JOURNAL ARTICLE

Consumer Tax Credits for EVs: Some Quasi-Experimental Evidence on Consumer Demand, Product Substitution, and Carbon Emissions.

  • Published In: Management Science (INFORMS), 2023, v. 69, n. 12. P. 7759 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: He, Cheng; Ozturk, O. Cem; Gu, Chris; Chintagunta, Pradeep K. 3 of 3

Abstract

This article examines the effectiveness of state-level tax credit incentives on the adoption of green vehicles—specifically plug-in hybrid electric vehicles (PHEVs)—and their impact on carbon emissions in the U.S. auto industry. Using detailed county-level sales data and quasi-experimental methods, the study finds that a $2,000 tax credit incentive increases PHEV sales by an average of 3.7%, with stronger effects in Democratic-leaning and lower middle-income counties, primarily by improving conversion rates rather than expanding consumer consideration. The incentive does not significantly affect sales of non-incentivized green vehicles or gasoline vehicles overall but induces substitution from gasoline vehicles with high fuel efficiency to PHEVs, resulting in no market expansion. Simulation analyses estimate the cost of carbon emissions avoided via these tax credits at approximately $109 per metric ton, which is lower than costs associated with some other green subsidies but higher than the $75 per ton carbon tax recommended by the International Monetary Fund. The findings highlight the nuanced effectiveness of delayed monetary incentives like tax credits and underscore the importance of consumer awareness and local demographic factors in policy design.

Additional Information

  • Source:Management Science (INFORMS). 2023/12, Vol. 69, Issue 12, p7759
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2023
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.4781
  • Accession Number:174253194
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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