Green Driver Training within the City of Calgary s Municipal Fleet: Monitoring the Impact Project Team:

Transcription

1 Green Driver Training within the City of Calgary s Municipal Fleet: Monitoring the Impact Project Team: Written by: Michelle Rutty, Lindsay Matthews, and Tania Del Matto March 2012

2 My Sustainable Canada, 2012 For inquiries into this study, please contact: My Sustainable Canada 743 Avondale Avenue Kitchener, ON N2M 2W / info@mysuscan.org The authors of this report would like to greatly acknowledge the City of Calgary s Environmental & Safety Management Department and the Development & Building Approvals business unit for their contributions in time and resources. A special thank-you to: Kyle White, Environmental Specialist Malcolm MacLean, Policy, Programs and Reporting Student -Environmental and Safety Management Shauna Collier-Jarvis, Fleet Administrator - Development & Building Approvals We also would like to acknowledge Green Communities Canada for their leadership on designing and delivering the EcoDriver training, without which this study would not have been possible: Beth Jones, Associate Director Michele Rich, EcoDriver Trainer Paul Barrett, EcoDriver Trainer Disclaimer The findings of this study are a result of the research conducted and do not reflect the specific opinions of the study participants or the funders of this study. Green Driver Training: Monitoring the Impact 1

3 Table of Contents Executive Summary... 4 Introduction... 7 Research Objectives... 9 Method... 9 The Drivers Phase I Phase II Phase III Results Baseline Data Acquisition Gasoline Vehicles Hybrid Vehicles Opportunities for Improvement Post-Training Data Acquisition Gasoline Vehicles Hybrid Vehicles Discussion and Limitations Limitations Final Thoughts References Appendix Appendix Appendix Green Driver Training: Monitoring the Impact 2

4 List of Tables Table 1. Vehicle Details Table 2. Variables Monitored and Calculated Table 3. Training Schedule Table 4. Phase I Fleet Results Table 5. Phase III Post-Training Data Acquisition Results for Gasoline Vehicles Table 6. Phase III Post-Training Data Acquisition Results for Hybrid Vehicles Table 7. Phase I Individual Daily Results Table 8. Phase I Individual Annual Results (Extrapolated) Table 9. Phase III Individual Daily Results Table 10. Phase III Individual Annual Results (Extrapolated) Table 11. Phase III Behavioural Change Daily Results Table 12. Phase III Behavioural Change Annual Results (Extrapolated) Green Driver Training: Monitoring the Impact 3

5 Executive Summary Operators of municipal fleets are becoming increasingly aware of the impact their vehicles are having on the environment. The need to control greenhouse gas (GHG) emissions is now widely accepted, with much evidence to suggest that stabilization can be achieved using currently available technologies. While a common barrier faced by local governments in greening their municipal fleets is cost, there are low cost solutions available that can reduce fuel use, improve the quality of the local environment and lower operating costs. This report will demonstrate the use of ecodriver training as a cost-efficient and near term strategy as a means to successfully reduce the economic and environmental impacts of the operation of a fleet of municipal vehicles in the City of Calgary, Alberta. As a leader among Canadian municipalities to take local action against climate change, the City of Calgary is investing in ways to reduce fuel consumption and associated CO2 emissions. Most recently, the City has sought to quantify the potential reduction of CO2 emissions from their municipal fleet as a result of ecodriver training. A total of 404 city employees that use light duty vehicles in the course of their work were engaged in a pilot Green Driver Training program. To assess the impact of this training, 15 self-selected drivers from the Development & Building Approvals Business Unit had in-vehicle monitoring technology (CarChips ) installed into their vehicles (11 gasoline, 4 hybrid) as part of a three phase research process: (1) baseline data acquisition (pre-eco-driver training, July 2011), (2) behavioural intervention (eco-driver training, August 2011), (3) post-training data acquisition (post-eco-driver training, October 2011). An assessment of the effectiveness of the eco-driver training between the baseline data acquisition (Phase I) and the post-training data acquisition (Phase III) reveals positive results for both the gasoline and hybrid vehicles. Of the 11 gasoline vehicles analyzed in this study, average daily hard decelerations decreased 0.21 counts per vehicle per day, which can reduce fuel consumption up to 40%. Moreover, average daily idling time decreased 0.34 hours per vehicle (or 4%), leading to an average daily decrease of 1.1kg of CO2, which is equivalent to 0.5L of fuel at CAD$0.58 per vehicle per day. When extrapolated annually, this sample of 11 gasoline vehicles would achieve a total reduction of hard decelerations of 5,943, while further eliminating 859 hours of idling, which would be responsible for 2,761kg of CO2, which is equivalent to 1,205L of fuel at a cost of CAD$1,447. The sample of hybrid vehicles was analyzed separately because of their ability to operate on both an electric and gasoline engine, to which the CarChip records only the latter engine use. When comparing the post-eco-driver training results with the pre-training results, the drivers of the 4 hybrid vehicles reduced their per vehicle Green Driver Training: Monitoring the Impact 4

6 average daily idling by 0.33 hours per day (or 10%), which leads to a decrease in 0.6kg of CO2, which is equivalent to 0.3L at a cost of CAD$0.30 per vehicle per day. On an annual basis, this sample of hybrid drivers would reduce idling by 817 hours, decreasing CO2 emissions by 1,414kg, which is equivalent to 618L of fuel at a cost of CAD$741 per year. The results were further extrapolated to the department level, whereby Development and Building Approvals has 115 gasoline vehicles and 39 hybrid vehicles. Based on the results from the study sample, the gasoline vehicles within the department would decrease annual idling time by 9,873 hours, reducing the department s CO2 emissions by 31,749kg, which is equivalent to 13,870L of fuel at a cost of CAD$16,644 per year. The department s hybrid vehicles would further reduce annual idling time by 3,185 hours, which is equivalent to 5,513kg of CO2 and 2,408L of fuel at a cost of CAD$2,890 per year. Two additional notable outcomes from the results of the hybrid sample emerge from this study. First, the benefits of better fuel economy and reduced environmental impacts of a hybrid vehicle versus a gasoline vehicle are underscored, as the gasoline and hybrid drivers within the department drive similar average daily distance (40km and 35km, respectively), but the hybrid drivers only use a fraction of the gas (gasoline engine is engaged for 1.5 hours instead of 3 hours). Second, having an eco-trained hybrid driver further enhances these benefits, optimizing the use of the electric motor over the gasoline motor to reduce overall fuel consumption and climate altering vehicle emissions. In fact, education activities may find it desirable to offer different training drivers of hybrids vs. gasoline vehicles. Of the 404 city staff that were engaged in Green Driver Training, 379 were trained through a one hour classroom based training, while 25 participated in a three hour (intensive) course that included an in-vehicle practice component. Of the 15 participants engaged with this study, 12 drivers were enrolled in the one hour EcoDriver course, and 3 drivers were enrolled in the three hour (intensive) EcoDriver course. Of the 3 drivers that participated in the intensive training course, all achieved positive results, including one driver who achieved the highest reduction in daily idling (21%) and another driver with the highest reduction in hard accelerations (0.5 per day). Beyond these specific observations, the very small sample size utilized in this study restricts the ability to decipher definitive statements regarding the difference in influence between the two training approaches on driver behaviour. Driver feedback upon completion of both training sessions showed positive opinions, with 97% of the drivers rating the course as good to excellent, and 87% of the participants committing to implementing at least some of the eco-driver training techniques they learned during the training sessions. A further 74% of the participants specifically committed to reducing idling at work. This data can be used to reinforce the social norms amongst drivers. Green Driver Training: Monitoring the Impact 5

