CLASS PROJECT REPORT
SUSTAINABLE AIR QUALITY, EECE 449/549, SPRING 2010
WASHINGTON UNIVERSITY, ST. LOUIS, MO
INSTRUCTORS: PROFESSOR RUDOLF B. HUSAR, ERIN M. ROBINSON
THE ENERGY ANALYSIS AND CARBON FOOTPRINT OF
WASHINGTON UNIVERITY AND BEYOND
Project List
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Global and Regional Carbon Causality Analysis
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Electricity Use by Space and Application: Danforth Campus
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Lindsay Aronson, Alan Pinkert, Will Fischer
WUSTL Transportation Carbon Footprint Update
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Sarah Canniff, Dan Zernickow, Elliot Rosenthal, T.J. Pepping, Brittany Huhmann
Electricity Use by Space and Application: DUC, Seigle
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Matt Mitchel, Jacob Cohen
DUC Energy Consumption
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Nick Thornburg, Will Hannon, Will Ferriby, Chris Valach
Michal Hyrc, Ryan Henderson, Billy Koury, Eric Tidquist
University Carbon Footprint Comparison
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Shamus Keohane, Chris Holt, Kristen Schlott, Sonny Ruffino
Project List
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Global and Regional Carbon Causality Analysis
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Nick Thornburg, Will Hannon, Will Ferriby, Chris Valach
Global/Regional Trend Objectives
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National causality trend analysis of carbon emissions of
specific world countries
Comparison of the causal commonalities within and among
different world regions and the United States
Comprehension of global and regional patterns of carbon
dioxide emissions over time for insight into the driving forces of
climate change
Quantified causality model of data from 60 world countries
and US for future project use
Approach and Methodology
CO2 Emissions =
Population x GDP/Person x Energy/GDP x CO2/Energy
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Population: The total number of people living in a country at a certain point
in time.
GDP/Person: Total GDP in a country divided by its population. Indicates the
national economic development and prosperity.
Energy/GDP: Total kg oil consumed per unit GDP. Indicator of the energy
intensity of a country’s economy.
CO2/Energy: Metric tons of CO2 emitted per kg oil consumed. Measure of
the carbon intensity and content of energy consumption.
Causality Factors for Saudi Arabia
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Increases in Population and
Energy/GDP
Decrease in GDP/Person and
CO2/Energy
The Population and Energy/GDP both
drive Carbon Emissions up while
GDP/Person and CO2/Energy drive
it down.
Increase in Population and
GDP/Person
Decrease in Energy/GDP and
CO2/Energy
Now the forces driving CO2 up are
GDP/Person and Population while
Energy/GDP and CO2/Energy drove
it down.
Causality Factors for South Africa
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Transition from population
as the driving force to GDP
as the driving force
CO2 emissions have
decreased because of
lowering of population and
a lowering of energy per
GDP.
Regional Causality: Europe
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Convergence to two points of CO2 emissions per capita
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Eastern European Countries: decreasing their emissions to get to these points.
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Western European countries: remaining relatively the same in their
Carbon/Capita emissions.
Regional Causality: South America
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Principal Causality Factor: GDP/Person:
Economy is responsible for footprint.
GDP/Person: skyrocketing trend from 19602005. Shift in economic nature.
Energy/GDP: net decrease over 35 year
time period.
CO2/Energy: relative stability,near-zero
trend evolution changing fuel type is
responsible.
Note the uncanny relativity between causal
factor magnitudes in countries.
Slight convergence over time: Evolution from
14-fold to only 3-fold difference!
975% increase!
Regional Causality: Southeast Asia
1732% increase!
1663%
Increase!
