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Bundling up actions to electrify: a fuzzy-set qualitative comparative analysis of e-mobility policies in 16 cities in the United States of America

Tobias Held et al.

Journal of Public Policy2026https://doi.org/10.1017/s0143814x26101068article
AJG 2ABDC B
Weight
0.50

Abstract

Policymakers in cities in the United States of America (U.S.) must tailor the right bundle of policies to promote the diffusion of battery electric vehicles (BEVs). We investigate the interplay of (non-)monetary incentives on U.S. state, city and utility-related policy levels. For doing so, we deploy fuzzy-set Qualitative Comparative Analysis (fsQCA) of BEV policies in 16 cities in the U.S. to identify policy configurations that promote BEV uptake. We provide a first-ever study to systematically evaluate BEV policy bundles across different U.S. policy levels. Results indicate two policy configurations: monetary incentives received upon/after BEV purchase in conjunction with (non-)monetary incentives for home charging infrastructure extension in conjunction with recurring monetary incentives by utilities (I); the absence of recurring (non-)monetary incentives at the local level in conjunction with (non-)monetary incentives for home charging infrastructure extension in conjunction with recurring monetary incentives by utilities (II) lead to successful progress in BEV adoption.

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https://doi.org/https://doi.org/10.1017/s0143814x26101068

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@article{tobias2026,
  title        = {{Bundling up actions to electrify: a fuzzy-set qualitative comparative analysis of e-mobility policies in 16 cities in the United States of America}},
  author       = {Tobias Held et al.},
  journal      = {Journal of Public Policy},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1017/s0143814x26101068},
}

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Bundling up actions to electrify: a fuzzy-set qualitative comparative analysis of e-mobility policies in 16 cities in the United States of America

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

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