Transcript Uncertainty in EIO-LCA / Hybrid LCA Models 1

Uncertainty in EIO-LCA /
Hybrid LCA Models
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Admin Issues
• Setting Office Hours
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Uncertainty in LCA
• Uncertainty exists for all LCA data: mass
flows, emissions, impacts, weights and
change effects, e.g.
– Proprietary data problems
– Boundary problems: Lenzen (2000, Journal
Industrial Ecology) finds truncation errors on
the order of 50% for Australian LCA. Similar to
Hocking result.
– Measurement, transfer, change, etc.
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Uncertainty Implications
• Consistency and reproducibility of results (e.g.
are paper or plastic cups superior).
• Certainty of conclusions and usefulness of LCA.
• Uncertainty for LCA studies in general obvious we will focus on EIO-LCA
– Data problems, combining data problems, allocation..
• Numbers of significant digits – how many digits
appropriate for www.eiolca.net result?
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EIO-LCA Uncertainty Sources
• Survey Errors: sampling and reporting errors –
depends on companies and census agencies.
• Old Data: IO tables are typically 2 to 7 years old.
Last US benchmark: 1997 released 12/2002.
• Incomplete Data: reports from only some sectors
or plants (e.g. tri sector and threshold limits,
holes in census surveys). Note: similarity to
boundary problem in conventional LCA!
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EIO-LCA Uncertainty (cont)
• Missing data: Census data missing many
topics, such as habitat destruction. Nonmonetary inter-sector dependencies also
not represented, e.g. congestion effects
from truck services.
• Aggregation: Sectors too large for detailed
analysis on specific products. Problem
sectors: ?
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Uncertainty (cont.)
• Imports: EIO treats imports as similar to
domestic production.
• Model form: Linearity of EIO, lack of
substitution as scale economies change.
• Mapping and Allocation Problems
• Product Prices
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Uncertainty (Pacca 2003)
Incomplete data
Collection
Missing data
Data
Inaccuracy
Interpretation
Other assumptions
Old data
Temporal constraint
Constant technology
Sources of problems
Intra-sectoral resolution
Imported goods
Economic boundary
Indirect outcomes
Constant returns to scale
No substitution effect
Methodological constraint
National averages
Inventory method
Price based flows
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Mitigating Factors and Approaches
•
•
•
•
•
Parameter stability over time
Positive Correlations
More and better data
Simulation analyses
User adjustments
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Parameter Stability Over Time
• Requirements matrix relatively stable over
time:
– Using 1961 final demand from IO tables of
1939 to 1961 found similar intermediate
outputs (Carter, 1970).
– Intermediate use relatively constant (Ma,
2003)
• Environmental impact vectors more
dynamic.
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Intermediate Use 1972-1997
Total Intermediate Use Percentages
1972
1987
1992
1997
Manufacturing
49%
41%
39%
35%
Natural Resources
11%
9%
8%
9%
Trade
16%
19%
20%
27%
Services
22%
29%
31%
28%
Miscellaneous
2%
2%
2%
2%
Infrastructure
22%
24%
25%
26%
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Positive Correlations
• Deciding on the best of two designs may
be more certain than overall impact due to
positive correlations. The designs may
share many elements in common, and
these elements would be positively
correlated. If the element is bad, it is bad
for both. If good, it is good for both.
• Numerical analysis of effect – Cano
(2000).
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Difference of Correlated Variables
• Suppose impact of design a is X and
impact of design b is Y. We are interested
to know if X > Y or X – Y > 0.
• E[X-Y] = E[X] – E[Y]
• V[X-Y] = V[X] + V[Y] – 2 cov[X,Y] –
correlation means variability is reduced.
• Ex: X ~N(1,1), Y ~ N(0,1), Cov (0.5), then
E[X-Y] = 1, V[X-Y] = 1, Pr(X-Y>0) = 0.84
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More and Better Data
• Mixed picture for more and better data.
• No water use data since 1980s in US.
• No workfiles for 1997 benchmark
released.
• Better industrial environmental
management systems to collect data.
• More international co-operation and public
data – international tri.
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User Adjustments
• Many adjustments possible due to known
aggregation or emissions problems
– Hybrid models including EIO and process models.
– Parameter adjustments to reflect non-linearities.
– Disaggregating individual EIO sectors.
• Bayesian methods applicable here – adjusting
estimates based on expectations.
• Multiple approaches: EIO-LCA and Conventional
LCA.
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Advantages of
Conventional LCA vs. EIO-LCA
Conventional LCA
 Detailed process-specific
analyses
 Specific product comparisons
 Process improvements/weak
point analyses
 Future product development
assessments
EIO-LCA
 Economy-wide, comprehensive assessments
(all direct and indirect environmental effects
included)
 Sensitivity analyses/scenario planning
 Publicly available data, reproducible results
 Future product development assessments
 Information on every commodity in the
economy
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Disadvantages of
Conventional LCA vs. EIO-LCA
Conventional LCA