Regional Causality: United States
838%
C02/Energy
CO2 Emiss
-10%
-7%
C02/Energy
CO2 Emiss
-42%
-88%
Energy/GDP
708%
-74%
Energy/GDP

900%
800%
700%
600%
500%
400%
300%
200%
100%
0%
-100%
GDP/Pers
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Much more than north
Due to increase in Population
South also had a larger drop
in Carbon per Energy, less
significant than the
population change
Pop
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182%
98%
Penn. 1960-2005
5%
Pop
Southern and
experienced a
emissions
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Georgia 1960-2005
GDP/Pers
900%
800%
700%
600%
500%
400%
300%
Western states 200%
100%
0%
significant
-100%
Overall US Emissions were
driven up by GDP increases,
moderated by decreases in
Energy/GDP
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Summary and Conclusions
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Regional causality frameworks and case studies of
countries prove strong socioeconomic and historical
dependence of causal factors
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Parallel of trends and driving factors in the US
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No such “master formula” for causality analysis
Intrinsic relationship with economic development
Significance of geographical placement
Economic development mostly responsible, dampened by
lowered energy intensity
Establishment of framework for sustainable future
Project List
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Electricity Use by Space and Application: Danforth Campus
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Matt Mitchel, Jacob Cohen
Approach/Methodology: Danforth Campus
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Obtained space breakdown data from the
Department of Space Utilization
Eliminated and grouped together specific spaces
Electricity Breakdown: Danforth Campus
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Electricity consumption=
ΣAreai * (cons/sq.ft.)i
Final Analysis:
23,000,000 kWh/y
consumed on Danforth
Campus.
Compared to previous
observed value of
68,500,000 kWh/y.
(33.5% accounted for)
Electricity Breakdown by Space (kWh)
Research Labs
2%
Class Labs
2%
Knight Center
11%
Support
Facilities
6%
Circulation Area
28%
General Use
5%
DUC
5%
Seigle Hall
5%
Elevators
1%
Toilets
3%
Offices
10%
Study Areas
17%
Classroom
4%
Custodial Area
1%
Project List
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DUC Energy Consumption
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Sarah Canniff, Dan Zernickow, Elliot Rosenthal, T.J. Pepping, Brittany Huhmann
DUC Energy Consumption Objectives
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Find total energy use, CO2 emissions, and cost for
natural gas, electricity, hot water, and chilled water
in the DUC for one year
Identify the portion of the DUC’s total energy use
that goes to individual components of the HVAC
system and the portion that goes to non-HVAC uses
Identify daily, weekly, and seasonal trends in the
above parameters
Begin to understand the influence of outdoor
temperatures and student use of the DUC on these
daily, weekly, and seasonal trends
Approach and Methodology
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Data from Metasys for 5:00 PM April 16, 2009 to
5:00 PM April 16, 2010
electricity, natural gas, hot water, chilled water
 supply fans, relief fans, and heat recovery fans for the 3
AHUs
 pumps for hot and chilled water
 outdoor air temperature
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All energy data converted to MMBTUs for
comparative purposes
Natural Gas
Electricity
Hot Water
Chilled Water
Natural Gas, Electricity, Hot and Chilled Water
Summary and Conclusions
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Annual energy use: 17,300 MMBTU
Annual CO2 emissions: 2,140,000 kg
Annual Cost: $126,000
Electricity is biggest source of all three metrics
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HVAC electricity is 29% of total electricity consumption
Energy reduction strategies should focus on non-HVAC electricity
Two peaks in daily energy consumption corresponding to lunch
and dinner rush
Lower energy consumption on weekends vs. weekdays & during
academic-year breaks
Seasonal patterns based on outdoor temperatures
Project List
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Electricity Use by Space and Application: DUC, Seigle
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Lindsay Aronson, Alan Pinkert, Will Fischer
Electricity Use Objectives
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We aimed to :
 Examine
lighting and appliances for the Danforth
University Center and Seigle Hall
 Look at energy consumption by appliance and by
space
 Show trends and suggest improvements to reduce the
carbon footprint of Washington University
Approach and Methodology
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Started by identifying how to breakdown spaces
within each given area
Researched appliances found in the different kind
of spaces identified and determined their wattage
Determined hours of use for appliances/lighting
To confirm, took metered energy data, subtracted
HVAC consumption, and compared calculations
Hourly Average Consumption
Hourly Average Consumption
Hourly Average Consumption
Results for the DUC (excluding kitchen)
Total By Category (kWh/week)
TVs
1%
Printers/Cop Other Total
1%
iers
Projector
3%
Total
Break Rooms
11%
0%
Weekly Total By Space (kWh)
Student Media
Formal
1%
Visitors
Lounge
Elevators
2%
0% Mechanical Orchid Center
Room 2%
Custodial
1%
1%
0%
North Commons
2%
Corridors
28%
Dining Area
2%
Fun Room
Event
3%
Services
Bathroom 3%
4%
Commons
4%
Meeting Rooms
4%
Lighting Total
13%
Computers
71%
Grad Center
4%
Career
Center
12%
Stairway
6%
SU
Floor 2
9%
StudLife
7%
SU Floor 1
6%
Results for DUC Food Service
Weekday Usage
Energy Use (Weekday)
100
90
Café
80
Consumption (kWh)
70
60
Beverage
Prep
Café
Misc.
