Companies that wish to assess the CO2 emissions caused by the production of their products are facing the challenge of gaining detailed information about their processes. Moreover, a comparison of technologies used with other more sustainable alternatives (e.g., the shift from chemical to biotechnical processing) is necessary to evaluate the life cycle of products (see Chapter 1) , showing the impact of possible process changes on CO2 emissions. To assign a specific assessment with valid data, two complementary paths are conceivable. Firstly, second-hand data can be obtained and adjusted for the own issue. This kind of research is associated with lack of precision. In general, data has been generated for other issues involving different circumstances, for example an average case translates into mean values with broad assumptions of basic conditions and technologies. Furthermore, some studies offer data that is too aggregated to be exploitable for other situations. For example, in a study by the IFEU Institute the production of ethanol emits 42.0 gCO2-eq./MJ in the agrarian stage and 62.6 gCOreq./MJ in the industrial stage, leaving 0.4gCO ) -eq./MJ for the distribution and the storage of fuel grade ethanol (see also Table 12.7) ) 36] ) But if, for example, an ethanol producer uses more efficient production techniques and sources its feedstock from more sustainable producers, the emissions for the own case could be more favorable compared with the average branch. Secondly, individual research could be conducted to obtain more detailed and specific information. Particularly, small or medium-sized companies, however, rarely have the resources to conduct intensive screening. Therefore, a compromise between effort and accuracy is necessary to deliver a satisfactory proxy for actual conditions involved. Internally modeling the LCA is conducted in general specifically enough and efficiently, if second- hand data is used for parameters that cannot be influenced directly by the regarded company (e.g., the CO 2 - emissions of average suppliers or for transport operations). Several institutions offer databases with common l ife cycle inventory (LCI) data which deliver standardized environmental and casually economical information for a specific region. For example, the average GHG emissions for the transport of goods per kilometer and weight in certain regions. A list of databases containing typical LCI is provided by the European Commission )22]. As location, capabilities, resource disposability and available technologies vary between companies and regions in general, these data need to be adjusted to fit the specific internal case. This leaves much room for interpretation upon conversion to real world conditions.
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