Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach

Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach CO2 emissions caused by private motorized traffic for the city of Graz, a typical European inland city with about 320 000 citizens, are investigated. The main methodology is a newly developed agent-based model that incorporates empirical data about the mobility behavior of the citizens in order to calculate the traveled routes, the resulting traffic and subsequent emissions. To assess the impact of different policies on CO2 emissions, different scenarios are simulated and their results are compared to a base line scenario. The model features a local and temporal resolution, effects like congestion and stop-and-go traffic as well as commuters to and from other regions. In addition to the evaluation of certain policies (like a focus on electric cars, telecommuting or an improvement of the road infrastructure), a method is provided, that makes it possible to compare many diverse scenarios, featuring technological changes, societal changes or changes in the road network, all within the same framework. The findings suggest that one of the most promising strategies to decrease urban CO2 emissions is to focus on the use of electric cars, especially if it is combined with offering alternatives to private car traffic and incentives for telecommuting. Banning the use of old cars only yields a significant result if a large amount of cars is affected, which would make such a policy difficult to implement. Expanding the road network has no significant positive effect and may even encourage using cars, therefore leading to even more CO2 emissions. Due to its flexible structure the presented model can be used to evaluate policies beyond what is presented in this study. It can easily be adapted to other conditions and geographical regions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Cleaner Production Elsevier

Large scale simulation of CO2 emissions caused by urban car traffic: An agent-based network approach

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier Ltd
ISSN
0959-6526
D.O.I.
10.1016/j.jclepro.2018.02.113
Publisher site
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Abstract

CO2 emissions caused by private motorized traffic for the city of Graz, a typical European inland city with about 320 000 citizens, are investigated. The main methodology is a newly developed agent-based model that incorporates empirical data about the mobility behavior of the citizens in order to calculate the traveled routes, the resulting traffic and subsequent emissions. To assess the impact of different policies on CO2 emissions, different scenarios are simulated and their results are compared to a base line scenario. The model features a local and temporal resolution, effects like congestion and stop-and-go traffic as well as commuters to and from other regions. In addition to the evaluation of certain policies (like a focus on electric cars, telecommuting or an improvement of the road infrastructure), a method is provided, that makes it possible to compare many diverse scenarios, featuring technological changes, societal changes or changes in the road network, all within the same framework. The findings suggest that one of the most promising strategies to decrease urban CO2 emissions is to focus on the use of electric cars, especially if it is combined with offering alternatives to private car traffic and incentives for telecommuting. Banning the use of old cars only yields a significant result if a large amount of cars is affected, which would make such a policy difficult to implement. Expanding the road network has no significant positive effect and may even encourage using cars, therefore leading to even more CO2 emissions. Due to its flexible structure the presented model can be used to evaluate policies beyond what is presented in this study. It can easily be adapted to other conditions and geographical regions.

Journal

Journal of Cleaner ProductionElsevier

Published: May 10, 2018

References

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