Advanced LCA – 12-716

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 32 sector model, year 2000 (http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm)

14. Disaggregating energy use for iron/steel in Japan 102 sector model, year 2000 (http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm) TOE = Tonne of Oil Equivalent

15. Disaggregating energy use for iron/steel in Japan 188 sector model, year 2000 (http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm)

16. 399 sector model, year 2000

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.