Journal of Sustainable Tourism, 2014 Vol. 22, No. 5, 787–800, http://dx.doi.org/10.1080/09669582.2013.855221
Using vehicle monitoring technology and eco-driver training to reduce fuel use and emissions in tourism: a ski resort case study Michelle Ruttya*, Lindsay Matthewsa, Daniel Scotta and Tania Del Mattob a Geography & Environmental Management, University of Waterloo, 200 University Avenue West, Waterloo, Ontario N2L 3G1, Canada; bMy Sustainable Canada, 743 Avondale Avenue, Kitchener, Ontario N2M 2W6, Canada
(Received 5 March 2013; accepted 23 September 2013) Ground-based transport moves more tourists than any other form of transportation and contributes c. 32% of tourism’s carbon dioxide (CO2) emissions – yet remains a largely neglected area of emission/carbon management research. This study examines the value of vehicle monitoring technology (VMT) and eco-driver training as a means to improve fuel efficiency and reduce CO2 emissions for a fleet of vehicles at the largest ski resort operation in Ontario, Canada. The VMT was installed in 14 fleet vehicles. After eco-driver training, the fleet reduced its average daily speed (14%), hard decelerations (55%), hard accelerations (44%), and idling time (2%), resulting in decreased fuel costs (8%) and CO2 emissions (8%). The process requires very low capital expenditures, can pay for itself in as little as one year, and has safety paybacks. It also has valuable externalities: tourism businesses that instill sustainability awareness and values to their employees contribute to environmental prosperity generally, because eco-trained drivers also drive more efficiently in their everyday lives. This is the first known study to quantify the benefits of driver training and behavioral intervention within a tourism context, demonstrating the potential to enhance environmental sustainability while simultaneously reducing operating costs. Technicalities, issues, and future application possibilities are discussed. Keywords: eco-driving; engine idling; sustainability; ski resorts; vehicle transport; emissions
Introduction The tourism literature is replete with accounts of adverse environmental impacts caused by tourism development, with many authors warning that the tourism sector is not only developing in an environmentally unsustainable manner, but also becoming globally increasingly unsustainable (Buckley, 2012; G€ossling, Hall, Ekstr€om, Engeset, & Aall, 2012; Hall, 2011; Peeters, 2012; Peeters & Dubois, 2010; Scott, Peeters, & G€ossling, 2010; Weaver, 2009). Conventional mass tourism development often faces criticism, leading attention toward small-scale, environmentally and culturally appropriate forms of tourism as means to achieve greater sustainability (e.g. Beaumont, 1998; Clarke, 1997; Fullagar, Markwell, & Wilson, 2012; Wearing & Neil, 2009). Given the continuing strong demand for mass tourism, however, it can confidently be assumed that it will not easily, or fully, be replaced by these “alternative” tourism forms, thereby highlighting the need to enhance the sustainability of existing and future mass tourism markets and infrastructure (Peeters, 2012; Weaver, 2012). Related to this is the question of how the effects of *Corresponding author. Email:
[email protected] Ó 2013 Taylor & Francis
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tourism can be better monitored to help determine how sustainability might be achieved and to measure progress (Buckley, 2012). The global tourism industry consumes a considerable amount of energy to transport tourists (travel to, from, and within a destination) as well as to provide amenities (accommodation) and supporting facilities (e.g. restaurants) at destinations (G€ossling, 2012; Scott et al., 2010). Since the majority of this energy is derived from fossil fuels, energy use within the tourism sector is linked to greenhouse gas (GHG) emissions. These emissions are not only altering global climate, but also a significant cause of diminished local air quality, and are compromising the visitor experience at a number of tourism destinations (G€ ossling, 2002; Holden, 2000; Kelly & Williams, 2007; Laube & Stout, 2000; Martin-Cejas & Sanchez, 2010). The GHG emissions from tourism were first discussed by Bach and G€ossling (1996), who focused on the emissions from the transportation of millions of tourists worldwide. A decade later, the United Nations World Tourism Organization (UNWTO), United Nations Environment Programme, and the World Meteorological Organization commissioned a climate change assessment that developed the first quantitative estimate of the global tourism sector’s contribution to climate change of approximately 5% of global anthropogenic emissions of CO2 in 2005 (Scott et al., 2008). The transport of tourists contributed the largest portion, accounting for 75% of the total CO2 emissions in 2005 (Scott et al., 2008). The impact of tourism transport on climate change has received increasing attention within the sustainable tourism literature, with a main focus on aviation (e.g. G€ossling