7 Perhaps the greatest challenge moving forward will be to ensure the effectiveness of the behavioural intervention continues and that the fleet drivers do not relapse into their older (inefficient) driving habits. The initial eco-driver training provided through this project should be complemented with the provision of additional periodic review sessions for the drivers, as well as reinforcing efficient driving behaviours through performance feedback or incentives. Recent studies have indicated that necessary CO2 reductions can be achieved locally through a number of relatively simple behavioural modifications on an individual level. This study lends support to this assertion, demonstrating that eco-driver training can be a cost-effective fleet management practice for the City of Calgary that can and has reduced climate altering GHG emissions, while simultaneously saving money and improving the livability of the City. Best practices from this project can be used as a model for additional municipalities, as well as other operations with large fleets of vehicles, with the aim of reducing climate altering emissions related to transportation across Canada. Green Driver Training: Monitoring the Impact 6

8 Introduction The need to control greenhouse gas (GHG) emissions is now widely accepted, with the effectiveness of mitigation efforts over the next several decades to have a large impact on the ability to lower, stabilize, and ultimately reduce GHG emissions. There is high agreement and much evidence to suggest that stabilization can be achieved using currently available technologies (IPCC 2007). Through the adoption of ecodriver training, in conjunction with vehicle monitoring technology (VMT), this report will demonstrate the potential to successfully reduce CO2 emissions that are generated through the operation of a fleet of municipal vehicles in the City of Calgary, Alberta. Municipalities in Canada own and operate thousands of vehicles to deliver a variety of services to local citizens. Operators of municipal fleets are becoming increasingly aware of the impact their vehicles are having on the environment. For instance, Repair Our Air, an anti-idling program that analyzed engine data from 60 municipal fleets across Canada between 2000 and 2005 found that 30-50% of the time municipal service fleets were operating, the vehicles were idling (Fleet Challenge 2008). Moreover, the average Canadian city fleet is accountable for 5% of a municipality s total GHG emissions (Transportation Canada 2010a). Pending the size of the municipality and the services provided, this percentage of emissions from transportation can be even greater. For example, between 1990 and 2004, Calgary s municipal fleet, which comprises over 4,000 vehicles, was the second largest source of GHG emissions in the City (30%), represented 22% of the municipality s total GHG emissions (95 kt) and was the fastest growing source of corporate GHG emissions (5% increase) (City of Calgary 2006). In 2002, Calgary City Council directed the municipality to reduce corporate GHG emissions to 6% below 1990 level by the year 2012 (which includes all corporate emissions, not just the municipal fleet). Well surpassing this goal (by 2009 GHG emissions from municipal operations decreased 34% over 1990 levels from 461kt to 306 kt), the City took further action, aiming to reduce GHG emission by 50% below 1990 levels by 2012 (i.e. Target 50). In October 2009, the mayor of Calgary, along with nine other world energy cities, signed a landmark accord (i.e. Calgary Climate Change Accord) that commits Calgary to bold GHG reduction targets for their municipal operations, including a reduction in the city s corporate GHG emissions by 80% by 2050 from 2005 levels (WECP 2009). The City of Calgary is increasing its use of alternative fuels and technologies having minimum environmental impact (i.e. hybrid and biodiesel fuelled vehicles), promoting an idling reduction policy, as well as right sizing vehicles (i.e. employing the right vehicle size for the task) and utilizing preventative maintenance strategies in an effort to green its municipal fleet (City of Calgary 2009). An additional low- 7

9 cost solution available that can further reduce CO2 emissions and reduce the operating cost of the municipal fleet is eco-driver training. While municipalities can purchase the most fuel efficient vehicles available on the market, if drivers are unaware of how their driving habits influence environmental and economic sustainability, maximum fuel efficiency will not be realized. Reducing fuel consumption by educating drivers on how to change their driving behaviour has great potential to be a cost-efficient approach to reducing energy use, improve the quality of the local environment and lower fuel costs. Ecological, economical and safe driving (eco-driving) is a relatively new concept that was first developed and integrated into driver training courses by the German Federation of Driving Instructor Associations in the mid-1990s (Dandrea 1996). There are three key facets that govern eco-driving (Figure 1); (1) Smooth and gradual acceleration and deceleration by anticipating traffic flow and leaving space between the vehicle ahead; (2) Maintaining a steady speed while adhering to the posted speed limit; (3) Avoid idling by turning off the engine when not in use (Barkenbus 2010, NRCan 2009a). Automobile maintenance measures (e.g. maintaining optimum tire pressure, regular changing of air filters) are also often included in the definition of eco-driving (NRCan 2009b). Figure 1. General Guidelines for becoming an Eco-Driver Although there are very limited studies that have evaluated eco-trained drivers, the results are promising (e.g. International Energy Agency 2005, Wahlberg 2007, Zarkadoula et al. 2007, Beusen et al. 2009, Enviance 2009). A recent study by the City of Edmonton has found that after training more than 800 fleet drivers, annual fuel consumption decreased 10%, which is approximately 200,000 litres of fuel per year, with a reduction in GHG emissions of 310 tonnes (Transport Canada 2010b). 8

10 Research Objectives To quantify the potential reduction of CO2 emissions from Calgary s municipal fleet as a result of eco-driver training, a three phase research process was initiated: (1) baseline data acquisition (pre-eco-driver training), (2) behavioural intervention (eco-driver training), (3) post-training data acquisition (post-eco-driver training). To realize this goal, three objectives have been formulated to guide this research: 1. Assess the effectiveness of eco-driver training on reducing (i) harmful vehicle emissions and (ii) fuel consumption within the City of Calgary s fleet of vehicles. 2. Examine how two differing approaches to eco-driver training (i.e. a 1 hour inclass session versus a 3 hour in-class and in-vehicle session) influences the degree of positive behavioural change. 3. Evaluate the difference in CO2 emissions between gasoline and hybrid vehicles within the City of Calgary s fleet. Best practices from this Eco-driving study can be used as a model by other municipalities, as well as other operations with large fleets of vehicles, with the aim of reducing GHG emissions related to transportation across Canada. Method Comprehensive data collection and analysis is critical for effective fleet operation, cost management and environmental sustainability. Unfortunately, the field of emissions accounting is not only relatively new, but in many cases, municipalities do not have the resources or tools required to quantify GHG reductions associated with certain fleet projects. When selecting a tool, it is essential that it both define the areas where corrective actions are necessary, as well as measure whether the desired amendment of the situation was achieved (Schianetz et al. 2007). For this reason, in-vehicle monitoring technology (CarChip ) (Figure 2) was selected as a costefficient means of identifying opportunities to reduce GHG emissions within the City of Calgary fleet and to measure the outcome of the behavioural intervention (i.e. eco-driver training). In this way, CarChips can be utilized to build capacity among municipalities to enable better reporting on their fleet management achievements. Figure 2. The CarChip Recognizing that each municipality and their fleet drivers are unique, it was important to acquire baseline data on driving behavior to identify the driving habits that lead to the greatest fuel inefficiency. From this data, recommendations can then be made on an individual basis regarding behavioural adjustments that hold the 9

