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Double Double Your Drive-thru Emissions
A Case Study of an Edmonton Tim Horton’s Facility and a City Wide Analysis of This Service
Prepared by:
Miranda Baniulis, Curtis Boyd, Amanda Dacyk, Jody Gobert,
Jocelyn Howery, Kendra Issac, Todd Keesey, Jennifer Martin
Submitted to:
Dr. Peter Boxall
Methods and Applications in Environmental Economics (ENCS 410)
Edmonton, Alberta
April 27, 2006
1. INTRODUCTION
Air pollution is one of the most pressing issues facing communities around the
world today. Emissions from the commercial, industrial and private sectors contribute to
global problems including ozone layer depletion, acid rain and the degradation of air
quality in urban centers. The growth of the human population and our increasing
demands on the environment continue to challenge governments and NGOs trying to
address air quality issues. One of the biggest problems with air pollution is that it cannot
be contained within domestic borders. The fact that air pollution does not obey borders
means international agreements are often necessary to prevent some countries from
abating air pollution while others do not. This issue of free-riding can make international
efforts to curb emissions difficult, as it is often costly to take on abatement technologies
or increase regulations that could potentially deter foreign investment. This can be
especially true for developing countries that make extensive use of manufacturing and
production to increase their income. These nations may be willing to trade environmental
degradation for a better standard of life.
It is estimated that the global temperature increase resulting from greenhouse gas
(GHG) induced climate change will have severe and far-reaching implications. Although
it can be difficult to attribute precise weather events to climate change, severe weather
events, such as the devastating hurricanes seen in the southern United States in 2005, are
expected to increase. Droughts and floods may also become regular occurrences or could
take place in areas that had previously never experienced these phenomena. Other issues
that may arise include severe water shortages, as glaciers that normally feed streams and
river recede, the inability to grow food in drought stricken areas, and severe heat waves
like the one that killed 150,000 Europeans in 2003 (Government of Canada 2005). The
Government of Canada (2005) fears that these climate related incidents may lead to the
displacement of millions of climate change refugees.
Canada became a signatory to the Kyoto Protocol in 1997, agreeing to reduce
GHG emissions by 270 Megatons during the 2008 to 2012 commitment period
(Government of Canada 2005). The 2005 federal plan to meet this target included
capping emissions by Large Final Emitters, creating a domestic offset credit system and
1
enticing source reductions and sink development. The plan also addressed the fact that
individual Canadian citizens are collectively responsible for 28% of Canada’s total
emissions (Project Green 2005).
According to the United State’s Environmental Protection Agency (EPA),
emissions from personal automobiles make up the largest proportion of urban air
pollution in numerous cities across the continent (EPA 1994). The Government of
Canada (2005) estimates that 50% of the average Canadian’s GHG emissions come from
their use of passenger vehicles. In order to fully understand this component of GHG
emissions literature regarding the chemical makeup of vehicle emissions, how these
emissions vary by vehicle type and the environmental and human health impacts
associated with these compounds was reviewed. Once this information is considered, the
true costs of actions creating GHG emissions may be recognized. It is then possible to
debate the necessity of these activities and discuss the possibility of their reduction.
This study looks at the amount of emissions created by vehicles idling in drive-
thru lines in Edmonton, Alberta, Canada. Average daily emissions produced by a single
drive-thru, daily and weekly usage patterns and total daily emissions by all Edmonton
drive-thrus were calculated. This process shows the relative significance of drive-thrus as
an emission source. It is hoped that this study will initiate discussion on the necessity of
drive-thrus and the policies surrounding them.
2.0 BACKGROUND
2.1 Chemical Composition of Vehicle Emissions
Though the by-products of perfect combustion are relatively innocuous (carbon
dioxide and water), the typical process is less benign. This process includes the
production of a variety of gaseous compounds including hydrocarbons (HC), nitrogen
oxides (NOx), carbon monoxide (CO) and carbon dioxide (CO2) (EPA 1994). In a study
of over one-hundred thousand vehicles, Beydoun and Guldmann (2006) found CO
emissions varied between 0 and 899 grams/mile (g/mile), (median = 4.93 g/mile); HC
emissions between 0 and 97.3 g/mile, (median = 0.51 g/mile); and nitrous oxides between
2
0 and 5.65 g/mile, (median = 1.17 g/mile)1. In addition to these main components, other
chemicals such as sulphur, formaldehyde and acetaldehyde are often found in vehicle
exhaust (Kirchstetter et al. 1996).
Concerns about these chemicals are related to their environmental and health
impacts. The reaction of hydrocarbons and certain nitrogen oxides produces ground-level
ozone, a major component of smog and currently one of the most prevalent urban air
pollution problems (EPA 1994). The EPA (1994) also lists hydrocarbons as potential
carcinogens. Nitrogen oxides contribute to acid rain. Carbon monoxide reduces a
person’s ability to utilize oxygen (EPA 1994). Though carbon dioxide is not a direct
health hazard, this gas plays a large role in global warming (EPA 1994). This impact is
particularly important, as fourteen percent of vehicle exhaust by volume is carbon dioxide
(Bishop and Stedman 1996). The identification of the negative implications of these
chemicals indicates that emission-reduction policies are desirable, and thus, studies on
potential sources of emissions, such as the one proposed, are socially desirable.
In relation to the proposed drive-thru study, it is important to note that the above
figures may not be representative of the emissions of idling vehicles, and instead should
serve as an exaggerated illustration of the potential amounts of chemicals produced and a
basis for assessing negative affects. This caution is supported by the findings of Wenzel
et al. (2000) who determine that NOx emissions are generally very low during idling.
These investigators also find that HC and CO emissions, measured when a vehicle is
under load, can differ greatly from those produced in resting situations (Wenzel et al.
2000).
2.2 Factors Influencing Emission Rates
Many factors influence the emissions rates of vehicles. These include: vehicle
make, vehicle age and maintenance, vehicle weight and engine size, seasonality and
others. Variation in emission rates may span several orders of magnitude between vehicle
manufacturers (Wenzel et al. 2000). Differences in technology, engine design and
mechanical components may result in certain automobile makes producing fewer
emissions than others, that is, being environmentally “cleaner” (Beydoun and Guldmann 1 Medians are present instead of mean values because emission distributions are strongly skewed to the right.
3
2006). In their analysis of data from the Enhanced Emissions Testing Program from
three American states, Beydoun and Guldmann (2006) argue that emission levels vary
significantly by make. They name manufacturers whose vehicles failed emission tests in
less than 5% of observations (Kia, Lexus, Infiniti, Saturn, Honda, Suzuki and Toyota)
and in more than 12% of cases (American Motors, Eagle, Mercedes-Benz, Cadillac and
Audi) (Beydoun and Guldmann 2006) 2. In a regression analysis, these researchers
analyzed the influence of a vehicle’s manufacturer on its probability of failing an
emissions test. It was found that BMWs were least likely to fail, while Hyundai,
Mitsubishi, Chrysler and General Motors vehicles were most likely to fail. The extent of
this variation is such that the probability of failure of the cleanest vehicles is ten times
less likely than that for the dirtiest vehicles (Beydoun and Guldmann 2006). These
results conform closely to the findings of Bin (2003) who determined the rate of
emissions test failure is significantly lower for foreign vehicles than domestics.
