The Yale Climate Initiative: GHG emissions from transportationMarco ButtazzoniKathryn A. Zyla

2

Presentation Overview

• Study background– Yale Climate Initiative (YCI)– Yale’s Inventory

• GHGs from transportation– Boundaries and methodology– Emissions calculations approaches

• Conclusions

3

YCI Overview: Purpose

Student-initiated study to:– Understand Yale’s greenhouse gas (GHG) emissions

drivers– Develop a GHG emissions inventory for Yale– Analyze approaches to make the University more

climate friendly

An opportunity to reflect on existing inventory methodologies and tools

4

Greenhouse Gases at Yale

CO2CH4N2OHFCs

CO2e offsets?

PFCsHFCsSF6CO2N2O

CO2CH4N2O

CO2CH4N2O

CO2. CH4, N2O, CFCs

5

Yale’s GHG Emissions: Year 2002Yale University GHG Emissions (2002)

Total 291,696 Tons CO2 equivalent

38,653

13,254

246,080

-6,291

-50,000

0

50,000

100,000

150,000

200,000

250,000

300,000

Power Plants/Buildings Transportation Other Sources Sinks

tons

of C

O2

equi

vale

nt

Other sources 4%

Transport. 13%

Power Plants/ buildings 83%

6

Transportation: System boundaries

Yale owns 366 vehicles, over half of which are

trucks or vans

• Institutional (WRI Scope 1)– Yale-owned vehicles

• Work-related (WRI Scope 3)– Conferences, meetings, research trips(flights, train, ground transportation)

• Commuters (WRI Scope 3)– Daily (faculty, students)– Travel home (students)

Yale spends about $20 million in travel each year

Yale commuters travel over 57 million miles per year

System boundaries chosen to:– Understand Yale’s total footprint– Explore methodologies for quantifying more than the usual direct

fleet emissions

7

Uncertainty

Importance of uncertainty– General need to understand

range of potential values – Very large for transportation

emissions– Several assumptions needed

to calculate emissions

The ‘inventory community’ has an opportunity to define more standardized factors and procedures

Our approach– Develop calculation tool– Estimate the variability

deriving from single variables– Evaluate total uncertainty– Identify most significant

variables and assumptions

8

Institutional Emissions: Data

• Vehicles owned and operated by Yale University– Vehicle purchasing decisions made by Yale– Fuel purchases made by Yale drivers

Data sources– Kept centrally

– Up-to-date list of vehicles– Accurate fuel purchase information from credit card transactions– Inconsistent identification of vehicles being refueled– Inaccurate vehicle mileage data

9

Institutional Emissions: Analysis

Diesel Fuel Consumption

Unleaded Fuel Consumption

Activity Data+/- 5%

Emissions Factors

Calculated Emissions

+/- 3%

Total CO2Emissions

1,622 t

Diesel Emissions

Factor

Unleaded Emissions

Factor

+/- 8%

aaa Activity Data Assumptionsbbb ccc Calculations +/- x% Uncertainty

10

Institutional Emissions: Conclusions

• Low uncertainty for total institutional emissions, but • High uncertainty on the emissions and efficiency of

individual vehicles and users→ Difficult to translate to mitigation opportunities

Case studies and “Best management practices” to help companies gather and analyze more granular data?

11

Work-related emissions: Data

• Travel directly linked with university activities – Conferences, meetings, research trips

• Includes many modes of transport– Flights, train, ground transportation

Data sources– Financial records (from expenditure reports etc.)– Yale’s travel agency records (with good granular information

about flights)– Some purchases not made through centralized process– Many types of trips are aggregated monetarily, not separated

by mode

12

Administration expenditure

data

% spent per

mode

Air and train

Car rental

Ground transp.

Lodging & food

Miscellan.

Travel agency data

% spent per mode

Emissions per $

Emissions (air, train, rented car,

ground transpor.)

Not considered

aaaData

Assumptions

Calculations

bbb

ccc

Work-related emissions: Analysis

Train

Air

13

Air travel analysis v. 1

# InternationalSegments

# DomesticSegments

CityDistances

GallonsConsumed

Miles/ gallon

Total Energy

Consumed

Energy/ gallon

Travel Agent CO2

Emissions

Tons CO2/GJ

Total CO2Emissions

8,792 t

TA as % of

total $Travel

Agency data

+/- 3%+/- 5% +/- 38% +/- 2% +/- 11%

Miles per segment

(web)

+/-59%

bbbaaa cccData Assumptions Calculations +/- x% Uncertainty

14

Air travel analysis v. 2

Total CO2Emissions

8,384 t

TA as % of total $

+/- 11%# International

Segments

# DomesticSegments

Travel Agent CO2Emissions

CityDistances

Miles per segment (web)

Emissions/ mile long

trips

Emissions/ mile medium

trips

Emissions/ mile short

trips

Travel Agency data

+/- 5%

+/- 10%

+/- 10%

+/- 10%

+/- 26%

bbbaaa cccData Assumptions Calculations +/- x% Uncertainty

15

Work-related emissions• Using financial data to calculate GHG emissions

required a variety of assumptions. E.g.ParameterMiles/$ train

GHG/passenger-mile train

$/day car rentalMiles/day car rental

GHG/pass. mile car rental

Miles/$ ground transportation

GHG/pass. mile ground t.