11 greatest potential to reduce GHG emissions through the adoption of eco-driver training. As such, a three phase project was initiated to help plan for a greener municipal fleet (Figure 3). Figure 3. Project Schedule The Drivers All vehicles participating in this study operate within the Development & Building Approvals Business Unit. The drivers selected for the study perform a variety of daily inspections, including electrical, plumbing and gas, buildings and compliance. Each driver does between 12 to 25 inspections per day, with the summer months (June, July, August) slightly busier, as many of the drivers take vacation during this period (i.e. the same workload is then dispersed among fewer employees). Each driver typically operates their vehicle from 7:30am to 3:00pm, Monday to Friday. Route planning aims to ensure that the majority of the daily driving occurs during the initial and final commute to and from the main depot, with the bulk of the site inspections located close together. The vehicle serves as a mobile office for the site inspectors, with a great deal of time spent in the vehicles completing inspections paperwork. Moreover, the vehicles are equipped with laptops, printers, inverters and GPS units all of which operate off of the vehicle s power supply. As a result, there is high potential that the vehicles within this department are idling a long period of time. There are currently 154 vehicles within the department s fleet 1 ; 151 are Ford Escapes (39 of which are hybrids), two GMC Canyons and one 2010 Chevrolet utility van. All of the daily drivers (i.e. inspectors who have been assigned their own vehicle) were instructed to take the eco-driver training course provided through this study, with the remaining drivers offered the option to voluntarily take the course. Of the 200 daily drivers in the department, a total of 15 self-selected (i.e. volunteer) drivers were examined in this study. Phase I Phase I of the project began in July 2011, with the programming and installation of the CarChips into 15 medium class fleet vehicles (Ford Escape 11 gasoline, 4 hybrid). Information on each vehicle was recorded, including vehicle year, manufacturer and model, engine size and fuel type (Table 1). The labeled and programmed CarChips, a netbook and an instruction booklet (see Appendix 1) were then mailed to the City of Calgary from My Sustainable Canada. The City of Calgary then installed the CarChips by plugging the device into the On-board Diagnostic (OBD) port found under the dashboard. To limit the influence these 1 Note: the number of vehicles within the department is in constant flux due to the commissioning and decommissioning of vehicles over the course of the year. 10

12 devices may have on driver behaviour, fleet drivers were notified of the installation, but details on which driving parameters were being recorded was not made available. Table 1. Vehicle Details CarChip Year Vehicle Model Engine Size (L) Gasoline G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape AWD 2.5 G Ford Escape 2WD 2.5 G Ford Escape 2WD 2.5 Hybrid H Ford Escape Hybrid 4WD 2.3 H Ford Escape Hybrid 4WD 2.3 H Ford Escape Hybrid 4WD 2.3 H Ford Escape Hybrid 4WD 2.3 Once installed, the CarChips continuously read the driving and engine performance data from the vehicle s on-board computers and store the data on an internal memory card. Selected parameters were recorded based on their relevance for eco-driver training, focusing on environmental performance and fuel consumption. Table 2 presents an overview of the parameters, their units, as well as a description and how each parameter was calculated. The baseline data acquisition (Phase I) concluded on August 12 th, 2011, removing the CarChips from the participating vehicles, and downloading the data using a universal serial bus (USB) cable onto the netbook provided. DriveRight Fleet Management Software Package was used in conjunction with Microsoft Excel, to view, analyze and calculate the data at varying degrees of detail. 11

13 Table 2. Variables Monitored and Calculated Parameter Description Number of Trips Drive Time (hours) Distance Driven (km) Average Trip Distance (km) Average Speed (km/h) Average Top Speed (km/h) Hard Acceleration Count Hard Deceleration Count Idling A trip is defined as the period between when the vehicle s ignition is turned on, to when it is turned off regardless of the distance travelled Total time the vehicle is driven Total distance travelled Average distance travelled The average speed the vehicle travelled An average of the highest speed the vehicle reached in each trip Number of times the vehicle performs a speed difference of 30km/h in 2.8 seconds Number of times the vehicle performs a speed difference of 30km/h in 2.4 seconds When the vehicle engine is turned on, but not moving (speed = 0 km/h), this includes all time spent stopped at traffic intersections. Total amount of time the vehicle is idling Percentage of time vehicle is idling Kilograms of CO2 emitted when the vehicle is idling 2 Idling Time (hours) Percentage of Idling Time (%) CO2 Emissions from Idling (kg) Fuel Consumed from Idling (L) Litres of fuel consumed while the vehicle is idling 3 Fuel Cost from Idling ($CAD) Cost of fuel consumed from idling 4 Phase II Phase II of the project behavioural intervention, was launched over the course of one month between August 22 nd and September 28 th, Based on EcoDriver, a course developed by Green Communities Canada, this phase of the study aimed to promote environmentally friendly driving habits and reduce fuel consumption and GHG emissions. These habits include improvements to vehicle maintenance and operation, selection of a fuel-efficient vehicle, consideration of alternatives to driving and avoidance of unnecessary driving. The EcoDriver curriculum was tailored for the City of Calgary by developing scenarios and examples specific to the operation of the municipality s fleet, which included the following topics: Trip planning; Right sizing (i.e. use the size of vehicle that fulfills the travel/hauling requirements and shifting to the use of smaller vehicles); Tire pressure awareness kg/co 2 /L of gas (Environment Canada, 2008). 3 Idling time*fuel flow*60, with fuel flow = engine size* 0.6 / 60 (Environment Canada, 2008). 4 Fuel consumed from idling*price of fuel (CAD$1.20/L). 12

14 Vehicle maintenance; Smarter driving style: accelerate gently, follow speed limits, anticipate traffic flow, coast to decelerate; Reducing unnecessary idling; Reducing warm-up times; Carpooling. A total of 404 drivers were enrolled in the EcoDriver training course throughout the duration of this study, with 15 drivers self-selected from the Development & Building Approvals Business Unitto be participants in this vehicle monitoring study. Of these 15 drivers, 12 were enrolled in a one hour eco-driver training course and three were enrolled in a three hour eco-driver training course. The one hour training session was in-class, while the three hour course also included an invehicle training component. Using in-vehicle feedback systems (i.e. ScanGauge), this more intensive training enables drivers to experience the immediate impact of instituting behavioural changes to their driving habits and to observe how these changes will result in meaningful reductions in fuel consumption. Immediately prior to the training, participants were directed to drive a vehicle outfitted with a ScanGauge around a set course as they normally would, and then after a period of in-class instruction they were then asked to drive the same course again, while putting in to practice what they had learned. The ScanGauge results provided immediate feedback, with participants averaging an 18% improvement in fuel efficiency after the training. Once participants completed the second in-vehicle exercise, they returned to the in-class setting to share their experiences and learn more of the theory that explains those experiences. All of the participants that received eco-driver training were asked to complete a short exit survey at the end of the session, and as well as a follow-up survey distributed by 10 weeks later. The results from these surveys are to assist with the continued development of effective green driver education programs, as well as to assess driver commitment to implementing the eco-driver techniques that were provided through the training session. Results of the exit surveys suggested that the training had been effective, with 97% rating the training as good to excellent. Of the 328 respondents, 74% committed to reducing idling at work. 53% said they planned to reduce highway speeds, 76% said they planned to combine trips for work, 74% planned to maintain tire pressure on their work vehicles, and 69% planned to carpool more often. The vast majority also indicated they planned to always or sometimes coast to a stop, practice gentle accelerations, maintain steady speeds, and anticipate traffic to reduce the need for braking. Phase III Immediately following Phase II, the post-eco-driver training data acquisition (Phase III) was launched for the month of October to quantify the differences in driver behavior between the pre- and post-eco-driver training courses. The same method 13

