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)
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Cynthia Klein-Banai, Ph.D.
Associate Chancellor for Sustainability
ACUPCC requires reporting using EmissionsWRI 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 reportedMethods
Reporting year and start date of year Emissions
FT faculty, FT staff, FT students
PT faculty, PT staff, PT students
Gross square feet
Health care space, laboratory space, residential space
Red = mandatoryContextual data from the ACUPCC
Size and setting classification (residential nature)
Land-grant institutionData from other sources
Most recent year for schools reporting Emissions
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 variablesData analysis
Metric tons CO2-e Emissions
Created new variable
ONSF = GSF – LSF – HCSF – RSF where
LSF = square feet of laboratory facilities
HCSF = square feet of health care facilities
RSF = square feet of residential facilitiesFinal data set (n=135)
S12 = Scope 1 and 2 emissions
GSF = Gross square feet
CDD = total cooling degree days/yr
HDD = total heating degree days/yr
log (S123) = -1.25 + 0.92 log (GSF)
p<0.0001; R2=0.795Scope 1 and 2 vs. Gross Emissions
S123 = -10489 + 0.007 ONSF + 0.085 LSF + 0.019 RSF + 38100 M + 1.406 CE + 1.369 FTE
CE = 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 institutionGross emissions model n=135
S123 + 1.406 CE + 1.369 FTE<50,000 = 3654 + 0.001 ONSF +
0.015 LSF + 0.019 RSF + 0.627 CE +
Took out medical school since only 1Gross emissions < 50,000 metric tons CO2e (n=107)
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
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
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