SourcesTA, Market prices

WRI

TA, Market pricesTA benchmark

WRI

Expenses mile rebate Market prices

WRI, DOT

NotesSignificant variability in train pricesOnly one factor for the US at the time of the analysis (now improved)

Based on limited random sampleBased on industry data

Based on limited sampleFew data available

None of these parameters influenced the work related emissions by more than 3.3% of the total

16

Work related emissions: Conclusions• Several parameters can affect uncertainty when

calculating work related emissions• The inventory community could improve consistency, if

not accuracy, if it agrees on parameters such as:– Miles per $ spent for various transportation methods (ideally at

state or local level)– Emissions per mile for different transportation methods (requires

aggregating different options)• With transportation companies looking at offering GHG

emissions offset services we should also think about information protocols to transfer relevant data between transportation companies and their customers

17

Personnel commuting: Data• Overall Yale employees

about 12.500 people– Faculty– Researchers– Management – Support

Where employees live Zip codes data

0%

5%

10%

15%

20%

25%

30%

less than1

between1 and 5

between5 and 10

between10 and 25

between25 and 50

more than50

Miles from Yale

% e

mpl

oyee

sData sources

– Zip code records from Human Resource Department

18

Zip code data

Correction for record mistakes

CT-DOT transport

parameters

Edited zip

code data

Mode of transport

ation

Total miles

traveled

aaa Data

Assumptions

Calculations

bbb

ccc

# of trips per

week

Miles per trip

Miles traveled

per mode of

transport

Emissions parameters

Emissions per mile

per mode

Emissions per mode

of transport.

Car occup. rate

FES & parking dep. data

Personnel commuting: Analysis

19

Zip code data

Correction +/- 2%

CT-DOT transport

parameters

Edited zip

code data

Cars only vs. PT

and bike

Total miles

traveled

aaa Data

Assumptions with change in the parameter

Calculations

bbb

ccc

# trips +/- 1%

Miles+/-5%

Miles traveled

per mode of

transport

Emissions parameters

Cars +22 -22%PT +120 –50%

Total CO2Emissions16,665 t

+ 19%- 12%

xx% Change in emissions

+ 6%- 9%

+ 4.8%- 4.8%

+ 1.5%- 3.7%

FES & parking

dep. data

Occ. Rate

+/- 17%

+ 20%- 14%

car +21.6%- 21.6%

PT +3.24%- 1.35%

+ 80%- 50%

Personnel commuting: Analysis

20

Personnel commuting: Conclusions• Work related emissions estimate could have benefited

from– A mobility study for the University– More precise and current data on personnel and students

residence– Vehicle-specific parking data (dept name doesn’t matter, but

type of data desired seems important)– State level and local data on transportation behavior– State level and local emission parameters (e.g. for car, train or

bus emissions)– Average (non-technology-specific) automobile emissions

parameters

21

Summary: Emissions from Transportation

4151,700 1,688

16,700

8,3945,4002,734

1,622

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

Inst

itutio

nal

trave

l

Wor

k re

late

dai

r tra

vel

thro

ugh

trave

lag

ent

Oth

er w

ork

rela

ted

trave

l

Em

ploy

ees

com

mut

esan

d vi

sits

Stu

dent

sco

mm

utes

and

visi

ts

Stu

dent

sre

turn

ing

hom

e (d

om.)

Stu

dent

sre

turn

ing

hom

e (in

t’l.)

Tran

spor

t'o

ther

activ

ities

'

tons

CO

2 eq

uiva

lent

Other sources 4%

Transport. 13%

Power Plants/ buildings 83%

22

Conclusions: Data Quality

• Lack of Data– Define management systems and templates for data

gathering and emissions calculation– Prepare standard templates for mobility studies for

Universities– (In the future?) Define communication protocols

between the travel industry and end users• E.g. For airline or train companies to communicate CO2

emission data to their users

23

Conclusions: Emissions factors

• Create a repository with agreed/standard parameters for: – Miles per $ spent per mode of transportation– Emissions per mile traveled per mode of transportation– Regional and local vehicle use parameters

• Occupation rate• Distance traveled for commutes• Mode of transportation chosen

– Average automobile emission factors

Parameters of increasing level of granularity can be provided and associated with decreasing level of uncertainty

24

Conclusions: Methodology

• Current calculations tools for transportation emissions can be a basis for improving scope and precision– New calculation tools and protocol can broaden calculations– More granular analysis is possible if suitable parameters are

available (and provided by reputable sources)– More explicit uncertainty analysis

• Case studies, ‘best practices’ and training to help companies improve:– Internal data management systems and – Processes and incentive structures

Thank you!Kathryn Zyla, zylak@pewclimate.orgMarco Buttazzoni, mbuttazzoni@ert.net