15 that was applied in Phase I was replicated during Phase III, such that the CarChips were installed back into the same participating vehicles (Table 1), recording the same parameters (Table 2), with the removal and downloading of the data from the devices onto the netbook computer between October 18 th and 21 st, A complete list of the dates trained, including length of training, as well as the number of days recorded during Phase I and Phase III are highlighted in Table 3. Table 3. Training Schedule CarChips Date Trained Training length First trip recorded Last trip recorded Total days recorded (Phase I) Total days recorded (Phase III) Gasoline G-1 Sept hour July 13 Oct G-2 Sept hour July 14 Oct G-3 Sept hour July 13 Oct G-4 Sept hour July 20 Oct G-5 Sept. 8 1 hour July 13 Oct G-6 Sept hour July 13 Oct G-7 Sept hour July 13 Oct G-8 Sept hour July 13 Oct G-9 Aug hour July 12 Oct G-10 Sept. 8 1 hour July 18 Oct G-11 Sept hour July 18 Oct Hybrid H-1 Sept hour July 13 Oct H-2 Aug hour July 13 Oct H-3 Aug hour July 12 Oct H-4 Sept hour July 7 Oct Results This section is subdivided into the baseline data acquisition (Phase I) and the postbehavioural intervention (Phase III). Each of these two sections highlights the results on a fleet level (data analysis on individual driver results are in Appendix 2). It is important to note that since the engine operation of a gasoline versus a hybrid vehicle is fundamentally different, the fleet results have been divided by the two vehicle types. The differences between the two engine types and how this influences the results recorded by the CarChip are discussed further in this section. All of the detailed results summaries are given by parameter and include the daily average (i.e. average daily results from the sample study), annual vehicle total (i.e. total 14

16 sample extrapolated to one full working year 5 ) and annual department total (i.e. sample size extrapolated across the Development & Building Approvals Business Unit of 115/39 gasoline/hybrid fleet for one full working year). Baseline Data Acquisition Gasoline Vehicles Table 4 summarizes the fleet results for the Phase I baseline data acquisition. For the 11 gasoline vehicles analyzed in this study, the average daily drive time was 3 hours per day, with an average total distance of 40 km per day. The average number of daily hard acceleration and decelerations was 0.6 and 1.4, respectively. Average total idling among the sample was over 1.6 hours per day, or approximately 52% of the time the vehicles were in operation. Overall, average daily idling per gasoline vehicle leads to 5kg of CO2 emissions and consumes approximately 2L of fuel at a price of CAD$2.82 per day. Extrapolating the results from this sample of 11 gasoline vehicles to represent the Development & Building Approvals Business Unit total gasoline fleet (115 gasoline vehicles), it becomes highly evident that opportunities do exist to improve the economic and environmental efficiency of the fleet. For example, annual total hard acceleration and deceleration for the department is estimated to be over 18,700 and 43,600, respectively. Moreover, nearly 45,000 hours per year would be spent idling, which would constitute 155,000kg of CO2 emissions and consume over 67,000L of fuel at a cost of CAD$81,000 ( Table 4). Hybrid Vehicles As shown in Table 4, the average daily drive time for the 4 hybrid fleet vehicles over the course of the Phase I study was half of the gasoline sample (1.5 hours per day versus 3 hours). While distance driven by the hybrid drivers is similar to that of the gasoline drivers (35km and 39km, respectively), the daily drive time hours in the hybrid is half that of the gasoline vehicles because the CarChip is only able to record data when the gasoline engine is engaged, not when the battery (i.e. electric motor) is engaged. In essence, the hybrid vehicle is not required to rely solely on the gasoline engine since it has an alternate power source an electric motor and batteries. As a result, a hybrid vehicle is able to turn off the gasoline engine while the vehicle is in operation, subsequently disabling the CarChip s ability to record driving behaviour. This is also likely the reason why daily average hard deceleration counts (1.0) are lower for the hybrid sample than the gasoline sample in this study because as the Escape hybrid declerates, it does so under electric power once the engine reaches approximately 40kph (Grabianowski 2005). Hard accelerations are slightly higher in the hybrid fleet sample at One full working year is defined as five working days per week multiplied by 50 working weeks. 15

17 In terms of idling, the hybrid vehicles idled for approximately 1 hour less than the gasoline vehicles (0.5 hours versus 1.7 hours) or approximately 36% of the time the vehicle was turned on. The lower idling in the hybrid vehicles can be partially explained by the fact that when the Escape hybrid is turned on but stopped (e.g., at a stop sign/lights, traffic), the gasoline engine will shut-off, leaving the electric motor to run thereby disabling the CarChips from data acquisition. Idling is nevertheless evident, which is likely the result of the drivers running office equipment (e.g. laptop, printer) and/or temperature controls (e.g. air conditioning), which would require the gasoline engine to be in operation (Grabianowski 2005). This daily idling leads to an average of 1.4kg of CO2 per vehicle, which is equal to 0.6L of fuel, costing CAD$0.73 per day. The above results from this sample of 4 hybrid vehicles was extrapolated to represent the Development & Building Approvals Business Unit total hybrid fleet (39 hybrid vehicles) for one full working year. This projection highlight that although the most fuel efficient vehicles are hybrid, if drivers are unaware of how their driving habits influence environmental and economic sustainability, maximum fuel efficiency will not be realized. Based on the extrapolation, the department s hybrids will be responsible for 10,835 hard accelerations and 9,630 hard decelerations per year. Moreover, the department s hybrids will idle for 5,232 hours, which will lead to approximately 13,500kg of CO2 emissions, consuming 6000L of fuel, costing CAD$7000 per annum. Opportunities for Improvement Based on the results from Phase I of the study, a variety of behavioural changes can be targeted to both decrease climate altering vehicle emissions and reduce the operating cost of the fleet. For example, the high number of hard accelerations and decelerations recorded in the baseline acquisition can be responsible for an increase in fuel use by approximately 33-40% when compared to vehicles driven with smooth and gradual acceleration and decelerations (e.g. Ericsson 2001, NRCan 2009b, Saboohi & Farzaneh 2009, Thew 2007). Unfortunately the CarChip is unable to capture the precise data on the speed and time difference at which a hard acceleration or deceleration incident occurs, which renders the calucation of the specific fuel consumption and CO2 emissions that result from the incidents, value remains in identifying the frequency of their occurrence as behavioural changes can target the reduction of these events. Achieving zero kilometers per liter of gas, idling is the most inefficient use of fuel identifed in Phase I. With over one-third of the time that the fleet vehicle are in operation spent idling, tens of thousands of kilograms of CO2 are being unnecessarily emitted, in addition to thousands of dollars lost in unnecessary fuel consumption. This inefficiency can be avoided by simply turning off the engine when the car is not in use. In fact, more than 10 seconds of idling consumes more fuel than would have been used if the engine was turned off and restarted (NRCan 16

18 2009b). Individual driving behaviour cannot afford to be neglected, particularly when the collective contributions of the department are so large. The promotion of eco-driving is a relatively low-cost, high-payoff initiatve that can be taken in concert with other transportation mitgiation options. Table 4. Phase I Fleet Results Driving Parameter Daily Avg/ Vehicle Gasoline Annual total for 115 vehicles (250 days) Daily Avg/ Vehicle Hybrid Annual total for 39 vehicles (250 days) Drive Time (hours) , * 14, Distance (km) ,145, , Hard Acceleration Count per Day , Hard Deceleration Count per Day , Idle Time (hours) , , Idle Time (%) CO2 Emissions from Idling (kg) , , Fuel Consumed from Idling (L) , , Fuel Costs from Idling ($1.20/ L) $2.82 $81, $0.73 $7, * Note: The daily drive time hours in the hybrid is half that of the gasoline vehicles because the CarChip is only able to record data when the gasoline engine is engaged, not when the battery (i.e. electric motor) is engaged. Post-Training Data Acquisition First, a detailed summary of driver behaviour, by parameter, after the participants took the EcoDrive course, is provided below. Second, a comparison of the change in behaviour from the pre to post-eco driver training is provided. Similar to the baseline data acquisition, the post- training results outline the results of the fleet, including daily average per vehicle (i.e. average daily results from the sample study), annual vehicle total (i.e. total sample extrapolated to one full working year), and the annual total for 115/39 vehicles (i.e. sample size extrapolated across the Development & Building Approvals Business Unit gasoline/hybrid fleet for one full working year). For individual driver results, please refer to Appendix 2. Gasoline Vehicles As highlighted in Table 5, the average daily drive time for the 11 gasoline fleet vehicles during Phase III of the study was 2.6 hours per day, with an average total 17

