Interpretation Flashcards
What is the general definition of the interpretation phase?
Systematic method to identify, qualify, control, evaluate, and present conclusion based on the results of the LCI and/or LCIA phases, in order to fulfil the goal of the study.
Iterative method.
What is the interpretation procedure?
- Identification of significant issues
- Evaluation
- Conclusions, recommendations, limitations, and report
How to identify significant issues?
- Structure results of LCA and LCIA
- Account for different methodological choices (hypotheses, multifunctional processes, excluded processes, impact assessment method, etc.)
- The ISO standard does not indicate what are the significant issues for a specific case
How do you perform the evaluation step?
- Establish and increase confidence in study’s conclusions
- 3 types of checks: completeness, consistency, sensitivity
Define the completeness check during the evaluation process?
Ensure that all relevant information and data needed were available and used
Define the consistency check during the evaluation step?
To determine whether the assumptions, methods, and data were consistent with the G&S and were applied consistently throughout the study (esp for comparative)
Define the evaluation sensitivity check?
To assess the reliability of the final results and conclusions by determining how they are affected by uncertainties in the data and the various methodological choices made
(uncertainty analysis)
Define uncertainty, variability, and sensitivity?
Uncertainty: what we do not know
Variability: how much what we know may change/vary
Sensitivity: how much does a specific change affect the result
Why do we care about uncertainties in LCA?
- Support decision making by making available information on the confidence that one can have in LCA results
- Plan uncertainty reduction in a structured way
- Transparency, avoiding to exaggerate the value of the results
What are the types of uncertainty?
- Parameter uncertainty
- Model uncertainty
- Scenario uncertainty
- Relevance uncertainty
What is the definition of parameter uncertainty?
Variability and uncertainty of model input parameters. Part of overall uncertainty about the model result.
(Ex. Non-representative inventory data, inaccurate data, etc.)
What is the definition of model uncertainty?
Uncertainty about the model itself. Part of overall uncertainty about the model result.
(Ex. Lack of LCI data, no consideration of non-linear processes, lack of spatial or temporal information, etc.)
What is the definition of scenario uncertainty?
Uncertainty related to choices, preferences, and scenarios. Part of both the overall uncertainty about the model result and conclusion/decision.
(Ex. choice of functional unit, allocation method chosen, assumptions about future efficiencies, etc.)
What is the definition of relevance uncertainty?
Uncertainty in relevance and representativeness of indicators for the decisions at hand. Part of overall uncertainty of the conclusion/decision.
What are the different ways to represent an uncertainty?
Qualitatively (Pro: Quick, Con: Cannot be propagated, Subjective)
Quantitatively (Pro: Allows propagation, Con: Requires lots of data)
Semi-quantitatively (Pro: Quick + Prop, Con: Estimates)
What are the steps of parameter uncertainty assessment?
- Estimate uncertainty of individual parameters
- choose probability distribution function (PDF)
- estimate parameters of PDF (statistical data, expert judgement, literature, pedigree) - Cumulate uncertainty to find the final result
What is significant vs. insignificant uncertainty?
< 10% is not considered significant for the energy and global warming scores.
> 30% is significant for respiratory inorganics, acidification, and eutrophication
An order of magnitude difference is required for significance in toxicity categories
Describe the formula used for uncertainty in databases using the pedigree matrix approach?
Total uncertainty = Basic uncertainty x Additional uncertainty
(Look at specific formula in slides)
What are the different types of variability?
- Temporal variability
- Spatial variability
- Inter-individual variability
What are the two main ways sensitivity is used?
- Global sensitivity analysis
2. Local sensitivity analysis
Define accuracy and precision
Accuracy: describes the closeness of a measured or modelled value to its “true” value.
Precision: represents the quality of being reproducible in amount
or performance
How does uncertainty vary between midpoints and endpoints?
Midpoint indicator result will be more precise but less environmentally relevant, while it will be the opposite for an endpoint indicator (i.e. less precise but more environmentally relevant). Therefore, endpoint indicators are typically perceived as more uncertain based on their usually lower precision (due to a larger number of choices and hypotheses involved in their modelling compared to midpoint indicators).
What is a common misconception with uncertainty for midpoints and endpoints?
if minimal or avoided environmental consequences are the objective of a decision, choosing midpoint indicators because they can be quantified with higher precision will still not avoid the uncertainty of that decision’s environmental consequences since a midpoint indicator is less relevant (representative) for the environmental consequences to be avoided.
What are the three main methods for reporting uncertainty levels?
- Pedigree matrix approach used for example by ecoinvent for the quantification of variability and uncertainty of LCI data
- Monte Carlo simulation used in LCA software like SimaPro, GaBi, openLCA, and the more explorative/educational LCA tools CMLCA and Brightway 2
- Taylor series expansion used in CMLCA
Describe the types of uncertainty analyzed in the pedigree matrix approach?
It quantifies (exclusively) parameter uncertainty via combining two different kinds of uncertainty:
- Basic uncertainty due to variation and stochastic error of the values for elementary flows, from measurement uncertainties, activity specific variations, temporal variations, etc.
- Additional uncertainty based on data quality indicators using a qualitative assessment of “reliability”, “completeness” and representativeness in terms of “temporal correlation”, “geographical correlation”, and “further technological
correlation”.
What are the types of uncertainty distributions?
- Uniform
- Triangle
- Normal
- Log Normal (most used in LCA)
What is the outline of the Monte Carlo simulation method?
- Generate samples of random values for all input variables
- Apply the model on the generated values to calculate the model output in terms of LCA results
- Analyze statistically the model output
The model output is represented by a probability distribution instead of a single value, accuracy increases as number of iterations increases
