Robustness in constructing a network of induced emissions based on GPS-tracking data

University essay from Högskolan Dalarna/Mikrodataanalys; Högskolan Dalarna/Mikrodataanalys

Abstract: The mobility of people, freight and information is fundamental to economic and social activities such as commuting, manufacturing, distributing consumer goods and supplying energy. There are two major problems that arise as a result of mobility. The first is economic cost and the second is environmental impact which is of increasing concern in sustainable development due to emission levels, particularly as a result of car use. This study focuses on constructing a network of induced emissions (NOIEs) by using three models and checking the robustness of NOIEs under varying parameters and models. The three models are Stead’s model, the NAEI model, and Oguchi’s model. This study uses the Swedish city of Borlänge as the case study. Calculating CO2 emissions by constructing the NOIEs using Stead’s model appears to give an underestimation when compared to results from a NOIEs which applies Oguchi’s model. Results when applying the NAEI model in constructing a NOIEs also give an underestimation compared to a NOIEs applying Oguchi’s model. Applying the NAEI model is, however, more accurate than applying Stead’s model in constructing a NOIEs. The outcomes of this study show that constructing a NOIEs is robust using Oguchi’s model. This model is preferable since it takes into account more important variables such as driving behavior and the length of the road segments which have a significant impact when estimating CO2 emissions.

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