INCLUSION OF UNCERTAINTY IN LCA

Transcription

1 INCLUSION OF UNCERTAINTY IN LCA TIM GRANT Centre for Design at RMIT University, GPO Box 2476V, Melbourne 3001, ABSTRACT The calculation of uncertainty in life cycle assessment (LCA) has traditionally been difficult due to a lack of available uncertainty data. Recent developments in databases and software are enabling the routine inclusion of uncertainty assessment in LCA, which enables a better evaluation of results from comparative assessments. Uncertainty data has been included in Australian LCA data for energy and transport systems using estimates from the original source documents and an uncertainty estimation technique developed as part of the EcoInvent Swiss LCA database. The results are shown for electricity generation and transport, and a comparative assessment of blast furnace steel and electric arc furnace steel. Keywords: life cycle assessment, recycling 1. INTRODUCTION Uncertainty analysis has long been a complex and generally avoided aspect of life cycle inventory and impact assessment. In general, all scientific investigations benefit from an understanding of the uncertainties of results caused by uncertainties in the input data. However, for life cycle assessment (LCA), which involves great uncertainties and variability in much of its data, a consistent and methodical approach to uncertainty is an imperative. This paper discusses an approach for including uncertainty for Australian data inventories, which borrows substantially from the approach undertaken by the Ecoinvent Swiss LCA database (Ecoinvent). Some results from analysis of this data are also included. 2. APPROACH TAKEN BY THE ECOINVENT SWISS LCA DATABASE Advances in data quality and computer resources are making the calculation of uncertainty in LCA possible where it would not have been five years ago. The Ecoinvent database, released in 2003, includes uncertainty estimates for most flow data (Frischknecht and Jungbluth 2004). It also includes an uncertainty estimation procedure based on estimates of basic uncertainties for a given list of flow types, which have then been modified by a qualitative assessment of data quality as proposed by Weidema and Wesnaes (1996) in their pedigree matrix. The Ecoinvent software allows four different statistical distributions to be included with data points. These are: uniform, triangular, normal, and lognormal distributions, described in Table 1.

2 Table 1: Probability distributions used in Ecoinvent software. Distribution Characteristics Described by Uniform Triangular No preference for an average Less influence of max and min values Arithmetic mean Minimum Maximum Arithmetic mean Minimum Maximum Graphical description: Y axis is probability, X is spread of values Normal Equal distance from the middle to the left and right Most likely value 2 times standard deviation Log Normal Industrial processes & nature Most likely value Square of geometric standard deviation Source adapted from Frischknecht and Jungbluth (2004) All the uncertainty estimates are based on lognormal distributions. The estimation technique provides a value for the square of the geometric standard deviation (variance), which is then increased as data quality indicators deteriorate. The qualitative indicators which are used to modify the basic uncertainty are: reliability; completeness; temporal correlation; geographical correlation; technological correlation; and sample size. For example, CO 2 emissions have a variance of However, if the data for the completeness and temporal correlation are poor, the variance estimate increases to IMPLEMENTATION OF UNCERTAINTY ESTIMATES IN THE AUSTRALIAN LCA DATABASE The Centre for Design at RMIT University (Centre for Design) has a collection of Australian LCA data which the Centre uses for internal assessments and makes publicly available in EcoSpold and SimaPro data formats. The data are partly derived from a joint project conducted with the CRC for Waste Management and Pollution Control in 1998, but also contain new data developed in further projects at the Centre for Design and other publicly available LCA projects. The task of introducing uncertainty information into the data set began with the updating of fuels, electricity and transport models, generally undertaken on an annual basis. This regular update includes data on refinery operation, oil and gas extraction, coal production, electricity production, road, rail, air and water-based transport Data sources for these data include the National Greenhouse Gas Inventory (Australian Greenhouse Office. and National Greenhouse Gas Inventory Committee, 2004) National Pollutant Inventory (Australian Government, Department of Environment and Heritage, 2004), ABARE (Australian Bureau of Agricultural Research Economics, 2002), Electricity Australia (Electricity Supply Association of Australia 2003), and Apelbaum (Apelbaum Consulting Group, 2001). Uncertainty data are provided in the National Greenhouse Gas Inventory in the form of plus and minus percentage of the reported value. For example, CO 2 emissions from black coal combustion are given as plus or minus 6%. These values have been modelled in the Australian inventories as triangular distributions with the given value as the mean, the minimum value as the mean less the uncertainty percentage, and the maximum value as the mean plus the uncertainty percentage. The National Pollutant Inventory provides emission data for industrial facilities around Australia. It has been used in the Australian inventories to develop emission estimates for electricity production, refineries, aluminium