The significance of these findings suggests that, to maximize the accuracy of the
estimation of drive-thru emissions, researchers must record the makes of the vehicles they
time in drive-thru lines. Ignoring this variable implies all types of vehicles produce equal
emissions, which is inaccurate. The huge range of emission rates reduces the
effectiveness of using an average value. Instead, a profile of the vehicles most commonly
found in drive-thru lines should be developed and the appropriate adjustments made. It
must be recognized that the current study does not make distinctions between makes of
vehicles. Although, this would have given a better estimate of the emissions from fast
food drive-thrus, it was beyond the scope of the study.
Overall, older vehicles produce significantly higher emissions than newer ones
(Beydoun and Guldmann 2006). This finding is not surprising as increased mileage
(generally corresponding well to increased age) tends to increase the deterioration of
parts and reduce functioning of emission-control equipment (Beydoun and Guldmann
2006). The progression towards cleaner vehicles in recent years is also attributable to the
tightening of emission laws (Wenzel et al. 2000) and technological development (Bishop
and Stedman 1996).
2 Note that the average failure rate of the three analyzed states was 8.6%.
4
Vehicle condition has been shown to overshadow the impact of age (Bishop and
Stedman 1996). Bishop and Stedman (1996) document numerous cases of old non-
catalyst vehicles with good maintenance records producing lower emissions than newer
cars that had not received regular repairs and adjustments. In addition to poor
maintenance, excessive maintenance, known as tampering/modifications, often disrupts
the emission-control systems and increases pollution (Wenzel et al. 2000).
These findings suggest that the most accurate estimation of vehicle emissions
would require data on vehicle condition to be collected. The mechanics of doing this
could become quite subjective if non-disruptive sampling is the only method available. A
possible means of incorporating partially reliable data on age/condition would involve
adopting the previous recommendation, that is, recording vehicle make. Since the year of
introduction of each make is known, the maximum age of each vehicle can be
approximated and a model generated incorporating mechanical deterioration. It is likely
impossible to determine maintenance history accurately without inspecting the car or
interviewing the owners.
Vehicle size was found to significantly impact emission levels, with trucks
generating higher emissions than cars (Beydoun and Guldmann 2006). The EPA
provides idling emission rates for three major weight classes, with the emissions of
heavy-duty trucks being approximately double that of light-duty cars (Appendix C) (EPA
1998). As it is these values that would be most useful to the proposed study, cars passing
through the drive-thru were separated into three groups: car, small truck, van or SUV
(less than three quarters of a ton) and large truck, van or SUV (greater than three quarters
of a ton).
Beydoun and Guldmann (2006) found the probability of a vehicle failing an
emissions test in winter is significantly higher than that for the same vehicle in summer.
This is likely attributable to a reduction in the effectiveness of emission-control
technology at low temperatures (Wenzel et al. 2000). This trend implies that different
emission production rates should be used for spring and summer versus fall and winter.
In this way, the accuracy of annual city-wide emission predictions will be maximized. It
is important to note that, as average winter temperature in Edmonton is likely lower than
5
-1.1 degrees Celsius, the rates of idling emission production reported by the EPA (1998)
for this season (Figure 1.) will be conservative values when used in this study.
In addition to the previously mentioned determinants of emission rates, several
other factors exist with the potential to influence these values, including: fuel type (EPA
1998), fuel economy (Harrington 1997), driving patterns (Beydoun and Guldmann 2006)
and mechanical engine characteristics (Bin 2003). Due to the relatively small variation in
these factors between vehicles in the studied city these factors will not be discussed.
When all the above factors interact in a negative way; that is, when a vehicle is a
domestic make, old and poorly maintained, heavy and operates in a cold environment,
there is a high probability that the vehicle is a “gross-polluter” (Bishop and Stedman
1996). Wang et al. (2005) note that, though the exact figures vary slightly, it is generally
accepted that this minority of vehicles produces the majority of emissions. Beydoun and
Guldmann (2006) elaborate this conclusion by illustrating that gross-polluters, described
as including between 5 and 20 percent of urban vehicles, produce 50 to 80 percent of total
automobile emissions. The presence of this group is relevant to the proposed study, as it
will be important to make some allowance for the presence of gross-polluters in the
sampling strategy.
2.3 Health Risks from Vehicle Emissions
Due to the fact that vehicle emissions are harmful to human and environmental
health, it is important to include these impacts in our discussion. The relationship
between children’s health and air pollution is the subject of many studies. To test the
hypothesis that traffic-related air pollution causes childhood asthma, Gauderman et al.
(2005) sampled NO2 concentrations at the residences of 208 Californian children. These
researchers examined the influence of the proximity of the residence to the nearest
freeway, the average number of vehicles traveling within 150 meters of the residence
each day and the model-based estimates of traffic related air pollution at the residence
(Gauderman et al., 2005). This study strengthened the emerging body of evidence stating
that air pollution can cause asthma and that traffic related pollutants are partly responsible
for this effect (Gauderman et al., 2005).
6
McCubbin and Delucchi (1999) attempted to approximate the costs associated
with the ailments and deaths resulting from four criteria of vehicle pollutants and six
toxic vehicle pollutants. They did so by estimating emissions related to motor vehicle use,
and changes in exposure to air pollution. They then related changes in air pollution
exposure to changes in physical health effects, and changes in physical health effects to
changes in economic welfare (McCubbin and Delucchi, 1999). Four emissions sources
were studied. Tailpipe and evaporative emissions from vehicles are of particular
importance to this drive-thru study.
2.4 Mixed-Use Zoning and the Urban Environment
Urban sprawl is often characterized by vehicle dependence. Goldberg (2002)
states that the combination of neighbourhoods not suited to walking, sedentary lifestyles
and the drive-thru diet, causes one in four of today’s kids to suffer from diabetes as an
adult. As well, since urban sprawl may be associated with an increase in driving rates, the
gains made in reducing air pollution in other areas may be negated. This occurs at a time
when asthma rates among children are soaring (Goldberg, 2002).
City design largely determines how one travels within a city. Because restaurants
depend on a high volume of vehicular traffic and a high turnover rate of customers within
the site (Bedford and Dill, 2002), they are most often situated close to major roadways.
This can have significance for pedestrian safety and for the design of the urban landscape.
Angotti and Hanhardt (2001) discuss the issues city planners must address when
allocating land for mixed uses (residential, commercial and industrial) using New York
City to illustrate these issues. In this paper, two noteworthy observations were made. The
first is that, although pollution may decrease from people not using their vehicles,
increased pollution may result from the commercial and industrial sectors (Angotti and
Hanhardt, 2001). The second observation is that mixed-use areas with industrial uses are
disproportionately allocated to lower income neighbourhoods (Angotti and Hanhardt,
2001).