Lighting
Misc.
50
Waste
40
30
Post-Prep
Post-Prep
Prep
Lighting
Beverage
20
10
0
Waste
Energy Breakdown: Seigle
Seigle Trends
Summary and Conclusions
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Circulation area is the largest energy consumer
 Recommend
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installing motion sensor lights
Computers are another major energy drain
 Stand-by
should be used during the day, but at night
computers should be shut down completely
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Other recommendations:
 Install
motion sensors in bathrooms and classrooms
 Use “Night mode” lighting setting in hallways without
motion at night
 Schedule night classes and meetings on first and second
floors so that other floors’ lights can be turned off
Project List
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WUSTL Transportation Carbon Footprint Update
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Michal Hyrc, Ryan Henderson, Billy Koury, Eric Tidquist
Transportation Objectives
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To better understand the carbon footprint of
transportation at Washington University by:
 Ground
Transportation: Improving Past Estimates
 Air Travel: Novel Estimates
 Parking: What happens when we go underground?
Approach & Methodology
Flying
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Extracted student locations and
numbers from home zip code data
Found total passenger miles flown by
students
Commuting
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Estimated carbon footprint from total
number of passenger miles
Parking
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Used approximate appliance data to
estimate daily carbon emissions
Used approximate size data to
estimate initial carbon emission due
to pouring concrete
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Used school zip code data from a
similar project conducted in 2009
Calculated commuting distances by
mode of transportation
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Walk/Bike
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MetroLink
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MetroBus
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Drive Alone
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Carpool
Estimated carbon footprint
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Upper bound
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Lower bound
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Best guess
Driving Forces for CO2 Emissions
Student Aviation Carbon Footprint
Ground Transportation
Faculty Addresses
Student Addresses
Comparison of Bounds
Modes of Transportation and Total Carbon
The two leftmost charts represent the number of students
(left) and faculty (center) that commute to school in each
mode of transportation taken into consideration.
The chart to the right represents the total carbon
emissions from students and faculty.
Best guess total: 5627 metric tons of CO2
Emissions Due to a Parking Spot
Summary & Conclusions
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Our best estimates for annual transportation footprints are
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~23,000 metric tons of CO2 from student air commute
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~5,500 metric tons of CO2 from faculty and student regional ground commute
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~527 metric tons of CO2 from lighting and ventilation of parking on campus
This is an underestimation of the actual total footprint
The transportation footprint has been and will continue to
increase
To reduce the transportation footprint, we recommend the
University
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Merge fall and thanksgiving break to reduce flight emissions
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Try to reduce the number of people that drive to work by themselves
Project List
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University Carbon Footprint Comparison
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Shamus Keohane, Chris Holt, Kristen Schlott, Sonny Ruffino
University Carbon Footprint Objectives
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The primary objective of this project was to compile
GHG data from other Universities to make
comparative analysis with respect to Washington
University’s place among other schools when it comes
to sustainability.
An additional goal of the data analysis is a
qualitative subject investigation to see which areas
of a GHG inventory Wash U can improve upon or is
already succeeding in.
Approach and Methodology
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This project began with a review of the previous class’ report, where size
data was only available for 12 schools, and transportation data was only
available for 19. Their analysis only really compared these two subjects.
We expanded to include net GHG emissions, total campus area, purchased
electricity and student population.
Tufts, Smith, Lewis and Clark, Wellesley, College of Charleston, Cal St.
Polytech, College of William & Mary, and Occidental College were
removed due to lack of data.