et al., 2007; G€ ossling & Peeters, 2007; Lee et al., 2009; Peeters & Landre, 2012; Scott et al., 2010). Aviation not only represents the largest share (40%) of overall sectoral CO2 emissions (Scott et al., 2008), but the contribution made by aviation to global warming, measured by radiative forcing, is thought to be far greater owing to non-carbon emissions and impacts at high cruising altitudes (Lee et al., 2009). Several studies have examined technological innovations, management policies, and consumer behavior strategies to reduce tourism-related aviation emissions (e.g. Cohen & Higham, 2011; G€ossling et al., 2007; G€ossling & Peeters, 2007; Krosen, 2013; Peeters, G€ossling, & Becken, 2007; Scott et al., 2010). Ground-based transportation nevertheless moves more tourists, while also contributing to 32% of the sectoral CO2 emissions (roughly equal to accommodationrelated emissions). However, ground-based tourism transport is a largely neglected area of emissions/carbon management research, with recommendations that transport modal shifts in some tourism markets could reduce sectoral GHG emissions (G€ossling, 2012; Scott et al., 2008). Recent studies have found that positive changes related to GHG reductions can and have been achieved within the tourism sector when businesses get involved (Becken & Hay, 2007; Becken & Hay, 2012; Scott et al., 2008; Scott, Hall, & G€ossling, 2012). Businesses are increasingly approaching sustainability through self-regulated, voluntary measures, as evidenced through the abundant corporate social responsibility mission statements, codes of ethics, eco-certifications, and destination planning strategies (Lynes & Dredge, 2006; Weaver, 2012). However, the efficacy of such approaches has been questioned (Buckley, 2012; Peeters, 2012). To date, there has been a notable gap in the literature regarding the behavioral changes that can be adopted from a business operation perspective. An important component for achieving sustainability that is often overlooked is the necessary engagement and support of business employees (Bohdanowicz, Zientara, & Novotna, 2011). Employees must be motivated to carry out sustainability plan(s) or action(s), particularly since many corporate greening activities involve staff taking on an
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extra role, which is rarely required nor formally rewarded (Savage, 2007). The role of management is also critical in achieving and sustaining (i.e. normalizing) a desired behavioral change, which includes championing sustainability actions and ensuring employee empowerment and teamwork (Bohdanowicz et al., 2011; Daily & Huang, 2001). Recognizing the dependence of winter sports on the natural ecosystem and the potential impacts of climate change, members of the Ontario Snow Resorts Association (OSRA)1 made a commitment to become more sustainable, collectively raising interest, and seeking opportunities to reduce CO2 emissions within their fleet of on-resort vehicles. It is against this background that this research was launched, analyzing the potential for driver training, as a low-cost behavioral intervention (versus capital intensive fleet renewal), to reduce fuel use, engine idling, and GHG emissions from tourism operation vehicle fleets. Eco-driver training as a means to improve sustainability has long been discussed in the transportation literature, but not yet applied to tourism operations. Blue Mountain Resort Limited (BMR), located in the UNESCO Biosphere Reserve of the Niagara Escarpment in Ontario, Canada, operates the largest ski resort fleet in the province (55 vehicles) and agreed to participate in this pilot study. This is the first known study to quantify the relationship between the economic and environmental impacts of driver behavior within a tourism setting and represents a novel means for ski resort operations to enhance their sustainability through a measurable reduction in harmful vehicle emissions. Context Ecological, economical, and safe driving (eco-driving) was first developed and integrated into public driver training courses by the German Federation of Driving Instructor Associations in the mid-1990s (Dandrea, 1996). Three key facets govern eco-driving: (1) Smooth and gradual acceleration and deceleration. Driving based on sudden acceleration/deceleration uses approximately 33%–40% more fuel (Ericsson, 2001; Natural Resource Canada [NRCan], 2009a; Saboohi & Farzaneh, 2009; Thew, 2007). (2) Maintaining a steady speed by anticipating traffic flow while adhering to the posted speed limits. While each vehicle reaches its optimal fuel economy at different speeds, fuel efficiency decreases 10%–23% at speeds above 90 kilometers (km) per hour (55 miles per hour) (NRCan, 2009a; West, McGill, Hodgson, Sluder, & Smith, 1999). (3) Avoiding idling by turning off the engine when not in use. Idling is the most inefficient use of fuel at 0 km per liter of gas. More than 10 seconds of idling consumes more fuel than would have been used if the engine was turned off and restarted (NRCan, 2009b). Although there are limited studies that have evaluated either personal or corporate eco-trained drivers, the results are promising. A literature review for the European Conference of Ministers of Transport by the International Energy Agency (2005) found an average estimated reduction of fuel consumption in personally owned vehicles of 5% for Organization for Economic Cooperation and Development countries. Since then, additional studies have recorded decreased fuel use ranging from 2% to 18% after eco-driver training. In Sweden, five corporate bus drivers recorded an average decrease in fuel consumption of 2%, 12 months after being trained (Wahlberg, 2007). Zarkadoula, Zoidis, and Tritopoulou (2007) noted a decrease of 10%–18% in Greece during a post-training monitoring period of two months. In Belgium, Beusen et al. (2009) found that four months after receiving eco-driver training, average fuel consumption had fallen by 5%, with most drivers showing an immediate improvement in fuel consumption. This was
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also the only known study to detail the influence of eco-driver training on idling, with drivers realizing an average decrease in fuel use of 1.5%. The BMR operates the largest ski resort in the province, hosting over half a million skiers annually, and employing more than 2000 local area residents. BMR’s environmental vision is recognized as a leader among resort destinations in Canada for their exceptional commitment to the environment, striving to achieve resort-wide sustainability by reducing the environmental footprint of their operation (BMR, 2013). The BMR’s corporate policy on climate change also includes the National Ski Areas Association’s “Keep Winter Cool” program, raising guest awareness about the potential impacts of climate change on the ski industry. As such, the BMR agreed to have vehicle monitoring technology (VMT) installed in resort vehicles to assess opportunities to reduce emissions across their vehicle fleet. Methods Comprehensive data collection and analysis are critical for effective vehicle fleet operation, cost management, and environmental sustainability. The GHG emission accounting is relatively new to the tourism sector, and tourism businesses often do not have the resources or tools required to quantify GHG reductions associated with sustainability projects. When selecting a tool, it is essential that it both defines the areas where corrective actions are necessary and measures whether the desired amendment of the situation was achieved (Schianetz, Kavanagh, & Lockington, 2007). For this reason, the VMT was selected as a cost-efficient means of identifying opportunities to reduce GHG emissions within the ski resort vehicle fleet and to measure the outcome of the behavioral intervention (i.e. eco-driver training). Recognizing that each fleet driver is unique, it was important to acquire baseline data on driving behavior to identify the habits that are leading to the greatest fuel inefficiencies and highest emissions. From this data, feedback and recommendations could then be made on an individual basis through the identification of behavioral adjustments that hold the greatest potential to reduce GHG emissions through the adoption of eco-driver training. For this reason, the study was completed in three stages: (1) baseline vehicle/fuel/ emissions data acquisition (pre-eco-driver training); (2) behavioral intervention (eco-driver training); and (3) post-training vehicle/fuel/emissions data acquisition (post-eco-driver training). Phase 1 began in December 2009 with the programming and installation of VMT. A representative sample of the resort vehicles that are driven the most by resort staff was selected for this study, which includes vehicles driven by grounds and maintenance staff (e.g. snow and garbage removal vehicles), shuttle buses, security vehicles, and management staff vehicles (e.g. operations, information technology, and accounting). On-board data loggers (CarChipsÒ ) were placed into 14 light and medium class fleet vehicles. Information on each company vehicle was recorded, including vehicle year, manufacturer and model, engine size, and fuel type (Table 1). To limit the influence these devices may have on driver behavior, the BMR staff were notified of the VMT installation, but details of which driving parameters were being recorded as well as the purpose of the study were not made available. The device was installed out-of-sight of the driver by plugging the device into the on-board diagnostic port found under the dashboard. Once installed, the CarChipsÒ continuously read the driving and engine performance data from the vehicle’s on-board computers and store the data in an internal memory card. Selected parameters were recorded based on their relevance for eco-driver training,
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Table 1. Vehicle details. Vehicle 1 2 3 4 5 6 7 8 9 10 11 12 13 14
Vehicle model
Fuel type
Engine size (L)
Year
Department/vehicle function