3 production, primary steel production, and secondary steel production. When more than four emission estimates for the similar activity are available, minimum and maximum values are derived from the spread of values. For example, in NSW, emission values are available for coal-based power generation from eight power plants. Total emission values are divided by estimating generation in each plant over the same period, to give a common emission per MWh, which is then averaged to provide the best guess estimate. The minimum and maximum values are used as the extremes of the triangular distribution. For emissions where four or less values are available, the average is derived from the available numbers, but the uncertainty is developed using the uncertainty estimation technique taken from the Ecoinvent database. This involves taking basic uncertainty estimates for different emissions and adjusting them using data quality characteristics taken from the data pedigree matrix. 4. RESULTS The Australian electricity results shown in Table 2 and Figure 1 below were calculated using a Monte Carlo simulation in SimaPro 6. LCA software based on the uncertainty data is listed above. The inventory was based on 407 processes of which 50% had uncertainty data. It can be noted that values calculated without uncertainty data are the same as the mean and median for six of the indicators, while the mean and median for carcinogens result in an order of magnitude higher that that calculated from the single point results used in the inventory. Figure 1 shows that the uncertainty estimates for heavy metals and carcinogens are very high, the maximum being almost double and the minimum being almost half the median value. Water use has an uncertainty value of approximately 20%. Most other impacts have uncertainties of 5% to 10%. A similar set of results are shown for articulated truck transport in Table 3 and Figure 2. Table 2: Results for Australian electricity mix, showing results of uncertainty assessment Indicator Unit Value calculated without uncertainty assessment Median Mean SD CV (Coefficient of Variation) 2.50% 97.50% Acidification SO E % Carcinogens B(a)P 8.75E E E E % 1.55E E-08 Cumulative energy demand MJ LHV % Eutrophication PO E E E E % 8. 43E E-05 Global warming CO % Heavy metals Pb 1.10E E E E % 1.17E E-07 Photo oxidant. formation C2H4 8.27E E E E % 7.81E E-06 Solid waste % Water use KL H2O E %

4 Figure 1 Results for Australian electricity mix, showing results of uncertainty assessment Table 3: Results for articulated truck mix, showing results of uncertainty assessment Impact category Global warming Acidification Eutrophication Unit CO2 SO2 PO4 Articulated truck, 28 tonne load on 30 tonne truck, (freight task) Median Mean SD CV (Coefficient of Variation) 2.50% 97.50% % E % E E E E-06 16% 4.23E E-05 Heavy metals Pb 3.98E E E E % 2.72E E-07 Carcinogens Photo oxidant. formation Cumulative energy demand. Water use B(a)P C2H4 MJ LHV KL H2O 7.73E E E E % 6.08E E E E E E % 2.84E E % E E E E % 1.45E E-06 Solid waste 9.93E E E % 6.80E

5 Figure 2 Results for articulated truck mix, showing results of uncertainty assessment In comparing two systems using Monte Carlo simulation, care needs to be taken to remove any shared uncertainty in the systems. Life cycle inventories, like the economies they are based on, have many shared materials and services, such as electricity, fuels, materials, and transport. If a comparison is made between two life cycles that contain steel from the same supplier, but one shows 20% more steel than the other, there is no chance that the one with higher steel content could show a lower impact, regardless of the uncertainty in the measurement of the emissions from steel production. Uncertainty could only arise in relation to the size of the emissions from the 20% extra steel in the heavier product. In uncertainty analyses of different life cycles (or components of life cycles),

