A 2009 Mobile Source Carbon Dioxide Emissions Inventory for the University of Central Florida

Abstract

A mobile source carbon dioxide (CO₂) emissions inventory for the University of Central Florida (UCF) has been completed. For a large urban university, more than 50% of the CO₂ emissions can come from mobile sources, and the vast majority of mobile source emissions come from on-road sources: personal vehicles and campus shuttles carrying students, faculty, staff, and administrators to and from the university, as well as on university business trips. In addition to emissions from on-road vehicles, emissions from airplane-based business travel are significant, along with emissions from nonroad equipment such as lawnmowers, leaf blowers, and small maintenance vehicles utilized on campus. UCF has recently become one of the largest universities in the nation (with over 58,000 students enrolled in the fall 2011 semester) and emits a substantial amount of CO₂ in the Central Florida area. For this inventory, students, faculty, staff, and administrators were first surveyed to determine their commuting distances and frequencies. Information was also gathered on vehicle type and age distribution of the personal vehicles of students, faculty, administrators, and staff, as well as their bus, car-pool, and alternate transportation usage. The latest U.S. Environmental Protection Agency (EPA)-approved mobile source emissions model, Motor Vehicle Emissions Simulator (MOVES2010a), was used to calculate the emissions from on-road vehicles, and UCF fleet gasoline consumption records were used to calculate the emissions from nonroad equipment and from on-campus UCF fleet vehicles. The results of this UCF mobile source emissions inventory were compared with those for another large U.S. university.

Implications:

With the growing awareness of global climate change, a number of colleges/universities and other organizations are completing greenhouse gas emission inventories. Assumptions often are made in order to calculate mobile source emissions, but without field data or valid reasoning, the accuracy of those assumptions may be questioned. This paper presents a method that involves a survey, the use of the MOVES model, and emission factors to produce a mobile source emissions inventory. The results show that UCF mobile source CO₂ emissions are larger than most other universities, and make up about 2% of all the mobile source emissions in Orange County, Florida.

Introduction

The University of Central Florida (UCF) is a member of American Colleges & Universities Presidents' Climate Commitment (ACUPCC) and is committed to sustainable development, energy conservation, and reduction of emissions of global climate change gases. As a part the university's commitment to ACUPCC, a mobile source inventory of carbon dioxide emissions for 2009 for UCF (main campus) in Orlando, FL, was conducted. Previous work has shown that the average institutional member of ACUPCC emits over 52,000 metric tons of CO₂ equivalent (MTCO₂e) annually (including emissions from purchased electricity), and collectively all member institutions account for nearly 2% of total annual U.S. greenhouse gas (GHG) emissions (Sinha et al., 2010). Based on student population, UCF is now one of the largest universities in the nation and therefore is responsible for significant emissions of CO₂. A previous estimate of GHG emissions from UCF reported about 147,000 MTCO₂e in 2008 (Sustainability and Energy Management Office, UCF, 2009). UCF previously reported about 94,000 MTCO₂e from purchased electricity and on-campus combustion of natural gas, and about 53,000 MT from mobile sources (Sustainability and Energy Management Office, UCF, 2009). In that previous study, UCF made several simplifying assumptions about student commuting, and it now appears that the 2008 estimate of mobile source emissions was much lower than it should have been. This paper focuses only on the mobile source emissions and uses an improved methodology to make the new estimate. The data reported herein are in tons of CO₂, rather than MTCO₂e. Since the vast majority of vehicle emissions are gaseous CO₂, it was decided not to bother with CO₂e. There are 1.1 tons in 1.0 MT.

UCF is located in east Central Florida (see Figure 1) and its primary service area is large. Furthermore, a large percentage (about 80%) of students commute to campus. While many students and employees live within eight miles of campus, some commute over considerable distances. An emission inventory is an important tool in managing air quality for any region because it gives managers and decision makers a good tool for identifying and focusing their efforts on large sources when trying to reduce emissions from a variety of sources (U.S. Environmental Protection Agency, 2004). A mobile source emissions inventory was completed for UCF to quantify the university's mobile source CO₂ emissions, which were then compared with emissions from the mobile sources in Orange County, Florida. UCF mobile source emissions were also compared with the mobile source emissions from another large university.

Figure 1. Location map showing UCF and surroundings in central Florida.

This emissions inventory for UCF is production based and details the emissions from mobile sources only (U.S. Environmental Protection Agency, 2004). A survey of students, faculty, staff, and administrators was conducted to develop data; surveys have been used very effectively for obtaining data for emissions inventories (Durbin et al., 1999; Fukushima et al., 2008; Eldridge and Lobnitz, 2011). In making a carbon footprint study for the University of Cape Town, a survey of staff and students was conducted to ascertain modes of transport and distances traveled (Letete et al., 2011).

The mobile sources included in the study included the personal vehicles of students, faculty, administration, and staff; UCF shuttle buses; visitors to campus for various events (such as football games, other entertainment events, open houses, etc); UCF fleet vehicles; campus non-road equipment (lawn and garden, other); and included emissions from business-related travel (airplane, car and train) by UCF personnel. This last category was included because even though the emissions occur outside the UCF area, they are directly attributable to UCF operations. Emissions from delivery vehicles were estimated and found to be exceedingly small in comparison with the former categories. Emissions from students driving to other destinations (such as grocery stores, movie theaters, etc.) were not included because they are not attributable to UCF operations, but rather to normal activities of living.

