Green Growth in Arab Countries: A Predictive Analysis Based on Economic Complexity and Macroeconomic Indicators Using the XGBoost Machine Learning Algorithm

DOI

10.2991/978-94-6239-743-9_9How to use a DOI?

Keywords

Green Growth; Economic Complexity; Trade Complexity; Research Complexity; XGBoost Machine Learning Algorithm

Abstract

This study examines how trade and research complexity predict Arab green growth trajectories. A comprehensive framework of institutional, environmental, and macroeconomic indicators frames this analysis. A composite green growth index (GGI) was created using the directional distance function to monitor GDP and undesirable outputs (carbon emissions and energy intensity) from 2000 to 2022. The dataset includes 13 oil-dependent, diversified emerging, and structurally constrained Arab countries. The model uses the Trade Complexity Index (TCI) and Research Complexity Index (RCI) and 12 control variables to assess institutional and governance, development and human capital, market and economic structure, innovation and technological readiness, energy composition and sustainability, environmental pressures, and external influences. XGBoost, optimized via Bayesian hyperparameter tuning, is used in the systematic design to capture nonlinear relationships and interaction effects across multidimensional and heterogeneous data. Gain was used to determine each factor’s predictive performance contribution. The RCI predicted green growth best, followed by the Anti-Corruption Index (CC) and TCI. This analysis shows the importance of national innovation capacity, institutional integrity, and trade’s technological content. Traditional environmental indicators like carbon dioxide emissions and renewable energy use were less predictive, suggesting that structural and institutional factors drive green growth in Arab economies more than direct environmental interventions. These findings suggest replacing narrow environmental strategies with integrated policy frameworks that link innovation, trade development, and institutional reform to environmental goals.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Okba Abdellaoui
AU  - Issam Djouadi
AU  - Lotfi Mekhzoumi
AU  - Moussa Hezla
PY  - 2026
DA  - 2026/08/11
TI  - Green Growth in Arab Countries: A Predictive Analysis Based on Economic Complexity and Macroeconomic Indicators Using the XGBoost Machine Learning Algorithm
BT  - Proceedings of API Conference 2025: Empowering the future: Energy Transition and Economic Diversification in Arab Countries
PB  - Atlantis Press
SP  - 157
EP  - 180
SN  - 3005-155X
UR  - https://doi.org/10.2991/978-94-6239-743-9_9
DO  - 10.2991/978-94-6239-743-9_9
ID  - Abdellaoui2026
ER  -