6 only those elements which are different need to be assessed for uncertainty. In SimaPro, this is done by analysing only the difference between options; that is Option A minus Option B. If the result is a positive value, then Option B has a lower impact than Option A. If the result has a negative value, then Option A has a lower impact than Option B. For example, data from blast furnace steel production was assessed against data for electric arc furnace production. The data was based on public reports (BHP Minerals, 2000), National Pollutant Inventory data for Bluescope Steel, and Smorgan steel plant data. (Australian Government, Department of Environment and Heritage, 2004) The results without uncertainty assessment are shown in Figure 3 for a range of indicators. Figure 4, on the left, shows the results of the uncertainty assessment in terms of the probability of electric arc furnace steel having lower impacts than blast furnace steel, and, on the right, the probability of blast furnace steel having a lower impact than electric arc furnace steel. For water use, there is a 53% probability that electric arc furnace steel has a higher impact than blast furnace steel. All other indicators show no uncertainty, and are always higher for blast furnace steel, with the exception of solid waste, which is always higher for electric arc furnace steel. Note that only the uncertainty of data entered into the inventory is included. While this appears obvious, it needs to be pointed out that inclusion of uncertainty is usually based on data points about which we know something. Yet the deficiencies in life cycle inventories may have more to do with data about which we know very little. Bearing this in mind, it may be appropriate to include data with high uncertainty in an inventory as long as the information about the uncertainty is also included. Figure 3: Comparison of electric arc furnace steel and blast furnace steel

7 Figure 4: Results of uncertainty assessment comparing 1 of electric arc furnace steel and 1 of blast furnace steel. Values left of centre show where electric arc furnace steel has a lower impact than blast furnace steel, and values to the right show where blast furnace steel has a lower impact than electric arc furnace steel. 5. DISCUSSION AND CONCLUSIONS Life cycle inventory data is difficult to obtain as point estimates for many emissions. At first sight, including additional uncertainty data may make the task of inventory more difficult. However, the inclusion of uncertainty data may actually make some data easier to model because the inaccuracy of the point estimate can be incorporated into the model rather than having to be resolved to a level of certainty where it would be appropriate for inclusion. The results of the analysis in this paper show high uncertainties for carcinogens and heavy metal emissions, but much lower uncertainties for greenhouse emissions, smog, eutrophication, and so on. Apart from the electricity data, which included an empirical assessment of uncertainty ranges for emissions, much of the uncertainty of heavy metals and carcinogens was based on the uncertainty estimation technique developed in Ecoinvent. Values for heavy metals are inherently uncertain, and this translates into high uncertainty in the calculation of results. While this is in some ways a self-fulfilling prophecy for the modeller of the data, it is nevertheless important to include the uncertainty information in data which is to be shared and used in other studies. It is not sufficient simply to advise users of the results that the data contain uncertainties; the uncertainty assessment should be provided with the data. This is to ensure that any future calculations using the results will accurately reflect the uncertainty in the source data. 6. REFERENCES Apelbaum Consulting Group (2001). Australian Transport Facts, 2001:Tables in Excel Format. Blackburn, Victoria Australian Bureau of Agricultural Research Economics (2002). ACS Petroleum Tables. Accessed. Australian Government - Department of Environment and Heritage (2004). National Pollutant Inventory Data for Year Accessed.

8 Australian Greenhouse Office. and National Greenhouse Gas Inventory Committee. (2004). National greenhouse gas inventory 2002: with methodology supplements. Canberra, Australian Greenhouse Office. BHP Minerals (2000). LCA of Steel and Electricity Production - ACARP Project C Report A1Steel from an integrated steelworks, Revision 2.3. Newcastle. Electricity Supply Association of Australia (2003). Electricity Australia Melbourne, Electricity Supply Association of Australia Frischknecht, R. and N. Jungbluth (2004). Ecoinvent Report No. 1 - Overview and Methodology. Dübendorf, Swiss Centre for Life Cycle Inventories, Weidema, B. and M. S. Wesnaes (1996). "Data quality man-agement for life cycle inventories - an example of using data quality indicators." International Journal of Cleaner Production 4(3-4):