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Ballast eller ballastfritt, det är frågan : Modellförslag för icke–monetär nyttovärdering av två olika järnvägskonstruktionerPersson, Madeleine January 2020 (has links)
To cut transport carbon dioxide emissions by 70% by the year of 2030, the Swedish government is investing in new railway infrastructure, hoping to stem the emissions caused by the transport sector. It is planned to stretch between Stockholm–Malmö and Stockholm–Gothenburg. The first section Ostlänken, is located between Järna in Södertälje municipality (Stockholm) and Linköping. It will be a 160 kilometers long railway with double track in ballast. All railways in Sweden are built with ballast, but internationally, ballast–free methods, so called slab tracks, are increasing in popularity. This master thesis aims to compare ballast tracks with a general model of slab tracks, in regard to carbon dioxide emissions. The study also aims to evaluate how several criteria can be compared to each other as non-monetary, in an evaluation model. Three criterions will be considered: availability, economic costs and carbon dioxide emissions. A case study of Ostlänken was used as a methodological framework. Further, a literature review has been implemented as well as several interviews. Calculations of carbon dioxide emissions from the two tracks respectively were performed through a life cycle analysis (LCA). Through a multicriteria analyse, the three criteria were evaluated in respect of each other. All calculations were performed in Excel. The result that could be drawn from the study, was that from a carbon dioxide point of view, ballast tracks have a much greater need for maintenance to preserve the track geometry. Slab track on the other hand, generate higher emissions in the start of the life cycle, due to its large amount of concrete and steel. Multicriteria analysis were shown to be a helpful tool when making decisions in complex systems. The multicriteria analysis could benefit from including other case specific factors as well. This might however also lead to a far too complex model, from which fewer conclusions can be drawn.
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