advanced lca – 12-716 lecture 3. admin issues group projects or take-home final? your choice...
TRANSCRIPT
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Advanced LCA – 12-716
Lecture 3
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Admin Issues
• Group Projects or Take-Home Final? Your choice (individual choice)
• EIO-LCA MATLAB version - some slight improvements coming.
• HW 1 discussion
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Today’s lecture
• Data sources and issues for EIOLCA
• Data consistency checks
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Sectoral Classification Schemes
• 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)
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History of SIC, NAICS• 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
• North American Industrial Classification System (NAICS) - made in 1990s by US, CA, MX– Production-process based classification (similar groups)– Standard categories, country-specific adjustments– Maintains ability to compare across countries– Is in alignment with UN ISIC standard
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NAICS Industry Sectors
• 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]
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SIC vs. NAICS - High Level
• Agriculture, Forestry, Fishing• Mining• Construction• Manufacturing• Transport/Infrastructure• Wholesale Trade• Retail Trade• Financial/Business Services• Other Services• Public Admin (Gov’t)
• 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
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IO Model Organization
• 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
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Notes on Mappings• “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’
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Sample Data Mappings• For electricity consumption of some electricity
sectors, data from MECS (DOE)1
– NAICS mapping -> IO sector (easy!)
• Other manufacturing data comes in SIC– SIC -> NAICS -> IO sector (harder)
• Some no longer provided, rely on old model– Old IO -> SIC -> NAICS -> New IO sector
• Repeat 500 times (for all sectors)
1: Manufacturing Energy Consumption Survey
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Old vs. New Example1992 Benchmark IO Model
Sector Economic($mill)Total for all sectors 1.671098Electric services (utilities) 1.007134Coal 0.102573Repair / maint. constr. 0.087334Crude petrol. / nat’l gas 0.041535Natural gas distribution0.037961Railroads & rail services 0.032541Wholesale trade 0.024300Petroleum refining 0.023055Real estate mgmt. 0.021044Banking 0.017472
1997 Benchmark IO ModelSector
Economic($mill)Total for all sectors 1.708177Power generation / supply 1.007417Oil and gas extraction 0.093182Coal mining 0.073502Pipeline transportation 0.031778Rail transportation 0.029385Wholesale trade 0.024219Maint. & repair constr. 0.022235Petroleum refineries 0.022115Lessors intangible assets 0.021955Real estate 0.019175
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Conclusions
• Change in basis (and new data) requires considerable conversion efforts– Roughly 1000 hours to date this year
• Payoff is more up-to-date estimates of economic and sustainability metrics
• New NAICS basis should increase power for international comparisons
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EIO-LCA Data Example
• Hopefully read documentation excerpt• Where does EIO-LCA data come from?• What all needs to be done to make it
useable on the web?
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NAICS to IO Mapping
• Many 1:1, some complex– This comes directly from BEA
• 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..
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SIC-NAICS-IO Bridge
• See documentation for details– Done in Access– Also shown in Excel (see web page)
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Commodities and Industries
• Use Table basis• Commodities are produced by
industries• We have “data on industries”• Sometimes causes classification
problems• Electricity example
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Electricity: As Commodity and Industry
• We have an industry called “Power generation and supply”– But the commodity “electricity” is produced by
several sectors: power gen, fed utils, state utils• 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
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Stability of IO Data
• 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
• Recall, 3 levels of detail in US IO data– Sector (~12), Summary (~90), Detail (500)
• Annual and benchmark tables– Benchmarks only every 5 yrs (next 2002!)
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Stability in Sector table• Coefficients for Agriculture-Agriculture
– 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
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Allocating Inventory Data
• 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
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Case Study: Agriculture
• Mostly aggregate data.• Implications of using the data at 500
sector level• http://www.usda.gov/oce/global_change/inventory_1990_2001/
USDA%20GHG%20Inventory%20Chapter%205.pdf
• 1122 trillion BTU of energy used.• Effects on modeling?• Ideal way to deal with / show effects?