System boundary setting
EIO-LCA

aggregate data
subjective

Tend to be time intensive
and costly

New process design
difficult
Some product assessments contain

Process assessments difficult

Difficulty in linking dollar values to
physical units

Economic and environmental data may
reflect past practices

Use of proprietary data

Imports treated as U.S. products

Cannot be replicated if

Difficult to apply to an open economy (with
confidential data are used

Uncertainty in data
substantial non-comparable imports)

Non-U.S. data availability a problem

Uncertainty in data
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References
• Cano-Ruiz, Alexandro Jose, (2000). “Decision
Support Tools for Environmentally Conscious
Chemical Process Design,” unpublished PhD
Dissertation, MIT.
• Lenzen, Manfred, (2000). “Errors in
Conventional and Input-Output-based Life-Cycle
Inventories,” J. of Industrial Ecology, 4(4), pp.
127-148.
• Pacca, S., (2003). “Global Warming Effect
Applied to Electricity Generation Technologies,”
PhD Thesis, UC Berkeley.
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Hybrid Life Cycle Assessment
Combining process models and
EIO-LCA
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Models of LCA
• “Conventional” LCA, developed by SETAC and EPA, based on process
models
• Economic input-output analysis-based LCA (EIO-LCA), developed by
Carnegie Mellon’s Green Design Initiative and Others
• Hybrid models:
– Using eiolca model to guide boundary and scope of process models.
– Disaggregating or augmenting io model.
– Using eiolca for some processes, products and supply chain elements
(where sector aggregation is not a major issue), with process models for
remainder.
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Utility of Two LCA Approaches
EIO-LCA
SETAC-EPA
LCA
SETA
LCESETAC
LCA
specific specific
product
ssystem materials processes
s for product design
range of information
industry inter
wide industrial
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Goals of Hybrid LCA Models
• Incorporate the advantages of the two models,
reduce disadvantages
• Include detailed, process-level data, as well as the
economy-wide effects
• Provide environmental and economic information
about every major product and process in the
economy
• Quantify the widest range of environmental data
• Two obvious high level alternatives for hybrid models
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Integration of EIO-LCA Data into
Conventional LCA
commodity
EIOLCA
C11
Process
models
Cn
C1
commodity
Cn
system
boundary
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Integration of Conventional LCA Data
into EIO-LCA
process results
Cj
Cj1 Cj2
commodity
EIOLCA
product
commodity
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Economic and Environmental
Implications of Online
Retailing and Centralized
Stock Keeping in the United
States
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E-Commerce Retail Quarterly
Volume ($B)
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Traditional Retail Logistics
System
• Factory to warehouse
to warehouse to
retailer.
• Last leg of trip by
private vehicle
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Single Facility Sales
• LL Bean, Lands End catalogue sales
• Amazon (original),
MusicOutpost - web
based sales from a
single facility
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Comparison of Freight Modes
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Air
Truck
Rail
10
0
Total Energy (TJ/$1M)
Direct Energy
(TJ/$1M)
Total Energy (MJ/tonmile)
Direct Energy (MJ/tonmile)
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How to Model E-Commerce
for LCA?
