How Institutional Factors are Related to Greenhouse Gas Emissions

1. How Institutional Factors are Related to Greenhouse Gas Emissions Cynthia Klein-Banai, Ph.D. Associate Chancellor for Sustainability

2. Outline • Background • Methods used • Results • Implications

3. Background • 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

4. 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

5. 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 space Red = mandatory Contextual data from the ACUPCC

6. 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

7. 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

8. Metric tons CO2-e

9. Used only FT enrollment for population factor 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 facilities Final data set (n=135)

10. log (S12) = -2.433 + 1.027 log (GSF) + 0.000129 CDD + 0.000032 HDD p<0.0001;R2=0.823 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=0.795 Scope 1 and 2 vs. Gross Emissions

12. S123 = -10489 + 0.007 ONSF + 0.085 LSF + 0.019 RSF + 38100 M + 1.406 CE + 1.369 FTE p<0.0001; R2=0.954 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 institution Gross emissions model n=135

14. S123<50,000 = 3654 + 0.001 ONSF + 0.015 LSF + 0.019 RSF + 0.627 CE + 1.08 FTE p<0.0001; R2=0.642 Took out medical school since only 1 Gross emissions < 50,000 metric tons CO2e (n=107)

16. 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

17. 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

18. 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

19. Cynthia Klein-Banai cindy@uic.edu 312-996-3968 http://sustainability.uic.edu Questions?