19 distance of 37 km per day. The average number of daily hard acceleration and decelerations was 8 and 13, respectively. An average total of over 1.2 hours per day was spent idling, or approximately 48% of the time each vehicle was in operation. Overall, average daily idling per gasoline vehicle leads to approximately 4kg of CO2 emissions, consuming 2L of fuel at a price of CAD$2.24 per day. When extrapolating the data across the entire department fleet (115 gasoline vehicles), post-training results reveal that annual total hard acceleration and decelerations would be approximately 220,000 and 370,000, respectively. It is also projected that the fleet would idle nearly 38,000 hours per year, resulting in over 122,000 kg of CO2 emissions and consume nearly 54,000L of fuel at a cost of over CAD$64,000 per annum. When comparing the baseline data acquisition (Phase I) with the post-training data acquisition (Phase III), positive behavioural changes are evident in Table 5. For example, hard decelerations decreased by 2.3 counts per vehicle per day. Moreover, average daily idling time decreased by 0.3 hours, thereby reducing CO2 emissions by 1.1kg and fuel consumption by 0.48L (or CAD$0.58) per vehicle from idling. Once extrapolated, eco-driver training would result in annual department reductions of nearly 33,000 hard decelerations. It would also eliminate almost 10,000 hours in unnecessary idling, decreasing CO2 emissions by almost 32,000kg and thereby consuming almost 14,000L less in fuel, saving over CAD$16,000 per year (or approximately 25% of the total fuel consumed by idling). However, not all parameters were positively influenced by the behavioural intervention. Hard acceleration increased an average of one count per vehicle per day, along with a 1% increase in idling during the first trip of the day. The former may be the result of collecting data in different seasons for the pre- and post-ecodriver training phase (summer versus fall). During the Phase III, data acquisition occurred in October, a time period in which the roads may be more congested as families return to Calgary after summer vacations and children return to school. While this may provide some justification for the slight increase, it is nevertheless a continued opportunity to target this inefficient driving behaviour and push for change among fleet drivers. The latter parameter may actually not be an increase at all, but rather only appear to be an increase. That is, since overall idling decreased by 4% from Phase I to Phase III, the percentage of idling time that occurs during each vehicle trip in Phase III becomes concentrated. 18

20 Table 5. Phase III Post-Training Data Acquisition Results for Gasoline Vehicles Post- Training Results by Parameter Gasoline Phase III Summary Behaviour Change (Pre- to Post-Training) Daily Avg/ Vehicle Annual total for 115 vehicles (250 days) Daily Avg/ Vehicle Annual total for 115 vehicles (250 days) Drive Time (hours) , , Distance (km) ,056, , Hard Acceleration Count per Day , , Hard Deceleration Count per Day , , Idle Time (hours) , , Idle Time (%) CO2 Emissions from Idling (kg) , , Fuel Consumed from Idling (L) , , Fuel Costs from Idling ($1.20/ L) $2.24 $64, $0.58 -$16,

21 Hybrid Vehicles As summarized in (Table 6), the average daily drive time for the 4 hybrid fleet vehicles during Phase III of the study was 0.8 hours per day, which is approximately 1.5 hours less than the gasoline vehicles, which is due to the fact that the Carchip is unable to record when the vehicle is operating on its electric engine. The hybrid vehicles had similar average daily hard accelerations and decelerations as the gasoline vehicles at an average of 1.4 counts per vehicle per day. An average total of over 0.2 hours per day was spent idling (approximately 1 hour less than the gasoline vehicles given the operation time on the electric motor), or approximately 26% of the time each vehicle was in operation. Average daily idling per hybrid vehicle during Phase III led to approximately 1kg of CO2 emissions, consuming 0.4L of fuel at a price of CAD$0.43 per day. When extrapolating the data across the entire department fleet (39 hybrid vehicles), annual total hard acceleration and decelerations would be approximately 11,000 each. It is also projected that the fleet would idle nearly 2,000 hours per year, resulting in over 8,000 kg of CO2 emissions and consume nearly 6,000L of fuel at a cost of over CAD$4,000 per annum. When comparing the baseline data acquisition (Phase I) with the post-training data acquisition (Phase III), positive behavioural changes are evident (Table 6). For example, average daily idling time decreased by 0.3 hours per vehicle, thereby reducing CO2 emissions by 0.6kg and fuel consumption by 0.3L or CAD$0.30 per vehicle per day. Once extrapolated, eco-driver training would result in annual department reductions of over 3,000 hours in unnecessary idling, decreasing CO2 emissions by over 5,000kg and thereby consuming more than 2,000L less in fuel, saving nearly CAD$3,000 per year (a 75% savings). Similar to the results of the gasoline fleet vehicles, not all parameters were positively influenced by the behavioural intervention. Daily average hard acceleration and deceleration counts both marginally increased from the baseline data acquisition (0.1 and 0.4, respectively). However, these parameters may not have actually increased, but rather appear to have increased because the parameters are concentrated as a result of the decrease in average daily drive time and average distance per vehicle from Phase I to Phase III (0.7 and 13, respectively). 20

22 Table 6. Phase III Post-Training Data Acquisition Results for Hybrid Vehicles Post- Training Results by Parameter Hybrid Phase III Summary Daily Avg/ Vehicle Annual total for 39 vehicles (250 days) Behaviour Change (Pre- to Post- Training) Daily Avg/ Annual total Vehicle for 39 vehicles (250 days) Drive Time (hours) , , Distance (km) , , Hard Acceleratio n Count per Day Hard Deceleratio n Count per Day Idle Time (hours) Idle Time (%) CO2 Emissions from Idling (kg) Fuel Consumed from Idling (L) Fuel Costs from Idling ($1.20/ L) % , , , , $0.43 $4, $0.30 -$2,