Toyota Tundra Ford F150 Toyota Yaris Chevrolet Venture Van Chevrolet Express Van GMC Sierra Ford 250 4 4 GMC Sierra Ford CTV Bus Ford Senator 24-passenger Ford E450 Bus GMC Safari Van Dodge Caravan Honda Fit
Unleaded Unleaded Unleaded Unleaded Unleaded Unleaded Unleaded Unleaded Diesel Diesel Diesel Unleaded Unleaded Unleaded
4.7 5.4 1.5 3.4 5.7 5.4 4.6 5.0 7.5 6.0 7.3 4.3 3.0 1.5
2007 2002 2010 1999 2000 2004 1998 1997 1995 2005 1999 2002 2000 2008
Operations Operations Accounting Information technology Grounds/maintenance Grounds/maintenance Grounds/maintenance Grounds/maintenance Shuttle Shuttle Shuttle Security Security Information technology
focusing on environmental performance and fuel consumption. Table 2 presents an overview of the parameters, their units, corresponding abbreviations, and a description, with footnotes denoting how the parameter was calculated. The CarChipsÒ were removed from each vehicle bi-weekly and the data were downloaded. The CarChipÒ memories were then cleared, reprogrammed, and reinstalled into the corresponding vehicle to continue data logging. Data were recorded for the duration of the 2009–2010 ski season, with the devices removed in April 2010. The DriveRight Fleet Management Software package was used to analyze the data. Table 2. Monitored variables. Parameter Daily drive time Distance driven Average speed Average top speed (km/h) Hard acceleration count (per 100 km) Hard deceleration count (per 100 km) Idling Daily idling time (hh:mm) Percentage of daily idling time (%) Daily idle time, first trip of the day CO2 emissions from idling (kg) Fuel consumed from idling (L) Fuel cost from idling (CAD$) 1
Environment Canada, 2008.
Description Total time the vehicles are driven Total distance travelled Average speed travelled Average top speed reached during a trip Number of times the vehicle performs a speed difference of 30 km/h in 2.8 seconds Number of times the vehicle performs a speed difference of 30 km/h in 2.4 seconds When the vehicle engine is turned on, but not moving (speed ¼ 0 km/h) Total amount of time the vehicle is idling Percentage of time vehicle is idling Time spent idling during the first trip of the day Kilograms of CO2 emitted when the vehicle is idling; 2.289 kg/CO2/L of gas and 2.663 kg/CO2/L of diesel 1 Liters of fuel consumed while the vehicle is idling; idling time fuel flow 60, fuel flow ¼ engine size 0.6 1 Fuel consumed from idling; consumption fuel cost (CAD$0.78/L, as per BMR pricing)
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The second phase of the project involved eco-driver training as the behavioral intervention. Launched in October 2010, this phase aimed to improve driver behavior to help achieve a more economically and environmentally sustainable ski resort fleet. Ten ecodriver training courses were delivered to 64 staff members at BMR (or approximately 60% of BMR staff that operate the fleet vehicles analyzed in this study).2 There were two additional training sessions for department managers at BMR that focused on “training the trainer”, aiming to build sufficient capacity among management staff for sustained training of seasonal staff(s) and staff in future years. Results from phase 1 were incorporated into the training, thereby tailoring the curriculum of the eco-driver training courses to focus on those parameters that BMR staff were the most inefficient in (e.g. idling during the first trip of the day). Following the eco-driver training, phase 3 consisting of post-intervention data acquisition began for the duration of 2010–2011 ski season. Phase 3 sought to quantify the differences in driver behavior between the pre- and post-eco-driver training courses. The phase 1 data collection method was replicated during phase 3, such that the CarChipsÒ were installed back into the same vehicles (Table 1) to record the same parameters (Table 2). BMR staff were once again notified of the VMT installation, but details on which driving parameters were being recorded and the purpose of the study were not made available. The devices were removed in April 2011. The cost for the VMT was CAD$130 per CarChipÒ and the tailored eco-driver training course was CAD$1250 per session, with a group of drivers attending one of the three sessions. Due to a confidentiality agreement with the BMR, only fleet aggregate results are presented here. Results for individual drivers or vehicles are not presented. Also, because multiple drivers have access to the vehicles monitored in this study, it is not possible to differentiate the results of eco-driver training based on individual drivers. Results Pre-eco-driver training Parameters of particular relevance for improving economic and environmental sustainability of the vehicle fleet include speed, acceleration, deceleration, and idling. It is among these parameters that opportunities can be sought to introduce behavioral changes that can reduce fuel consumption and limit harmful idling emissions. Data from VMT indicated that the average daily drive time for the 14 fleet vehicles over the course of the