeys taken, the distance travelled and the time spent on each mode by respondents. The width of each box-plot indicates the number of respondents who used that mode in each case. The results show a large variation in the number of journeys reported by individuals on each mode of transport. 3.1.1. Travel costs & CO2 emissions Survey respondents were asked to provide some details of their travel costs. Their annual costs of travel calculated following the methodology described in section 2.2.1 are provided in Table 2. The average travel costs and average CO2 emissions for the chosen samples, calculated using the methodology described in section 2.2.1 & section 2.2.2 are provided in Table 1. 3.2. Census 2011 data To identify the potential CSS users (LU) the Small Area Population Statistics generated from the 2011 Irish census and published by the Central Statistics Office (2011b) were used. A small area is the smallest area unit in the census for which socio-demographics and all travel related information are published (Central Statistics Office, 2011b). The 2011 Irish census was carried out on the night of Sunday, April 10th, 2011 (Central Statistics Office, 2012a) and the results of the census are published as aggregate data, in small areas consisting of 264 people or 97 households on average. 3.3. Existing car sharing organisation in Ireland GoCar is a third party organisation operating a CSS in Dublin and Cork. In 2012, the company was operating 11 car bases. An anonymized breakdown of all customer bookings made from February 1st, 2012 to July 31st, 2012 was obtained. Members who joined or left CS during the study period were excluded from calculations. This data was used to establish the behaviour of new CS members, i.e. frequency of use, distances travelled etc. On average, the customers made 1 trip every 2 months, cars were booked for 2.3 h, travelled 21.67 km and were on the membership rate that charges V5/month, with V0.45/km and an additional V5.75/hr (GoCar Website, 2013). For reasons of commercial confidentiality it is not possible to give a full breakdown of this data set, however overall the dataset revealed low levels of usage among GoCar members and a large section of the members did not make a booking during the study period.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
6
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
Table 1 Summary of sample survey data. Active traveller
Car traveller
Public transport traveller
All respondents
Non-car owner
Car owner
Non-car owner
Car owner
Non-car owner
Car owner
Dublin Number of respondents % of Total % Female Average Age Average Household Size Average Km/Year Average No. of Trips/Yr Average Travel Time (Hrs) Average Annual Travel Cost Average CO2 Emissions(kg/yr)
189 23.16% 47.6% 33.0 2.20 7813 986 366.1 V1108 283.2
122 14.95% 47.5% 37.6 2.55 8164 944 336.3 V3750 496.5
45 5.51% 57.8% 32.4 2.64 12,809 900 348.3 V2570 1006.4
248 30.39% 57.7% 37.5 2.57 14,232 1070 391.5 V5559 1457.8
123 15.07% 52.8% 32.7 2.39 7139 933 387.8 V1784 448.9
89 10.91% 58.4% 37.9 2.54 10,057 1008 458.6 V5256 731.3
816 100.00% 53.2% 35.5 2.54 10,235 995 381.7 V3491 785.8
Ireland Number of Respondents % of Total Average Age Average Household Size Average Km/Year Average No. of Trips/Yr Average Travel Time (Hrs) Average Annual Travel Cost Average CO2 Emissions(kg/yr)
234 19.97% 33.1 2.3 10,946 960 356.9 V1190 483.2
149 12.71% 38.2 2.6 8243 945 325.3 V3827 541.6
63 5.38% 32.2 2.5 14,446 895 361.9 V2687 1197.2
422 36.01% 37.2 2.7 19,487 1133 440.6 V5944 2202.4
166 14.16% 32.9 2.5 10,058 926 440.1 V1974 614.3
138 11.77% 39.1 2.6 14,402 1049 497.9 V5652 986.3
1172 100.00% 35.9 2.6 14,147 1022 411.7 V3954 1225.9
Fig. 3. Per Fortnight, summary of (a) Number of Journeys Undertaken, (b) Distance Travelled and (c) Time Spent by Mode of Transport.