23 Discussion and Limitations Objective 1: Assess the effectiveness of eco-driver training on reducing (i) harmful vehicle emissions and (ii) fuel consumption within the City of Calgary s fleet of vehicles. To assess opportunities to improve the economic and environmental sustainability of a fleet of vehicles within the City of Calgary s Development & Building Approvals Business Unit, and to quantify the outcome of eco-driver training, CarChips were installed in 15 vehicles (11 gasoline, 4 hybrid). An assessment of the effectiveness of behavioural intervention are positive, with this sample of vehicles achieving an overall reduction in harmful CO2 vehicle emissions by over 3,000kg per year and a reduction in fuel consumption by over 1,500L per year (or approximately CAD$2,000) from idling alone. Moreover, 12 of the 15 trained drivers achieved a reduction in daily average CO2 emissions (between 0.02 and 4 kg per driver per day) and a decrease in daily average fuel consumption (between 0.01 and 1.7L per driver per day) from idling alone (Appendix 2). There was also a decrease in the average daily hard deceleration (0.21) counts within the sample of gasoline vehicles, which would further reduce CO2 emissions and fuel consumption. Unfortunately it is not possible to calculate the exact degree in reduction, however previous studies have found that smooth and gradual acceleration and decelerations can reduce fuel consumption by 33-40% (e.g. Ericsson 2001, NRCan 2009b, Saboohi & Farzaneh 2009, Thew 2007). Upon completion of the eco-driver training course, drivers were asked to complete a short exit survey (see Appendix 4) to assist in the continued development of effective green driver education programs. The feedback from the drivers revealed that 97% of the drivers that participated in the course found the session to be good to excellent. Perhaps more importantly for this study, 87% of participants committed to implementing the eco-driver training techniques they learned during the training sessions including other fuel savings habits such as coasting to a stop, maintaining tire pressure, etc. It can be anticipated that there will be further savings based on the implementation of these fuel saving habits. The CarChip technology that was used to assess changes in idling, accelerations and braking is not designed to provide feedback on many of these techniques, so it is not possible to concretely assess the impact of the training on such behaviours. Nevertheless the results of the follow-up surveys distributed by 10 weeks after the training reconfirmed the participants commitment and suggest that savings will be realized. For example 55% reported that they regularly practiced gentle accelerations, 64% reporting reduced speeds on the highways, 60% reporting that they coasted to a stop, 58% reporting that they combined trips at work. While this type of self reporting on surveys carries with it the potential for overreporting, evidence from the Car Chip data suggest that this is not the case. The follow up surveys asked participants to report on changes in their idling habits, with 22

24 65% reporting that their idling had reduced. Meanwhile data from the Car Chips indicated that in fact 80% of the study participants had reduced their idling, suggesting that if anything the survey respondents may be under-reporting rather than over-reporting their actual behaviour changes. Further studies with refinement of data gathered may be helpful in further establishing the effectiveness of training in influencing such driver behaviors as well as the impact on fuel savings. Perhaps the greatest challenge moving forward will be to ensure the effectiveness of the behavioural intervention continues. Previous studies have shown that the positive effects of eco-driver training can diminish in the weeks and months following the completion of the course, with drivers relapsing into their older (inefficient) driving habits (e.g. Civitas 2008, Beusen et al. 2009, Barkenbus 2010). The degree to which the positive effects of eco-driving are retained is relatively unknown, with no known studies that conclusively document the average rate and degree of reduced effects among eco-driver participants. One recent exception is a study by the Quebec Ministry of Natural Resources (2011). Among the drivers that had been trained, up to 56% applied eco-driving techniques in the first month after training, which remained stable for approximately 6 months before half of the drivers abandoned their new driving habits by month 9 (reducing the application of eco-driving techniques to just under 20%). To ensure the effectiveness of the behavioural intervention is maintained, the initial eco-driver training should be complimented through the provision of periodic review sessions for the drivers. Efficient driving behaviours could also be reinforced through performance feedback (e.g. installing ScanGuages into the vehicle) or incentives (e.g. monetary incentive such as offering a portion of savings garnered through the reduction of daily fuel consumption). Motivated drivers with a form of reminder or incentive will tend to retain the positive effects of eco-driving when compared to those drivers who have no form of incentive at all (Civitas 2008, Barkenbus 2010). Objective 2: Examine how two differing approaches to eco-driver training (i.e. a 1 hour in-class session versus a 3 hour in-class and in-vehicle session) influences the degree of positive behavioural change. A total of 12 participants from the department were enrolled in the one hour EcoDriver course, and 3 participants were enrolled in the three hour (intensive) EcoDriver course. As highlighted in Table 9 and Table 10 (Appendix 2) with additional figures in Appendix 3, all 3 drivers that participated in the intensive ecodriver training did achieve positive behavioural results, with each driver reducing their average daily CO2 emissions and average daily fuel consumption. For example, G-9 achieved the second highest reduction in daily idling (21%), however this is partially correlated to the reduction in total daily drive time (1.2 hours and 1.7 km). H-2 achieved the lowest level of emissions reductions (1% in total daily idling), but 23

25 the highest reduction in hard accelerations (0.5) and highest reduction in percentage of idling time during the first trip of the day (9%), with H-3 achieving the second highest level emissions reductions (4% in total daily idling). Overall, the two training approaches reveal comparable results among drivers, with the very small sample size utilized in this study restricting the ability to decipher definitive statements regarding the influence between the two training approaches on driver behaviour. Previous studies have found that drivers who participate in an in-vehicle training component positively benefit from the experience. For example, a study by Rutty et al. (2011) found ski resort fleet drivers that used the in-vehicle feedback systems (i.e. ScanGauge, Carchip with activated audible alarm) favoured this training approach over the classroom setting because the immediate feedback of the ScanGuage allowed the drivers to better understand and experience the difference between inefficient and efficient driving practices. More specifically, the in-vehicle component enabled the drivers to understand the impact of instituting behavioural changes to their driving habits and to observe how these changes can and will result in meaningful reductions in both fuel consumption and harmful vehicle emissions. Additional research that focuses on the influence and effectiveness of in-vehicle versus classroom based training remains a productive avenue to explore. Objective 3: Evaluate the difference in CO2 emissions between gasoline and hybrid vehicles within the City of Calgary s fleet. Two primary goals of driving a hybrid versus a traditional gasoline powered vehicle are to both achieve better fuel economy and to reduce environmental impacts through a reduction in vehicle emissions. As shown in Table 4, the data gathered from this study underscore these benefits, with gasoline and hybrid drivers within the department driving similar average daily distances (40km and 35km, respectively), but the hybrid drivers are only using a fraction of the gas. For example, the average daily drive time with a gasoline engine in the hybrid vehicles was 1.5 hours, whereas the gasoline vehicles average daily drive time was 3 hours per day. Over the course of the year, for 10 hybrid vehicles, this translates to a difference of nearly 10,000kg of CO2 emissions and over $5,000 in fuel per year (or approximately 4,000L less in fuel consumption) when compared to 10 gasoline drivers. Having an eco-trained hybrid driver further enhances these benefits. Based on the results from this study (Table 11, Table 6), 10 eco-trained hybrid drivers, when compared to 10 untrained eco-drivers, would further reduce CO2 emissions by more than 1,400kg per year, saving over 600L of fuel, or approximately $750 per year. In essence, eco-driving styles are essential to maximize the economic and environmental benefits of hybrid vehicles due to the ability of eco-driving to optimize the use of the electric motor over the gasoline motor. 24

