The Dutch government, in alignment with the Paris climate agreement, has expressed the ambition to reduce CO 2 emissions in the Netherlands by 49% in 2030 compared to 1990. As freight transport is recognized as a serious CO 2 emitter, this sector is confronted with a substantial part of the target. For cities, the reduction of the urban freight transport emissions is, next to the CO 2 reduction, also important to improve the air quality. Dutch municipalities take an active role in coordination, facilitation and acceleration of the emission reduction processes, not only via regulation but also by using their public procurement power. This paper describes the City of Rotterdam's experiences from the EU Horizon 2020 BuyZET project. This project was launched in November 2016 and includes the cities of Rotterdam, Oslo and Copenhagen. The project aims at understanding and optimising the impact of public procurement activities on transport patterns and emissions in cities as well as to find innovative and sustainable delivery solutions for goods and services-related transport in order to reduce emissions.
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Over the last years a large growth in Electric Vehicles (EV) and charging infrastructure (CI) development has been observed. Particularly in metropolitan areas this growth has led to a system in which multitudes of interactions between EV users take place. While many researchers have focused on EV user charging behavior and deployment strategies for CI, little attention has been paid to conceptualizing the problem domain. This research provides a brief overview of complex systems theory, and derives six characterizing elements of complex systems that may be applicable for CI. The paper investigates both theoretically but also empirically how these characterizing elements apply for CI and provides implications for the further roll-out of CI for both policy makers and researchers. We illustrate our findings with preliminary results form ongoing research. Recommendations include the further development of simulation tools that are capable of exploring effects of e.g. non-linear behavior, feedback loops and emergence of new patterns on CI performance. In the end this paper aims to provide directions to enable policy makers to be better prepared for the anticipated exponential growth of EVs and CI.
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City authorities want to know how to match the charging infrastructures for electric vehicles with the demand. Using camera recognition algorithms from artificial intelligence we investigated the behavior of taxis at a charging stations and a taxi stand.
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