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The existing system of land-based transport based on the individual use of automobiles for passenger transport and trucks for freight transport is not sustainable. Problems of pollution and congestion are pressing public authorities to take action but this is far from easy. One reason why it so difficult for authorities to do something about transport-related problems is that there appear to be no attractive alternatives. Sustainable technologies that fulfil important user requirements in terms of performance and price are most often not available on the market. Ideas of what might be more sustainable technologies and systems exist, but the long development times, uncertainty about market demand and social gains, and the need for change at different levels — in organisation, technology, infrastructure and the wider social and institutional context — provide a great barrier. This paper offers a way to escape the gridlock by engaging in what we call “Strategic Niche Management” (SNM). SNM aims at taking the new technologies to the market place to try and use them in selected settings — niches — by real users. The niches consist of domains of application in which the technology (or new mobility system) is already attractive to use due to specific circumstances (a local problem or the availability of specific, low cost resources). Experiences in the niche are then used to inform decisions about technical improvement and support policies, aimed at expanding the original niche and making a transition towards a more sustainable transport system. The protected space, in which the technology is temporary protected from the full force of normal selection pressures, acts as a test bed and incubator for the new technology. It may pave the way for private and government support policies in a gradual, non-distortionary, and thus feasable way. The paper provides three examples of SNM in Europe: the large-scale experiment with light-weight electric vehicles in Mendrisio (CH), organised car sharing in Switzerland and Rolling Highway in Sweden. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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