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Book Publishing Case Study
• Traditional System:
– logistics: printer > warehouse > warehouse >
retailer > home, all by truck/car
– unsold returns - roughly 35% for bestsellers
• E-commerce System:
– logistics: printer > warehouse > distribution
center >home, by air and truck.
– No unsold returns
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Comparative Analysis:
* is EIOLCA Sector Use
• Traditional:
–
–
–
–
–
truck transport (1000 mi)*
Warehousing*
production of returns*
reverse travel of returns*
private automobile
transport
• E-Commerce
– air transport (500
mi)*
– truck transport (500
mi)*
– Warehousing*
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Comparative Costs ($ 1000s for
$ 1 M or 290,000 books)
Traditional
W/o Returns
or Auto
W Returns but
w/o Auto
W/o Returns
but w/ Auto
W returns and
auto
E-Commerce
700
992
1,300
992
1,170
992
1,780
992
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Why are E-Commerce Costs
Lower?
• Higher transportation costs for ecommerce, but:
– Returns of unsold copies
– Lower retail transactions costs
– Lower (private) automobile cost
• Result is cost advantage for e-Commerce
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Energy
(TJ)
*Co nventional
RCRA
Greenhouse
Air Pollutants Hazar dous Gas Emissions
(mT)
Was te (mT) (CO2 Equiv.,
mT)
8.9
9.1
354
Truck ing (with return s)
5.3
Production
Packaging
Passenge r Trips
Pass. Fuel Prod.
Total
9.45
1.2
9.7
7
33
8.1
1.1
42
1.7
62
23
3.5
0
30
66
612
84
611
337
2000
Truck ing
Air
Production
Packaging
Deli very Trips
Pass. Fuel Prod.
Total
% Difference
1.2
7
7
4
11
0
30
9
2
3
6
3
18.5
0
33
47
2
9
17
11
19
0
58
12
80
440
453
254
736
0
1963
2
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Summary Environmental
Impacts
(per-book basis)
Trad. E-Com.
Energy (MJ)
115
105
Conventional Air (kg)
0.2
0.1
Hazardous Waste (kg)
0.2
0.2
7
7
Greenhouse Gas (kg)
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Sensitivity Analysis
• ‘Traditional’ becomes better if:
– Local distance to bookstore < 3 miles
– Air transport of books > 700 miles
– Orders not shipped together
• Ecommerce better if:
– Switch from Air transport
– Multiple origin sites
– Greater density of sales.
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Harry Potter Case
• 250,000 books shipped on release date by
Amazon.com
– 9,000 trucks and 100 airplanes
• 2.5 lb. book, 0.7 lb. packaging (3.2 lbs.)
– Bookstores got 10 per box
• Shopping trips for books avg. 11 miles
– Marginal effects
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This is Research….
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Some Analysis Issues
• What are E-commerce future scenarios?
• What will happen with local manufacturing
technology?
• What will be impact of new business
models for controlling inventory
(warehousing), manufacturing and
shipping.
• What is appropriate time scale of
analysis?
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Analysis Boundary Issues
(cont.)
• Buildings - decrease in retail or warehouse
space?
• Shopping - will individuals substitute other
travel for reduced shopping travel?
• Computers - what fraction of personal
computer burdens should be allocated to
E-commerce?
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Will E-commerce Improve or
Degrade the Environment?
• Net Effect - hypothesis: depends upon product
and processes and upon the analysis boundary.
• Appropriate Public Policy – Don’t ignore service industries in environmental
policy.
– Consider life cycle costs including social costs.
– Take advantage of cost savings to create
environmental benefits
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