Transcript EIO-LCA Data Issues / Allocation in LCA

Advanced LCA – 12-716
Lecture 3
Admin Issues
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Group Projects or Take-Home Final?
Your choice (individual choice)
EIO-LCA MATLAB version - some slight
improvements coming.
HW 1 discussion
Today’s lecture
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Data sources and issues for
EIOLCA
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Data consistency checks
Sectoral Classification
Schemes
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ISIC – International Standard Industrial
Classification
NAICS – North Amer. Classification
System
NACE – Statistical Classification of
Economic Activities in the EC
Point: There’s a lot. For bridges
between systems and comparisons, see
Eurostat’s RAMON system (Google it)
History of SIC, NAICS
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IO models ‘sector based’ (but have their own different - classification!)
Standard Industry Classification (SIC) - originally
developed in 1930s
– Structures economy for data/comparative purposes
– Since 30s, significant econ. changes - last updated ‘87
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North American Industrial Classification System
(NAICS) - made in 1990s by US, CA, MX
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Production-process based classification (similar groups)
Standard categories, country-specific adjustments
Maintains ability to compare across countries
Is in alignment with UN ISIC standard
NAICS Industry Sectors
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6-digit NAICS codes (vs. 4-digit SIC)
First 5-digits fixed, 6th for country specifics
Example:
33 Manufacturing [Industry Sector]
334 Computer and Electronic [Industry Subsector]
3346 Manufacture/Reproduction [Industry Group]
33461 Manufacture/Reproduction [Industry]
334612 Pre-recorded Computer CDs [Country-specific]
SIC vs. NAICS - High Level
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Agriculture, Forestry, Fishing
Mining
Construction
Manufacturing
Transport/Infrastructure
Wholesale Trade
Retail Trade
Financial/Business Services
Other Services
Public Admin (Gov’t)
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11 Agric., Forestry, Fishing, Hunting
21 Mining / 22 Utilities/ 23 Construction
31-33 Manufacturing
42 Wholesale Trade/ 44-45 Retail
48-49 Transportation / Warehousing
51 Information
52 Finance and Insurance
53 Real Estate and Rental
54 Professional, Technical Services
55 Management of Companies
56 Admin, Support, Waste Management &
Remediation Services
61 Education Services
62 Health Care and Social Assistance 71
Arts, Entertainment, and Recreation 72
Accommodation and Food Services 81
Other Services
92 Public Administration
IO Model Organization
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1997 benchmark IO tables organized into
about 500 sectors
Many IO sectors 1:1 with 5-digit NAICS
Others are 1:1 with 2, 3, or 4-digit NAICS
Others are 10:1 - e.g. agriculture
This can get really confusing!
On EIO-LCA, see “About the Model-> Sectors
in EIO-LCA
Notes on Mappings
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“More high level sectors” does not alone mean “better
data” - just a different model!
Most environmental/resource data is still given in SIC
format (not yet NAICS)
Thus need multiple mapping functions
Use of (re)-mapping functions leads to additional
data/model uncertainties - hard to quantify
Auxiliaries - offices classified by ‘what they do’ rather
than ‘who they serve’
– Corporate headquarters have their own sector
– These offices not considered with ‘their sector’
Sample Data Mappings
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For electricity consumption of some electricity
sectors, data from MECS (DOE)1
– NAICS mapping -> IO sector (easy!)
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Other manufacturing data comes in SIC
– SIC -> NAICS -> IO sector (harder)
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Some no longer provided, rely on old model
– Old IO -> SIC -> NAICS -> New IO sector
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Repeat 500 times (for all sectors)
1: Manufacturing Energy Consumption Survey
Old vs. New Example
1992 Benchmark IO Model
Sector
Economic($mill)
Total for all sectors
1.671098
Electric services (utilities) 1.007134
Coal
0.102573
Repair / maint. constr.
0.087334
Crude petrol. / nat’l gas
0.041535
Natural gas distribution
0.037961
Railroads & rail services
0.032541
Wholesale trade
0.024300
Petroleum refining 0.023055
Real estate mgmt.
0.021044
Banking
0.017472
1997 Benchmark IO Model
Sector
Economic($mill)
Total for all sectors
1.708177
Power generation / supply 1.007417
Oil and gas extraction
0.093182
Coal mining
0.073502
Pipeline transportation
0.031778
Rail transportation
0.029385
Wholesale trade
0.024219
Maint. & repair constr.
0.022235
Petroleum refineries
0.022115
Lessors intangible assets 0.021955
Real estate
0.019175
Conclusions
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Change in basis (and new data)
requires considerable conversion efforts
– Roughly 1000 hours to date this year
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Payoff is more up-to-date estimates of
economic and sustainability metrics
New NAICS basis should increase
power for international comparisons
EIO-LCA Data Example
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Hopefully read documentation excerpt
Where does EIO-LCA data come from?
What all needs to be done to make it
useable on the web?
NAICS to IO Mapping
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Many 1:1, some complex
– This comes directly from BEA
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Part 2: SIC->NAICS also from Census
– http://www.census.gov/epcd/ec97brdg/
– Drilldown:
– http://www.census.gov/epcd/ec97brdg/E97B1311.HTM
– See “6% of, 94% of” notes..
SIC-NAICS-IO Bridge
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See documentation for details
– Done in Access
– Also shown in Excel (see web page)
Commodities and Industries
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Use Table basis
Commodities are produced by
industries
We have “data on industries”
Sometimes causes classification
problems
Electricity example
Electricity: As Commodity
and Industry
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We have an industry called “Power
generation and supply”
– But the commodity “electricity” is produced by
several sectors: power gen, fed utils, state utils
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We use industry by industry A matrix to better
match the industry data we have
Downside: we are modeling average
production from the industry, not “of the
commodity”
There are commodity-commodity models
Stability of IO Data
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Can see Chapter X of EIO-LCA book for
more detail if needed.
Interactive BEA IO Data Site:
– http://www.bea.gov/industry/iotables/prod/table_list.cfm?anon=732
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Recall, 3 levels of detail in US IO data
– Sector (~12), Summary (~90), Detail (500)
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Annual and benchmark tables
– Benchmarks only every 5 yrs (next 2002!)
Stability in Sector table
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Coefficients for Agriculture-Agriculture
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Direct requirements matrix values
2005 backward to 1997
2005 - 0.229
2004 - 0.227
2003 - 0.233
2002 - 0.236
2001 - 0.230 … (only 0.4% different)
Can see similar stability in other cells
Allocating Inventory Data
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What happens if we don’t have source
data at the “491 sector detail” level?
– Running model at 12, 100, 500 level?
– Compare effects of $1M production
– Similar to plastic problem on HW 3/4
Case Study: Agriculture
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Mostly aggregate data.
Implications of using the data at 500
sector level
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http://www.usda.gov/oce/global_change/inventory_1990_2001/U
SDA%20GHG%20Inventory%20Chapter%205.pdf
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1122 trillion BTU of energy used.
Effects on modeling?
Ideal way to deal with / show effects?
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