how institutional factors are related to greenhouse gas emissions
DESCRIPTION
How Institutional Factors are Related to Greenhouse Gas Emissions. Cynthia Klein-Banai, Ph.D. Associate Chancellor for Sustainability. Outline. Background Methods used Results Implications. Background. ACUPCC reporting tool (>450 schools) - PowerPoint PPT PresentationTRANSCRIPT
World-Class Education. World-Class City.
Click to Add TitleHow Institutional Factors are Related to
Greenhouse Gas Emissions
Cynthia Klein-Banai, Ph.D.Associate Chancellor for Sustainability
World-Class Education. World-Class City.
• Background• Methods used• Results• Implications
Outline
World-Class Education. World-Class City.
• ACUPCC reporting tool (>450 schools)• Colleges and universities are 1.6% of US GHG
emissions• Depending on the year of construction, inpatient
health care, education, lodging, public assembly, and other building types are approximately 1.5-3 X more energy intensive than an office building
Background
World-Class Education. World-Class City.
• ACUPCC requires reporting using WRI Greenhouse Gas Protocol
• Most campuses use the Campus Carbon Calculator
• Scope 1, 2 and 3 emissions, including components of each such as air travel and commuting are to be reported
Methods
World-Class Education. World-Class City.
• Reporting year and start date of year• State• Carnegie class• FTE enrollment• FT faculty, FT staff, FT students• PT faculty, PT staff, PT students• Residential students• Gross square feet• Health care space, laboratory space, residential spaceRed = mandatory
Contextual data from the ACUPCC
World-Class Education. World-Class City.
• Heating and cooling degree days (National Weather Service)
• Carnegie Foundation – Locale– Size and setting classification (residential nature)– Region– Medical school – Land-grant institution
Data from other sources
World-Class Education. World-Class City.
• Most recent year for schools reporting • Small data set with all of above information
(n=52)• Principal factor analysis to tease out most
significant factors• Population showed a lot of colinearity, as did
square footage although to a lesser extent• HDD and CDD explained less of the variance
than the other independent variables
Data analysis
World-Class Education. World-Class City.
Metric tons CO2-e
World-Class Education. World-Class City.
• Used only FT enrollment for population factor
• Created new variableONSF = GSF – LSF – HCSF – RSF where LSF = square feet of laboratory facilitiesHCSF = square feet of health care facilitiesRSF = square feet of residential facilities
Final data set (n=135)
World-Class Education. World-Class City.
• log (S12) = -2.433 + 1.027 log (GSF) + 0.000129 CDD + 0.000032 HDDp<0.0001;R2=0.823S12 = Scope 1 and 2 emissions GSF = Gross square feetCDD = total cooling degree days/yrHDD = total heating degree days/yr
• log (S123) = -1.25 + 0.92 log (GSF) p<0.0001; R2=0.795
Scope 1 and 2 vs. Gross Emissions
World-Class Education. World-Class City.
World-Class Education. World-Class City.
S123 = -10489 + 0.007 ONSF + 0.085 LSF + 0.019 RSF + 38100 M + 1.406 CE + 1.369 FTE
p<0.0001; R2=0.954CE = calculated greenhouse gas emissions from
commuting to campus (metric tons CO2-e) M = medical school (0=no; 1=yes)FTE = full-time equivalent enrollment as calculated by
the institution
Gross emissions model n=135
World-Class Education. World-Class City.
World-Class Education. World-Class City.
• S123<50,000 = 3654 + 0.001 ONSF + 0.015 LSF + 0.019 RSF + 0.627 CE + 1.08 FTE
p<0.0001; R2=0.642Took out medical school since only 1
Gross emissions < 50,000 metric tons CO2e (n=107)
World-Class Education. World-Class City.
World-Class Education. World-Class City.
• For gross emissions CDD and HDD are not important parameters.
• The parameter estimate for LSF is more than 10X than for ONSF in both models and the parameter estimate for RSF is at least 2X ONSF. This shows a greater influence on emissions from these types of uses.
• Smaller schools less influenced by student population and building space than larger schools.
Conclusions
World-Class Education. World-Class City.
• Find a contextual predictor for commuting
emissions, such as number of parking spaces or permits, to improve the simplicity of this model.
• Conduct an in-depth examination of the institutional factors that influence emissions.
Conclusions
World-Class Education. World-Class City.
• Examine necessity of building space and use. Is it essential to mission?
• Do we need multiple and/or large offices for faculty and staff, especially when we can telecommute?
• Is space fully utilized for intended function?• Is it necessary that the institution support activities such
as facility- (and energy-) intensive research? • Has vacated space been fully decommissioned and how
can it be renovated and reassigned?
Beyond technical solutions
World-Class Education. World-Class City.
• Cynthia Klein-Banai• [email protected] • 312-996-3968• http://sustainability.uic.edu
Questions?