Article ID: | iaor20125001 |
Volume: | 46 |
Issue: | 9 |
Start Page Number: | 1477 |
End Page Number: | 1489 |
Publication Date: | Nov 2012 |
Journal: | Transportation Research Part A |
Authors: | Zubaryeva Alyona, Thiel Christian, Zaccarelli Nicola, Barbone Enrico, Mercier Arnaud |
Keywords: | ecology, forecasting: applications |
This study presents a modeling approach that focuses on the identification of potential lead markets for electric‐drive vehicles (EDVs) in Europe. It is based on a combination of several selected economic, social, environmental, and transport‐related factors. The modeling approach is implemented in a GIS‐based multi‐criteria decision support process with fuzzy measures, enabling an assessment at different spatial and temporal scales under different EDV market penetration scenarios for Europe. The decision support system embeds a multi‐criteria analysis based on selected expert‐weighted market penetration drivers. The spatial scale chosen for the application of the decision support process are NUTS2 regions and cities within EU27 member states. Three scenarios are investigated, a business as usual, a moderate change, and an accelerated innovation scenario. Across the scenario horizon, it is shown how lead regions for EDVs will be changing in time between first early‐adopter areas towards other long‐term potential lead regions, depending on the evolution of the market drivers. The European regions and cities which will have a higher lead market potential score in 2020 and 2030 are identified. Our model solution suggests that with the business‐as‐usual scenario there will be a few insular lead market areas in 2020 and a relatively limited number of more connected lead regions in 2030. The other two scenarios explored suggest a more positive picture leading for the case of the 2030 accelerated scenario to a wide distribution of EDVs across most of Germany, the Netherlands, France, the UK, Ireland, and Italy. The cities of London, Madrid, Berlin and Rome would show high EDV sales under this scenario.