Table 2 Breakdown of average annual costs and CO2 emissions of travel for survey respondents. Outside Dublin
Opportunity Costs of Car Ownership Depreciation Costs of Car Ownership Annual Contributions towards all private vehicle Fortnightly Costs for Travel in private vehicle Annual Train and Bus Tickets Cost Fortnightly costs for travel in buses and trains converted to yearly costs Annual Taxi Spend Total Travel Cost Carbon Dioxide Emissions (kg/yr)
Dublin
Total
No car
Car owner
No car
Car owner
416
372
1215
636
2639
VVV184.50 V498.44 V353.53 V823.40 V243.84 V2103.72 1192.1
V360.31 V1390.81 V1262.95 V2324.08 V289.48 V259.29 V142.81 V6029.74 3365.2
VVV134.20 V293.46 V286.62 V434.09 V248.26 V1396.63 794.1
V306.20 V1212.46 V1115.86 V1555.14 V162.20 V275.28 V259.19 V4886.33 1023.8
V124.58 V488.26 V537.82 V916.08 V267.59 V432.55 V235.33 V3002.21 1274.6
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
4. Analysis & results In this section the results of the analyses done following the methodology described in section 2 is provided. 4.1. Identification of LU The 2011 Irish census data was used to identify areas suitable for establishing car-sharing stations with sufficient densities of probable CSM. The results and analysis are same as that presented in Rabbitt & Ghosh, 2013. The results showed that the population of LU was concentrated in areas with higher population densities, principally in the four main cities: Dublin, Cork, Galway and Limerick. 97% of the LU population inhabited in these four cities in areas with the potential for early rollout of CS. For the entire country at the early rollout stage, 2.5% of the entire adult population were estimated as LU. In a late rollout, around 20% of the adult population could join CSS. For the early and middle rollout stages Dublin County provided the only continuous area with a high density of suitable locations. Hence, in the later subsections the overall impact of introduction of CSS has been studied in further detail for Dublin County along with the entire country. 4.2. Individual economic & environmental analysis This analysis was undertaken to establish the economic and environmental changes generated from the switch to CSS for individual users. The travel costs and CO2 emissions of individuals classified by travel style and car ownership are presented in Table 3. The reported costs & emissions are presented along with estimated costs & emissions as calculated considering travel behaviour changes as described in section 2.2.4 under three hypothetical scenarios. The calculations were carried out for Dublin county (N ¼ 816) and Ireland (N ¼ 1172) separately. In all cases, the average travel costs and emissions for the country was higher than that in Dublin. The average CO2 emissions were much higher for Ireland compared to Dublin.
7
Individuals of non-car based travel styles, AT & PT, who were car owners gained the most from joining CSS. The AT, car-owners saved around 74% and PT car-owners saved around 60% on joining CSS in both Dublin & Ireland under BCS & MPS. However, for non-car owners in AT & PT style, in most cases joining CSS might incur extra costs. However, it can be argued that this extra cost to non-car owners was reasonable, particularly when compared to the alternative option of a car purchase. The results for CO2 emissions were similar to the travel costs, i.e. car owner, could reduce their CO2 emission by joining CSS (AT 65% & PT 14e20%) and for non-car owners joining CSS would increase their CO2 emissions slightly. However, if the CSS vehicles were run by electric then both car owners and non-car owners would be able to reduce their CO2 emissions under all three scenarios especially in WCS. 4.3. Collective economic & environmental analysis This section illustrates the potential economic and environmental benefits that can be gained by introducing CSS extensively in Ireland. As indicated in 4.1, the early rollout and middle rollout of CSS is mainly viable in Dublin. The later rollout stages are possible only through policy and infrastructure support. The monetary saving to the users and travel related emission reductions were calculated following the methodology described in section 2.3. The immediate and the ideal impacts are presented for both Dublin and Ireland in four combinations of membership compositions in Table 4. The impacts for Dublin were calculated using average cost and emission savings as simulated for Dublin residents. The impacts for Ireland were calculated using average cost and emission savings as simulated from the entire sample size of 1172 survey respondents. It was assumed that the average value of cost and emission estimations from Table 3, can be applied to LU, LUCO and RC users in all small areas in Ireland. It can be argued that the introduction of CSS can be viable at a middle rollout stage with some policy support and a very likely membership composition of 70%LU, 25%LUCO & 5%RC can reduce CO2 emissions by 25.4 kt/yr in Dublin and 69.6 kt/yr in Ireland with electric CS vehicles.