In the paper written by Handy et al. (2005), it was hypothesized that an
environment, where residents are closer to destinations and that has viable alternatives to
driving, is associated with less driving. As well, moves to environments where residents
7
are closer to destinations and have viable alternatives to driving, are associated with a
decrease in driving (Handy et al., 2005). The change in neighbourhood design was
captured by questioning five hundred individuals who had recently moved to California
neighbourhoods and five hundred individuals who were already situated in these
neighbourhoods (Handy et al., 2005). Results showed significant associations between
changes in travel behaviour and changes in neighbourhood design (Handy et al., 2005).
This supports prior evidence which states that mixed land-use decreases vehicle travel,
thereby reducing vehicle emissions.
In contrast to Handy et al. (2005), Lam and Niemeier (2005), constructed a simple
model that found mixing residential and business uses in one city may result in increasing
residential housing prices, which could displace incumbent residents to a neighbouring
city. Their model, which was a simplified theoretical model used to look at policy
options, found that an increase in housing price, due to mixing of residential and
commercial uses, may increase inter-city traffic which would increase vehicle emissions
(Lam and Niemeier, 2005). This could be an interesting factor relating to drive-thru
usage in Edmonton, as Edmonton has several surrounding communities. This factor,
however, was beyond the scope of this study.
2.5 Current and Future Trends in the Fast Food Industry
Trends in food consumption are continuously changing. Currently, there is a strong
reliance on fast food restaurants. Research has shown a decline in in-home activities, such as
cooking meals, while there has been an increase in activities requiring travel (e.g. picking up
food) (McGuckin and Nakamoto 2004). Sales at fast food restaurants surpassed sales of full
service restaurants for a short period in the mid 1990s, but there is constant competition
among food service firms for the consumer’s away-from-home-dollar (Stewart et al. 2004).
The focus on convenience and on low priced food has allowed fast food chains such as
McDonald’s to capture a significant portion of this growing market. In an attempt to make
their food more available to consumers, many fast food outlets now have two or three drive-
thru windows (Jekanowski 1999). These trends have sparked growing concerns about the
health impacts, environmental impacts and energy use resulting from the consumption of fast
food.
8
Byrne et al. (1998) described the demographic and socioeconomic variables that lead
to spending on food away from home at different types of food facilities. They developed a
framework for the decision process and estimated how socioeconomic variables would affect
each household’s choice of facility type. Their findings suggest that a large family size has a
positive effect on expenditures at fast food restaurants, as does seasonality (summer) and
increased hours worked by the home manager (Byrne et al 1998).
Stewart et al. (2004) examined the changing economic and demographic trends that
they felt would affect the future demand for food away from home. They observed trends in
key U.S. characteristics that influence the demand for food away from home and found that
incomes are rising, the population is aging, and household sizes are getting smaller. They
also identified the changing structure of households from “traditional families” to single
person or multiple adult dwellings with no children. Using the Shonkwiler and Yen statistical
model, they estimated the relationship between household characteristics and spending at fast
food and full service restaurants. They then used projected changes of these characteristics
(for the year 2020) in a simulation and found the effect was increased per capita spending of
18% at full-service restaurants and 6% in fast-food outlets. This simulation was based on the
assumptions that there will be no change in the relative prices of fast and full-services foods,
and that household characteristics will continue to influence consumer spending in the same
manner. The model also holds the number and locations of restaurants and the food and
service mix constant (Stewart et al. 2004).
There is a prevailing idea in North America that a strong economy is founded on
cheap food (Cummings, 1999). Fast food companies have become vertically integrated in all
aspects of food production, processing and retailing giving them significant market influence.
Through their buying practices, these businesses have the ability to influence the agricultural
sector, but usually not in favour of small-scale farms. Cummings (1999) also suggests that
these corporations have used this influence to manipulate public perception by marrying food
and entertainment, thus increasing their influence in consumers’ spending habits. She feels
that their sway has been instrumental in inciting demand for convenience food, which has led
to obesity and health problems for many North Americans (Cummings, 1999).
Jekanowski (1999) suggests that it is unlikely that time spent preparing meals in the
home will increase. He says that, even if incomes decline, the growing time constraints faced
9
by working people, as well as the general decline in cooking knowledge, will continue to
increase the demand for convenient fast foods (Jekanowski, 1999). The advantage of
franchise chains to respond to consumer preferences through economies of scale as suggested
by Stewart (2005), may allow them to keep costs down by spreading them across many
stores. These cost advantages and consumer trends, along with the growing demand for
convenience in away from home food, support the perception that the fast food industry will
continue to play a significant role in Canadian diets.
Jekanowski (1999), reports that 60% of burger sales at Burger King and 54% of
burger sales at McDonald’s were made at the drive-thru window. Apprehensions about the
impacts of drive-thrus have led some communities in the United States, including Carrboro in
North Carolina, San Juan Capistrano and Sierra Madre in California, to ban construction of
drive-thrus (Guilford 1998). This has also become a predominant issue in Canada, as seen in
the push for a similar drive-thru ban in Toronto (Hume 2004). In this city, a ban was put into
place in 2003 and upheld in 2004, despite strong legal opposition from McDonald’s, Tim
Horton’s, Wendy’s, Burger King, five major banks and Canadian Tire Real Estate.
Much of the literature concerning the future state of the fast food industry suggests
that growth will continue be positive, but that it may be smaller than in previous years. These
predictions are based on general trends in attitudes and spending seen in the U.S.A. and
Canada. The application of current literature to our study would seem to indicate that we will
see small positive increases in future volumes of GHGs resulting from fast food drive-thrus,
ceteris paribus. This information will be useful to individuals and government in addressing
concerns about emissions from automobiles in the City of Edmonton.
3.0 OBJECTIVES
The objective of this study was to determine the number of cars, light duty trucks
and heavy duty trucks that utilize the drive-thru window at the Tim Horton’s restaurant
located at 11084 Street and 51 Ave NW in Edmonton, Alberta. Estimating the average
idling time per vehicle was another primary goal. By gathering this information, the
average daily emissions produced by this drive-thru could be determined, daily and
10
weekly usage patterns could be uncovered, and a regression on the significance of time
and day’s effect on emission rates could be created.
In addition to the illustration of consumer behaviour at one specific drive-thru
location, a second objective of this study was to extrapolate these basic results into an
estimation of the total daily emissions produced by all Edmonton drive-thrus. By using
measures of traffic density and relative franchise popularity, the daily emissions of all the
major drive-thru chains in this city could be approximated. This process would allow the
relative significance of drive-thrus as an emission source to be assessed and initiate
discussion on the necessity of policy changes regarding these establishments.
