1. Advanced LCA – 12-716 Lecture 3

2. Admin Issues Group Projects or Take-Home Final? Your choice (individual choice) EIO-LCA MATLAB version - some slight improvements coming. HW 1 discussion

3. Today’s lecture Data sources and issues for EIOLCA Data consistency checks

4. 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)

5. 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

6. 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]

7. 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

8. 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

9. 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’

10. 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

11. Old vs. New Example 1992 Benchmark IO Model Sector Economic($mill) Total for all sectors1.671098 Electric services (utilities)1.007134 Coal0.102573 Repair / maint. constr.0.087334 Crude petrol. / nat’l gas0.041535 Natural gas distribution0.037961 Railroads & rail services0.032541 Wholesale trade0.024300 Petroleum refining0.023055 Real estate mgmt.0.021044 Banking0.017472 1997 Benchmark IO Model Sector Economic($mill) Total for all sectors1.708177 Power generation / supply1.007417 Oil and gas extraction0.093182 Coal mining0.073502 Pipeline transportation0.031778 Rail transportation0.029385 Wholesale trade0.024219 Maint. & repair constr.0.022235 Petroleum refineries0.022115 Lessors intangible assets0.021955 Real estate0.019175

12. 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

13. 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?

14. 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/http://www.census.gov/epcd/ec97brdg/ –Drilldown: –http://www.census.gov/epcd/ec97brdg/E97B1311.HTMhttp://www.census.gov/epcd/ec97brdg/E97B1311.HTM –See “6% of, 94% of” notes..

15. SIC-NAICS-IO Bridge See documentation for details –Done in Access –Also shown in Excel (see web page)

16. Commodities and Industries Use Table basis Commodities are produced by industries We have “data on industries” Sometimes causes classification problems Electricity example

17. 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

18. 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=732http://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!)

19. 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

20. 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

21. 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 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?