Emissions from on-road vehicles were estimated using MOVES2010a (MOVES), the U.S. Environmental Protection Agency's latest motor vehicle emissions simulator. To calculate CO₂ emissions from nonroad sources and UCF fleet vehicles, emissions factors from the U.S. Department of Energy (Department of Energy, n.d.) and the UK Department for Environment, Food & Rural Affairs, as approved by the Intergovernmental Panel on Climate Change (IPCC), were used (Greenhouse Gas Protocol Initiative, 2009).

For any organization, the percentages by which greenhouse gases (GHGs) are reduced are dependent upon the results of an initial or baseline greenhouse gas inventory that examines GHGs, mainly CO₂, methane (CH₄), and nitrous oxide (N₂O). In 2006, a U.S. Environmental Protection Agency (EPA) study found that 96% of GHG emissions from motor vehicles were from engine exhaust (CO₂, N₂O, and CH₄), and 4% were from the release of hydrofluorocarbons (HFCs) from motor vehicle air conditioning (McCarthy, 2010). In the UCF mobile source emissions inventory, atmospheric CO₂ was calculated, in keeping with a previous Central Florida emissions inventory (see discussion later in the Results section). Atmospheric CO₂ was used because it is very understandable, and it is known that N₂O and CH₄ emissions account for less than 2% of all GHG emissions from motor vehicles (McCarthy, 2010). Thus, methane and nitrous oxide emissions are not included in the results shown in this paper.

The Climate Leaders GHG protocol (an EPA–industry partnership) prioritizes GHG emissions into two categories: core emissions and optional emissions. The World Research Institute defines GHG emissions as Scope 1, 2, or 3 (U.S. Environmental Protection Agency, 2005). When comparing the definitions between the Climate Leaders GHG Protocol and the World Research institute, core emissions (Scope 1) are direct emissions from the generation of electricity, heat, or steam, physical or chemical processing, and other direct activities. Scope 2 emissions are indirect emissions from the consumption of purchased electricity, heat, or steam, and Scope 3 emissions are indirect emissions from transportation in vehicles not owned by the organization. Any emissions from transportation-related activities are considered optional (Scope 3) emissions (Greenhouse Gas Protocol Initiative, 2009). Most corporations and universities consider emissions from mobile sources as secondary and these are often termed “indirect” or “optional” emissions. For a large urban university such as UCF, mobile sources will be one of the biggest contributors to CO₂ emissions and should be explicitly counted.

Background

The development of MOVES (Motor Vehicle Emissions Simulator) by the U.S. EPA Office of Transportation and Air Quality was in response to recommendations by the National Academy of Science. It replaced the existing MOBILE 6.2 model as the U.S. EPA's official model for state implementation plans (SIPs) and regional conformity determinations. MOBILE 6.2 produced emissions factors in units of grams per vehicle-mile, which must be used in postprocessing calculations to find emissions based on the number of vehicle-miles traveled (VMT). In contrast, MOVES requires little postprocessing by the user because the model completes those calculations and produces a final value for total emissions. MOVES requires that the user input a large amount of information into the model (preprocessing). For this UCF mobile source emission inventory, the model was run using the “Custom Domain” option within the County Domain/Scale. Within the MOVES model a custom domain is defined as a geographic area that may consist of multiple counties, parts of counties, or combinations of counties and partial counties that can be described using a single set of inputs. A summary of local input data required can be found in Table 1.

Table 1. Required inputs for a “Custom Domain” in MOVES

Local inputs for MOVES
Age Distribution	Age fractions of fleet by age and source type
Avg. Speed Distribution	Speed distribution by road type, hour, and source type
Fuel Supply	Market share of fuel blends
Fuel Formulation	Composition of fuel blends (RVP, sulfur, oxygenates, etc.)
Meteorology	Temperature and humidity inputs
Ramp Fraction	Fraction of freeway VHT occurring on ramps
Road Type Distribution	Fraction of source type VMT on different road types
Source Type Population	Number of local vehicles operating in the area
Vehicle Type VMT	Total VMT by HPMS vehicle class
Month VMT Fraction	Allocation factors to distribute annual VMT by month, day type (weekday vs. weekend), and hour
Day VMT Fraction
Hour VMT Fraction
I/M Programs	Description of I/M programs, if any
Zone Road Activity	Allocates source hours operating on each road type to zones
Notes: RVP = Reid vapor pressure; VHT = vehicle-hours traveled; HPMS = highway performance monitoring system; I/M Programs = inspection and maintenance programs.

Methodology

Because the driving habits of the UCF commuter population are likely significantly different from the countywide driving patterns, those habits must be determined. The majority of UCF commuters are undergraduate and graduate students. As a result of students graduating and new students enrolling each semester, the student population fluctuates between the fall, spring, and summer semesters. In addition, students may travel to campus as often as 10 times a week, or as little as a 4 or 5 times a semester. UCF consists of 13 different colleges, offering multiple undergraduate and graduate programs, each offering courses online and in classrooms. To try to represent the travel patterns accurately, a voluntary online survey was developed by the authors and emailed to the entire UCF population by the UCF Office of Administration & Finance.

