JOURNAL ARTICLE

Navigating the Transition to Green Mobility: Assessing the Impact of Tax Incentives on Vehicle Efficiency and Market Dynamics in Italy.

  • Published In: Corporate Social Responsibility & Environmental Management, 2025, v. 32, n. 3. P. 3167 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Ferraro, Aniello; Garofalo, Antonio 3 of 3

Abstract

The transportation sector is a significant contributor to global greenhouse gas emissions, underscoring the need for innovative fiscal measures to promote decarbonisation. This study evaluates the efficiency of car models based on price, emissions, and technical specifications, while examining the impact of fiscal policies on the adoption of green vehicles in Italy. A two‐stage Data Envelopment Analysis (DEA) was conducted utilising a comprehensive database of new cars registered in 2019, categorised by market segments. The results indicate a paradox: the most efficient cars are not necessarily the most environmentally friendly. Furthermore, while green cars are available in smaller segments, their prices often exceed those of comparable conventional cars by 100%. This highlights the need for eco‐bonus adjustments that consider factors beyond CO₂ emissions. Additionally, promoting environmentally friendly cars in higher‐end segments could drive innovation, reduce costs, and advance the automotive industry's transition to sustainability. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:Corporate Social Responsibility & Environmental Management. 2025/05, Vol. 32, Issue 3, p3167
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2025
  • ISSN:1535-3958
  • DOI:10.1002/csr.3121
  • Accession Number:185030934
  • Copyright Statement:Copyright of Corporate Social Responsibility & Environmental Management is the property of Wiley-Blackwell 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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