4. METHODS
4.1 Determining Consumer Behaviour for the “Base-Case” Tim Horton’s
Data was collected in 15-minute periods to allow for sampling flexibility,
although, samples were often taken in 3 hour blocks. To calculate the quantity of
emissions produced from this Tim Horton’s location, the number and type of vehicles
using the drive-thru as well as the time each vehicle spends idling in the drive-thru was
monitored. In order to complete this task, the following steps were taken. As each
vehicle entered the drive-thru line the time was recorded and the vehicle type
(classification) was recorded. Vehicles were classified into three categories; cars were
grouped into one type with an assumed maximum gross vehicle weight (GVW) of
6000lbs. As per the U.S. EPA categorization, light-duty vehicles include those vehicles
with a GVW of 6000lbs to 8500lbs, while heavy-duty vehicles include those with a GVW
greater than 8500lbs. To simplify this classification, heavy-duty vehicles were those
vehicles with an engine equal to, or greater than, 3500 (example an F-450). The time at
which the vehicle came to a stop at the end of the drive-thru line was recorded as “time-
in”. The time the vehicle began to pull away from the drive-thru window was recorded as
“time-out”. Observations continued until the last car arriving during the period left the
drive-thru window. All times were recorded to the nearest second.
216 samples, totalling 54 hours, were conducted at a single Tim Horton’s, located
at 11084-51 Avenue, Edmonton, through five weeks in February and March. This
11
location may be characterized as a very busy location due to its proximity to a traffic
corridor on 111th St. Sample times were distributed to ensure that every 15-minuted time
period was recorded at least once from 5am to 10pm. Samples were taken through
varying days to ensure a distribution such that an ‘average day’ could be computed.
All observations were entered into an Excel spreadsheet. Idling time was
calculated by taking the difference between the “time-out” and the “time-in” for each
observation. For each 15-minute sample, sub-total idling times were calculated for each
size of vehicle: cars, light duty trucks/vans/SUVs, and heavy-duty trucks/vans/SUVs.
The total idling time for each 15-minute sample was calculated by adding these three
values together. A full day (5am to 10pm) of idling time was compiled for this facility,
using the average idling time calculated for each hour if this period had been sampled
more than once. This full day was used in conjunction with U.S. EPA and Government
of Canada data to compute average GHG emissions per day and per year from this
restaurant’s drive-thru. The U.S. EPA data (Figure 1) outlines the amount of NOx
(g/min), CO (g/min) and VOCs (g/min) emitted from passenger cars, light-duty vehicles
and heavy-duty vehicles. Estimates of per day emissions of these various pollutants were
then calculated.
In addition to compiling the data to comprise an average day, data was combined
into its respective hourly blocks with dummy variables for time of observation, day of the
week, and week observed. SPSS 11.0 was then used to perform a regression analysis to
evaluate the existence of any significant difference in hourly idling times according to
these dummy variables.
Realizing that this 5am to 10pm data is an underestimate daily pollutants, average
hourly idle times were correlated with hourly traffic flow (City of Edmonton,
Transportation and Streets Department, 2006) to attempt to extrapolate a full 24 hours of
emissions.
4.2 Extrapolation of Observations for the City of Edmonton
In order to estimate the total quantity of GHG emissions produced by drive-thru
lines across Edmonton, it was necessary to determine the number of these restaurants in
the city and their rates of use. To accomplish the former, the address and the drive-thru
12
hours of every location of eight selected restaurant chains were determined by contacting
each locating. The restaurants were selected based on the groups’ informal observations
of common drive-thru restaurants and were not assumed to be complete. The traffic
density on the nearest roadway was determined for each location using a traffic density
map. If the location was between two roads, an average of the densities was used.
Once an estimate of the number of drive-thrus in Edmonton was complete, the
evaluation of relative use rates was performed. It was assumed that idling time at these
other restaurants was a function of nearby traffic density and relative restaurant
popularity. Although this assumption was undoubtedly simplifying, it allowed us to use a
traffic density map and a small number of observations to predict the usage of each
location’s drive-thru.
First, to establish the relationship between traffic density and idling time, idling-
times were observed at five Edmonton Tim Horton’s restaurants in areas of varying
traffic density (one low density and three medium density) (City of Edmonton,
Transportation and Streets Department 2006), between the times of 5 and 6 pm on Friday,
March 17th. Next, hour-long samples were conducted at seven drive-thru locations
around Edmonton, each at a different major restaurant chain, including McDonald’s,
Kentucky Fried Chicken (KFC), Burger King, Arby’s, Harvey’s, Dairy Queen, and Taco
Bell. Sampling occurred at a time and day that had previously been sampled at the
11084-51 Avenue Tim Horton’s. Since the relationship previously observed between
traffic density and idling time could only be assumed to hold between 5 and 6 pm, and
the sampling at other restaurants occurred at different times throughout the day, these
observations had to be adjusted to represent the idling time that would have been
observed if the sample had been taken between 5 and 6 pm. To do this, the compiled
average hourly idling times for the base case Tim Horton’s were presented as proportions
of the total daily idling time (see Appendix A for sample calculations). These
proportions were assumed to hold for all restaurant chains, however slight adjustments
were made for restaurants whose drive-thru were not open the entire day (5 am to 10 pm).
Once the proportion of total daily idling time was known for each hour period, the
samples taken at the other restaurants at varying times were adjusted to what they would
have been predicted to show if they had occurred between 5 to 6 pm. The original traffic-
13
density/idling time relationship was then used to predict the Tim Horton’s idling time for
a restaurant on a roadway of that density. The observed hourly idling time (adjusted for
time of day) for restaurant x was divided by the idling time predicted for a Tim Horton’s
at that traffic density to give a “popularity factor”. Total daily idling time for each
restaurant was calculated by multiplying the inverse of the proportion of daily idling time
occurring between 5 and 6 pm by the coefficient for traffic density for that restaurant by
the popularity factor for each restaurant chain.
The daily idling times of each restaurant were summed to give estimated total
daily idling time for Edmonton drive-thrus. This value was used to calculate daily and
annual emissions of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC)
and nitrous oxides (NOx). The coefficients used to calculate the non-CO2 emissions were
available from the U.S. Environmental Protection Agency and given for each vehicle
type. Winter production rates were used (Figure 1.). The rate of CO2 production varies
greatly depending on vehicle mileage (less-related to vehicle weight); therefore, a
constant rate of 64.9 g per minute of idling was used (Natural Resources Canada, 2006).
5. RESULTS
5.1 Results of the “Base Case” Tim Horton’s Analysis
Average emissions for one day at the Tim Horton’s location on 11084 Street and
51 Ave NW, are a product of the average idling time per day per vehicle type and the
emissions emitted by each vehicle type.
Overall, through the 54 hours of observations, there were 3756 vehicles observed
contributing 320 hours, 55 minutes and 48 seconds of idling emissions. From the entire
data set, an average idling time of five minutes and eight seconds was recorded with the
shortest average idling time being 59 seconds and the longest average idling time being
twelve minutes and 37 seconds.
Depicting the average day, Figure 2 displays the average hourly wait times and
observed vehicle classifications. 1085 vehicles used this drive-thru facility for a total
idling time of 88 hours 23 minutes and 49 seconds on an average day. The distribution of
vehicles categories using the drive-thru is seen in Figure 3.