Survey

The survey was conducted to gather data on travel behavior, distances, and frequency of trips (all of which affect VMT), the distribution of the personal vehicles (source types and ages) of students, faculty, administration, and staff, and other information. The online company SurveyMonkey was used to develop a website where a potential respondent could access and start the survey. Through this online service, data were recorded for each respondent who accessed the website. The survey was left open for a period of 3 months during the fall semester of 2010, and then the survey was closed and all of the data were analyzed. Even though this mobile source emissions inventory was for the calendar year of 2009, the 2010 time period was deemed representative of commuter behavior at the university in 2009. It is common practice to survey a population to gather data in order to complete an emissions inventory (Durbin et al., 1999; Fukushima et al., 2008;, Eldridge and Lobnitz, 2011).

All survey participants who stated they were (1) undergraduate students who lived off-campus, (2) graduate students, (3) administration, (4) faculty, and (5) staff/other were asked to estimate the mileage they traveled from home to campus. Each was then asked to identify what type of vehicle he or she drove to the university: “car,” “small SUV/pick-up truck or minivan,” “large SUV/pick-up truck or large van,” “motorcycle, moped or similar,” “I don't drive a vehicle to campus.” If a survey participant selected, “I don't drive a vehicle to campus,” that participant was then asked what alternate transportation he or she utilized to commute to UCF (UCF Shuttle, carpool, Lynx Bus, bicycle, walk) and was then directed to the end of the survey.

After survey respondents identified the type of vehicle used to commute to the university, each was asked to select the age of the vehicle from a dropdown menu from a range of 1979–2011 and whether the driver participated in a carpool. If “yes” was selected, he or she was then asked the average number of times per week he or she participated in a carpool during the fall/spring semester and the summer semester and how many persons typically rode in the vehicle. All drivers were then asked, “How many one-way trips (in your vehicle) from your residence to UCF main campus do you make each week during the FALL/SPRING semester?” Participants were also asked the average time of day they typically arrived on campus and how long it took them to find a parking spot. The final question asked was whether participants traveled to campus during the summer semester. If “yes” was selected, participants were asked the same three questions (how many one-way trips a week are made to campus, the time of arrival, and time spent searching for parking). If “no” was selected, they were taken directly to the end of the survey. As the survey was online, it was designed to be user interactive, and would only display those questions pertinent to each respondent. For example, if an undergraduate student indicated that he or she lived on campus and did not drive a car to classes, that student would immediately be directed to the end of the survey. A representation of this interactive survey is shown in Figure 2.

Figure 2. Schematic diagram representing online survey instrument.

In total, 3,270 people responded to the survey, but some were eliminated as explained in the following paragraph. This final database contained a total of 3,034 respondents (202 administrative personnel, 202 faculty, 392 staff/other, 459 graduate students, and 1,779 undergraduate students). All of these subsamples were of sufficient size to be statistically significant.

The UCF populations of undergraduate and graduate students, faculty, staff/other, and administration can be seen in Table 2 (Office of Institutional Research, UCF, n.d.), along with the percentages of those populations for which completed valid surveys were received (percent representation). Some respondents did not complete the online survey; these were discarded. Analysis of incomplete survey responses could not be accomplished. If one or more responses were left blank or a respondent exited the survey before completion, all the necessary data were not provided and VMT could not be computed. Some completed responses were judged to be invalid (see footnote to Table 2) and were eliminated. Some of the surveys were eliminated as statistical outliers using a Z-test. Considering all reasons, 236 responses were not used, leaving 3,034 valid usable surveys, which are represented in Table 2. After contacting UCF's Residence Life Office, it was determined that essentially no graduate students lived on campus and that about 22% of undergraduate students did reside on campus and therefore did not commute to the UCF main campus.

Table 2. UCF Campus Population for 2009 and Survey Representation

	Spring 2009		Summer 2009		Fall 2009
Group	Campus population	Survey representation (%)	Campus population	Survey representation (%)	Campus population	Survey representation (%)
Administrative	282	65	282	62	278	66
Faculty	1,771	10	1,502	10	1,727	11
Staff/other	3,015	12	3,015	10	2,971	12
Graduate students	7,212	6	5,316	6	8,246	5
Undergraduate commuter students	31,997	5	21,023	5	34,956	4
Undergraduate resident students	9,142	3	6,006	4	9,988	3
Total UCF population	53,418	5	37,144	5	58,620	5
Note: Some surveys were not used because those respondents exited the survey before completing it, or gave answers that were deemed unrealistic (e.g., several respondents said they commuted 50–100 miles [one-way] 9 or 10 times per week). Other surveys were excluded as statistical outliers. The survey representation is presented as (number of surveys used/campus population), percentage, for each semester.

