This research analyses the heterogeneous effects of digital infrastructure on sustainable economic growth in five developing countries (Egypt, India, Kenya, Saudi Arabia and Sudan) using the period 2014-2025. We use panel fixed effects with Driscoll-Kraay standard errors to investigate the effect of internet penetration, mobile broadband and fixed broadband on the GDP per capita growth. Our results suggest that digital infrastructure has a statistically and economically significant impact on economic growth. Internet penetration yields the largest benefits, followed by mobile broadband, whereas fixed broadband is not statistically significant in the full model, which is reflective of limited access in these countries. An exploratory Random Forest analysis with the important caveat of limited sample size suggests that internet penetration is the most important predictor of growth, followed by mobile broadband and human capital. However, these machine learning results should be considered exploratory given the small sample (N=60, or N=48 when excluding Sudan) and should not be over-interpreted. Our heterogeneity analysis finds that internet penetration drives growth in middle-income countries (Egypt, India), while mobile broadband drives growth in low-income countries (Sudan, Kenya). The moderation by human capital is large: the marginal impact of internet penetration more than doubles once average education exceeds six years. However, we note that this threshold is illustrative, based on the distribution of the sample and not necessarily a policy cutoff. The results have implications for SDG 4 (quality education), SDG 9 (infrastructure and innovation) and SDG 10 (inequality reduction). However, with our purposively selected five-country sample, these results are better considered as case-based evidence rather than statistically representative of all developing economies. The stark digital divide is evident in the 87% internet penetration in MENA countries compared to 44% in Sub-Saharan Africa and underscores the need for context-specific policy approaches. We suggest that low-income countries focus on expanding mobile broadband and middle-income countries make complementary investments in internet infrastructure and human capital, but emphasize that these policy suggestions are indicative rather than conclusive.

The Gulf Cooperation Council (GCC) countries face the challenge of balancing their reliance on hydrocarbon resources with ambitious renewable energy transition goals, including initiatives such as Saudi Vision 2030. Despite these commitments, renewable energy deployment in the region remains relatively limited, highlighting the need to better understand the factors associated with renewable energy consumption. This study investigates the macroeconomic factors associated with renewable energy consumption in the six GCC countries over the period 2000–2024 using a hybrid methodology combining panel econometric methods and machine learning. Panel data were compiled from the World Bank and the International Energy Agency. The econometric results identify research and development (R&D) expenditure and trade openness as the two most important predictors of renewable energy consumption, jointly explaining approximately 63% of the variation (adjusted R² = 0.629). The random forest model supports these findings by ranking R&D expenditure as the most influential predictor, followed by trade openness. GDP, foreign direct investment, and inflation were not found to be significantly associated with renewable energy consumption in the final models and the structural characteristics of GCC economies. The random forest model also achieved an out-of-sample R-squared of 0.425 with a low RMSE (0.032), suggesting satisfactory predictive performance for an initial model. Sub-period analysis further suggests that the associations of R&D expenditure and trade openness strengthened after 2015, coinciding with the implementation of national energy transition strategies across the GCC. These findings suggest that technological innovation and economic openness may support the region’s energy transition, with greater investment in R&D and stronger international trade integration potentially facilitating renewable energy adoption.

The Gulf Cooperation Council (GCC) states have to find a way to balance their reliance on hydrocarbon resources and their ambitious aims on moving to renewable energy (for instance, the Saudi Vision 2030.) Despite the policies in place, the implementation of renewable energy technologies in the region is relatively low indicating that more needs to be understood about the reasons behind the use of renewable energy. The present research will analyze the macroeconomic determinants of renewable energy consumption in the 6 GCC states for the periods 2000-2025 using a combination of panel econometric techniques and machine learning. Panel data were constructed from data from the World Bank and the International Energy Agency. The econometric results show that R&D expenditure and trade openness are the two most important determinants of renewable energy consumption, which jointly explain about 63% of the variation (adjusted R² = 0.629). The random forest model is consistent with these findings, with R&D expenditure as the most important predictor followed by trade openness. No statistically meaningful relationship was found between GDP, foreign direct investment, inflation and renewable energy consumption in the final models due to the structural characteristics of GCC economies. The random forest model also had a low RMSE value of 0.032 and an out-of-sample R-squared of 0.425, indicating satisfactory predictive performance for an initial model. Sub-period analysis also indicates that the relationships between R&D expenditure and trade openness became stronger after 2015, which coincides with the introduction of national energy transition strategies in the GCC. The study implies that innovation in terms of technology as well as the open nature of economy could promote energy transition in the region whereby high amount of investments in research and development are required as well as deeper international trade linkages might be supporting the acceptance of green energy approach.

