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36 3 Life cycle

36 3 Life cycle assessment of biofuels: methods and tools differences are to be regarded as the variability exclusively between the various countries only if each input value can be given with an uncertainty value of zero.) The significant uncertainties are derived from estimates of CVs (coefficients of variance) for input data and “MonteCarlo”-calculations. 3.3.2 Data format For the first run of data collection and exchange for validation and calculation, the data were stored in the so-called SPOLD format (SPOLD: Society for Promotion of Life Cycle Assessment Development). Each unit process of each chain was stored in a separate file (e. g. harvesting of rape seed in Denmark). The SPOLD format is an approach for standardising the storage and exchange of life cycle inventory data. The data are divided into eight classes of inputs and outputs from and to nature and technosphere. The format allows a comprehensive documentation of data origin, time and geographic reference, cut off criteria etc. The SPOLD files can be exported to Microsoft Excel ® . In order to accelerate the subsequent validation and calculation steps, consolidated Excel files were prepared from those related to the SPOLD files. These had the structure mentioned above (one file for each chain in all participating countries with defined lines and columns for all inputs and outputs of all unit processes). 3.3.3 Calculation tools The present project required two different kinds of tools in order to store and to handle the great amount of data collected for each country and for each chain under study. For these purposes two software tools were used: • Microsoft Excel ® : for the calculation of the inventories • Palisade @Risk ® : for the statistical analysis and the assessment of the quality of data based on a MonteCarlo simulation. A specific calculation tool was developed using Microsoft Excel ® . After the first run of calculation it was modified in order to speed up validation procedures and the statistical simulation. The calculation tool allows for each chain: • an aggregation of the emissions and the resource depletion relevant to each input data of each process unit into one "process system" (representing the whole chain). The structure of the inventory is based on the SPOLD data format that consists of eight categories of input and output data: 1. Input from technosphere – materials and fuels 2. Input from technosphere – electricity and heat 3. Input from nature 4. Output to technosphere – products and by-products 5. Output to technosphere – waste 6. Output to nature – air 7. Output to nature – water 8. Output to nature – soil • taking into account the uncertainty of data. Each data collected is characterised by a coefficient of variance (standardised for each category of data). The uncertainty of data is handled by a specific software tool (Palisade @Risk ® ) which, using appropriate formulas, calculates the mean, the minimum, the maximum and the standard deviation values of each data stored in the spreadsheet according to a log-normal distribution and using a MonteCarlo simulation model. Two kinds of data sets were used for the calculation of the inventories: • base data in form of standardised input data (grams of emissions and MJ of energy depletion relevant to the supply and use of machinery, buildings, plants, chemicals, etc.) • country specific data (hours per hectare of machinery use, cubic meters of storage buildings, kg of chemicals per hectare used in the process). For energy crops, the calculations were carried out as follows. The first data processing consisted of the conversion of base data into specific values per hectare (the relevant reference unit for these chains),

3.3 Inventory analysis 37 using the country specific data and taking into account default values set for some items (i. e. average speed of tractors, calorific values of fuels). The results of the first calculation are expressed as "g of emissions per hectare" and "MJ of energy per hectare" for each of the process units into which the chain under study is split. The second step consisted of the sum of impacts relevant to each process unit in order to obtain the total amount of impacts per hectare for the whole chain. The third step consisted of subtracting from the main chain the impacts due to the corresponding agricultural reference system (calculated in the same way as the main chain). The final results were converted in order to express all the impacts in the relevant functional unit (MJ or kWh of useful energy respectively). This step takes into account the energy production of the main chain. For the European results and certain individual countries the last calculation step consisted of a “normalisation" procedure (see Chapter 3.4.4). For the fossil fuels the calculations were carried out in an equivalent fashion, likewise taking into account all process units and including all aspects of resource acquisition, processing and utilisation. The results for the fossil fuel chains were then subtracted from those for the biofuel chains, so that a negative figure indicates an environmental advantage for the biofuel (because it implies that the impacts of the biofuel are smaller than those of the fossil fuel) and vice versa, i. e. a positive figure indicates an advantage of the fossil fuel. 3.3.4 Completeness, consistency and sensitivity analysis The completeness and consistency of the results are naturally directly dependent on the completeness and consistency of the input data. Therefore a close scrutiny of such data is essential. The input data can be divided into the following groups with regard to their generation and validation: Basic processes The following data were balanced and checked by means of comparisons with data from the literature and with regard to their plausibility: • data for all fossil energy carriers • data for those basic processes of bioenergy carrier chains that were not country specific – such as fertiliser and pesticide production or emissions and time specific energy demand of agricultural machines. Country specific input data The data were generated on a country specific basis, i. e. the representatives of each participating country were responsible for its specific data generation. For certain parameters, particularly those related to agriculture, significant differences were to be expected between the individual countries. Thus for example on small fields with steep slopes like in Austria and Switzerland, smaller machines are required than on larger and more level fields like in Germany – and smaller machines require a higher specific time input than larger ones. The completeness of the data was checked by means of a comparison with the instructions for the life cycle descriptions. With regard to consistency, spreads were estimated on the basis of expert judgements, which leave much scope for country specific differences and should therefore not be transgressed. Validation took place in several steps: In the first step missing data as well as extreme values for parameters with large spreads were simply recorded and the respective partners were asked for a thorough check and supplement or modification of the data. In the next round, default values were suggested which were either accepted or modified by the countries involved. This step was repeated with narrower spreads and finished with the acceptation of the input data sets. Here it was obvious that the last step did not lead to a decrease of the spreads. This means that the – in some cases very large – differences for certain parameters have to be regarded as real.

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