Arizona State University, Cornell, and Bates were added as they are known
to be sustainable schools
Data for most of the schools was available either on their sustainability
websites or through the ACUPCC website. The latter providing a nice and
unified way of reporting and measuring GHG emissions
The data was tabulated into a Google Doc. work space along with general
statistics for each school (area, pop., etc). From this common source of data,
we began to analyze the information for trends
Fig. 1
Overall GHG Emissions Time Comparison
Fig.1 This is a time comparison of total GHG emissions, from the 2008 group data to current data. Note
that Wash U ranks 3rd amongst the analyzed schools in terms of gross emissions of CO2, despite Wash
U’s size compared to other schools. Also noteworthy is the fact that schools are generally trending to
emit more GHG than previously evaluated, this is most likely due to many schools expanding their GHG
inventories to account for transportation effects. The large disparity between transportation reporting
from the 2008 report to this report is likely the cause of the overall increase in emissions seen in this
time period. More information on transportation data reporting can be seen in figures 4a and 5b.
•
Immediately attention grabbing in this figure is Harvard’s dramatic decline since the time of the previous
inventory. More information on this is included in figure 5a.
Fig. 2
Per Capita Comparison
INCLUDING MED SCHOOL
Without Med School
Fig. 2 Per Capita Emissions: Gross emissions per number of students. This graph includes results from the
most recent GHG Index results from Wash U, including the medical school. Also, there is no 2008 data
for Wash U, but rather there is data for Wash U including only the Danforth Campus (not med school).
We included both values to show the dramatic impact medical schools can have on overall emissions.
For Gross GHG Emissions, all other indices studied included medical schools. Additionally, the student
population counts are a total count, including graduate and medical students. We think this graph
(including Wash U + med school) is the most accurate indication of per capita emissions, because of
the all inclusiveness of using graduate school campuses + graduate and medical school students,
where applicable.
Fig. 3
Gross Emissions & Population Trends Time Comparison
(W/ Med School)
Student Populations
Fig. 3 This is a time comparison of the gross emissions normalized by population.
Total 2010 Transportation Emissions per Capita
Fig. 4b
***for schools that report all categories
Fig. 4a 2010 Transportation Data Reported
4b) Not all schools had the same information
available, so we felt that a comparison of the
2010 transportation emissions by school should
be normalized. This graph compares only
schools that reported data in all three
4a) This chart shows the breakdown of transportation data that was available in
transportation categories: university fleets,
each school’s GHG Emissions Index. Most schools had a good log of
student and faculty commuting, and air travel.
transportations emissions data, but not all. As mentioned above,
This graph represents the total combined
transportation can have a huge impact on overall emissions, when included in
emissions for those three categories, controlled
emissions reports. For example, as seen from the report by the transportation
by university population. It is the only graph that
group, international student travel can have a major impact on Transportation
is not also a time comparison to the 2008 group
GHG emissions. Yale currently has 8% international students while Duke has
data. This is because we could not be sure
13%. The 2008 group mentioned great inconsistency and difficulty tracking
which transportation data the 2008 group
data, so we are doing an isolated study of 2010 data only.
included in their graphs, though they did include
mention of their raw data’s inconsistencies.
Fig. 5a
Emissions Resulting from Purchased Electricity Time Comparison
Fig. 5b
Index Data Reporting Time Comparison
Abbrev.
Data Category
PE
Purchased Electricity
RE
Renewable Energy
ST
Stationary Sources
Tr-UF
Transp: University Fleet
Tr-CST
Transp: Commuting, Students
Tr-CSF
Transp: Commuting, Faculty
Tr-A
Transp: Air
Ag
Agricultural Waste
SW
Solid Waste
Figure 5 Analysis
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5a)This graph shows a comparison over time of the total emissions resulting from
electricity purchased. As mentioned above, Harvard in particular shows a dramatic
decrease in their EP emissions. This is because of the installation of a new on-campus
power plant since the previous inventory, drastically reducing their GHG emissions
from purchased power.
5b) This graph is a time comparison of available data in each school’s GHG index.
The 2008 group included this bar graph in their data to demonstrate the
inconsistencies in reporting, as well as the dramatic differences in reporting methods
from school to school. We decided this was a pertinent graph for comparison.
