Uncertainty in LCA can be reduced by following standards, most importantly the ISO LCA standard (ISO, 2006a, b), but since this standard only provides guidance on a high level, more targeted standard documents can be used to ensure consistency in calculation methods. Uncertainty in input data can be reduced by e.g. improving data collection and measurements, using data from well-regarded databases and validating data. Uncertainty due to choices can be reduced using critical reviewing and model uncertainties can be reduced by using a higher resolution model with higher precision (Björklund, 2002; Heijungs & Huijbregts, 2004).
Uncertainty in LCA can only be reduced to a certain extent. The remaining uncertainty needs to be illustrated and presented as part of the results. The ISO LCA standard has a requirement for the inclusion of uncertainty assessment: “An analysis of results for sensitivity and uncertainty shall be conducted for studies intended to be used in comparative assertions intended to be disclosed to the public.” Uncertainty is defined in the ISO LCA standard as:
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“Uncertainty analysis is a systematic procedure to quantify the uncertainty introduced in the results of a life cycle inventory analysis due to the cumulative effects of model imprecision, input uncertainty and data variability”
and sensitivity analysis as:
“Sensitivity analyses are systematic procedures for estimating the effects of the choices made regarding methods and data on the outcome of a study.”
Uncertainty and sensitivity analyses are further described in the next two sections. It should be noted that uncertainty and sensitivity analyses are used not only when presenting and interpreting LCA results but, since LCA is an iterative process, also for improving the study. For example, if uncertainty is too large in the final results it might be possible to improve the certainty with better data, while if sensitivity analysis shows that some scenario choices are crucial to the results, it might be possible to improve the reliability of the results through a more refined analysis (Curran, 2013).
3.3.1 Uncertainty analysis
Uncertainty analysis involves quantification and propagation of uncertainty. When uncertainty in input data is described using probability distributions, it is possible to use stochastic stimulation to propagate the uncertainty in the input data though to the end results. Several such techniques have been employed in LCA and in calculating the CF, most commonly Monte Carlo (MC) simulation (Rubinstein and Kroese, 2007; Röös et al., 2010, 2011).
In MC simulation, parameters are described by a probability distribution rather than a single deterministic value, and the calculation of the CF is repeated for a large number of times, for each of which a random parameter value from the probability distribution is used. The results of a MC simulation consist of a number of possible outcomes of the calculation, hence giving a representation of the probability of different results depending on the uncertainty and variation in the input data (Figure 10).
33 Figure 10. Schematically view of Monte Carlo simulation.
Although MC simulation is technically easy to perform, finding probability distributions that describe input data can be time-consuming and difficult. It is also highly important to take correlations between parameters into account when performing MC simulations, since failing to do so could lead to uncertainty in the end results being heavily overestimated (Bojacá & Schrevens, 2010).
Other ways of performing uncertainty analysis, using e.g. classical or Bayesian statistics or fuzzy logic, have been used to a limited extent in LCA. It is often complicated to use traditional statistical methods in LCA due to limited abundance of data, complex models and several correlations between parameters. Hence it has proven easier to use stochastic simulation techniques. Uncertainty analysis in the CF of livestock products is further discussed in section 5.3.
3.3.2 Sensitivity analysis
Sensitivity analysis aims at illustrating how choices in models and data affect the end results. It is useful for testing the robustness of models and results. It can give knowledge about the relationship between input and output variables in a model and thereby identify input parameters causing strong effects on the outputs. Sensitivity analysis can be carried out in several different ways in LCA. Some are exemplified here and the subject is further discussed in section 5.3.
Sensitivity analysis can be performed by allowing one input parameter value to change by a certain predefined percentage while all other parameters are kept constant. The change in the end result shows how sensitive the results are to uncertainties or variability in this specific parameter. By using actual min and max
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values, or e.g. a 95% confidence interval, for input parameters instead of an arbitrarily chosen percentage value, a better picture of the sensibility of the model is provided. This is called uncertainty importance analysis and one example of results from such an analysis is shown in Table 3.
Table 3. Uncertainty importance analysis when calculating the carbon footprint of Swedish wheat (from Röös et al., 2011), testing how boundary values for different input parameters affect the final carbon footprint of wheat.
Boundary values Change in wheat CF
(%)
Humus content (%) 2.4 11 −3 +23
Yield (kg/ha) 3,700 11,000 +37 −20
Amount of N (kg/ha) 49 357 −38 +7
EF production of mineral fertilisers (kg CO2e/kg N)
5.2 9.0 −7 +9
EF N2O from mineral fertilisers (kg N2O-N/kg applied N)
0.003 0.03 −14 +41
EF N2O crop residuals (kg N2O-N/kg applied N)
0.003 0.03 −4 +11
EF N2O leakage (kg N2O-N/kg applied N)
0.0005 0.025 −2 +5
Scenario analysis is a type of sensitivity analysis in which different modelling assumptions such as system boundaries, allocation methods, data choices etc. are tested in order to see how these choices affect the results. Scenario analysis can also be used to investigate future scenarios or alternative production strategies. Sensitivity analysis also includes testing different methods for calculating the emission sources (Chapter 4).
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