Table 3 Individual Financial and Environmental Impacts of joining a Car Sharing Service. Dublin
Ireland
Active traveller
Car traveller
Non-car owner
Car owner
Non-car owner
V3750 V965 V965 V3111
Annual average travel costs in Euros Reported V1108 Best Case Scenario (BCS) V1055 Most Probable Scenario (MPS) V1286 Worst Case Scenario (WCS) V1744 Annual average travel related CO2 emission in kg Reported 283.2 496.5 Conventional Best Case Scenario 240.6 cars (BCS) Most Probable Scenario 293.4 (MPS) Worst Case Scenario 394.4 (WCS) Electric cars Best Case Scenario 148.5 (BCS) Most Probable Scenario 283.0 (MPS) Worst Case Scenario 179.8 (WCS)
Public transport traveller
Active traveller
Car traveller
Public transport traveller
Car owner
Car owner
Non-car owner
Non-car owner
Car owner
Non-car owner
Car owner
Car owner
Non-car owner
V2570 V3786 V2570 V6827
V5559 V3261 V5559 V8827
V1784 V1822 V1964 V2305
V5256 V2115 V2115 V3633
V1190 V1082 V1343 V1903
V3827 V979 V979 V3205
V2687 V3802 V2687 V7596
V5944 V3708 V5944 V11,471
V1974 V2001 V2123 V2639
V5652 V2170 V2170 V3984
1006.4 179.9
1457.8 669.2
448.9 598.0
731.3 428.0
483.2 585.6
541.6 427.6
1197.2 199.3
2202.4 792.0
614.3 888.6
986.3 591.4
850.6
179.9
1006.4
1457.8
458.4
585.6
493.1
199.3
1197.2
2202.4
623.6
850.6
689.4
1560.2
1943.7
448.9
731.3
605.8
723.4
1794.3
2677.8
702.3
1161.7
92.2
462.5
380.0
223.9
363.3
297.1
109.8
531.7
590.0
322.3
472.0
92.2
1006.4
1457.8
448.6
363.3
483.0
109.8
1197.2
2202.4
614.0
472.0
91.4
172.2
98.1
351.6
367.2
360.6
102.2
221.8
139.3
503.4
617.9
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
8
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
Table 4 Estimation of collective economic and environmental impacts of car sharing rollout in Dublin & Ireland. Dublin
Ireland
Immediate % Members per category
Projected number of Members
CO2 saved in Conventional Car CSS (kt/yr)
CO2 saved in Electric Car CSS (kt/yr)
Cost Saved by Members (Million Euros)
LU LUCO RC Early Adopters Middle Adopters Late Adopters Maximum Limit Early Adopters Middle Adopters Late Adopters Maximum Limit Early Adopters Middle Adopters Late Adopters Maximum Limit Early Adopters Middle Adopters Late Adopters Maximum Limit
80% 20% 0% 4676 174,883 416,137 450,151 0.20 7.34 17.47 18.89 0.28 10.54 25.09 27.14 V2.16 V80.78 V192.22 V207.93
70% 25% 5% 5344 199,866 475,586 514,459 0.52 19.60 46.63 50.44 0.68 25.48 60.64 65.60 V3.95 V147.74 V351.55 V380.28
55% 30% 15% 6801 254,375 605,291 654,766 1.34 50.25 119.58 129.35 1.68 62.81 149.46 161.68 V7.75 V289.77 V689.52 V745.88
Ideal
Immediate
10% 70% 20% 5344 199,866 475,586 514,459 1.83 68.41 162.78 176.08 2.24 83.79 199.38 215.68 V13.35 V499.39 V1188.31 V1285.44
80% 20% 0% 5373 306,632 849,112 1,206,189 0.23 12.86 35.61 50.58 0.51 28.97 80.22 113.96 V2.83 V161.27 V446.58 V634.38
Ideal 70% 25% 5% 6140 350,437 970,413 1,378,502 0.74 42.27 117.04 166.26 1.22 69.62 192.78 273.84 V4.96 V283.32 V784.56 V1114.49
55% 30% 15% 7815 446,010 1,235,072 1,754,457 2.07 118.37 327.79 465.63 3.00 171.02 473.57 672.72 V9.45 V539.40 V1493.67 V2121.81
10% 70% 20% 6140 350,437 970,413 1,378,502 2.31 86.35 205.47 222.27 4.01 228.68 633.26 899.56 V16.22 V925.57 V2563.04 V3640.88
Fig. 4. Collective economic and environmental impact for middle adapters in Dublin (A) collective economic impact (B) collective CO2 emission in conventional car CSS (C) Collective CO2 emission in electric car CSS.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
9
Fig. 5. Collective economic and environmental impact for middle adapters in Ireland (A) Collective economic impact (B) collective CO2 emission in conventional car CSS (C) Collective CO2 emission in electric car CSS.