14
Using the average day, it was possible to combine this data with the U.S. EPA
data to find the average daily emissions from this site (Figure 4). The total emissions,
including HC, CO, NOX, and CO2, total 385.5 kg per day. 82% of these total emissions
are comprised of CO2 emissions. In noting the quantity of emissions produced it should
be again noted that these numbers are assumed to be an underestimate, and because there
is no correlation between daily traffic distribution and hourly drive-thru usages, it was not
possible to extrapolate a full day with certainty.
The SPSS regression output did depict some significant variation from the base as
defined as 7:00 Sunday in the first week observed. The regression reveals an R2 value of
84% which is relatively large. The regression output (figure 5) shows that Wednesday
and Saturdays are significantly busier than Sunday. There were no significant differences
between weeks, although those times which are significantly less busy than 7:00AM are
5:00AM, 5:00PM and 6:00PM. The fact that there were fewer observations recorded in
the tails of the day may impact the reliability of these numbers as depictions of an
average day.
5.2 Results of the City Wide Analysis
Using the observations made at the base case Tim Horton’s, the total levels of
certain vehicle emissions (CO2, CO, HC and NOx) produced from drive-thru restaurants
throughout the City of Edmonton were predicted. In total, 114 branches from nine
restaurant chains were considered (Figure 6). Both traffic density and restaurant
popularity were assumed to play a role in determining the relative amount of use of each
drive-thru (compared with that of the base case Tim Horton’s). Thus, this results section
includes findings on the influence of these factors as well as actual estimations of city-
wide emissions.
Firstly, it was found that traffic density predicted approximately 64% of the
variation in Tim Horton’s drive-thru usage between locations between 5 and 6 pm (Figure
7). As this relationship could only be assumed to hold for this particular hour, we were
forced to assume that the daily trends in idling times observed for the base case Tim
Horton’s remained constant across all restaurants in the city (Figure 8). It was determined
that idling observed between 5 and 6 pm made up between three and six percent of a
15
restaurant’s total daily idling time (depending on the hours of operation of the drive-thru
window).
It was also found that drive-thru use at Tim Horton’s was substantially higher than
the use of this service for all other restaurant chains. That is, the popularity of Tim
Horton’s drive-thrus far exceeded that of any other restaurants included in this study
(Table 1).
Daily idling times for each restaurant were estimated to range from 233 hours/day
for a Tim Horton’s to 1.7 hours/day for a Burger King. The average daily idling time for
each drive-thru location in Edmonton was predicted to be 47 hours/day. The estimated
total daily drive-thru idling times for Edmonton was 5383 hours/day. This idling time
was distributed unequally between the restaurant branches, with McDonald’s and Tim
Horton’s together accounting for 81% of total daily drive-thru idling time in this city.
The total amount of GHGs produced by vehicles in drive-thru lines in Edmonton
was estimated to be 23.5 tonnes per day [approximately 8600 tonnes per year]. Carbon
dioxide constituted approximately 90% of these emissions at 21 tonnes per day, while
carbon monoxide, nitrous oxides and hydrocarbons together made up the remaining 10%
(2.5 tonnes per day). Of the non-carbon dioxide emissions, carbon monoxide was the
most highly produced (Figure 9). Although cars were the most common category of
vehicle observed in drive-thru lines, light trucks were the largest contributor to
Edmonton’s daily drive-thru line emissions (Table 2).
6. DISCUSSION
6.1 Limitations of the Base-Case Analysis
The majority of data was collected by pairs of samplers to increase the accuracy
of the results however, some observations were collected by individuals and may be less
accurate if the sampler was busy and failed to notice the exact time a vehicle entered or
left the drive-thru line-up. Lengthy drive-thru line-ups and multiple entrances to line-ups
made it difficult to see when vehicles arrived. As a result, some observations may be an
underestimate of the actual idling time. Time restrictions were a major hindrance in data
collection, limiting the total number of samples that could be taken.
16
Another limiting factor of this study was the lack of detailed vehicle knowledge of
some surveyors, potentially causing them to assign vehicles to the wrong class. It was
also difficult to tell, without directly speaking with the driver (which we did not do), the
make, age, and maintenance of a vehicle. As Wenzel et al. (2000) demonstrate that these
factors are highly influential in determining a vehicle’s emission rates, ignoring these
characteristics reduces the accuracy of our findings.
6.2 Extrapolation of Observations for the City of Edmonton
6.2.1 Limitations of this Study
This study was performed under conditions where sampling effort and
research/analysis time were limited. In many cases, these limitations forced us to make
large assumptions reducing the accuracy of our findings. The shortcomings of our
methods must be presented in some detail, in order that possible errors in our results are
understood.
Firstly we were forced to assume that there is no seasonality in drive-thru usage.
As well, our methods imply that all drive-thrus follow the same daily usage patterns as
Tim Horton’s. In addition, we were forced to assume that sampling was sufficient to get
representative samples of the relative busyness of each drive-thru chain.
These assumptions are unlikely to be completely realistic. For example, the
number of people visiting a Dairy Queen drive-thru in July is likely to be higher then it is
in April. As well, variation in the busy times for each restaurant was not considered.
This could be an important factor that may have led us to over/underestimate the
emissions. Since we did not have a full day of sampling at each restaurant we were
forced to assume that idling time observed between 5 and 6 pm made up the same
proportion of total daily idling time for all restaurants sampled. This figure was based on
data from the main Tim Horton’s. As Tim Horton’s is a coffee/breakfast restaurant and
does a large portion of their business in the morning, using this franchise as our base-case
potentially skews our results.
Adding to the problem of a lack of detailed data for the additional restaurants is
the lack of sufficient data for the traffic density-idling time correlation. Although our R2
was quite high (0.64), additional samples can only decrease our confidence in the linear
17
regression. Our ‘Traffic Density’ linear regression (which was used to predict idling
minutes based on vehicles per week on the nearest roadway), was based on 5 samples,
covering a range of 7500 to 28 700 vehicles per week on the nearest roadway. This
regression was extrapolated out to 57 100 vehicles per week, it was assumed that the
regression was linear and went through the origin. We are also assuming that each
sampling time was a representative sample. These assumptions may not hold true and
thus, decrease the reliability of our citywide emissions predictions.
To add reliability to our predictions, more data would need to be collected to add
to the strength of our correlation, make our data more representative and overcome the
problems of different restaurants having different daily time distributions. As well, data
from different seasons would be necessary to account for seasonality in drive-thru usage.
The current lack of detailed data may considerably decrease the reliability of our results.
Therefore, our results should not be considered as fact, but rather an estimation which is
subject to many sources of error. The findings of this investigation should be presented
only in context with the limitations of the methods.
