Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Archivio istituziona...arrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Research in Transportation Business & Management
Article . 2022 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
versions View all 3 versions
addClaim

This Research product is the result of merged Research products in OpenAIRE.

You have already added 0 works in your ORCID record related to the merged Research product.

Simulating electric vehicle uptake in Italy in the small-to-medium car segment: A system dynamics/agent-based model parametrized with discrete choice data.

Authors: Scorrano, Mariangela; Danielis, Romeo;

Simulating electric vehicle uptake in Italy in the small-to-medium car segment: A system dynamics/agent-based model parametrized with discrete choice data.

Abstract

The main goal of the paper is to provide a simulation of the potential uptake of electric vehicles in Italy up to the year 2030, as a base for transport and energy planning by public and private decision makers. We develop a hybrid model, integrating an agent-based approach for the demand module and a system dynamics approach for the supply module. The demand module is parametrized with data derived from a discrete choice survey to car users (N = 1521), representative of the Italian population. The supply module interacts with the demand module and incorporates the available data on the evolution of battery production costs. Because of the characteristics of the choice data, the model is parametrized with data relative to the small-to-medium sized car segment only, and does not include PHEVs. Word-of-mouth and advertisement induce a growing number of potential buyers to include BEVs in their choice set. Car buyers choose between the two propulsion systems based on the relative utility. We estimate that in the period 2019–2030 BEVs will gradually overtake conventional vehicles (CVs) in Italy. In terms of annual sales, the share of BEVs will be equal to that of CVs in July 2030. By the end of 2030, BEVs will represent 52.4% of new sales. A total fleet of almost 5 million BEVs will be on the Italian roads by 2030, i.e. about a sixth of the Italian car fleet. Scenario analyses lead us to conclude that BEV subsidies are important but that they are likely sub-optimal.

Country
Italy
Related Organizations
Keywords

Agent-based model, Supply-demand model, Electric car, System dynamics model, Electric car; Simulation; Supply-demand model; Agent-based model; System dynamics model, Simulation

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    14
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Top 10%