For membership compositions varying between 20 and 80%LU, 0e20% RC, the costs and emission reductions in middle rollout stages are plotted for both Dublin (Fig. 4) and Ireland (Fig. 5). It is apparent that in ideal impact situations, i.e. percentage of LUCO > LU, the savings would be higher and also with higher percentage of RC members the savings would be even bigger. However, the changes in savings did not follow the same pattern for both Dublin and Ireland. For the entire country, a 50%LU users with high percentage of RC members generated maximum benefits. 5. Discussion The inferences drawn from the analyses in the previous section and their implications for policy changes are discussed in this section. The cost analysis indicated that owning a car significantly increases the annual travel related costs of an individual. Individuals
who are not dependant on a privately owned car to satisfy the majority of their travel needs would save significantly on joining CSS. The non-car owners can join CSS as an alternative to purchasing a car as CSS would provide access to a car without bearing the ownership and maintenance costs. The car owners would also benefit by saving ownership and maintenance costs; however CSS membership may not replace their total need for car travel if they do not intend to change a percentage of their trips to more sustainable modes. The financial benefits gained by the LUCO and noncar owners on joining CSS makes a definite business case for introducing public or private CSS to Ireland. The introduction of CSS would also provide significant CO2 savings at all rollout stages. The more car owners join CSS, the bigger the environmental benefit will be. If policy changes are made to support wide-spread introduction of CSS, it can contribute to the National Climate Change Strategy (Dept. of the Environment, Heritage, & Local, Government, Ireland, 2007), prove to be a
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001
10
N. Rabbitt, B. Ghosh / Research in Transportation Economics xxx (2016) 1e10
major step towards sustainability by promoting sustainable travel modes such as walking, cycling and public transport, by indirectly restricting car ownership levels and number of car trips. It would also reduce congestion levels, parking space demand and other externalities associated with increased car mode share. The environmental benefits of CSS can be best realised through provision of electric cars as CS vehicles. This will promote use of electric cars, further reduce CO2 levels and may bring in private investment from electric car manufacturers. The geographic analysis indicated that there exists a large group of individuals (denoted as LU in the paper) who are quite likely to join CSS. Accordingly, even without much policy support CSS is viable in areas with high population density within Dublin County. Outside Dublin, there is a lack of high density areas with suitable users. With governmental policy support such as tax incentive or work place mobility management schemes, a CSS could operate successfully in the major cities (Cork, Limerick, Galway) and other medium density areas in Ireland. The immediate benefits of introducing CSS may seem modest at early roll-out stages. However, the expected long term impacts are significant; the 30%LU/60%LUCO/10%RC spilt in the middle rollout areas in Dublin seems to be the most probable projection for introducing CSS which would save approximately V453million/ year for customers and 86 kt CO2 emissions per annum in Dublin with electric cars (Fig. 4). The savings will increase to V485million/ year and 104 kt/year when the entire country is considered (Fig. 5). This reduction in CO2 emissions per annum is important as transport is one of the largest contributing sectors in non-emission trading scheme (non-ETS) emissions and it is projected that Ireland may cumulatively exceed its GHG emission obligations by 4 Mt CO2eq over the period 2013e2035 (Environmental Protection Agency, 2015). Introduction of CSS may reduce the projected increase of 13% by 2020 in CO2 emissions in transport sector to a smaller figure. 5.1. Limitations Due to lack of available information, the study did not include the investment required for infrastructure development, operation and maintenance of CSS in Ireland, hence the cost savings are calculated on the basis of customer savings and not in terms of profits that could be obtained by CSS operators such as government or third parties. 6. Conclusions The study in this paper estimates individual and collective, economic and environmental impacts of introduction of CSS in Dublin and in Ireland. The study presents multiple alternative scenarios which examine the financial and environmental factors influencing CS adoption and use. The scenarios were developed using observations from existing CSS in other countries. Some observations from the small scale CSS, GoCar, operating in Ireland have also been used to compare the travel behaviour change among CSM in Ireland and elsewhere. These scenarios were applied to the available and collected travel information of the Irish population to estimate the potential impact of introducing CSS in Ireland. The travel information was collected through an activity diary based survey administered to over 2500 respondents. Information