26 The City of Calgary has their own fuelling stations, and all employees have access to Esso stations (via fleet cards). Esso fuel data is available in a read-only format through a secure website, which is available to city managers, but unfortunately, is not accessible for this project. City managers also receive fuel data from the City of Calgary system on a quarterly basis. Management at the City of Calgary has indicated that approximately half of the departmental fuel consumption is derived from the on-site fueling stations, and the other half is from the Esso stations. Unfortunately access to this data was not possible, however should it be made available in the future, an analysis of gas consumption by the hybrid drivers-posttraining would be a productive avenue to explore. Limitations There are three key limitations as they are applied in this study. The first relates to the inability of the CarChip to record all driving behavior in the hybrid vehicles, such as when the electric motor is engaged. Without this data, it is possible that the number of hard accelerations and decelerations is higher than what was collected during Phase I and Phase III. It was therefore not possible to directly compare the driving behavior of the gasoline and hybrid vehicles, resulting in a very small sample size for the hybrid drivers. This is particularly true when assessing the effectiveness of the three hour eco-driver training versus the one hour course, as it further limits the already small sample of three drives down to one (gasoline) and two (hybrid) drivers making it difficult to draw definitive statements. The second limitation relates to the CarChips inability to calculate specific fuel consumption and CO2 emissions for the parameters of hard acceleration and hard deceleration counts. The CarChips are unable to capture precise data on the speed and time difference at which the incident occurred, rendering the counts unusable in terms of quantifying fuel consumption loss due to inefficient behaviour. Although this data would be helpful, value does remains in identifying the frequency of their occurrence as behavioural changes can nevertheless target the reduction of these events. The third limitation relates to idling. There are a few circumstances in which individuals may be required to idle their vehicle. This includes to warm the engine, to warm (heat) or cool (air condition) the interior of the vehicle, to wait for something unrelated to traffic (e.g., a passenger), and while commuting (e.g., at a stop sign, traffic lights, railway crossing) (Carrico et al 2009). This latter idling circumstance is difficult to avoid for functional and safety purposes and can therefore be deemed necessary idling and should not be included in the daily average and total idling time. Unfortunately the CarChip quantified idling at every point when a vehicle was at zero kilometers per hour. Ideally, second by second data could be collected to calculate those circumstances when the vehicle is idling for 60 6 seconds or less as necessary idling, thereby removing these circumstances from the daily averages and totals. To do so would demand high memory space on the 6 Idling for 60 seconds or greater has been identified by NRCan as unnecessary idling (NRCan 2008). 25

27 CarChip, thereby requiring regular (i.e. weekly) data downloads. Unfortunately, to access the CarChips during the study period required the City staff to request access through the fleet management department, which would then require the vehicle to be taken off the road to uninstall, download and then reinstall the device a time consuming and a logistically difficult procedure. Final Thoughts The urgent need to stabilize and curb global GHG emissions is now widely accepted, with the effectiveness of current mitigation efforts to have a large impact on the degree of current and future climate change. Recognizing the need to reduce the environmental impact of fleet operations, the City of Calgary is a leader in developing programs and policies that aim to reduce GHG emissions and associated pollutants resulting from the use of fossil fuels. Among the local action taken against climate change, the City of Calgary had staff from Development & Building Approvals Business Unit partake in eco-driver training, in conjunction with vehicle monitoring devices, resulting in a successful reduction in CO2 emissions that are generated through the operation of its fleet of vehicles. A primary challenge among Canadian municipalities such as the City of Calgary, is to identify the sources of GHG emissions that have the greatest capacity for substantial reductions, while simultaneously planning and implementing means to achieve reduction goals. The current debate predominately focuses on the use of economic regulation (e.g. carbon taxation, cap and trade systems) within industry sectors, including transportation. While such regulatory measures may be critical to achieving such goals on a much larger scale (e.g., nationally and globally), recent studies have indicated that necessary CO2 reductions can be achieved locally through a number of relatively simple behavioural modifications on an individual level. This study lends support to this assertion, demonstrating that eco-driver training can be a cost-effective fleet management practice for the City of Calgary that can and has reduced climate altering greenhouse gases, while simultaneously saving money and improving the livability of the City. Best practices from this project can be used as a model for additional municipalities, as well as other operations with large fleets of vehicles, with the aim of reducing GHG emissions related to transportation across Canada. 26

28 References Barkenbus, J.N. (2010). Eco-driving: An overlooked climate change initiative. Energy Policy, 38: Beusen, B., Broekx, S., Denys, T., Beckx, C., Degraeuwe, B., Gijsbers, M., Scheepers, K., Govaerts, L., Torfs, R., and Panis, L.I. (2009). Using on-board logging devices to study the longer-term impact of an eco-driving course. Transportation Research Part D, 14, Carrico, A.R., Padgett, P., Vandenbergh, M.P., Gilligan, J., and Wallston, K.A. (2009). Costly myths: An analysis of idling beliefs and behaviour in personal motor vehicles. Energy Policy, 37, Civitas. (2008). Eco-driving for municipal employees. Malmö, SMILE Project. Sweden: Guard. Conference Board of Canada. (2011). Greenhouse Gas Mitigation in Canada. Ottawa: The Conference Board of Canada. Ericsson, E. (2001). Independent Driving Pattern Factors and their Influence on Fuel-Use and Exhaust Emission Factors. Transportation Research Part D, 6: Environment Canada. (2010). National Inventory Report, : Greenhouse Gas Sources and Sinks in Canada. Ottawa: Environment Canada. Environment Canada (2011a). National Inventory Report. Greenhouse Gas Sources and Sinks in Canada. Accessed from 6C392078D1A9%5CNationalInventoryReportGreenhouseGasSourcesAndSinksIn Canada ExecutiveSummary.pdf Enviance. (2009). Denver s driving change program reduces vehicular CO2 emissions. Accessed from px?id=53 Federation of Canadian Municipalities (FCM). (2009). Act Locally. The Municipal Role in Fighting Climate Change. Ottawa: FCM. Fleet Challenge. (2008). Municipal Best Practices Manual. Accessed from pdf Government of Canada (GC). (2008). Regulatory Framework for Air Emissions. Accessed from eng.pdf Government of Canada (GC). (2011). Transportation Emissions by Mode. Accessed from Grabianowski, B. (2005). How the Ford Escape Hybrid Works. Available from International Energy Agency. (2005). Saving Oil in a Hurry. International Energy Agency Publications, Paris, France. Matthews, L., Rutty, M., Andrey, J. and Del Matto, T. (Accepted). Effects of Weather on Vehicle Idling, International Journal of Biometeorology. 27

29 Natural Resources Canada (NRCan). (2009a). Idling Wastes Fuel and Money. Accessed from Natural Resources Canada (NRCan). (2009b). Auto$mart Thinking Driving and Maintaining Your Vehicle. Accessed from Quebec Ministry of Natural Resources. (2011). Projet Pilote de Formation a L Ecoconduite pour Vehicules Legers. Accessed from ort/cahierecoconduite_2011-lowres.pdf Rutty, M., Matthews, L. and Del Matto, T. (2011). Analyzing Engine Idling Reduction Opportunities at Three Ontario Ski Resorts. Available from analyzing-engine-idling-reduction-opportunities-at-three-ontario-skiresorts.html Saboohi, Y., and Farzaneh, H. (2009). Model for developing an eco-driving strategy for a passenger vehicle based on the least fuel consumption. Applied Energy, 86, Schianetz, K., Kavanagh, L., and Lockington, D. (2007). Concepts and Tools for comprehensive Sustainability Assessments for Tourism Destinations: A Comparative Review. Journal of Sustainable Tourism, 15(4), Thew, R. (2007). United evidence and research strategy: driving standards agency. Version number 1.2, prepared for the International Commission for Driver Testing, Belgium. Transport Canada. (2010a). Biodiesel in Transit and Municipal Fleets. Accessed from Transport Canada (2010b). Fuel Sense: Making Fleet and Transit Operations More Efficient. Accessed from United Nations Framework Convention on Climate Change (UNFCCC). (1992). United Nations Framework Convention on Climate Change. Accessed from Wahlberg, A.E. (2007). Long-term effects of training in economical driving: fuel consumption, accidents, driver acceleration behaviour and technical feedback. International Journal of Industrial Ergonomics, 37, Zarkadoula, M., Zoidis, G., Tritopoulou, E. (2007). Training urban bus drivers to promote smart driving: a note on a Greek eco-driving pilot program. Transportation Research Part D, 12,

