1. Advanced LCA – 12-716 Lecture 6

2. Admin Issues Group Projects or Take-Home Final? –Default: Final - yell otherwise EIO-LCA MATLAB version - posted HW 1 discussion –Help me improve! HW 2 Out

3. HW 1 Recap Looked good so far. “A” work. –Solutions handed out Tues - comments Thoughts in meantime, and for hw 2 –1992, 1997 of course different definitions.. –How to aggregate A matrices to compare? –How different after this? A vs Leontief? –What did Appendix IV say?

4. HW 1 (cont) Allocating energy from MECS –1991/1992 and 1997/1998.. –I got 237 kWh/$ and 235 kWh/$.. –5 years apart… why so similar? Allocating agriculture data by farms, land –Problems with data? –Results?

5. In Prep for HW #2. EIOLCA Matlab revised and posted How to run purchaser prices How to edit “environmental data” –Spreadsheet interface for matrices after uploaded (run EIOLCA4 once..) How to import data –Import function, import wizard (file menu) –Can import file or clipboard (back button)

6. Recap from Chris W last mini… While I focus on I-O examples, of course the problem is a general one to all LCA.

7. Data uncertainty Typical data types: Material and energy input/output Bill of materials (.e.g grams steel per product) Use characteristics (e.g.lifespan) Economic input/output Environmental IO vectors Economic “bill of prices”

8. Model uncertainty – cutoff error Cutoff or truncation error in process LCA: not all processes are included. Lenzen study simulated cutoff error via EIOLCA (Australia) by comparing full answer with second tier subsum. 31% of 135 industries had truncation error greater than 50% In hybrid LCA computer study, cut-off error was about 50%

9. Aggregation uncertainty In practice, for both process and EIOLCA we bundle processes into larger aggregates. In general aggregation uncertainty for EIOLCA is potentially larger, fewer sectors, difficult (but possible) to add sectors by hand

10. Aggregation uncertainty EIOLCA At level of single process, clear problems with aggregation: e.g. aluminum and copper have similar prices (about $1.50 per kg) but very different energy profiles (e.g. 214 MJ/kg energy for Al, 94 MJ/kg for copper) The key question is the extent to which high and low estimates tend to cancel each other out.

11. Aggregation uncertainty in EIOLCA History of work (50’s-70’s) by input- output economists on the effect of aggregation on results (Leontief also worked on it). Computation was real challenge then. Early environmental aggregation work by Bullard and Sebald (1978): Monte Carlo analysis of error propagation, errors to cancel instead of multiply.

12. Aggregation uncertainty in EIOLCA Still no definitive answers on the precise degree of uncertainty in LCA due to aggregation. Topic for future work Whatever the answer is, it will depend on considering how the product of interest is embedded in its parent sector. Next illustrate aggregation using concrete example for iron/steel sector in Japan.

13. Disaggregating energy use for iron/steel in Japan Sector #Item Econ output Unit direct energy consump tion Embodied energy intensity (I-A) -1 type Column code on producer price basisMillion yen TOE/Million yen 9Iron and steel171595382.345.45 11Metal products134523880.171.6 (http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm) 32 sector model, year 2000

14. Disaggregating energy use for iron/steel in Japan (http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm)http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm TOE = Tonne of Oil Equivalent 102 sector model, year 2000 Embodied energy intensityItem Domestic productio n (gross outputs) Unit direct energy consumption Embodied energy intensity (I-A)- 1 type Column codeon producer price basis Million yenTOE/Million yen 38 Pig iron and crude steel ￥ 45141000.16312.730 39Steel92499930.0377.055 40 Cast and forged materials17541811.2363.582 41 Other iron or steel products16412641.8124.063

15. Disaggregating energy use for iron/steel in Japan (http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm) 188 sector model, year 2000 Embodied energy intensityItem Domestic productio n (gross outputs) Unit direct energy consumption Embodied energy intensity (I- A)-1 type Column codeon producer price basis Million yenTOE/Million yen 2611Pig iron and crude steel45141000.00812.323 2621Hot rolled steel45384470.0249.344 2622Steel pipes and tubes8226590.0095.082 2623 Cold-finished steel and coated steel38888870.1985.097 2631 Cast and forges materials(iron)17541810.0893.595 2649Other steel products16412640.0433.681

16. 399 sector model, year 2000 Embodied energy intensityItem Domestic production (gross outputs) Unit direct energy consumption Embodied energy intensity Column codeon producer price basisMillion yen TOE/Million yen 261101Pig iron12562110.06326.580 261102Ferro alloys1127530.30313.860 261103Crude steel (converters)21330810.07415.265 261104 Crude steel (electric furnaces)10120550.2566.726 262101Hot rolled steel (?)45384470.0809.586 262201Steel pipes and tubes8226590.0355.197 262301Cold-finished steel25754090.0245.983 262302Coated steel13134780.2264.108 263101Cast and forged steel2957000.4314.702 263102Cast iron pipes and tubes1488430.8043.327 263103 Cast and forged materials (iron)13096380.1353.714 264901 Iron and steel shearing and slitting14327140.0144.013

17. Aggregation uncertainty in EIOLCA Conclusions: For many products such as steel pipes, energy intensity changes less than 20% across aggregations For some aggregation induces large error. Worst case for iron/steel is pig iron, where answer changes by a factor of 5

18. Geographical uncertainty Intra and international variations in technologies, materials, producer prices. Limited availability of international process data and EIOLCA models requires assuming region specific data applies elsewhere. Given globalization these geographical uncertainties Global production system.

19. Geographical uncertainty - process Technology differences: e.g. in the chlor- alkaki sector, US uses mercury cell, Japan uses membrane Material differences: e.g. China has low grade bauxite compared to US, higher sulfur coal

20. Geographical uncertainty - EIOLCA Geographical uncertainty is probably larger due to additional factor of varying producer prices Sample energy intensities for industry (as one overall sector) in 2000 are –United States 6.14 MJ/$ –Japan 3.73 MJ/$ –China 24.4 MJ/$ –Malaysia 10.2 MJ/$ –World 9.6 MJ/$ Will revisit this with Regional, MR models soon

21. Uncertainty Modeling Most of you took my BCA course.. Lots of tools for uncertainty, sensitivity analysis.. Tornado, one-two-way, etc. Distributions At the end of the day, though, we still seek “dominant outcomes” not just distributions to compare.

22. Think about source data.. Ciroth reading, Figure 1 –True value and measured value are probably correlated, but not equal –How to treat this?

23. Uncertainty Chain Uncertainty in input values –E.g., R matrix. How to address? –What about final demand? Uncertainty in model –E.g., IO parameters, propagation –How much should we trust the IO values? –What did hw 1 say? A versus Leontief? Leads to uncertainty in outputs –How to represent and Interpret?

24. Uncertainty Can do uncertainty analysis in Matlab, but its pretty high-tech Probably easier to dump into excel for graphing, analysis, etc.

25. Conclusions Many different types of uncertainties Many are difficult to characterize, rarely done in practice Personal opinion: a bad estimate of uncertainty is better than no estimate at all… bounding approach is useful.