Within each of the five separate groups to be analyzed (administration, faculty, staff/other, graduate students, and undergraduate students), an average weekly VMT was calculated from the survey data. This average was determined separately for the summer term and the fall/spring terms. For each survey participant within a group who indicated he or she traveled to UCF in his or her personal vehicle, an estimated mileage driven from home to the university was determined, as well as an average number of trips per week.

From these data, an average weekly VMT was calculated for the fall, spring, and summer semesters because the populations of students change each semester (due to graduations etc). Included in this VMT is the estimated mileage driven while searching for a spot to park on campus. UCF has nine parking garages and many surface lots. However, it is not uncommon to hear a student complain that it took more than 30 minutes to park on campus. If such delays are common, that would cause a significant increase in emissions. It is also likely that while searching for parking, a vehicle would either idle for a period of time in one place or would slowly cruise the lot or garage while “hunting” for a spot. Thus, the authors decided that simply using the emissions from trips from home to campus and back home again would be incomplete, so we estimated additional parking-related emissions by assigning additional VMT to the commute. Based on local observations, it was estimated that on average a vehicle travels at a speed of 7.5 mph while searching for parking at UCF. The following equations were used to calculate the weekly VMT for each survey respondent:

(1)

where t = time spent looking for parking (minutes) and

(2)

A statistically representative weekly VMT was calculated for each UCF subgroup by totaling the weekly VMT of respondents for each UCF subgroup for the fall/spring and summer semesters and using the percent representation of each subgroup.

This representative weekly VMT was then multiplied by the appropriate number of weeks in each semester of the 2009 calendar year in order to tabulate the VMT driven by each UCF subgroup for each semester.

MOVES methodology

The emissions from the personal vehicles of the UCF commuter population were calculated by running MOVES using the “Custom Domain” option within the County Domain/Scale. The “Custom Domain” option creates a generic county for which there are no data available in the default database (U.S. Environmental Protection Agency, 2010). To utilize this customized option within the model, 15 separate databases were created for spring, summer, and fall semesters of the 2009 calendar year. The 15 databases consisted of five different distributions to represent each UCF commuter population (Administration, Faculty, Staff/Other, Graduate and Undergraduate students) for each of the three different semesters of 2009.

The “Custom Domain” option is required by the U.S. EPA for state implementation plans (SIPs) and regional conformity analyses. One benefit of using the “Custom Domain” option for this mobile emissions inventory is that it uses the same meteorology, age distribution, average speed distribution, road type distribution, ramp fraction, fuel formulation and supply, and inspection and maintenance (I/M) program to describe the area; thus, the majority of data needed for these distributions were the same. However, the methods suggested by the U.S. EPA to gather data to input into the distributions were thought to be too difficult and time consuming for this non-regulatory-driven study (not an SIP), and most local metropolitan planning organizations have yet to develop local-specific data needed to support the U.S. EPA-recommended methods. The MOVES default information for Orange County, Florida, was deemed to provide a sufficient representation for this work. The authors' methods are described next.

While using the “Custom Domain” option, a user cannot use default data within the MOVES database. To supply the UCF databases with MOVES default data, a Run Specification file was created at the county domain/scale for Orange County, Florida, for 2009 for all road types and certain source types (motorcycle, passenger car, passenger truck, and light commercial truck). From the “County Data Manager,” default data were exported from the MOVES database. The export data were then transferred into input files used to create the databases used in this project. Since UCF is located in Orange County (which has demographics, roadways, and vehicles very similar to those of Seminole County) and since UCF is so large, the Orange County default data from MOVES likely do not differ significantly from data that would be supplied specifically for the UCF population.

As previously stated, we used default data rather than the U.S. EPA-recommended methods for the “Custom Domain” option. For the average speed distribution for UCF, the default input values were exported from the MOVES database for Orange County, Florida. MOVES utilizes 16 different “speed bins” that describe the average driving speed on all road types. Input data is needed for average speed data specific to vehicle type, road type, and time of day/type of day. The user must enter the fraction of driving time in each speed bin for each hour/day type, vehicle type, road type, and average speed. The two day types considered in MOVES are weekdays and weekend days. The fractions entered must sum to one for each combination of vehicle type, road type and hour/day type specified. It is important to note that while we recognize that travel to and from the university decreases between weekdays and weekend days, it was addressed in the “vehicle type VMT” distribution, because it is the total VMT that would differ and not the average speed driven that would vary between weekdays and weekend days.

The fraction of VMT by road type can vary among different geographic areas and can affect overall emissions from mobile sources. For each source (vehicle) type, the “Road Type Distribution” table stores the distribution of VMT by road type. Each road type from the MOVES model was considered for the mobile UCF emissions Inventory: Off-network, Rural Restricted Access, Rural Unrestricted Access, Urban Restricted Access, and Urban Unrestricted Access. Off-network describes locations where the predominant vehicle activity is vehicle starts, parking, and idling, for example, parking lots, truck stops, rest areas, and so on. While all 15 databases utilized the default data for “average speed distribution,” “fuel formulation,” “ramp fraction,” and “road type distribution,” other inputs were unique to each of the semesters, and to each of the UCF commuter groups. It was postulated that a vehicle age distribution for UCF would differ from the MOVES default national age distribution. To create an “age distribution” for UCF, information was supplied from the Parking & Transportation office. In order to purchase a parking permit, the purchaser is required to supply the model year of his or her vehicle; from this information a vehicle age distribution profile was created. It was found that the UCF “age distribution” had a slightly smaller percentage of old vehicles (12 years and older) and a slightly larger percentage of newer vehicles (11 years old and newer) when compared to the national MOVES default distribution, but the differences were not significant.

