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A multi‐criteria decision‐making approach for assembling optimal powertrain technology portfolios in low GHG emission environments

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  • Florian Kellner
  • Bernhard Lienland
  • Sebastian Utz

Abstract

Environmental regulations force car manufacturers to renew the powertrain technology portfolio offered to the customer to comply with greenhouse gas (GHG) emission targets. In turn, automotive companies face the task of identifying the “right” powertrain technology portfolio consisting of, for example, internal combustion engines and electric vehicles, because the selection of a particular powertrain technology portfolio affects different company targets simultaneously. What makes this decision even more challenging is that future market shares of the different technologies are uncertain. Our research presents a new decision‐support approach for assembling optimal powertrain technology portfolios while making decision‐makers aware of the trade‐offs between the achievable profit, the achievable market share, the market share risk, and the GHG emissions generated by the selected vehicle fleet. The proposed approach combines “a posteriori” decision‐making with multi‐objective optimization. In an application case, we feed the outlooks of selected market studies into the proposed decision‐support system. The result is a visualization and analysis of the current real‐world decision‐making problem faced by many automotive companies. Our findings indicate that for the proposed GHG restriction at work in 2030 in the European Union, no optimal powertrain technology portfolio with less than 35% of vehicles equipped with an electric motor exists.

Suggested Citation

  • Florian Kellner & Bernhard Lienland & Sebastian Utz, 2021. "A multi‐criteria decision‐making approach for assembling optimal powertrain technology portfolios in low GHG emission environments," Journal of Industrial Ecology, Yale University, vol. 25(6), pages 1412-1429, December.
    • Handle: RePEc:bla:inecol:v:25:y:2021:i:6:p:1412-1429
    DOI: 10.1111/jiec.13148
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