phase 1 study was just under five hours per day, with an average total distance of 96 km per day (Table 3). VMT data also indicated that the average daily speed of the fleet was 20.5 km per hour, with an average daily top speed of 50 km per hour. While this may seem low, 10 of the 14 vehicles are restricted to the resort property, where the maximum posted speed limit is –40 km per hour. The four vehicles that do travel off resort were driven approximately three hours per day, at speed equal to or greater than 110 km per hour, which decreases fuel efficiency up to 23% versus following the posted speed limit of 100 km per hour (NRCan, 2009a; West et al., 1999). The average daily hard accelerations across the fleet were approximately nine per 100 km, with seven hard decelerations per 100 km. The lack of gradual acceleration and deceleration increased fuel consumption by 30%–40% (Ericsson, 2001; NRCan, 2009a; Saboohi & Farzaneh, 2009; Thew, 2007). Idling is another driving behavior where significant fuel savings and emission reductions could be realized. Drivers spent an average of nearly two hours per day idling, or approximately 34% of the time the vehicle was turned on. The first trip of the day was
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Table 3. Results by parameter. Parameter Daily drive time (hh:mm) Distance driven (km) Average speed (km/h) Average top speed (km/h) Hard acceleration count (per 100 km) Hard deceleration count (per 100 km) Daily idling time (hh:mm) Daily idle time, first trip of the day (hh:mm) Percentage of daily idling time (%) CO2 emissions from idling (kg) Fuel consumed from idling (L) Fuel cost from idling (CAD$)
Phase 1
Phase 3
Absolute difference
Percentage difference (%)
04:56 95.98 20.50 50.05 8.63 7.21 01:46 0:23 36 17.64 7.71 7.32
5:56 111.84 17.57 47.86 4.86 3.17 2:00 0:17 34 16.30 7.12 6.76
1:00 15.86 2.92 2.19 3.77 4.04 0:14 0.11 – 1.34 0.59 0.56
þ21 þ17 14 4 44 56 þ14 27 2 8 8 8
identified as having particularly high idling times, with average idling times during this first trip at 39 minutes per day, or approximately 22% of total daily idling time. Recorded idling leads to an average daily consumption of almost eight liters (L) of fuel and emissions of more than 18 kilograms (kg) of CO2 per vehicle. In total, the fleet vehicles consume approximately 24% of daily fuel consumption in idling. Based on the average daily totals, engine idling equates to 24.7 tons of CO2 emissions and over 10,000 L of fuel over the average ski season (approximately 100 days between the months of December and March – Bruce Haynes, OSRA President, personal communication, April 2011). When these findings are extrapolated across the entire BMR ski resort fleet of 55 vehicles, assuming average daily totals remained the same for each vehicle type, idling consumes more than 42,000 L of fuel and emits in excess of 97 ton of CO2. Post-eco-driver training Phase 1 demonstrated the usefulness of VMT in identifying opportunities to improve sustainability through reduced fuel use and GHG emissions, while phase 3 aimed to demonstrate that VMT can also be used to assess the effectiveness of a behavioral intervention – in this case, eco-driver training. Table 3 summarizes the changes recorded in the posteco-driver training. The 2010–2011 ski season was a busier season for BMR, with lower temperatures and increased snow fall subsequently leading to increased skier visits (þ4%) (Bruce Haynes, OSRA President, personal communication, April 2011). As such, the average daily drive time and the average daily total distance driven by BMR fleet staff increased by 21% and 17%, respectively, from the 2009–2010 season, as there would be, for example, more snow to be ploughed and guests to be shuttled. Nevertheless, the post-eco-driver training phase recorded several notable changes in vehicle-use efficiency. There was a reduction in the average daily speed (14%) and a reduction in the average daily top speed (4%). The greatest improvement between the pre- and post-eco-driver training was recorded for the parameters of average daily hard accelerations (44%) and average daily hard decelerations (55%). The percentage of daily idling time decreased by 2%, but total idling time increased because the fleet vehicles were driven more during the busier post-training season, thereby increasing the recorded necessary idling times (discussed further in the next section). Most importantly, average daily CO2 emissions, average daily fuel use
