BIOENERGY FOR EUROPE: WHICH ONES FIT BEST?

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