6.2.2 Comparison to Existing Literature
Published literature on emissions produced by vehicles in drive-thru lines is
somewhat limited, however, the results of this investigation do show some similarities to
those obtained by a group of students in Massachusetts in 1999. These researchers
estimated the daily amount of emissions produced by the drive-thru at one selected
McDonald’s location in their county (Shusas, 2000). Their recorded average of 2237
idling minutes per day (Shusas, 2000) is approximately 20% lower than the overall daily
average by store obtained in our study (2833). It is approximately 40% lower than the
daily idling time average obtained for Edmonton McDonald’s restaurants. This finding is
slightly curious, however, it may be explained partially by the fact that that Tim Horton’s
restaurants were included in our investigation. Since daily idling times at Tim Horton’s
are well above average rates (McDonald’s showed only approximately 50% of the daily
idling time in our study), including this chain predictably raised the average daily idling
time estimated for all Edmonton drive-thrus. As well, although some adjustments were
made for restaurant popularity, since a Tim Horton’s was used for the base-case (given a
18
value of 1 for popularity), daily emissions could potentially be slightly skewed upwards
for the more popular restaurants like McDonald’s.
The difference in observed emission rates between the studies is relatively small
and appears logical when the influence of certain factors is examined. Shusas (2000)
estimated the daily production of about 11 kg of non-carbon dioxide emissions by their
studied drive-thru. In our study, it was found that the average production of non-carbon
dioxide emissions was approximately 22 kg per day per drive-thru. The variation in
observed daily production of these emissions is likely attributable our higher average
daily idling time and the higher proportion of our sampled vehicles in the light truck and
heavy truck categories (our study found that 42% of observed vehicles were light trucks,
while only 32% of their observed vehicles were in this class). Since these vehicle classes
produce emissions at a greater rate than cars, observing more of light and heavy duty
trucks is likely to yield inflated emissions rates. Another contributor to the observed
variation could be the choice of emission production rates used. Although the U.S.
Environmental Protection Agency’s figures were used by both research groups, Shusas
(2000) used those rates applicable for summer temperatures while researchers in this
study used those adjusted for winter conditions. Since the emission rates are higher for
summer than for winter, it is likely that this factor also accounts partially for the higher
average daily emission production for each store.
Since Shusas (2000) did not account for the production of carbon dioxide, it is
impossible to compare methods. If however the rate of carbon dioxide production used in
this study is applied to the average daily idling time observed by Shusas (2000), we may
predict that this McDonald’s emits approximately 145 kg of carbon dioxide daily. This is
relatively close to our average estimation of 184 kg/drive-thru/day. The variation is again
explained by a combination of the above factors and their lower average daily idling time.
6.2.3 Significance of These Emissions
Despite the limitations of this study, it is undeniable that idling vehicles at
Edmonton fast-food drive-thrus contribute to local, provincial, and national annual total
vehicle emissions. Edmonton fast-food drive-thrus contribute approximately 8600 tonnes
of emissions (mainly CO2, CO, HC, and NOx) per year to the atmosphere accounting for
19
approximately 0.1865% of Alberta’s annual transportation-related GHG emissions and
0.0037% of Alberta’s annual total GHG emissions. Edmonton’s residential
transportation emissions, including idling activities, account for approximately 15.84% of
Edmonton’s total annual GHG emissions (CO2RE 2005).
Commonly used Air Quality Indices report Edmonton’s air quality history as
primarily “good” with occasional occurrences of “fair” (Alberta Environment 2005). As
the city of Edmonton has not previously had air quality problems and emissions from
drive-thrus do not seem to contribute a considerable amount to Edmonton’s total, it may
be difficult to argue that action is needed to reduce drive-thru use in this city.
6.2.4 Negative Impacts of These Emissions
The argument in favour of policy-induced adjustments to drive-thru use is based
upon the recognition that vehicle emissions can cause a variety of environmental
problems. Climate change, an issue currently at the forefront of environmental policy,
results primarily from the burning of fossil fuels, creating GHGs. Actions are being
taken both on a provincial level, through Alberta’s climate change action plan, as well as
on a national/international level through the Kyoto Protocol. Alberta Environment’s
(2002) climate change action plan is directed towards reducing GHG emissions: “The
Alberta government will reduce the GHG emissions intensity of its economy (emissions
relative to GDP) by 50 per cent below 1990 levels by the year 2020. 60 million tonnes by
2020 is a translation of what that level of intensity reduction would mean in tonnes of
carbon dioxide equivalent.” Completely eliminating Edmonton drive-thrus until the year
2020 would result in a 114,128 tonne reduction in emissions – keeping in mind pollutants
other than those emitted from vehicles contribute to climate change. Maintaining the
current level of drive-thru use does not help either Alberta or Canada in meeting their
stated reduction targets (Government of Canada 2001).
As well as posing risks to the environment, vehicle emissions are known to have
negative implications towards human health, including eye irritation, headaches, acute
and chronic respiratory illness, and death (McCubbin and Delucchi 1999). McCubbin
and Delucchi (1999) apply a dollar value to health risks caused by vehicle emissions in
the United States. They estimate the upper limit cost per kilogram of motor vehicle
20
emissions in 1990 for CO and NOx as $0.09 and $17.29, respectively. If we apply this
value to our estimated production of NOx (approximately 38 kilograms per day), the cost
of drive-thru emissions of this gas is approximately $250,000 per year. People living in
residential areas near drive-thrus and fast-food employees who typically work the drive-
thrus are particularly at risk for the above noted health risks.
6.2.5 Taking Action
In spite of the current low contribution of Edmonton drive-thrus to total city
emissions, two main factors suggest that taking action today is justifiable. Firstly, the
fast-food industry is continuing to grow as evident from the 125 new Tim Horton’s
locations opened in Canada between 2004 and 2005. This increase brings the number of
these stores in Alberta to 205 (Wendy’s International Inc. 2006). Comparatively,
McDonald’s Canada has opened an average of 35 locations per year throughout the
country since its emergence here in 1966 (McDonalds Canada, 2006). As actual yearly
figures are not available to the public, it is difficult to determine modern trends in fast-
food growth. It is defensible however, to assume that the trend of an increase in new
franchise stores in the United Kingdom are likely to be experienced in Canada (Biz/ed,
2006). In the United States in the 1990s, an average of 1000 new McDonald’s stores
appeared each year (Reuters Newsroom, 2002). Though it is unlikely that this rate of
growth will ever be seen in Canada, recognizing the enormous potential of the fast-food
(drive-thru) industry and its current rate of growth supports the conclusion that the issues
and risks associated with idling vehicle emissions will only intensify unless action is
taken.
The second factor justifying taking action to reduce drive-thru use is the fact that
this service is not essential for the vast majority of the population. Eliminating or
reducing this feature would not prevent individuals from consuming a meal of their
choice. This policy would only alter the mode of consumption. Though it has been
shown that drive-thru revenues substantially contribute to the revenues of many
franchises it is unlikely that all current drive-thru users would simply stop consuming
fast-food if this service was removed. The losses that would accrue due to this policy
21
(though potentially minimal) should be compared to the benefits that would arise from
less air pollution and lower GHG emissions to determine the true value of such a policy.