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from idling, and the average daily cost of idling, all decreased by 8%. There was also a significant (27%) decrease in the time spent idling during the first trip of the day between the pre- and post-eco-driver training sessions. These results are particularly notable given that poor weather conditions, including cold temperatures, have been linked to higher incidences and durations of idling, particularly during the first trip of the day (Matthews, Rutty, Andrey, & Del Matto, 2011). While it is not possible to control such outside parameters as weather or number of skier visits, it provides important context to the results, with the possibility that the post-training results may have yielded further improvements, had the 2010–2011 season been similar to the 2009–2010 season. After eco-driver training, the BMR fleet improved its fuel efficiency, reducing fuel consumption and associated CO2 emissions. Based on an average operating ski season, the reduction in idling led to a decrease of 821 L of fuel and close to 1.9 ton of CO2 emissions from just the 14 sample vehicles. Extrapolated across the entire BMR ski resort fleet (55 vehicles), the BMR achieved a reduction of over 3200 L of fuel (CAD$3000) and nearly 7.4 ton of CO2 emissions from the pre- to post-eco-driver training winter seasons.
Discussion: advancing the sustainability of ground transport in tourism This study assessed the value of VMT and eco-driver training as a means to improve fuel efficiency and reduce associated CO2 emissions for a fleet of vehicles at the largest ski resort operation in Ontario, Canada. After eco-driver training, the 14 fleet vehicles examined showed an overall improvement, with drivers reducing average daily speed (14%), hard decelerations (55%), hard accelerations (44%), and idling time (2%). As such, decreases in fuel costs (8%) and CO2 emissions (8%) were achieved. While the results demonstrate that eco-driver training has much potential in the tourism sector, there are three key limitations of the CarChipÒ as applied in this study. The first relates to the inability to collect data on just those drivers who had completed the eco-driver training courses. Regrettably due to logistical issues beyond the control of the research team (e.g. the hiring of seasonal staff after the eco-driver training courses commenced), only 60% of the drivers were eco-driver trained. To make more detailed statements regarding the effectiveness of eco-driver training on improving the economic and environmental sustainability of the BMR fleet, it would require that only those drivers who were trained be included in the study. However, the operation of the fleet requires that each vehicle be driven by multiple drivers on a daily basis, including dozens of seasonal drivers who are hired during the winter season (and therefore unavailable during the launch of the eco-driver courses). It was consequently not possible to assess only those drivers who were eco-driver trained. It is plausible that 40% of untrained drivers may be masking greater improvements achieved by those who were trained, with the possibility that greater gains would have been recorded if all drivers were eco-trained. However, if the “train the trainer” program worked, and managers relayed eco-driving messages to the seasonal staff, perhaps little further improvement could be expected. The second limitation relates to idling. There are three circumstances in which individuals may idle their vehicle, including to warm the engine, waiting for something unrelated to traffic (e.g. a passenger), and while in transit (e.g. at a stop sign, traffic lights, and railway crossing). 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 calculating idling time. Unfortunately, the CarChipÒ quantified idling at every point when a vehicle was at 0 km per hour. Future studies could collect data by the
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second and calculate those circumstances when the vehicle is idling for 60 seconds or less as necessary idling,3 thereby removing these circumstances from idling estimates. The third limitation was the inability to calculate specific fuel consumption and CO2 emissions for the parameters of speed, hard acceleration, and hard deceleration. The CarChipsÒ are not programmed to measure the exact degree of speeding, but rather to identify the duration of speeds over 110 km per hour. Without such data, the precise decrease in fuel efficiency cannot be derived. This is similar for the acceleration and deceleration parameters, to which the CarChipÒ is unable to capture precise data on the speed and time difference at which the incident occurred. Although this data would be helpful, value remains in identifying the frequency of their occurrence as behavioral changes can affect the reduction of these inefficient driving habits. An important challenge moving forward will be to ensure that the effectiveness of the behavioral 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. Barkenbus, 2010; Beusen et al., 2009; Civitas, 2008). The degree to which the positive effects of ecodriving 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 drivers that had been trained, up to 56% applied eco-driving techniques in the first month after training, which remained stable for approximately six months, but by nine months, half of the drivers abandoned their new driving habits, reducing the application of eco-driving techniques to just under 20%. To ensure that the effectiveness of the behavioral intervention is maintained, the initial eco-driver training