Considering that a) we studied fewer schools b) that emissions from student vs. teacher
commuting have been combined and in 2010 is simply referred to as overall
"commuting," and c) considering that agricultural waste no longer seems to be included
in most GHG inventories, a general trend shows increased reporting for all data
categories. Air travel and renewable energy reporting has increased the most. It
should also be noted that data reporting seems to be much more standardized (most
schools were included in the comprehensive ACUPCC GHG Emissions Index) in 2010
than in 2008. We didn't have to resort to any "alternative methods" for GHG
inventories, and another recent trend is that significantly more inventories were
available as a university sponsored report (including Harvard
and Wash U), indicating
54
increased interest and university involvement in GHG inventories.
Summary and Conclusions
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It is clear from the previous data that Wash U has reported drastically more CO2
emissions from the last group’s report in 2008. Wash U currently still does not include
transportation, so the current estimates for Wash U emissions are lower than they are
in reality.
Wash U’s poor rank among other Universities in GHG emissions can primarily be
attributed to the amount of electricity Wash U purchases and the source of that
Electricity. If Wash U were to contract with utility companies to purchase electricity
produced from renewable resources, Wash U could greatly improve its standing in
the academic community.
In conclusion, while Wash U may take an open and active stance toward it’s
sustainability goals, the University need to look to new areas that can have greater
impacts in reducing the University’s Carbon Footprint.
Questions?
References (Global)
1.
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23.
24.
http://www.google.com/publicdata/overview?ds=d5bncppjof8f9_
https://www.cia.gov/library/publications/the-world-factbook/geos/ve.html
http://inflationdata.com/inflation/inflation_Rate/Historical_Oil_Prices_Table.asp
http://web.archive.org/web/20080226202420/http://www.jica.go.jp/english/global/pov/profiles/pdf/sau_eng.pdf
http://www.state.gov/r/pa/ei/bgn/35639.htm
http://www2.census.gov/prod2/statcomp/documents/1980-02.pdf
https://www.cia.gov/library/publications/the-world-factbook/geos/br.html
https://www.cia.gov/library/publications/the-world-factbook/geos/ar.html
http://en.wikipedia.org/wiki/France#Economy
http://www.bea.gov/regional/index.htm#gsp
http://www.census.gov/compendia/statab/
http://en.wikipedia.org/wiki/Economy_of_Thailand
http://web.worldbank.org/WBSITE/EXTERNAL/COUNTRIES/LACEXT/HONDURASEXTN/0,,contentMDK:21035522~pageP
K:141137~piPK:141127~theSitePK:295071,00.html
http://www40.statcan.gc.ca/l01/cst01/econ40-eng.htm
http://www.statcan.gc.ca/pub/88-221-x/2008002/part-partie1-eng.htm
http://capitawiki.wustl.edu/ME449-07/index.php/Image:All_State_Energy_BTU_EmissionR.xls
http://www.eia.doe.gov/emeu/states/_seds.html
http://www.epa.gov/climatechange/emissions/state_energyco2inv.html
http://www.eia.doe.gov/oiaf/1605/state/state_emissions.html
http://www.eia.doe.gov/oiaf/1605/ggrpt/carbon.html
http://open.worldbank.org/countries/AFG/indicators/EN.ATM.CO2E.KT?per_page=100&api_key=4kzbhfty3mz6v293vr
q5uphw&date=1960:2005
http://datafedwiki.wustl.edu/index.php/2010-02-15:_World_Bank_Coutry_Data
http://capitawiki.wustl.edu/EECE449/index.php/Global-Regional_Trends_of_Carbon_Emissions
http://capita.wustl.edu/me449%2D00/
References (University)
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Duke University (2007) http://acupcc.aashe.org/ghg-report.php?id=225
Penn State University Park (2009) http://www.ghg.psu.edu/campus_inv/default.asp
Washington University in St. Louis (2009) http://www.wustl.edu/sustain/GHGEmissions.pdf
