Decision Support via Uncertain
Energy / Carbon Footprints
H. Scott Matthews
Mili-Ann Tamayao
Rachna Sharma
Carnegie Mellon University
Green Design Institute
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Background
• Energy, carbon footprinting / inventories for 10+ years
– Footprints for every sector in US from 1987-2002 (EIO-LCA)
• Recent applications: Pittsburgh, CMU campus
– Several person-years of effort to estimate footprint
– Generally done to inform ‘policy decisions’, e.g., climate action
plans or set reduction targets
• Summary findings
–
–
–
–
Too much time spent on inventory step
Existing methods inconsistent, not comparable, credible
Data quality poor, estimates uncertain (but typically ignored)
Not easily compared..
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Goal
Inventory
Action Plan
Reductions
• Streamline “front end” (generating inventory,
footprints) via single, consistent data archive
• Enable stakeholders to quickly leapfrog to planning
efforts and make reductions
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Example 1
• College GHG inventories (self-reported to a website)
• Reported data:
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–
–
–
GHG emissions data by Scope (1-3)
Full time students, staff, faculty
Floor space
Climate Zones
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Reporting of Climate Zones
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Pennsylvania schools
12,000
10,000
8,000
6,000
4,000
2,000
2,000,000
1,800,000
1,600,000
1,400,000
1,200,000
1,000,000
800,000
600,000
400,000
200,000
0
0
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Colorado College
Colorado College
Colorado College
Colorado College
Colorado State…
Colorado State…
Colorado State…
Colorado State…
Colorado State…
Community…
Community…
Metropolitan…
Naropa University
Naropa University
Naropa University
University of…
University of…
University of…
University of…
University of…
GHG emission factors (all lb/MWh)
California schools
1,200
1,000
800
600
400
200
0
Colorado schools
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Re-focus
• The problem is not merely that these organizations are
unable to do an inventory.
• The problem is that they’re reporting this data, in
support of commitments, and making plans based on
erroneous inventories.
• They will make bad decisions as a result
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Example 2
• To support climate action planning and goal setting
for regions, estimated energy and carbon footprints of
every county in US (~3,000)
• Found consumption-based emissions (emissions
attributed to county not just emitted by county)
– Have not yet included all possible categories (e.g., food)
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Metropolitan Statistical Area Codes
Coding example for the area around Pittsburgh metro area
C – Central
O – Outlying
N – Nonmetropolitan
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Top Total Emitters for US Counties (2002)
** Done with uncertainty ranges (not shown). Have also found per-capita emissions 2
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Validates well with Public Inventories
Public inventory figures
(X) consistently in middle
of range of estimates for
each county.
We continue to “casually” validate but have seen no consistent needs for adjustment
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Work In Progress
• Assessing feasibility/need for beyond county level
– Balancing more work with “good enough” numbers
• Splitting current “electricity” sector back into
residential, commercial, industrial components
– Won’t change totals but will improve sectoral estimates
• Looking at cross-county flows (e.g., commuting)
• Visualizations for peer comparisons
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Peer Group Analysis Tools
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Peer Results
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Vision
• Short-term: Credible “open inventory” website for
counties, campuses. Maybe companies?
• Counties and interested parties access for “first best
guess” estimates, including uncertainty
– Allow them to upload / compare their numbers vs. ours
• Enable peer analysis (“what are emissions of
counties like me in population, area, etc.”?)
• Medium-term: develop consistent planning tools for
same entities to use
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Questions?
Scott Matthews
hsm@cmu.edu
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Indicator Analysis
• Use FTE, sq ft as normalizations of GHG emissions
• Also do separate analysis by climate zone (most fair)
Metrics vary
from 5% to
500% of
average
When
analyzed,
outliers due
to basic
errors
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Model for Estimating County-level
Consumption-based Emissions
Indirect Emissions from
Electricity
Consumption
Direct
Emissions
County-level
Consumptionbased
Emissions
=
Vulcan (2002):
Industrial,
Residential,
Commercial,
Onroad, Nonroad,
Aircraft, and
Cement
Vulcan limitation:
contains productionbased estimates only
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Electricity
Consumptio
n Estimate
x
Data scarcity:
county-level
electricity
consumption is
scarce
Emission
Factor
(E.F.)
Uncertainty:
Origin of
electrons
cannot be
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ascertained
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Variation in Mixes Across all US Counties
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