should be complimented through the provision of continued or periodic feedback to the drivers regarding the techniques taught. The absence of feedback from energy-conserving actions in order to maintain a desired action or behavior has been a familiar lament for decades (Barkenbus, 2010). Behavior theory strongly confirms that unless the individual can see or feel the results of his/her actions on an immediate and continual basis, that individual is unlikely to maintain the behavior over time (Kluger & DeNisi, 1996). To overcome this, eco-driver training could be combined with other emergent technology to encourage the continued behavioral upkeep while further reducing fleet fuel use and GHG emissions. For example, many automakers are installing feedback devices into their vehicles with monitoring displays that provide real-time information to the driver on the vehicle’s fuel economy performance. While studies indicate that drivers welcome these devices and alter their driving habits as a result (e.g. Barkenbus, 2010; Kurani, 2007), purchasing a new fleet of vehicles with the latest on-board devices would require a substantial one-time investment. External monitoring devices are a cheaper alternative, the most common being the ScanGauge, which can be purchased online and mounted to the dashboard of any vehicle (CAD$100-200) (Froehlich et al., 2009). The most costattractive alternative is using mobile technology to provide drivers with eco-driving feedback. This approach requires the smallest financial investment and the quickest time to implement due to the comparably low (or free) cost for a smartphone application (e.g. EcoDrive, GreenMeter, EcoDriver, DriveGain) and the high penetration rate of smartphones (Tulusan, Steggers, Staake, & Fleisch, 2012). Computer modeling and simulation programs are also available, including FleetCarma, a technology-based company that works with fleet managers to create driving profiles to demonstrate the available gains that can be achieved by shifting from gasoline-powered to hybrid- or electric-powered vehicles (FleetCarma, 2013). An important line of future research includes an appraisal of the best type(s) of
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feedback to encourage the behavioral adoption of and successful maintenance of eco-driving in diverse real-world tourism operations (e.g. tour-bus companies, airport-hotel/resort shuttle services, etc.). As awareness of tourism’s contribution to GHG emission increases, future research must inform sector-specific carbon management policies, plans, and strategies (G€ossling, 2012). Evidence in the literature suggests that tourism is highly inefficient in its energy consumption, leaving numerous opportunities to improve efficiency (e.g. Becken, 2013; G€ossling, 2012; Kelly & Williams, 2007). Unfortunately, unlike energy use in tourist accommodation, which has been studied for over a decade (Becken, 2013), energy audits for tourism vehicle fleets have yet to be inventoried and analyzed to generate quantitative information on energy-saving opportunities. A recent exception is Kelly and Williams (2007), who grouped vehicle fleet data in with accommodations, restaurants, and retail stores data, to estimate that ski hill operations and tourism service functions in Whistler, Canada, consume 39% of total internal energy and emit 31% of internal GHG emissions. The study suggested that by transitioning passenger vehicles to hybrid models, using vehicles with smaller engines, and adopting fuel additives to improve fuel economy, the overall efficiency of the gasoline fleet could be improved by 50% and the diesel fleet by 15%. While such measures may be critical to achieving large reductions in fleet fuel use and emissions, this requires substantial capital investments. It must also be noted that the fuel economy of hybrid vehicles is extremely sensitive to driving style and remains contingent on efficient driving behavior (Barkenbus, 2010; Rutty, Matthews, Andrey, & Del Matto, 2013). Therefore, the incentive remains for tourism businesses and operations to engage in eco-driver training as a strategy to reduce fuel consumption and vehicle emissions, and in a way that can be maximized with phased in strategic capital investment in fleet renewal. Carbon management should also involve a strategic reduction of fuel use and emissions that includes economic considerations (Scott et al., 2012). This is a particularly important factor in operational costs given the volatile and escalating prices of energy (Becken, 2013; G€ ossling, 2012; Weaver, 2012). Effective eco-driver training programs have the potential to result in a relatively low-cost carbon management initiative, able to pay for itself in as little as one year. By including a “train the trainer” program, the initiative also lends itself to long-term gains, with savings incurred over time at no additional cost to disseminate the course to new staff via an external agency. Innovative environmental management is also often positively noted in the media – leading to both greater public recognition and free marketing (G€ ossling, 2012). Hence, engaging proactively in carbon management and emissions reductions is an increasingly integral component of sustainable tourism operations. There are also positive externalities beyond the destination, as tourism businesses those instil sustainability awareness and values within their employees, also contribute to the environmental prosperity of society at large (Bohdanowicz et al., 2011). Eco-trained drivers can drive more efficiently not only at work, but also in their everyday lives. Actions that individuals can take are, in the aggregate, significant. Barkenbus (2010) estimates that if one-third of all USA drivers adopted eco-driving, it would save 33 million metric tons of CO2, and result in a societal savings of up to $15 billion in gasoline annually. With the UNWTO (2013) projecting the global tourism sector to host 296 million jobs by 2019, the opportunities to enhance sustainability are evident. The advantages of eco-driving also include tangible and well-known safety benefits such as fewer accidents and traffic fatalities (Barkenbus, 2010; Barth & Boriboonsomsin, 2009; Zarkadoula et al., 2007). It is estimated that by 2035, CO2 emissions from tourism will increase by 135% (from 2005 levels) if tourism remains business as usual (BAU) (Scott et al., 2008). This is incompatible with the strong emission reduction goals of the international community
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(Scott et al., 2010). World tourism leaders, through the Davos Declaration on Climate Change and Tourism, have called on all tourism stakeholders to reduce climate altering emissions (UNWTO et al., 2008; WTTC, 2010). From a climate change mitigation perspective, there remains insufficient integration of research that offers technical solutions into the tourism context (G€ ossling, 2012; Scott & Becken, 2010). Moreover, behavioral changes necessary to reduce tourism’s carbon footprint are only just being explored by tourism researchers and operators. This study highlights a novel opportunity for tourism businesses and operations to adopt eco-driver training to improve the sustainability of fleet vehicles. Best practices from this project can be used as a model for all kinds of other resorts with large fleets of vehicles, holding promise for reducing the industry’s CO2 emissions. Finally, the findings of this research are that – unlike G€ossling and Peeters (2007) claims that little technical progress can be made with air transport – in other transport sectors there are valuable behavioral and technical changes that can help create more sustainable tourism. Acknowledgements The authors would like to thank the Tourism Industry Association of Ontario (TIAO) and the Ontario Tourism Marketing Partnership Cooperation (OTMPC) for recognition of this research through the Industry Award of Excellence—Sustainable Tourism Award. The authors would also like to recognize Dr. Jean Andrey (University of Waterloo) for her kind supervision and guidance during the project. Gratitude is further extended to the study participants for their contributions in time and resources: Bruce Haynes (President, Ontario Snow Resorts Association), Lindsay Ayers (Planning & Environmental Specialist, Blue Mountain Resort Limited) and the staff at Blue Mountain Resort. The financial support of the MITACs Accelerate Program and Natural Resources Canada is also gratefully recognized.
Notes 1. OSRA is the voice for the Ontario snow resort industry, with over 60 Alpine and Nordic snow resort members. 2. The remaining 40% are seasonally hired staff, and were, therefore, unavailable during the offseason when the eco-driver training took place. 3. Idling for 60 seconds or greater has been identified by NRCan as unnecessary idling (NRCan 2008).
Notes on contributors Michelle Rutty is a PhD candidate in the Department of Geography and Environmental Management at the University of Waterloo, Canada. Her research interests are primarily focused on the role of weather and climate for tourist decision-making. Lindsay Matthews is a masters candidate in the Department of Geography and Environmental Management at the University of Waterloo, Canada. Her research efforts focus on sustainability and transportation. Daniel Scott is a Canada Research Chair in Global Change and Tourism at the University of Waterloo, Canada. He has been a contributing author and expert reviewer for the United Nations Intergovernmental Panel on Climate Change Third and Fourth Assessment Reports. He is also the Chair of the World Meteorological Organization’s Expert Team on Climate and Tourism and Co-Chair of the International Society of Biometeorology’s Commission on Climate Tourism and Recreation. Tania Del Matto is the co-founder and director of My Sustainable Canada, a national not-for-profit organization that seeks to further our understanding of sustainable consumption and encourage citizens to be mindful consumers.
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