U of Pennsylvania (2008) http://acupcc.aashe.org/ghg-report.php?id=258
Cornell (2008) http://acupcc.aashe.org/ghg-report.php?id=237
Yale (2008) http://sustainability.yale.edu/sites/default/files/GHG2008.pdf
Arizona State University (2008) 2008: http://acupcc.aashe.org/ghg-report.php?id=628
2007: http://acupcc.aashe.org/ghg-report.php?id=386
U of Illinois at Chicago (2008) http://acupcc.aashe.org/ghg-report.php?id=102
UT Knoxville (2009) http://acupcc.aashe.org/ghg-report.php?id=1018
Colorado State University (2009) http://acupcc.aashe.org/ghg-report.php?id=932
UC Berkeley(2008) http://acupcc.aashe.org/ghg-report.php?id=142
U of Connecticut (2007) http://acupcc.aashe.org/ghg-report.php?id=587
Harvard(2007) http://www.provost.harvard.edu/institutional_research/FACTBOOK_2007-08_FULL.pdf
Tulane University (2008) http://green.tulane.edu/PDFs/Inventory_Complete_2008_FINAL.pdf
University of Central Florida (2008) http://acupcc.aashe.org/ghg-report.php?id=1108
Utah State University (2008) http://acupcc.aashe.org/ghg-report.php?id=971
Rice (2009) http://acupcc.aashe.org/ghg-report.php?id=843
UC Santa Barbara (2009) http://acupcc.aashe.org/ghg-report.php?id=963
University of New Hampshire (2007) http://www.sustainableunh.unh.edu/climate_ed/greenhouse_gas_inventory.html
Oberlin College(2007) http://acupcc.aashe.org/ghg-report.php?id=367
Middlebury College (2007) http://acupcc.aashe.org/ghg-report.php?id=441
Carleton College (2007) http://acupcc.aashe.org/ghg-report.php?id=236
Colby College (2008) http://acupcc.aashe.org/ghg-report.php?id=801
Bates College (2008) 2008: http://www.bates.edu/Prebuilt/GHGInventory.pdf
2007: http://acupcc.aashe.org/ghg-report.php?id=329
Connecticut College (2009) http://www.conncoll.edu/green/greenliving/GreenlivingDocs/CC_greenhouse_gas_emissions_inventory_0809.pdf
References (Application)
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Tom Dixon, DUC General Manager
DUC Electrical Binder: http://capita.wustl.edu/me44909/Elect%20Binder.pdf
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Leslie Heusted, Director, Danforth University Center
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Kellie Briggs, Assistant Director, Facilities, Danforth University Center
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Jessica Stanko, Career Center Assistant; Lauren Botteron, Hatchet
Yearbook; Alan Liu, StudLife staff member
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Frank Freeman
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Larry Downey and Kevin Watkins in Facilities
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Seigle Construction Plans
http://capitawiki.wustl.edu/EECE449/images/0/0c/Seigle_Hall_Constructi
on_Plans.pdf
Excel files with the data for graphs shown in this presentation can be found
on our wiki report page.
References (Transportation)
1.
http://hypertextbook.com/facts/1999/KatrinaJones.shtml
2.
http://apps.olin.wustl.edu/mba/casecompetition/PDF/oscc_case2.pdf
3.
http://www.engineeringtoolbox.com/garage-ventilation-d_1017.html
4.
http://www.docstoc.com/docs/2392070/Overview-of-Existing-Regulations-for-VentilationRequirements-of/
5.
http://www.epa.gov/ttnchie1/conference/ei13/ghg/hanle.pdf
6.
http://capitawiki.wustl.edu/EECE449/index.php/Commuting
7.
http://capitawiki.wustl.edu/EECE449/index.php/Shuttles
8.
http://capitawiki.wustl.edu/EECE449/index.php/Transportation
9.
http://www.bts.gov/xml/air_traffic/src/index.xml#CustomizeTable
10.
http://www.ghgprotocol.org/
11.
http://www.eia.doe.gov/oiaf/1605/coefficients.html
12.
http://www.whatsmycarbonfootprint.com/faq.htm
13.
http://www.carbonfund.org/site/pages/carbon_calculators/category/Assumptions
14.
http://www.epa.gov/oms/climate/420f05001.htm
15.
http://capitawiki.wustl.edu/EECE449/index.php/Transportation