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

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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.

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Notes

  1. ICT can be measured with either non-monetary (e.g., mobile cellular subscriptions, fixed telephone subscriptions, percentage of individual using the Internet, fixed broadband subscriptions) or monetary variables (e.g., ICT investment, ICT capital stock). Due to the unavailability of monetary data, we limit our selection to non-monetary data (i.e., mobile cellular subscriptions and percentage of individual using the Internet) to make our study comparable to recent studies that used these variables to measure ICT use and penetration.

  2. The PSTR specification procedures are based on the assumption that all variables in Eq. (1) are I(0) stationary in level (González et al., 2005; 2017).

  3. For more details on these two tests, please refer to González et al. (2017).

  4. HAC stands for Heteroskedasticity and Autocorrelation Consistency.

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Authors and Affiliations

  1. IPAG Business School, Paris, France

    Béchir Ben Lahouel

  2. École Supérieure des Sciences Économiques et Commerciales Tunis Université de Tunis, Tunis, Tunisia

    Lotfi Taleb

  3. Laboratoire LARIMRAF LR21ES29, École supérieure de commerce de Tunis, Université de la Manouba, Tunis, Tunisia

    Lotfi Taleb

  4. Kyushu University, Fukuoka, Japan

    Shunsuke Managi

  5. CRECC, Paris School of Business, Paris, France

    Khaled Guesmi

Authors
  1. Béchir Ben Lahouel
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  2. Lotfi Taleb
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  3. Shunsuke Managi
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  4. Khaled Guesmi
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Correspondence to Béchir Ben Lahouel.

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Appendix

Appendix

See Appendix Tables 5, 6

Table 5 Descriptive statistics of variables used in the PSTR model
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Table 6 Pearson correlation
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Ben Lahouel, B., Taleb, L., Managi, S. et al. The threshold effects of ICT on CO2 emissions: evidence from the MENA countries. Environ Econ Policy Stud 26, 285–305 (2024). https://doi.org/10.1007/s10018-022-00346-w

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