Using Public Data to Support Low-
Carbon Products and Supply Chains
Scott Matthews (hsm@cmu.edu)
Departments of Civil and Environmental Engineering /
Engineering and Public Policy (EPP)
Carnegie Mellon University
NSF Low Carbon Supply Chains Workshop
October 14, 2010 -- Washington DC
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Acknowledgements of Key
Collaborators:
Dr. Chris Weber
(Science, Technology and Policy Institute)
Dr. Anny Huang
(California Air Resources Board)
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Context
• Companies and others need methods to estimate what their operations and supply chains look like
• .. And benchmark them inter-intra industry
• GOAL: Decision support, not 4-digit numbers
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Corporate Carbon Methods
• Past focus on Scope 1 and 2 emissions
• Scope 3 – “everything else” (WRI: 15 categories)
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Data and Models Used
• Process / internal data
– Equipment level, emissions factors, etc.
– Bottom up approach (get data for everything)
– Allocation rules if needed to get to per-product level
– If good primary data sources, will aggregate to useful result
• Alternative – Input-output models
– Top down approach
– For scoping and estimating, but may be useful for reporting
– Helps to define boundaries and areas of focus
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Developed CMU 1995 - full supply chain
Available on Internet ( www.eiolca.net
)
First free LCA tool, 1 million uses to date
Actively used by companies
Data and model - continual development
Renewed interest - carbon footprinting
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EIO-LCA Overview
• IO tables describe production recipes (source:
BEA’s Economic Census)
Computer Mfg
Recipe
($k)
Wholesale Trade 23
Semiconductors 21
Components
Software
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11
Storage devices 9
Peripherals
...
9
Will identify entire upstream supply chain
Semiconductor
Recipe
Wholesale
Trade
Management
Components
Chemicals
...
7
($k)
2
1
1
1
RECIPE ...
RECIPE ...
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Other Data Sources
• Energy use and emissions of GHGs by sector
– US DOE – MECS, CBECS, etc.
– USDA – use of fertilizers, etc. for farming
– EPA – National GHG Inventory
• “Economic emissions factors” for each sector
– e.g., 50 kWh/$, 50 kg CO2/$
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EIO-LCA : Result for One Industry
•$100k of Coatings
Sector Total ($k) CO2 (tons)
Paint and Coatings
Materials and resins
Organic chemicals
Wholesale Trade
Management of Companies
Dyes and pigments
Petroleum refineries
Truck transportation
Oil and gas extraction
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10
8
5
5
5
102
13
12
10
~0
17
3
8
4
13
5
5
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EIO-LCA Sample Results
• Questions Benefits of bio-based products
Sector
Total
Electricity
Paint and Coatings
Materials and resins
Organic chemicals
Wholesale Trade
Management of Companies
Dyes and pigments
Petroleum refineries
Truck transportation
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Total ($k) CO2 (tons)
100
2
102
23
13
13
12
10
10
8
5
5
5
5
1
~0
17
3
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Fast and convenient (and free)
Great for first-pass analyses (screening)
Not a substitute for a complete project, certainly not a substitute for primary data
Need IO model for each relevant country
Represents upstream only
“Internal production” only, no use phase, EOL
No commuting/etc impacts
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Our Innovation:
Product Carbon Calorimeter TM 1125
•
Calculates total energy /
GHGs embodied in product
- 4 sig digits
• Detail by Fuel / electric
• Detail by Industry and
Country of origin
Use phase calculation coming soon
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The Catch
• This piece of equipment does not exist
• And can not exist
• Problem is policymakers (and others, like consultants and GHG accounting industry) presume that it already does
• We need to question “calculator methods” and also their application
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Supply Chain GHG Research Questions
• What is contribution of Scope 1, 2 emissions?
• What are major sources of upstream Scope 3 emissions?
• What are the effects of protocol thresholds?
• How much carbon footprint is within direct control of a regulating entity?
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Scopes 1 and 2 for All Industries
• Total = Scopes 1, 2, everything else
• For 90% of sectors, Scopes 1+2 < 25% of Total
– Few exceptions, e.g., power generation is 90% Scope 1
• For energy-intensive products, Scopes 1+2 can be less than 5%.
• We joined WRI Steering Committee and Working
Groups on Product and Scope 3 Corporate Standards
– Source: Matthews, Hendrickson, and Weber, “The Importance of Carbon Footprint Estimation Boundaries”, ES&T, 2008.
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Coding Scheme
Huang, A., Life Cycle Management of Reducing Impacts on Climate Change at a Regional Level , in Engineering and Public Policy . 2009, Carnegie Mellon University: Pittsburgh. p. 1-230.
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80%
Threshold
Rule?
Portion of Footprint by Scope
2
Huang, Y. A.; Weber, C. L.; Matthews, H. S., Categorization of Scope 3 Emissions for Streamlined Enterprise Carbon Footprinting.
Environmental Sci.& Technology 2009
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Structural Path Analysis
• Instead of showing results aggregated by industry, break them up into discrete “paths”
• Imagine “carbon tree” for a product or industry
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EIO-LCA – Structural Paths
Huang, A., Life Cycle Management of Reducing Impacts on Climate Change at a Regional Level , in Engineering and Public Policy . 2009, Carnegie Mellon University: Pittsburgh. p. 1-230.
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Threshold / Capture Rate Relationship
Huang, A., Life Cycle Management of Reducing Impacts on Climate Change at a Regional Level , in Engineering and Public Policy . 2009, Carnegie Mellon University: Pittsburgh. p. 1-230.
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EIO-LCA: Example for Global Products
• Context: how hard will it be to manage emissions?
– Global production and consumption
– Easy options like RPS, LCFS.. Will they be enough?
• Model used: Multi-Region IO-LCA model
– Different Economic, GHG Data for each region
– Results by region and energy sources
• Funded by CARB (still studying carbon labeling)
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90%
80%
70%
60%
50%
40%
30%
20%
10%
0%
100%
Results:
GHG Emissions, by energy source
Other
Scope 1 emissions from non-gas+diesel
Transp Fuel LC: ROW
Electricity LC: ROW
Transp Fuel LC: RUS
Electricity LC: RUS
Transp Fuel LC: CA
Electricity LC: CA
Huang, A., Life Cycle Management of Reducing Impacts on Climate Change at a Regional Level , in Engineering and Public Policy . 2009, Carnegie Mellon University: Pittsburgh. p. 1-230.
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Supply Chain GHG Future Research
• Decision Support !
– What is a low GHG product?
– What can be achieved through energy efficiency?
– What is technical and policy life cycle potential?
• Linking real data with public (industry) average data
– EPA Mandatory Reporting, CDP Voluntary data, etc.
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