Some input information was specific to each semester but was independent of the UCF population. This information includes “fuel supply” and “meteorological” data. “Fuel supply” information varies depending on the market share values (U.S. Environmental Protection Agency, 2009). MOVES has default gasoline and diesel fuel formulation and supply information for every county–year–month combination available. The default fuel information was developed from NMIM County Database and RFG Fuel Surveys for years up to 2005, and from the Energy Information Administration's Annual Energy Outlook from 2007, which projected fuel usage for 2012. Values for fuel properties between 2005 and 2012 were interpolated in order to achieve a consistent trend. The default information for Orange County, Florida, for 2009 was used to supply the “fuelformulation” and “fuelsupply” input tables for UCF. The MOVES database is supplied with region-specific market share values for fuel blends; thus the default data were used. A temperature and relative humidity are necessary for each hour that is to be modeled. The MOVES default “meteorological” data, temperature and relative humidity, for Orange County, Florida, were checked and found to be consistent with independent sources and so were used in this study. The default MOVES information is based on 20-year averages from the National Climatic Data Center for the years 1971 to 2000. In addition to temperature and relative humidity, sea-level pressure in inches of mercury must be entered. Sea-level pressure is only required by MOVES for the “Custom Domain” option (U.S. Environmental Protection Agency, 2010).

Other information that had to be entered into the UCF databases is particular to each UCF group analyzed for the spring, summer, and fall semesters of 2009. This information includes “source type population” and “vehicle type VMT.” “Source type population” information is the amount of vehicles of each source type. The source types that were included in this database are motorcycles, passenger cars, passenger trucks, and light commercial trucks. “Vehicle type VMT” is the VMT sorted by HPMS vehicle class. HPMS vehicle classes identify vehicle types in a different manner than source types. For the inventory, the source types, passenger trucks, and light commercial trucks were identified as one HPMS class, “Other 2-axle-4 tire vehicles.” For motorcycles and passenger cars there is an equivalent HPMS vehicle class for each. Information for both the “source type population” and “vehicle type VMT” was supplied from survey analysis. Fifteen databases were created (one for each UCF commuter group for each semester).

Within the “vehicle type VMT” database there exists a monthly VMT fraction and a day VMT fraction. For a custom domain the monthly VMT fraction is not designated by the 12 months of the year, but rather by the fraction of VMT driven during the months represented in each run specification file. For example, in the run specification for the undergraduate students for fall semester 2009, the months represented were September, October, November, and December. The VMT was distributed according to the number of weeks of the fall semester in each month, which also matches up with the meteorological input for those months. The VMT driven commuting to campus is much less on the weekend days than on weekdays, and the authors thought to represent that by a bigger difference than what is represented by the MOVES default day VMT fraction. We used a value of 0.16 to represent VMT driven on weekend days and a value of 0.84 for the fraction of VMT driven on weekdays. This was the only temporal allocation factor that was slightly altered to better represent UCF commuter behavior; in all other instances the default temporal allocation factors were used for the run specification files.

After the input information was compiled and entered into Excel files, run specification files were created in MOVES for the 15 databases. A generic countyID was created for the university and was used for all run specification files. Various pollutants were modeled (for reasons of other ongoing work). The processes modeled were running emissions exhaust, start emissions exhaust, processes for evaporative emissions, and processes that account for refueling emissions. For this work, only CO₂ emissions were used. Each database was populated by uploading Excel files containing input information into the MOVES graphical user interface (GUI). After each run specification file was populated with the appropriate database, the model was run and emissions were calculated. After each MOVES run, the results were exported from the MySQL output database files.

UCF campus shuttle

UCF has an active and growing student shuttle bus service. It not only shuttles students to and from the main campus and nearby apartment complexes, but the service also runs shuttles from the main campus to both the Rosen College of Hospitality Management campus in southwest Orlando and the College of Medicine, located at the Health Sciences Campus in Lake Nona, FL (see Figure 1). The shuttles that transport students to and from student living facilities and UCF main campus are also in service on UCF college football game days (five hours before game and two hours after the game). On-campus shuttles (Black & Gold Line) transport students around campus to the most populated and important buildings and facilities. Another option for students who commute to campus is the “Park & Ride” shuttle that runs from the Orlando Tech Center off Research Park to the Health Center (Lot C3), and Lot E8 to Burnett Honors College (Lot H2), by the UCF Brighthouse Networks Stadium. American Coach Lines keeps a record of the number of riders, the miles driven, and the gallons of diesel fuel consumed for every month of the year. In order to calculate CO₂ emissions from the UCF shuttle service, an emission factor from the U.S. Department of Energy was used. The amount of CO₂ emitted from the campus shuttles was estimated by multiplying the number of gallons of diesel fuel consumed by the DOE emission factor of 9.9 kg CO₂/gallon of diesel fuel (Department of Energy, n.d.).