If the benefits of action are shown to outweigh its costs, there are three primary
policy approaches Edmonton could take: completely eliminate drive-thrus, reduce the use
of drive-thrus or do nothing. Banning drive-thrus is an unrealistic approach to reducing
idling vehicle emissions. Not only would it likely result in lost profits to the fast-food
industry, but it would likely result in foregone capital investment and be extremely
unpopular with society. Reducing drive-thru use is more reasonable. Individuals can be
deterred from idling in drive-thru line-ups through a variety of economic mechanisms
such as a discount on purchases made inside and taxes on purchases made at the drive-
thru. Equity issues arise when imposing the above taxes or ban. Such mechanisms
penalize individuals for whom getting out of their vehicle is difficult – individuals with
small children or pets, or individuals with disabilities. Education campaigns could serve
as alternatives to economic measures. Since these programs, designed to generate
voluntary emission reductions, are common (eg. One-Tonne Challenge) the introduction
of new campaigns, specifically targeting drive-thru use, could be a more acceptable
method of curbing this practice (Climate Change, 2006).
6.2.6 Future Research
Any further research into vehicle emissions from drive-thrus should begin with
some of the limitations discussed above. Taking more samples at more fast-food
establishments could greatly reduce some of the inaccuracies present in our data.
Sampling all hours of the day, taking into account drive-thrus that are open 24hrs, would
also provide more representative samples. Our study can be expanded to include a
variety of other variables including: seasonality, vehicle characteristics such as make,
age, and maintenance and more restaurant franchises.
Expanding our research and improving its accuracy will play a key role in
determining the true costs and benefits of policies designed to reduce drive-thru usage.
Establishing the physical amount of emissions produced each day by drive-thrus is a
fundamental part of determining the above benefits, and thus, is a key component of
performing an accurate cost/benefit analysis. Eventually, other costs of drive-thru usage,
22
such as a reduction in transportation route efficiency and social implications, should be
considered along with benefits of this service to businesses and users.
Though seemingly small when compared to other emission sources in this province,
emissions from drive-thru lines appear more substantial when this practice is recognized as
almost completely avoidable. Since the use of this service creates negative health and
environmental effects, a review of the expansion of drive-thrus is certainly warranted.
7.0 CONCLUSION
Currently, average Canadian lifestyles are vehicle dependent. Many individuals face
long commutes to work and as a result are constrained for time. The fast-food industry has
responded to the demand for quick, convenient service by creating and expanding drive-thru
operations. Fast-food restaurants, banks, and dry cleaners are just some industries that utilize
drive-thrus. Although convenient, this service contributes to environmental and public health
degradation as a result of the emissions created by idling vehicles.
Vehicle emissions are a major source of GHGs (CO, NOx, HC, and CO2), which not
only contribute to such health risks as eye irritation, asthma, and even death, but are also
linked to the growing climate change issue. 163 countries have ratified the Kyoto Protocol to
combat climate change. Urging the public to reduce the use of their vehicles is one
mechanism through which the Canadian government is attempting to meet their Kyoto
commitments.
This study was conducted to achieve two objectives. The first objective was to
estimate the number of cars using the drive-thru at a local Edmonton Tim Horton’s restaurant
as well as the average time each vehicle idled in line at the drive-thru. The second objective
was to estimate the total daily vehicle emissions produced by all fast food drive-thrus in the
city of Edmonton. Several sources of error plague this study, the majority stemming from a
lack of sampling effort. This leads us to caution readers that the findings of this investigation
should be treated as estimates only. However, as this study has achieved its objectives, both
characterizing the use of a single drive-thru location and estimating the amounts of certain
emissions produced from city-wide drive-thrus, these estimations should not be ignored. It is
recommended that further research be conducted to produce more conclusive results
23
regarding the societal and environmental impacts of emissions from idling vehicles at drive-
thrus. In this way, debate on potential policies to curb this practice may occur in an educated
and fact-based manner.
24
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Bedford P.J., and P.M. Dill. 2002. Proposal to amend the zoning by-law for the former City of Toronto regarding development standards to address drive through restaurants and other drive-thru operations. Available online at www.welivehere.ca/interim%20control%20bylaw.pdf
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Byrne, Patrick J., Oral Capps, Jr. and Atanu Saha. “Analysis of Quick-serve, Mid-scale and Up-scale Food Away From Home Expenditures.” International Food and Agribusiness Management Review 1(1)(1998): 51-72.
City of Edmonton, Transportation and Streets Department. 2006. 2005 Traffic Flow Map Average Annual Weekday Traffic.
City of Edmonton. Anti-Idling Infromation. Accessed March 10, 2006http://www.edmonton.ca/portal/server.pt/gateway/PTARGS_0)2)688762_0_0_18/Anti-Idling+Information.htm
Climate Change. 2005. Taking Action on Climate Change. Government of Canada.Accessed on April 25, 2006. http://www.climatechange.gc.ca/english/
Cummings, Claire Hope. “Entertainment foods (the health of individuals threatened bythe spread of fast food culture).” The Ecologist 29.1 (Jan-Feb 1999): 16(4).
Enns, P. (2000), ‘A Framework for Modeling In-Use Deterioration of Light-Duty VehicleEmissions Using MOBILE6’, Journal of Transportation and Statistics 3(2), 39-48.
EPA (1998), Emission Facts: Idling Vehicle Emissions. United States EnvironmentalProtection Agency: Office of Mobile Sources, http://www.epa.gov/otaq/consumer/f98014.pdf, February 16.
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Gauderman, J.J. 2005. Childhood Asthma and Exposure to Traffic and Nitrogen Dioxide. Epidemiolgoy. 16:737-743
Goldberg, D. 2002 Children, Youth and Families and Smart Growth: Building Family Friendly Communities. Network for Smart Growth. 9
Guildford, Roxanne. “America's Car Culture Collides with Bans on Drive-Thrus”. Christian Science Monitor 8/24/98, Vol. 90 Issue 189, p3, 0p,1c
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Handy, S., S. Cao, and P. Mokhatarian. 2005 Correlation or causality between the builtenvironment and travel behaviour? Evidence from Northern California. Transportation Research Part D:427-444
Harrington, W. (1997), ‘Fuel Economy and Motor Vehicle Emissions’, Journal ofEnvironmental Economics and Management 33, 240-252.
Hume, Christopher. “For a McHappy Ending: No New Drive Throughs” The Globe and Mail February 10, 2004
IPSOS World Monitor. “Fast Food Faces an Uphill Battle.” IPSOS World Monitor Research Alert. December 07, 2004.
Jekanowski, Mark D.. “Causes and Consequences of Fast Food Sales Growth”. Food Review. 22.1 (Jan. 1999).
Kirchstetter, T., B. C. Singer, R. A. Harley, G. R. Kendall and W. Chan (1996), ‘Impactof Oxygenated Gasoline Use on California Light-Duty Vehicle Emissions’, EnvironmentalScience and Technology 30(2), 661 -670.
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Appendix ASample Extrapolation Calculation
i) Determining popularity factor
Observed 32 minutes of idling time (IT) at Harvey’s between 11 am and 12 pm. Traffic density at this location is 24000 vehicles per week.
