Mångfunktionell grön infrastruktur har en betydande roll för städers resiliens tackvare de ekosystemtjänsterna de bidrar med. På grund av den snabba expansionen avstäder skapas ofta målkonflikter gällande markanvändning vilket har lett till att dengröna infrastrukturen i många fall bortprioriterats. För att motverka denna utvecklingär det viktigt att beakta den gröna infrastrukturen och dess ekosystemtjänster tidigt iplaneringsskedet. Syftet med arbetet är att skapa en modell för att utvärderamångfunktionalitet samt hur ekosystemtjänster påverkas vid förändring i urban miljö.Metoden som legat till grund för arbetet har varit analyser av öppna rumsliga datakombinerat med multikriterieanalys. Arbetet har avgränsats till endast inkluderamiljömångfald, konnektivitet, klimatreglering, samt rekreation. Resultatet av arbetetvisar att genom kombinationen av rumsliga analyser och multikriterieanalys kan fleraekosystemtjänster och ekologiska funktioner analyseras samtidigt. Vidare kanmodellen användas som planeringsunderlag för att identifiera styrkor och utmaningarvid förändring av den gröna infrastrukturen i urbana miljöer. / Multifunctional green infrastructure (GI) is central for the resilience of cities, due tothe ecosystem services (ES) it provides. The rapid expansion of cities often createsconflicting interests regarding the land use, which has led to the undermining of theurban green infrastructure (UGI). It’s, therefore, important to consider GI and ES inthe early stages of urban planning. The aim of this study is to develop a model for GImultifunctionality and ES evaluation. The method used to accomplish this is acombination of a multi-criteria analysis (MCA) and spatial analyses. This study’sscope only incorporates environmental variety, connectivity, climate regulation andrecreation. The results of this study show that the combination of MCA and spatialanalyses allow the evaluation of several ES and ecological functions simultaneously.Furthermore, the model can be used as a basis for the urban planning process throughthe identification of strengths and challenges of the UGI.

In this study, I have investigated the effects of station-near development in Kumla to smaller urban areas with sustainable transport. To be able to promote sustainable means of transport and station development in smaller municipalities and urban areas. Transport is mostly done by car and replacing the need for transport with other sustainable modes of transport can be problematic in the smaller towns if it’s not properly encouraged. As similar research has previously been conducted in larger towns, the purpose of this study is to investigate the effect of the station’s proximity in Kumla to smaller towns and encourage sustainable transport to the station from the urban areas. The method for the investigation is a smaller inductive case study that’s applied qualitatively and scenario-work that Kumla municipality has participated in. The strategy Transit-Oriented Development has been used as a point of departure for the scenario work and as a theoretical framework to discuss the results. The result show a current situation analysis and a destination analysis based on the existing environment in the municipality. The result also show the municipality's work with building scenarios, which were then analyzed in a multi-criteria-analysis based on Transit-Oriented Development. Based on these analyses, Kumla Municipality is well placed to develop its objectives and strategies. Transit-Oriented Development is a good guide for Kumla Municipality to develop near-station within the scope of the strategy with sustainable transport. It is also something that can be extended and further used in the routes between the towns based on the winning scenario. / I denna studie har jag undersökt de effekterna med stationsnära utveckling i Kumla till mindre tätorter med hållbara transporter. För att kunna främja hållbara transportmedel och stationsutveckling i mindre kommuner och tätorter. Transporterna sker för det mesta med bil och ersätta transportbehovet med andra hållbara trafikslag kan vara problematisk i de mindre orterna om det inte främjas ordentligt. Då motsvarande forskning har tidigare genomförts i större orter, syftet i denna studie är att undersöka effekten av stationens närhet i Kumla till mindre orter, samt främja kopplingen med hållbara transporter till stationen från tätorterna. Metoden till studien är en mindre induktiv fallstudie som är tillämpad kvalitativt och scenarioarbete som Kumla kommun har deltagit i. Strategin Transit-Oriented Development har använts som en teori i scenarioarbetet och för att diskutera resultatet. Resultatet visar en nulägesanalys och en resmålsanalys utifrån den befintliga miljön i kommunen. Resultatet visar även kommunens arbete med scenariomatrisen, som sedan analyserades och utvärderades i en multikriterieanalys utifrån Transit-Oriented Development. De förutsättningarna utifrån analyserna har Kumla kommun goda förutsättningar att utveckla dess mål och strategier. Transit-Oriented Development en bra vägledning för Kumla kommun att utveckla stationsnära utveckling inom strategins räckvidd med hållbara transporter. Det är även något som kan utvidgas och användas vidare i stråken mellan orterna utifrån det vinnande scenariot.

This study aims to explore the ability of a multi-criteria decision making with analytical hierarchy process (MCDM-AHP) model to emulate the results of a cost benefit analysis (CBA) model in the context of offshore wind farm siting within the Swedish exclusive economic zone (EEZ). The research question addressed is whether the MCDM-AHP analysis produces similar results to the CBA analysis. In addition to this, the strengths and weaknesses of each model is explored. The MCDM-AHP model employs the spatial criteria in a more basic manner compared to the CBA model, simplifying the evaluation process while still explaining 89.5% of the variation in the CBA model and defining similar areas as suitable. Thus, it can be concluded that the MCDM-AHP model adequately emulates the CBA model within the context of offshore wind farm siting within the Swedish EEZ. However, it is crucial to note that the two models produce outputs on different scales. While the CBA model provides levelized cost of energy (LCOE) values that can be thresholded for investment viability comparisons, the suitability score generated by the MCDM-AHP model remains a relative and arbitrary score within the model. Both models entail uncertainties, limiting their usage beyond making general assumptions or identifying areas of interest. The findings reveal that the CBA model demonstrates greater robustness when confronted with changes in spatial input parameters compared to the MCDM-AHP model. This discrepancy is attributed to the iterative computation process and consideration of flat cost inputs in the CBA model, whereas the MCDM-AHP model represents a linear combination of various spatial parameters. However, the calculated LCOE values in the CBA model are highly sensitive to changes in modeling assumptions regarding external parameters, resulting in significant linear variations. The LCOE values obtained from the CBA model baseline case fall within a range of 52.1 - 98.9 EUR/MWh, which aligns with similar studies, validating the CBA model. Nonetheless, caution should be exercised when considering these results as an accurate representation of the real world due to inherent uncertainties in cost inputs and the LCOE measure. The strengths of the MCDM-AHP model lie in its robustness when the order of relative importance remains stable for key spatial evaluators. It is sensitive to significant changes in water depth and wind speed, which heavily influence its output. The model's simplicity allows for a quick overview of the problem, but it requires assumptions that introduce uncertainties. Validation of the MCDM-AHP model using existing and planned offshore wind farms within the Swedish EEZ was possible but limited by the arbitrary scale and limited validation areas. The comparison between the two models could be enhanced with more comprehensive spatial and economic data for an in-depth CBA model, which could serve as a ground truth for the MCDM-AHP model. Nevertheless, the comparison made in this study considers the CBA model to be closer to the truth, acknowledging the underlying assumptions that should be considered during evaluation. In conclusion, within the context of offshore wind farm siting, the MCDM-AHP model produces outputs that are similar to the CBA model.