{"id":1384,"date":"2018-03-25T16:57:02","date_gmt":"2018-03-25T16:57:02","guid":{"rendered":"http:\/\/mitmgmtfaculty.mit.edu\/jsterman\/?page_id=1384"},"modified":"2018-04-05T13:40:31","modified_gmt":"2018-04-05T13:40:31","slug":"transitionchallenges-2","status":"publish","type":"page","link":"https:\/\/mitmgmtfaculty.mit.edu\/jsterman\/transitionchallenges-2\/","title":{"rendered":""},"content":{"rendered":"

Transition Challenges for Alternative Fuel Vehicle and Transportation Systems<\/h2>
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J. Struben and J. D. Sterman (2008) Transition Challenges for Alternative Fuel Vehicle and Transportation Systems.\u00a0Environment and Planning B<\/em>\u00a035:1070-1097.<\/p>\n

DOI:\u00a0http:\/\/doi.org\/10.1068\/b33022t<\/p>\n

Abstract<\/strong><\/p>\n<\/div>\n

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Automakers are now developing alternatives to internal combustion engines (ICE), including hydrogen fuel cells and ICE-electric hybrids. Adoption dynamics for alternative vehicles are complex due to the size and importance of the auto industry and vehicle installed base. Diffusion of alternative vehicles is both enabled and constrained by powerful positive feedbacks arising from scale and scope economies, R&D, learning by doing, driver experience, word of mouth, and complementary resources such as fueling infrastructure. We describe a dynamic model of the diffusion of and competition among alternative fuel vehicles, including coevolution of the fleet, technology, consumer behavior, and complementary resources. Here we focus on the generation of consumer awareness of alternatives through feedback from consumers\u2019 experience, word of mouth and marketing, with a reduced form treatment of network effects and other positive feedbacks (which we treat in other papers). We demonstrate the existence of a critical threshold for sustained adoption of alternative technologies, and show how the threshold depends on economic and behavioral parameters. We show that word of mouth from those not driving an alternative vehicle is important in stimulating diffusion. Expanding the model boundary to include learning, technological spillovers and spatial coevolution of fueling infrastructure adds additional feedbacks that condition the diffusion of alternative vehicles. Results show scenarios for successful diffusion of alternative vehicles, but also suggest that marketing programs and subsidies for alternatives must remain in place for long periods for diffusion to become self-sustaining.<\/p>\n