UCF fleet and nonroad vehicles and other vehicles

The University of Central Florida has an on-campus fuel pumping station. At this station, biodiesel fuel (B20), gasohol (E10), and ultra-low-sulfur off-road diesel fuel (ULSD) are available. The UCF Fleet vehicles and campus nonroad equipment obtain fuel from this station. The UCF Fleet consists of total of 417 vehicles. The 2009 data for gallons of fuel pumped from the on-campus station and data for gallons of fuel purchased from commercial gas stations for UCF Fleet vehicles were obtained from records provided by the Department of Sustainability and Energy Management. The CO₂ emissions for both the UCF Fleet and nonroad vehicles and for maintenance equipment (which were also fueled from the on-campus station) were calculated from fuel consumption using emission factors (see Table 3).

Table 3. Emission factors used in the UCF Mobile Source Emissions Inventory

Source	Emissions factor
GHG Protocol	0.23 kg CO2/passenger mile (car)
UK DEFRA	0.163 kg CO2/passenger mile (train)
	0.308 kg CO2/passenger mile (airline)
U.S. DOE	8.115 kg CO2/gallon (B2 biodiesel)
	7.978 kg CO2/gallon (E10 gasohol)
	9.942 kg CO2/gallon (ultra low sulfur off road diesel)

Other people drive to UCF (e.g., to watch football or basketball games, to come to concerts or other entertainment events, to attend open houses for prospective students). Obviously, these people could not be surveyed. An estimate was made of the VMT attributable to visitors based on the typical number and type of on-campus events held each year, along with an average fuel economy for passenger vehicles. It was estimated that more than 5.8 million VMT was attributable to visitors during a typical year. The fuel consumption was then estimated, and the emission factor method described earlier was used to estimate CO₂ emissions from visitors. A similar method was used to estimate emissions from delivery vehicles (e.g., courier service trucks, office supply trucks, food delivery trucks, fuel trucks). It was estimated that about 75 delivery vehicles arrive on campus each week. The emissions from those vehicles were found to be negligible compared with all the other sources.

The CO₂ emissions from business travel were calculated using records for rental car, train, and airline mileage reported from the Office of Accounting. The emissions factors used in these calculations are from the U.S. EPA and the UK Department for Environment, Food & Rural Affairs (UK DEFRA), which are approved by the Intergovernmental Panel on Climate Change (IPCC) and other nongovernmental organizations (NGOs), such as the World Research Institute (Greenhouse Gas Protocol Initiative, 2009).

Results and Discussion

The annual mobile source-related CO₂ emissions inventory for the UCF main campus in Orlando, FL, for 2009 is summarized in Table 4. As can be seen in Table 4, UCF contributed nearly 134,000 tons of CO₂ emissions from mobile sources in 2009. Of that, nearly 123,000 tons, or about 92%, came from commuters—students, faculty, administrators, and staff. A small amount (about 1%) came from university maintenance activities (mostly lawn and garden), and about 4% came from business-related travel (about 5,100 tons of CO₂). As shown in Table 5, students are responsible for about 85% of all commuting emissions.

Table 4. CO₂ mobile source emissions by group for UCF

UCF group	Tons CO2/year
Commuters (administrators,	122,658
faculty, staff, and students)
UCF Shuttle	2,092
Campus visitors	2,600
UCF Fleet vehicles	1,451
Maintenance	8.93
Business travel
Car	547
Train	2.20
Airline	4,509
Total	133,868

Table 5. Emissions from the UCF commuter population

UCF Group	CO2, tons/year	Percentages(%)
Administrative	945	0.8
Faculty	4,902	4.0
Staff/other	10,757	8.8
Graduate students	18,179	14.8
Undergraduate students	87,875	71.6
Total	122,658	100

As mentioned earlier, the 2008 UCF GHG report states that, in 2008, purchased electricity and natural gas accounted for about 94,000 metric tons of CO₂e (Sustainability and Energy Management Office, UCF, 2009). Thus, it was estimated that only about 36% of total emissions came from mobile sources. If we assume about the same tonnage of emissions for purchased electricity and natural gas in 2009 as in 2008, then with our new (and presumably more accurate) estimate of mobile source emissions, UCF mobile sources account for about 61% of total GHG emissions from UCF. This large percentage is due to the large number of commuting students at UCF and is in stark contrast with universities like the University of Cape Town, which showed that commuting and other mobile sources accounted for only about 17% of total emissions (Letete et al., 2011). As expected, it was found that the majority of mobile source emissions at UCF are from commuters.

Commuting emissions are related directly to the percentage of VMT for each commuter population at the university (see Table 6). Like the percentage of emissions from students commuting in personal vehicles, 72% of VMT came from undergraduate students and 15% from graduate students. Again, in keeping with their contribution to emissions from UCF commuters, administration at UCF only drove 1% of VMT, faculty about 4%, and staff/other about 9% of VMT (from the commuter population).

