How Institutional Factors are Related to Greenhouse Gas Emissions

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

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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 Presentation

Transcript of How Institutional Factors are Related to Greenhouse Gas Emissions

Page 1: How Institutional Factors are Related to Greenhouse Gas Emissions

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

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World-Class Education. World-Class City.

• Background• Methods used• Results• Implications

Outline

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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

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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

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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

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• 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

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• 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

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Metric tons CO2-e

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• 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)

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• 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

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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

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• 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)

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• 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

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• 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

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• 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

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• Cynthia Klein-Banai• [email protected] • 312-996-3968• http://sustainability.uic.edu

Questions?