Digital Economy and Environmental Sustainability in MENA Region: The Role of ICT and Economic Complexity

Authors

  • Ihsen Abid Department of Finance, College of Business, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia Corresponding Author

DOI:

https://doi.org/10.47654/v30y2026i4p85-108

Keywords:

Ecological Footprint, Economic Complexity, ICT Exports, GDP per Capita, Population Density, Foreign Direct Investment, ICT Imports, Sustainable Development, MENA Region

JEL Classifications:

Q56, O13, F63

Abstract

Purpose – This paper investigates the determinants of the ecological footprint in the Middle East and North Africa (MENA) region over the period 1995–2023. It focuses on examining the roles of economic complexity, ICT exports, GDP per capita, population density, foreign direct investment, and ICT imports in shaping environmental pressures within a short-run and contemporaneous analytical framework.
Design/Methodology/Approach – The study employs a panel data approach based on stationary transformations of the variables to ensure econometric validity and avoid spurious regression. The empirical analysis is conducted using Fixed-Effects (FEM), Random-Effects (REM), Pooled OLS, and Mixed-Effects Generalized Linear Model (GLM) estimators. These complementary models allow for the examination of short-run dynamics while accounting for heterogeneity and cross-sectional dependence across countries.
Findings – The results indicate that ICT exports consistently contribute to reducing the ecological footprint, highlighting the role of technological advancement in improving environmental sustainability. Economic complexity shows mixed effects across model specifications, reflecting transitional dynamics in structural transformation. In contrast, GDP per capita emerges as a strong and robust driver of ecological pressure, while population density contributes to environmental strain in several cases. Foreign direct investment (FDI) and ICT imports do not exhibit statistically significant effects, suggesting that their environmental impact depends on contextual factors such as institutional quality and absorptive capacity.
Implications – The findings suggest that MENA policymakers should promote ICT exports, digital infrastructure, and green technological upgrading as tools for reducing ecological pressure. At the same time, economic growth strategies should be accompanied by environmental regulation, sustainable urban planning, and green investment policies to prevent short-run income growth from increasing ecological degradation.
Originality/Value – This study contributes to the literature by providing a consistent econometric framework based on stationary panel estimations, avoiding reliance on cointegration-based methods in the presence of mixed integration orders. It offers new insights into the short-run relationships between digital transformation, economic structure, and environmental sustainability in the MENA region, a context characterized by rapid economic growth and technological change.

References

Abid, I. (2025a). Balancing growth and sustainability: The role of economic factors in adjusted net savings in South Asia. International Journal of Sustainable Development and Planning, 20(4), 1487–1497. https://doi.org/10.18280/ijsdp.200412

Abid, I. (2025b). The role of economic and environmental variables in green growth: Evidence from Saudi Arabia. Engineering, Technology & Applied Science Research, 15(1), 20433–20439. https://doi.org/10.48084/etasr.9836

Abid, I., & Gafsi, N. (2025). Economic complexity, environmental sustainability, and technological integration in Saudi Arabia: Analyzing long-term trends. International Journal of Energy Economics and Policy, 15(3), 669–683. https://doi.org/10.32479/ijeep.18806

Abid, I., Hechmi, S., & Chaabouni, I. (2024). Impact of energy intensity and CO₂ emissions on economic growth in Gulf Cooperation Council countries. Sustainability, 16(23), 10266. https://doi.org/10.3390/su162310266

Ali, W., Gohar, R., Chang, B. H., & Wong, W. K. (2022). Revisiting the impacts of globalization, renewable energy consumption, and economic growth on environmental quality in South Asia. Advances in Decision Sciences, 26(3), 75–98. https://doi.org/10.47654/v26y2022i3p75-98

Arif, U., Arif, A., & Khan, F. N. (2021). Environmental impacts of FDI: Evidence from heterogeneous panel methods. Environmental Science and Pollution Research, 29(16), 23639–23649. https://doi.org/10.1007/s11356-021-17629-6

Asongu, S., Nwachukwu, J. C., & Pyke, C. (2018). The comparative economics of ICT, environmental degradation and inclusive human development in Sub-Saharan Africa. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3265084

Baltagi, B. H. (2008). Econometric analysis of panel data. Wiley.

Baltagi, B. H., Song, S. H., Jung, B. C., & Koh, W. (2007). Testing for serial correlation, spatial autocorrelation and random effects using panel data. Journal of Econometrics, 140(1), 5–51. https://doi.org/10.1016/j.jeconom.2006.09.001

Borensztein, E., De Gregorio, J., & Lee, J.-W. (1998). How does foreign direct investment affect economic growth? Journal of International Economics, 45(1), 115–135. https://doi.org/10.1016/s0022-1996(97)00033-0

Bretschger, L. (2015). Economic growth, sustainability, and environmental quality. In Economics of the environment (pp. 267–284). https://doi.org/10.1007/978-3-540-73707-0_16

Breusch, T. S., & Pagan, A. R. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47(5), 1287–1294. https://doi.org/10.2307/1911963

Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge University Press. https://doi.org/10.1017/cbo9780511811241

Cheng, Y., Hui, Y., Liu, S., & Wong, W. K. (2022). Could significant regression be treated as insignificant? An anomaly in statistics. Communications in Statistics: Case Studies, Data Analysis and Applications, 8(1), 133–151.

Cheng, Y., Hui, Y., McAleer, M., & Wong, W. K. (2021). Spurious relationships for nearly non-stationary series. Journal of Risk and Financial Management, 14(8), 366.