Table 6. VMT of the UCF commuter population

Group	Spring 2009 VMT	Summer 2009 VMT	Fall 2009 VMT	2009 Annual VMT
Administrative	662,592	487,473	614,768	1,764,836
Faculty	3,827,788	1,935,528	3,513,104	9,276,420
Staff/other	7,879,898	5,148,277	7,308,142	20,336,317
Graduate students	13,863,124	5,461,176	15,014,320	34,338,622
Undergraduate commuter students	73,435,954	24,508,464	75,509,856	173,472,274
Visitors				5,850,000
UCF shuttle	470,106	362,934	416,985	1,250,025
UCF Fleet vehicles				8,198
Business travel
Car	799,024	776,625	580,023	2,155,672
Train	2,505	8,615	977	12,097
Airline	3,475,078	4,767,435	5,057,335	13,299,848
Total UCF population	104,416,063	43,456,538	105,316,330	261,764,303

The VMT was developed from survey data, and consequently statistical uncertainty exists. This uncertainty was estimated by calculating a margin of error. The margin of error was calculated as shown in Eq. (3):

(3)

where e is the margin of error, z _(α/2) = 1.96 (for 95% confidence interval), σ is the standard deviation, and n is the sample population.

Using a 95% confidence interval, the commuter mileage for the fall semester had a margin of error of ±3.8 million miles out of a total VMT of 102 million (3.7%). The margin of error for the spring 2009 semester was ±3.8 million miles out of 100 million miles (3.8%); the summer semester mileage had a margin of error of ±2.2 million miles out of 38 million miles (6%).

The final results of the UCF mobile source emissions inventory were compared to the 2008 Central Florida Emissions Inventory (Ross, 2011). Only the on-road and nonroad mobile sources from the 2008 study were included in this comparison, so as to better compare results with the UCF 2009 mobile source emissions inventory. While the 2008 Central Florida Emissions Inventory did encompass a three-county area, only the results from Orange County are listed in Table 7. UCF's main campus is located in Orange County close to the Seminole County line. The Central Florida area includes Orange, Seminole, and Osceola counties (Ross, 2011). These three counties comprise the Orlando Metropolitan area, and the vehicle fleet in these three counties has essentially identical characteristics.

Table 7. Comparison of UCF and Orange County mobile source emissions

	Central Florida Area, Orange County	University of Central Florida
Source	CO2 (tons/year)	CO2 (tons/year)
On-road	8,185,289	129,348
Nonroad	792,303	4,520
Totals	8,977,592	133,868

The UCF inventory comparison with Orange County, Florida, is shown in Table 7. Interestingly, the on-road CO₂ emissions total due to UCF is about 1.5–2% of the total CO₂ emissions from all mobile sources for all of Orange County, Florida. At first, it might seem surprising that the UCF commuter population would contribute such a large percentage (1.5–2%) of the mobile source emissions for all of Orange County. However, the UCF population of 59,000 people (students plus employees) is about 5% of the population of Orange County (which was estimated to be about 1.1 million in 2009) (U.S. Census Bureau, 2011). Of course, there are a number of other factors besides population, such as commuter habits, that influence the university's impact on local air pollution emissions. Thus, it is more appropriate to consider VMT. The total estimated mileage driven from the personal vehicles of the UCF commuter population and business travel (car) was 1.5–2% of the total vehicle miles traveled in Orange County for 2008 as published by the Florida Department of Transportation in 2009.

A growing number of colleges and universities are completing an emissions inventory in an effort to decrease their carbon footprint and increase sustainability. ACUPCC has signatures from presidents/chancellors from approximately 680 different universities, colleges, and community colleges throughout the United States listed on its website (ACUPCC, n.d.). The previously completed inventory by UCF had underestimated CO₂ emissions from transportation by a factor of three, mainly due to the assumptions used to estimate the VMT driven by student commuters (Sustainability and Energy Management Office, UCF, 2009). This large discrepancy led the authors to conclude that surveying the university population is a better method to estimate VMT (and therefore emissions) compared with making assumptions about commuting habits.

The UCF mobile source inventory was also compared with studies recently completed by other universities. UCF emits larger amounts of CO₂ than most other universities. This is due mostly to the fact that UCF has a very large student population compared with most other universities (UCF is now the second largest university in the country based on student population). Also, UCF has a higher percentage of commuter students than many other universities, and many students travel far distances within our service area. Furthermore, several of the universities did not include calculations for the summer semester, whereas UCF has an active summer program, and its emissions in summer were included in our study. However, UCF has many part-time students, and based on full-time-equivalent (FTE) students, UCF emits about 5.5 MTCO₂e per FTE, compared with 7.8 MTCO₂e per FTE for the average U.S. institution (Sinha et al., 2010).

A comparable university emissions inventory for the University of Maryland College Park Campus (UMD) was found (UM, 2009). UMD is a large urban university and did a CO₂ emissions inventory for 2009. For the fall 2009 semester, the total student population enrolled at UCF was 53,466 with a total campus population (including faculty, staff, administrators, and others) of 58,620 (Office of Institutional Research, UCE, n.d.), while the University of Maryland had 34,437 students and a total campus population of 43,577 (UMD, 2009). A comparison of the 2009 CO₂ emissions between UCF and UMD is presented in Table 8.

