Life Cycle Inventory Analysis for Decision-Making: Scope-Dependent Inventory System Models and Context-Specific Joint Product Allocation
In this thesis, Life Cycle Inventory Analysis (LCI) is structured in view of its use in decision-making. Em-phasis is put on often encountered inconsistencies, namely the set-up of LCI system models, the representation of decisions and value choices of actors (e.g., firms) involved in a product system, and the re-pre-sen-ta-tion of changes within the economic system. An LCI system model con-sists of numerous individual processes. Their relations are identified according to economic (such as market information or contracts) instead of mere physical information. Based on such a system model, LCA provides environmental information consistently complementary to private cost statements. A disutility function is introduced, which is used for the default choice of (marginal) technologies or tech-nology mixes within the product system, and for joint product allocation. The disutility function adds up economic information (i.e., private costs) and environmental information to total "social" costs. For that purpose, an environmental exchange rate is introduced. The exchange rate mirrors the variable influence of environ-mental aspects on decisions in different political entities such as nations. It may also express differences in uncertainty perception of the actors directly and indirectly involved in the production of the good or service under analysis. To reflect the consequences of decisions, models capable of representing changes within the economic system shall con-sist of processes represented by marginal technologies, the technologies put in or out of operation next. The disutility function is used for the identification of the marginal technologies throughout the whole product system. System models are classified according to the distinction of planning tasks in firms, i.e., short-, long- and very long-term decisions. It is assumed that all firms connected within the process network of a product make their decisions based on the same time horizon (i.e., short-, long-, and very long-term). Aspects of non-linearity occur in the case of short-term optimisation. Semi-dynamic modelling in the case of very long-term planning shows its limited added value compared to static modelling. for more, please contact the author.
Where to find
|Institution||Energy Technology Department, ETH Zuerich (now: ESU-services, Uster)|
|Advisor||Prof. Dr. P. Suter, Prof. Dr. D. Spreng, both ETH Zuerich, Dr. G. Huppes, CML Leiden|