JOURNAL ARTICLE

The asymmetric impact of industrial growth on carbon dioxide emissions: Evidence for the Tunisian economy.

  • Published In: Journal of Renewable & Sustainable Energy, 2025, v. 17, n. 2. P. 1 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Dallali, Atef; Ben Jebli, Mehdi 3 of 3

Abstract

This article examines the asymmetric impact of industrial value-added (IVA) on per capita carbon dioxide emissions (CO₂em) in Tunisia from 1990 to 2021, incorporating real gross domestic product (GDP), renewable energy consumption (REC), and nonrenewable energy consumption (NREC) as explanatory variables. Using a nonlinear autoregressive distributed lag (NARDL) model and Granger causality tests, the study finds that economic growth, REC, NREC, and positive shocks in IVA increase CO₂em, while negative shocks in IVA reduce emissions. The results highlight the strong dependence of Tunisia's industrial sector on fossil fuels and the limited current impact of renewable energy on emission reductions, underscoring the need for policies promoting cleaner industrial practices, investment in renewable energy infrastructure, economic diversification, and environmental regulations. Granger causality analysis reveals bidirectional relationships between CO₂em, renewable energy, and IVA, emphasizing the importance of integrating sustainable energy practices within industrial growth to achieve long-term environmental sustainability in Tunisia.

Additional Information

  • Source:Journal of Renewable & Sustainable Energy. 2025/03, Vol. 17, Issue 2, p1
  • Document Type:Article
  • Subject Area:Science
  • Publication Date:2025
  • ISSN:1941-7012
  • DOI:10.1063/5.0242842
  • Accession Number:184884771
  • Copyright Statement:Copyright of Journal of Renewable & Sustainable Energy is the property of American Institute of Physics 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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