30 Appendix 1 If at any time you have any questions or concerns please call Lindsay Matthews at or at Lindsay@mysuscan.org When you receive the CarChips, please install the chips into the vehicles at your earliest convenience. All of the CarChips have been labeled according to their respective vehicles. Please ensure that each CarChip is installed in the appropriate vehicle. Instructions for Installing CarChip Devices within Fleet Vehicles at the City of Calgary 1. Install CarChip devices into fleet vehicles. - The OBDII port is found under the steering wheel of the vehicles Press the CarChip firmly into the OBDII port - Look to see that the faint green light is flashing this may be difficult to see in the sunlight, so you may need to cup your hand around the chip. 29

31 2. Write down the vehicle number (same as the CarChip number) and write down the odometer reading for that vehicle. All CarChips have been numbered according to the vehicle numbers provided by the City of Calgary. 3. When all CarChips have been installed into the vehicles, please the vehicle numbers and odometer readings to These readings are used to verify the data from the CarChip devices. Download Schedule for City of Calgary CarChip Study 1. Within 3 days of installing the Chips - Please download the CarChips from TWO hybrid vehicles, and from TWO nonhybrid vehicles. This is to ensure that the CarChips are working properly in the hybrid vehicles. Please see sheet Instructions for Exporting CarChip Data and export ONLY the trips (Instruction #2). 2. Within 2 weeks of installing the CarChips - Please download ALL CarChip devices. Follow the instruction sheets provided to complete the CarChip downloads. Send all five exported files, as well as odometer readings to Lindsay@mysuscan.org - Repeat the download schedule of every 2-3 weeks. Please download ALL CarChip devices. Follow the instruction sheets provided to complete the CarChip downloads. Send all five exported files, as well as odometer readings to Lindsay@mysuscan.org Instructions for Downloading CarChip Devices from Fleet Vehicles at the City of Calgary Log On to Computer Password = mysuscan 1. Open DriveRight Software 2. Plug USB cable into laptop attach CarChip 3. Download CarChip - Click CarChip -> Download CarChip The download will start. o Download could take a few minutes and in some cases up to half an hour, depending on the amount of data collected (i.e. amount of driving done) o When completed a popup will say download successful Click OK 4. Check that data downloaded 30

32 - Click on the icon that has a road on it (if you hold the mouse over it will say trip summary report) o Select the vehicle you just downloaded o Make sure the dates are set to the dates of the download o See that data is present If data is present proceed to next step 5. Clear CarChip memory (Do not perform this step until you have completed step 4, if unsure, please contact Lindsay Matthews: at or at Lindsay@mysuscan.org - Click CarChip -> Display memory -> write down amount of space used (please summarize this information and send to Lindsay@mysuscan.org) -> click OK 31

33 - Click CarChip -> Clear memory 6. Set Date and Time - Click CarChip -> Set Date and Time -> Set 7. Unplug CarChip- no need to click anything before removing chip 8. Plug back into vehicle. 32

34 Instructions for Exporting CarChip Data Data is to be exported after ALL Carchips have been downloaded. There is no need to export the data after each individual CarChips has been downloaded. The database is cumulative and new data is just added to the existing database. 1. File -> Export -> Odometer Logs - Save to desktop as odometerlogs(insertdate) 2. File -> export -> Trips - Save file to desktop as trips(insertdate) 3. File -> Export -> Parameter Logs - Save file to desktop as parameterlogs(insertdate) 4. File -> export -> Tamper Logs - Save file to desktop as tamperlogs(insertdate) 33

35 5. File -> export -> Days - Save file to desktop as days(insertdate) When completed, please send the 5 files to Lindsay@mysuscan.org 34

36 Hybrid Gasoline Appendix 2 Table 7. Phase I Individual Daily Results CarChip Drive Time (hours) Distance (km) Hard Accel. Hard Decel. Idling Time (hours) Idle Time (%) CO 2 from Idling (kg) Fuel Consume d from Idling (L) Fuel Cost from Idling ($1.20/L) G G G G G G G G G G G Total Sample H H H H

37 Hybrid Gasoline Table 8. Phase I Individual Annual Results (Extrapolated) CarChip Drive Time (hours) Pre-Eco-Driver Training Results by Parameter Annual Average (250 working days) Distance (km) Hard Accel. Hard Decel. Idling Time (hours) Idle Time (%) CO 2 from Idling (kg) Fuel Consume d from Idling (L) Fuel Cost from Idling ($1.20/L) G , , G , , G , G-4 1, , , ,031 1, G , , G , G , , G , , G , , G , , G , Total Sample H , H , H , H , , , ,740 4, , , , ,

38 Hybrid Gasoline Table 9. Phase III Individual Daily Results CarChip Drive Time (hours) Post-Eco-Driver Training Results by Parameter Daily Average Distance (km) Hard Accel. Hard Decel. Idling Time (hours) Idle Time (%) CO 2 from Idling (kg) Fuel Consume d from Idling (L) Fuel Cost from Idling ($1.20/L) G G G G G G G G G-9* G G Total Sample H H-2* H-3* H * Drivers in the 3 hour EcoDriver course 37

39 Hybrid Gasoline Table 10. Phase III Individual Annual Results (Extrapolated) CarChip Post-Eco-Driver Training Results by Parameter Annual Average (250 working days) Drive Time (hours) Distance (km) Hard Accel. Hard Decel. Idling Time (hours) Idle Time (%) CO 2 from Idling (kg) Fuel Consumed from Idling (L) Fuel Cost from Idling ($1.20/L) G , , G , , G , G , , G , , G , G , , G , G-9* , G , G , Total Sample H , H-2* , H-3* , H , , , , , , , , * Drivers in the 3 hour EcoDriver course 38

40 Hybrid Gasoline Table 11. Phase III Behavioural Change Daily Results CarChip Drive Time (hours) Distance (km) *Drivers in the 3 hour EcoDrive Course Behavioural Change Results by Parameter Daily Average Hard Accel. Hard Decel. Idling Time (hours) Idle Time (%) CO 2 from Idling (kg) Fuel Consumed from Idling (L) Fuel Cost from Idling ($1.20/L) G G G G G G G G G-9* G G Total Sample H H-2* H-3* H

41 Hybrid Gasoline Table 12. Phase III Behavioural Change Annual Results (Extrapolated) CarChip Drive Time (hours) Behavioural Change Results by Parameter Annual Average (250 working days) Distance (km) *Drivers in the 3 hour EcoDrive Course Hard Accel. Hard Decel. Idling Time (hours) Idle Time (%) CO 2 from Idling (kg) Fuel Consumed from Idling (L) Fuel Cost from Idling ($1.20/L) G G G G G G , G , G , G-9* G , G Total Sample H , H-2* , H-3* , H , , , , , ,

42 Number of Hard Accelerations per Day Appendix G-1 G-2 G-3 G-4 G-5 G-6 G-7 G-8 G-9 G-10 G-11 Vehicles Pre Training Post Training 41

43 Number of Hard Accelerations per Day H-1 H-2 H-3 H-4 Vehicles Pre Training Post Training 42

44 Number of Hard Decelerations per Day G-1 G-2 G-3 G-4 G-5 G-6 G-7 G-8 G-9 G-10 G-11 Vehicles Pre Training Post Training 43

45 Number of Hard Decelerations per Day H-1 H-2 H-3 H-4 Vehicles Pre Training Post Training 44

46 Average Daily Idling Time (hours) G-1 G-2 G-3 G-4 G-5 G-6 G-7 G-8 G-9 G-10 G-11 Vehicles Pre-Training Post-Training 45