Advanced LCA – 12-716

Lecture 6

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

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?

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?

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)

Recap from Chris W last mini…

While I focus on I-O examples, of course the problem is a general

one to all LCA.

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”

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%

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

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.

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.

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.

Disaggregating energy use for iron/steel in Japan

Sector # ItemEconoutput

Unit direct energy consumption

Embodied energy intensity

(I-A)-1 type

Column code

on producer price basis Million yen

TOE/Million yen TOE/Million yen

9 Iron and steel 17159538 2.34 5.45

11 Metal products 13452388 0.17 1.6

(http://www-cger.nies.go.jp/publication/D031/CGER/Web/eng/index-e.htm)

32 sector model, year 2000

Disaggregating energy use for iron/steel in Japan

(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 intensity Item

Domestic production (gross outputs)

Unit direct energy consumption

Embodied energy intensity (I-A)-1 type

Column code on producer price basisMillion yen TOE/Million yen

TOE/Million yen

38Pig iron and crude steel ￥ 4514100 0.163 12.730

39 Steel 9249993 0.037 7.055

40Cast and forged materials 1754181 1.236 3.582

41Other iron or steel products 1641264 1.812 4.063

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 intensity Item

Domestic production (gross outputs)

Unit direct energy consumption

Embodied energy intensity (I-A)-1 type

Column code on producer price basisMillion yen TOE/Million yen

TOE/Million yen

2611 Pig iron and crude steel 4514100 0.008 12.323

2621 Hot rolled steel 4538447 0.024 9.344

2622 Steel pipes and tubes 822659 0.009 5.082

2623Cold-finished steel and coated steel 3888887 0.198 5.097

2631Cast and forges materials(iron) 1754181 0.089 3.595

2649 Other steel products 1641264 0.043 3.681

399 sector model, year 2000Embodied energy intensity Item

Domestic production (gross outputs)

Unit direct energy consumption

Embodied energy intensity

Column code on producer price basis Million yen

TOE/Million yen

TOE/Million yen

261101 Pig iron 1256211 0.063 26.580

261102 Ferro alloys 112753 0.303 13.860

261103 Crude steel (converters) 2133081 0.074 15.265

261104Crude steel (electric furnaces) 1012055 0.256 6.726

262101 Hot rolled steel (?) 4538447 0.080 9.586

262201 Steel pipes and tubes 822659 0.035 5.197

262301 Cold-finished steel 2575409 0.024 5.983

262302 Coated steel 1313478 0.226 4.108

263101 Cast and forged steel 295700 0.431 4.702

263102 Cast iron pipes and tubes 148843 0.804 3.327

263103Cast and forged materials (iron) 1309638 0.135 3.714

264901Iron and steel shearing and slitting 1432714 0.014 4.013

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

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.

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

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

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.

Think about source data..

• Ciroth reading, Figure 1– True value and measured value are

probably correlated, but not equal– How to treat this?

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?

Uncertainty

• Can do uncertainty analysis in Matlab, but its pretty high-tech

• Probably easier to dump into excel for graphing, analysis, etc.

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.