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BIOENERGY FOR EUROPE: WHICH ONES FIT BEST?

BIOENERGY FOR EUROPE: WHICH ONES FIT BEST?

34 3 Life cycle

34 3 Life cycle assessment of biofuels: methods and tools eration particularly general developments within existing concepts without consideration of technical details. European Chains The European chains were calculated in two steps, based on the national results. In the first step, for those countries not participating in the project (or in a certain chain) the most appropriate country was selected for which the chain was calculated and the results were adopted. The main criteria were similarities in soil and climatic conditions. For example, the impacts of the life cycle of RME in the Netherlands were estimated based on the results for Denmark and Germany (50 % each). In the second step, the European impacts were calculated by weighting the national impacts according to the shares of the individual countries with regard to the European agricultural area. For SME (considered only for the south of Europe), traditional firewood and biogas (similar in all countries) the non-weighted means were used. (See Annex 7.5 for information on the data for the European chains.) For data sources, consistency and representativeness see the following Chapter 3.3.1. For information on data uncertainty see Chapter 3.3.4. 3.2.6 Assumptions, limitations and review Two major assumptions must be considered when interpreting the results: • the allocation procedures, especially when co-products are dealt with by system expansion: depending on the substituted system chosen, the resulting emissions (accounted as a bonus) can reach several orders of magnitude of the total of the emissions calculated for the other process units of the investigated chain. • the cut-off rules, especially each time when process units of the life cycle of the product were not considered because they are the same for the biofuel and the reference chains. The calculated difference between these two chains cannot be referred to the absolute value of the total chain in order to assess its respective relevance. No critical review according to ISO 14040 was performed. 3.3 Inventory analysis 3.3.1 Data acquisition and quality In theory, thousands of environmental parameters can be balanced within a life cycle analysis, depending on the priorities of the particular study or project. Thus it is necessary to select certain parameters that are of particular relevance. This choice has to be made in accordance with the aggregation methods in the impact assessment. The inventory parameters considered in this project are listed in Chapter 3.4.3. In order to collect data for all these parameters in a standardised way for all countries involved, detailed data collection guidelines were required. These included for example the following conventions: • for CO2 only fossil sources were considered, i. e. no organic ones, since these are part of the global cycle and therefore do not add to the greenhouse effect • the total carbon content of the combusted fuels was expressed in terms of CO2 rather than a mix of CO2 and other substances such as diesel particles etc. • all NOx were expressed in terms of NO2. For the purpose of this study the data were partly projected onto the reference year 2010. The input data were primarily obtained from the literature. Main sources were recent studies on related issues and agricultural handbooks (e. g. Borken et al. 1999, Ecoinvent 1996, Patyk and Reinhardt 1997). For certain parameters information was obtained from plant manufacturers and users. These data were partly modified by expert judgements in order to take into account future developments, particularly regarding increases in efficiency and emission reductions. Regarding the future reference year obviously estimates had to be used for representative specifications of plants and processes that are anticipated to be used

3.3 Inventory analysis 35 then. Every institute involved was responsible for its own data inputs, but these data were exchanged and discussed and validated by the project co-ordinator. Therefore the database can be largely regarded as being homogenous. For impact categories such as erosion or soil compaction the situation is generally more difficult. Within an LCIA these categories can usually only be described by very simplified models. For the purpose of this project country specific input data were required, particularly with regard to agricultural production. Different climatic conditions, soil quality and topography for example are likely to lead to different dry matter yields. For other parts of the life cycles of bioenergy carriers on the other hand, equal conditions can be assumed for all participating countries – or at least this can be expected for the future. These processes are listed below: • supply of conventional energy carriers • fertiliser production • use of agricultural machinery (fuel consumption and emissions per hour) • transportation (fuel consumption and emissions per km) • supply of the machines and plants („infrastructure“) For the description of these processes, uniform data sets are used without regional differentiation. The essential rules for data collection and generation respectively, as well as relevant conversion factors, were compiled in the data collection guidelines (see Annex 7.5). These included methods of obtaining relevant data for the following factors: 1. Agriculture: fertiliser application, yields, mechanical work, field emissions (N2O, NOx, NH3, phosphate, nitrate, heavy metals, pesticides) 2. Conversion and use: data for energy consumption and emissions of various substances to water and atmosphere (e.g. CO2, SO2, CH4, HCl, NH3, heavy metals) 3. Biodiversity and soil quality: ecosystem occupation, soil quantity, harmful rainfall 4. Normalisation For the complete data collection guidelines used for this project see Annex 7.5. The data required for the description of the life cycles of bioenergy carriers and their fossil counterparts show significant, in parts even extreme differences regarding availability and scientific reliability. Naturally, this has also an effect on the reliability and accuracy of the results. Using “technical” parameters only, as for example the diesel requirement for ploughing one hectare, or the NOx emissions from the production of one kWh electricity, the following ranking can be carried out: • The largest available amount and highest quality of data is that for the energy consumption of plants and machines working in accordance with established procedures. For individual sectors such as the mineral oil industry or electricity production, reliable mean values can be deduced from official statistics. These mean values can, if necessary, be used as a basis for updates as well as an assessment of marginal technology. • The emission data for those pollutants, like for example CO2 and SO2, whose values are calculated on the basis of the content of the relevant substance in the consumed resource, show comparable quality. • The reliability of data regarding “standard” pollutants, whose emission values are rather limited with respect to technical processes in many countries, is somewhat lower. This is true e. g. for NOx or NMHC, where the emission values often depend on the particular conditions for each process. • The reliability of data regarding limited and non-limited emissions of trace elements as well as certain other limited emissions (BaP, dioxins, heavy metals) is generally very low. The following ranking refers to sectors: the highest quality data come from the conventional energy industry, followed by the transport and raw material industry. Regarding the conversion of bioenergy carriers the data can show significant uncertainty, as is generally true for new technologies. In extreme cases basic data may be completely missing. For all input parameters CV (coefficient of variance) were calculated or estimated. With these data MonteCarlo calculations were made (the results of which however are not represented in the end result diagrams; see Chapter 4.1.3 for further information on this). On the level of the final results the differences between the countries can be interpreted as an approximate measure of the uncertainties. (These

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