Table 8. Comparison of the 2009 CO₂ Mobile Source Emissions Inventory for UCF and UMD

Group	University of Central Florida, tons CO2/yr	University of Maryland, tons CO2/year
Employee commuting	16,604	20,175
Student commuting	106,054	21,566
Shuttle	2,092	2,218
Visitors	2,600	NR
Fleet and maintenance vehicles	1,460	4,486
Business Travel	5,058	NR
Total	133,868	48,446
Note: NR, not reported.

As seen in Table 8, while the emissions from faculty/staff commuting and the student shuttle buses are almost identical for the two universities, student commuting emissions from UCF are much higher than at UMD. The large difference between UCF and UMD student commuting emissions likely is due to (1) the larger UCF population, (2) the greater percent of commuters at UCF, (3) the fact that summer semester travel was not included for the UMD study (University of Maryland, 2010), (4) the use of MOVES in the UCF study versus using a gasoline consumption model for student travel in the UMD study (University of Maryland, 2010), and (5) the fact that in the UMD study, several simplified assumptions about student travel behavior were made, whereas in the UCF study, a detailed survey of student travel behavior was conducted. The University of Maryland study estimated that the total of miles driven by student commuters for the fall and spring 2009 semesters was 106.1 million (University of Maryland, 2010), while the total of miles driven by the UCF student commuter population for the same semesters was 175.1 million miles. Furthermore, UCF students traveled an additional, 27.0 million miles in the summer 2009 semester.

In addition to total CO₂ emissions, the student emissions rate per capita was calculated for UCF and for UMD. The annual student commuting emissions were added to the shuttle emissions, and the total was divided by the total fall semester student population for each school. The per capita student emissions rate was about 0.6 tons per UMD student and about 1.8 tons per UCF student, or three times as high. The difference in the per capita emissions rates for UCF and UMD seems to be quite large, but perhaps can be explained by two key assumptions in the UMD study. Those assumptions made by the UMD researchers were (1) that students commuted only 160 days/year and (2) that students made only one round trip per day. Thus, in the UMD study, each commuting student made only 160 round trips per year (UMD, n.d.). In the UCF study, based on actual survey responses, we found that a significant number of students made more than one round trip per day. When dividing the total number of trips made per year (including summers) by the total fall enrollment, UCF students averaged about 250 round trips per year.

It is noted that university fleet vehicle emissions are significantly higher for UMD than for UCF. The university fleet for UMD includes equipment for the UMD golf course, building and landscape services, agricultural equipment, dining services equipment, and others (UMD, 2009). The UCF fleet includes lawn and garden equipment, maintenance equipment, and department/college vehicles. The University of Central Florida fleet does not provide maintenance for a golf course, nor does it include dining services equipment; this is most likely the cause of the difference in CO₂ emissions for the university fleet between UCF and UMD.

Conclusions and Recommendations

With a growing awareness of climate change, different organizations, corporations, and colleges/universities have become more aware of their sustainability efforts by focusing on energy management, with one of their tools being a GHG inventory. Many inventory efforts are consumption based and categorize emissions from transportation as “optional” or “indirect.” However, in nonindustrialized areas such as central Florida, and for universities such as UCF, the largest producers of CO₂ are from on-road mobile sources.

A mobile source emissions inventory of CO₂ for the University of Central Florida for the year 2009 was completed. The results were compared with the 2008 Emissions Inventory for Central Florida, and with a recent GHG inventory for the University of Maryland. In all three studies, mobile sources were found to emit large amounts of CO₂. At UCF, mobile sources are the largest contributor to CO₂ emissions, and commuters are by far the largest contributing group. Powering campus buildings (mainly heating and air conditioning) requires purchased electricity and is responsible for a sizeable amount of CO₂, but based on this study mobile sources are a significantly larger source. It was concluded that surveying the campus populations was a good method that avoided assumptions and produced accurate information to compute the UCF mobile source emission inventory. The most recent U.S. EPA mobile source emissions model (MOVES2010a) was used to generate the emissions from on-road vehicles, and fuel-use-based emission factors were used for nonroad equipment and visitor vehicles. Emissions from business-related airline travel were also included. Although it represented considerably more work than other approaches, the methodology of this study is thought to be more accurate and comprehensive than that used in past studies by UCF and by some other universities.

This study focused only on mobile sources; other sources of emissions were already well documented (Sustainability and Energy Management Office, UCF, 2009). This narrow focus may be viewed as a limitation of the work. Very small amounts of emissions may come from small combustion sources on campus or from emergency generators. Indirectly, a large amount of CO₂ is attributable to the usage of electricity (which mostly comes from nearby coal- and gas-fired power plants) that is purchased to heat and cool buildings (those emissions were mentioned and put into perspective). Another potential limitation is that we assumed default distributions for Orange County as being representative of UCF commuters' vehicles.

Acknowledgments

The authors gratefully acknowledge the Department of Sustainability and Energy Management at UCF for providing financial support for the 2009 UCF Mobile Source Emissions Inventory, as well as Metroplan Orlando for providing financial support for the 2008 Emissions Inventory of Central Florida.