Based on base-case Tim Hortons, 315 minutes of IT are observed on average between 11 and 12 pm. 164 minutes are observed on average between 5 and 6 pm
IT that would have been observed if measurement had occurred between 5 and 6 pm = 32*164/315 = 16.7
Now can use correlation observed for 5-6 pm between traffic density and IT [IT=0.009(traffic density)
If the restaurant had been a Tim Hortons would have seen [24000*0.009] 216 minutes of IT
Popularity factor= observed IT(adjusted for time of day)/predicted IT for a Tim Hortons= 16.7/216= 0.077
The IT observed at a Harveys is 7.7% that observed at a Tim Hortons on a road of comparable traffic density.
ii) Determining daily IT for an unsampled restaurant
eg. Harveys located on a street with weekly traffic density of 19100 vehicles per week
Total daily IT= (1/proportion of total daily IT occurring between 5 and 6 pm) (Popularity factor) (0.009*traffic density)
= (1/0.036) (0.077) (0.009*19100) = 368 minutes of IT/day
*Note, the first term must be included to expand the hourly IT value given by the last term to a full day. In this example the 5 to 6 pm period accounted for 3.6% of the total daily IT (as given by base-case analysis), therefore multiply by 1/0.036 to get a full day’s IT.
27
Appendix B - List of Tables and Figures
Winter Conditions (-1.1ºC, 13.0 psi RVP gasoline)
Pollutant Units Light-Duty Gasoline Cars
(<6000 lbs)
Light-Duty Gasoline
Trucks (6001-8500 lbs)
Heavy-Duty Gasoline
Trucks (>8501 lbs)
HC grams/minute 0.3525 0.512 0.734CO grams/minute 6.19 8.12 11.4NOx grams/minute 0.103 0.125 0.196Figure 1. Average rates of production for non-carbon dioxide emissions for three vehicle categories (EPA, 1998).
Average DayCar Light Duty Heavy Duty Total
Vehicle Numbers 576 461 48 1085Vehicle Idling Time (hh:mm:ss) 47:34:47 36:56:16 3:52:46 88:23:49
Figure 2. Distribution of daily idling time and total number of vehicles by vehicle category for base-case Tim Horton’s.
Distribution of Vehicle Types
Cars53%
Trucks/SUVs/Vans
42%
Large Trucks
5%
Figure 3. Proportion of each vehicle category passing through the drive-thru line at the base-case Tim Horton’s.
28
Average Daily Emissions
CarsLight Duty
Trucks
Heavy Duty
Trucks TotalHC (g) 1006.3 1134.7 170.9 2311.9CO (g) 17671.1 17996 2653.5 38320.6NOX (g) 294 277 45.6 616.6CO2 (g) 185275.2 143835.2 15106.1 344216.5Total emissions (g) 204246.7 163243.1 17976 385465.8
Figure 4. Average daily emissions (grams) produced by the base-case Tim Horton’s.
Coefficientsa
17017.592 7171.361 2.373 .025-1555.869 9894.830 -.025 -.157 .876-234.278 6461.205 -.014 -.036 .9714194.111 6374.433 .188 .658 .5163341.405 6596.773 .187 .507 .6177789.775 10222.429 .126 .762 .453
-4384.513 6646.718 -.166 -.660 .5151048.270 4753.308 .037 .221 .8277883.035 4489.571 .337 1.756 .0903318.641 5545.111 .166 .598 .5556982.360 5973.824 .282 1.169 .2539400.792 5425.128 .440 1.733 .095-15960.0 6068.080 -.362 -2.630 .0142149.530 6068.080 .049 .354 .726
46.109 3945.220 .002 .012 .9914087.979 3864.409 .165 1.058 .2995418.591 4629.556 .205 1.170 .252890.028 4646.273 .031 .192 .850713.485 4860.622 .025 .147 .884
-8260.105 7844.907 -.134 -1.053 .302-235.551 5861.423 -.005 -.040 .968
-2809.551 5861.423 -.064 -.479 .636-3992.150 5137.617 -.110 -.777 .444-15566.7 6040.937 -.429 -2.577 .016-11722.1 5454.678 -.323 -2.149 .041
-1402.484 9155.131 -.023 -.153 .879-2576.484 9155.131 -.042 -.281 .781-10534.5 9155.131 -.171 -1.151 .260
(Constant)BWEEKCWEEKDWEEKEWEEKFWEEKMONTUEWEDTHUFRISATFIVEAMSIXAMEIGHTAMNINEAMTENAMELEVENAMTWELVEAMONEPMTWOPMTHREEPMFOURPMFIVEPMSIXPMSEVENPMEIGHTPMNINEPM
Model1
B Std. Error
UnstandardizedCoefficients
Beta
StandardizedCoefficients
t Sig.
Dependent Variable: IDLEa.
Figure 5. Coefficients for the linear regression of daily idling time on dummy variables for days of the week and times of the day.
29
Arby's
KFC
Burger King
Taco Bell
Dairy Queen
Harvey's
A & W
Tim Horton's
McDonalds
Figure 6. Proportion of drive-thrus included in study belonging to each restaurant chain.
Figure 7. Observed correlation between traffic density and hourly idling time for five different Tim Horton’s locations between 5 and 6 pm. Line represents a linear relationship forced through the origin.
y = 0.009xR2 = 0.6438
0
50
100
150
200
250
300
350
0 5000 10000 15000 20000 25000 30000 35000
Traffic Density (Vehicles per Week on Nearest Roadway)
Obs
erve
d Id
ling
Tim
e Be
twee
n 5
and
6 p
m
(min
utes
)
30
0
50100
150
200
250300
350
400450
500
5 7 9 11 13 15 17 19 21
Start Time of Hour-Long Recording Period (24 Hour Clock)
Obs
erve
d Id
ling
Tim
e (M
inut
es)
Figure 8. Daily trend in drive-thru usage at the base case Tim Horton’s. Observed hourly idling times are averages from all sampled times within a three week period (individual values are averages of two to seven observations—each one hour long).
0
500
1000
1500
Car SmallTruck
LargeTruck
Vehicle Type
Daily
Em
issi
ons
(kilo
gram
s) Emissions of CO
Emissions of HC
Emissions of NOx
Total Daily Non-CarbonDioxide Emissions
Figure 9. Proportion of non-carbon dioxide emissions produced by each vehicle type per day in Edmonton.
Restaurant Popularity
Harvey's 7.7%
Arby's 6.5%
McDonald's 46.8%
Kentucky Fried Chicken 12.3%
Burger King 3.2%
Taco Bell 7.8%
Dairy Queen 31.5%
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Table 1. Drive-thru popularity, as measured by relative daily idling times, of seven fast-food restaurant chains in Edmonton. Figures are presented as a percent of the popularity of Tim Horton’s.
Vehicle Type Percent of Total Vehicles Observed in Drive-Thru
Lines
Percent of Total Daily Non-CO2 Emissions
Car 54% 46%
Small Truck 42% 47%
Large Truck 5% 8%
Table 2. Comparison of the frequency of use of drive-thru lines by each vehicle type and its contribution to total daily non-CO2 emissions. Note that data on CO2 emissions was not-available by vehicle category and thus, production rates were applied equally to all categories
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