Choi, I. (2001). Unit root tests for panel data. Journal of international money and Finance, 20(2), 249-272. https://doi.org/10.1016/S0261-5606(00)00048-6

Chu, L. K. (2021). Economic structure and environmental Kuznets curve hypothesis: New evidence from economic complexity. Applied Economics Letters, 28, 612–616. https://doi.org/10.1080/13504851.2020.1767280

Dietz, T., Rosa, E. A., & York, R. (2007). Driving the human ecological footprint. Frontiers in Ecology and the Environment, 5(1), 13–18. https://doi.org/10.1890/1540-9295(2007)5[13:dthef]2.0.co;2

Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49(4), 431–455. https://doi.org/10.1016/j.ecolecon.2004.02.011

Freitag, C., Berners-Lee, M., Widdicks, K., Knowles, B., Blair, G. S., & Friday, A. (2021). The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations. Patterns, 2(9), 100340. https://doi.org/10.1016/j.patter.2021.100340

Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press. https://doi.org/10.1017/cbo9780511790942

Global Footprint Network. (2023). National footprint accounts. https://www.footprintnetwork.org

Greene, W. H. (2003). Econometric analysis. Prentice Hall.

Hartmann, D., Guevara, M. R., Jara-Figueroa, C., Aristarán, M., & Hidalgo, C. A. (2017). Linking economic complexity, institutions, and income inequality. World Development, 93, 75–93. https://doi.org/10.1016/j.worlddev.2016.12.020

Hidalgo, C. A., & Hausmann, R. (2009). The building blocks of economic complexity. Proceedings of the National Academy of Sciences, 106(26), 10570–10575. https://doi.org/10.1073/pnas.0900943106

Hui, Y., Wong, W. K., Bai, Z., & Zhu, Z. Z. (2017). A new nonlinearity test to circumvent the limitation of Volterra expansion with application. Journal of the Korean Statistical Society, 46, 365-374. https://doi.org/10.1016/j.jkss.2016.11.006

Kashif, U., Shi, J., Naseem, S., Dou, S., & Zahid, Z. (2024). ICT service exports and CO₂ emissions in OECD countries: The moderating effect of regulatory quality. Economic Change and Restructuring, 57(3). https://doi.org/10.1007/s10644-024-09685-y

Maddala, G. S., & Wu, S. (1999). A Comparative Study of Unit Root Tests with Panel Data and a New Simple Test. Oxford Bulletin of Economics and Statistics, 61: 631-652. https://doi.org/10.1111/1468-0084.0610s1631

Mealy, P., & Teytelboym, A. (2020). Economic complexity and the green economy. Research Policy, 51(8), 103948. https://doi.org/10.1016/j.respol.2020.103948

Pesaran, M. H. (2006). Estimation and inference in large heterogeneous panels with a multifactor error structure. Econometrica, 74(4), 967–1012. https://doi.org/10.1111/j.1468-0262.2006.00692.x

Pesaran, M. H., & Yamagata, T. (2008). Testing slope homogeneity in large panels. Journal of Econometrics, 142(1), 50–93. https://doi.org/10.1016/j.jeconom.2007.05.010

Pinheiro, J. C., & Bates, D. M. (2000). Mixed-effects models in S and S-PLUS. Springer. https://doi.org/10.1007/978-1-4419-0318-1

Sadorsky, P. (2010). The impact of financial development on energy consumption in emerging economies. Energy Policy, 38(5), 2528–2535. https://doi.org/10.1016/j.enpol.2009.12.048

Salahuddin, M., & Alam, K. (2016). Information and communication technology, electricity consumption and economic growth in OECD countries: A panel data analysis. International Journal of Electrical Power & Energy Systems, 76, 185–193. https://doi.org/10.1016/j.ijepes.2015.11.005

Sbardella, A., Pugliese, E., Zaccaria, A., & Scaramozzino, P. (2018). The role of complex analysis in modelling economic growth. Entropy, 20(11), 883. https://doi.org/10.3390/e20110883

Stojkoski, V., Utkovski, Z., Jolakoski, P., Tevdovski, D., & Kocarev, L. (2022). Correlates of the country differences in the infection and mortality rates during the first wave of the COVID-19 pandemic: Evidence from Bayesian model averaging. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-10894-6

Wackernagel, M., Schulz, N. B., Deumling, D., Linares, A. C., Jenkins, M., Kapos, V., Monfreda, C., Loh, J., Myers, N., Norgaard, R., & Randers, J. (2002). Tracking the ecological overshoot of the human economy. Proceedings of the National Academy of Sciences, 99(14), 9266–9271. https://doi.org/10.1073/pnas.142033699

White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica, 48(4), 817–838. https://doi.org/10.2307/1912934

Wong, W. K., & Yue, M. (2024). Could regressing a stationary series on a non-stationary series obtain meaningful outcomes? Annals of Financial Economics, 19(3), 2450011.

Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT Press.

York, R., Rosa, E. A., & Dietz, T. (2003). Footprints on the Earth: The environmental consequences of modernity. American Sociological Review, 68(2), 279–300. https://doi.org/10.2307/1519769

Zhou, D., Saeed, U. F., Kongkuah, M., & Wiredu, I. (2024). Examining the moderating role of environmental regulations on financial development and ecological footprint in the MENA region. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-05430-7

Published

2026-06-01

How to Cite

Abid, I. (2026). Digital Economy and Environmental Sustainability in MENA Region: The Role of ICT and Economic Complexity. Advances in Decision Sciences, 30(4), 85-108. https://doi.org/10.47654/v30y2026i4p85-108