available through the census and National Travel Survey data of Ireland were also utilised. The study showed CSS holds immense potential in terms of cost savings to members, reduction of travel related CO2 emission and increased share of sustainable modes of travel. In almost all the scenarios designed in the study, financial savings were estimated for members of CSS. This is expected to be the most influential factor in attracting individuals to join CSS. The study in this paper was based on two-way CSS where cars are required to be returned to the same base from which they were rented. The one way CS, similar to a bike sharing scheme, where cars may be returned to a different station from the collection point needs to be investigated as well. The increased flexibility of oneway CS may lead to increased scope of CSS in Ireland. Acknowledgements The authors wish to thank the Irish Research Council for Science, Engineering and Technology for providing a grant to support this research. References Central Statistics Office, Ireland. (2011a). National travel survey 2009 (Prn A11/1239). Dublin, Ireland: Dublin: Stationery Office. Central Statistics Office, Ireland. (2011b). Census of population 2011: Preliminary results. Dublin, Ireland: Dublin: Stationery Office. Central Statistics Office, Ireland. (2012a). This is Ireland: Highlights from census 2011 Part 1. Dublin, Ireland: Dublin: Stationery Office. Cervero, R. (2003). City car share: First-year travel demand impacts. Transportation Research Record: Journal of the Transportation Research Board, 1839(1), 159e166. Dept. of the Environment, Heritage, & Local, Government, Ireland. (2007). Ireland : National climate change Strategy 2007-2012. Dublin: Dept. of the Environment, Heritage and Local Government. Dept. of Transport Tourism and Sport, Ireland. (2011). Irish bulletin of vehicle and driver statistics 2011. Enoch, M. P., & Taylor, J. (2006). A worldwide review of support mechanisms for car clubs. Transport Policy, 13(5), 434e443. Environmental Protection Agency Ireland. (2009). Change CMT calculator emission factor sources. EPA. Environmental Protection Agency Ireland. (2015). Ireland's greenhouse gas emission projections 2014-2035. EPA. GoCar Website. (2013). GoCar official website. Retrieved 14th April 2013, 2013, from http://www.gocar.ie/. Hensher, D. A., & Chen, X. (2011). What does it cost to travel in Sydney? Spatial and equity contrasts across the metropolitan region. Road and Transport Research, 20(2). Irish Tax and Customs. (2012a). Online vehicle registration tax (VRT) valuation enquiry system. Retrieved 1st May 2012, 2012, from https://www.ros.ie/evrt-enquiry/ vrtenquiry.html?execution¼e2s1. Irish Tax and Customs. (2012b). Vehicle registration tax, section 8, valuation system for new and used vehicles. Dublin: Revenue.ie Retrieved from http://www.revenue. ie/en/about/foi/s16/vehicle-registration-tax/vrt-manual-section-08.pdf. Loose, W. (2010). “The state of European car-sharing”, project momo final report d 2.4 work package 2. Martin, E. W., & Shaheen, S. A. (2010). Greenhouse gas emission impacts of carsharing in North America. United States., California: Mineta Transportation Institute (San State Jose, CA: Mineta Transportation Institute, College of Business, San Jose University). panier, M., Agard, B., Martin, B., & Quashie, J. (2007, September). Car Morency, C., Tre sharing system: What transaction datasets reveal on users’ behaviors. In 2007 IEEE Intelligent Transportation Systems Conference (pp. 284e289). IEEE. Rabbitt, N., & Ghosh, B. (2013). A study of feasibility and potential benefits of organised car sharing in Ireland. Transportation Research Part D: Transport and Environment, 25, 49e58. SEAI. (2014). Energy in transport 2014 report (SEAI, Trans.) energy policy statistical support unit. Dublin: Sustainable Energy Authority Ireland. Steininger, K., Vogl, C., & Zettl, R. (1996). Car-sharing organizations: The size of the market segment and revealed change in mobility behavior. Transport Policy, 3(4), 177e185.
Please cite this article in press as: Rabbitt, N., & Ghosh, B., Economic and environmental impacts of organised Car Sharing Services: A case study of Ireland, Research in Transportation Economics (2016), http://dx.doi.org/10.1016/j.retrec.2016.10.001