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The threshold effects of ICT on CO2 emissions: evidence from the MENA countries

Author

Listed:
  • Béchir Ben Lahouel

    (IPAG Business School)

  • Lotfi Taleb

    (École Supérieure des Sciences Économiques et Commerciales Tunis Université de Tunis
    Université de la Manouba)

  • Shunsuke Managi

    (Kyushu University)

  • Khaled Guesmi

    (CRECC, Paris School of Business)

Abstract

The objective of this paper is to investigate the nonlinear relationship between ICT and CO2 emissions by controlling for economic growth, foreign direct investment, energy consumption, and trade openness. Using data from 16 Middle East and North African (MENA) countries over the period 1990–2019, we apply the Panel Smooth Transition Regression (PSTR) model, as introduced by (González A, Teräsvirta T, vanDijk D (2005) Panel smooth transition regression models. SEE/EFI Working Paper Series in Economics and Finance, No. 604), to study the potential regime-switching behavior of the relationship between the variables. The results reveal the existence of a strong regime-switching effect between ICT and CO2 emissions. It was found that after reaching a certain threshold, ICT use and penetration starts to significantly mitigate environmental degradation. Our results show that high levels of ICT not only improve environmental quality but can also be part of the solution to combat the environmental challenges that the MENA region has faced over the past decades. In addition, to account for the potential endogeneity bias, we also develop and estimate a PSTR model with instrumental variables (IV-PSTR) using the approach of (Fouquau et al., Econ Model 25:284–299, 2008). The results obtained confirm those initially found by the PSTR model. The study concludes with policy implications.

Suggested Citation

  • Béchir Ben Lahouel & Lotfi Taleb & Shunsuke Managi & Khaled Guesmi, 2024. "The threshold effects of ICT on CO2 emissions: evidence from the MENA countries," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 26(2), pages 285-305, April.
    • Handle: RePEc:spr:envpol:v:26:y:2024:i:2:d:10.1007_s10018-022-00346-w
    DOI: 10.1007/s10018-022-00346-w
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    References listed on IDEAS

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    Cited by:

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    2. Wu, Wei & Bi, Shuochen & Zhan, Yunqiu & Gu, Xiao, 2025. "Supply chain digitalization and energy efficiency (gas and oil): How do they contribute to achieving carbon neutrality targets?," Energy Economics, Elsevier, vol. 142(C).
    3. Tao, Miaomiao & Wang, Jianda, 2025. "Harnessing the web: County-level perspectives on China's green economy," Energy, Elsevier, vol. 316(C).

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • O3 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics
    • Q58 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Government Policy

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