This paper investigates the macroeconomic determinants of renewable energy consumption in Saudi Arabia during 1990–2025 using a hybrid approach of ARDL bounds testing and Random Forest machine learning with SHAP (SHapley Additive ExPlanations) to improve model interpretability. We employ annual data from the World Development Indicators to examine the effect of high-technology exports, trade openness, foreign direct investment, inflation and GDP on renewable energy consumption. Our results indicate that renewable energy consumption is highly path dependent, with previous adoption playing a significant role in current consumption (β = 0.776, p < 0.01). Trade openness also shows a negative contemporaneous effect ( = 0.0013, p 0.05) in line with the carbon lock-in hypothesis that typifies hydrocarbon-dependent economies. High-technology exports as a proxy for technological innovation do not appear to be a significant driver, indicating that the innovation-led energy transition in Saudi Arabia is still maturing. The Random Forest model confirms the importance of persistence effects explaining about 73 % of the variance. The analysis of the different time periods indicates that, after 2016 (also known as Vision 2030), there has been a significant change in the structure of the analyzed data, with the statistical model of research becoming much stronger as its explanatory power has reached up to 83%. The analysis of the SHAP allows understanding of the obtained results by measuring the influence of each individual variable. The findings that were obtained pertain to Sustainable Development Goal 7 (which focuses on developing cheap and clean energy), Sustainable Development Goal 9 (which focuses on industry, innovation, and infrastructure), and Sustainable Development Goal 13 (which focuses on climate action). It is our proposal that Saudi Arabia adopt focused innovation strategies, careful management of its trade integration, and a commitment to the institutional framework in order to accelerate the transition to renewable energy. However, it is important to note that this recommendation is suggestive and not definitive.

The Gulf Cooperation Council (GCC) countries face the challenge of balancing their re-liance on hydrocarbon resources with ambitious renewable energy transition goals, in-cluding initiatives such as Saudi Vision 2030. Despite these commitments, renewable energy deployment in the region remains relatively limited, highlighting the need to better understand the factors that drive renewable energy consumption. This study investigates the macro-economic determinants of renewable energy consumption in the six GCC countries over the period 2000-2024 using a hybrid methodology of stepwise regression analysis and Random Forest machine learning. Panel data were compiled from the World Bank and the International Energy Agency. The econometric results identify Research and Development (R&D) expenditure and trade openness as the two most important determinants of renewable energy consumption, jointly explaining approximately 63% of the variation (Adjusted R² = 0.629). The Random Forest model supports these findings by ranking R&D expenditure as the most influential predictor, followed by trade openness. GDP, foreign direct investment, and inflation were dropped significantly from the final model because of multicollinearity and the struc-tural characteristics of GCC economies. The Random Forest model also achieved an out-of-sample R-squared of 0.425 with a low RMSE (0.032), demonstrating satisfactory predictive performance. Sub-period analysis further shows that the influence of R&D expenditure and trade openness become more pronounced after 2015, alongside the implementation of national energy transition strategies across the GCC. These findings underline the importance of technological innovation and economic openness in sup-porting the region’s energy transition that greater investment in R&D and stronger in-ternational trade integration can help renewable energy adoption.

This research analyses the heterogeneous effects of digital infrastructure on sustainable economic growth in five developing countries (Egypt, India, Kenya, Saudi Arabia and Sudan) using the period 2014–2025. We use panel fixed effects with Driscoll–Kraay standard errors to investigate the effect of internet penetration, mobile broadband and fixed broadband on the GDP per capita growth. Our results suggest that digital infrastructure has a statistically and economically significant impact on economic growth. Internet penetration yields the largest benefits, followed by mobile broadband, whereas fixed broadband is not statistically significant in the full model, which is reflective of limited access in these countries. An exploratory Random Forest analysis with the important caveat of limited sample size suggests that internet penetration is the most important predictor of growth, followed by mobile broadband and human capital. However, these machine learning results should be considered exploratory given the small sample (N = 60, or N = 48 when excluding Sudan) and should not be over-interpreted. Our heterogeneity analysis finds that internet penetration drives growth in middle-income countries (Egypt, India), while mobile broadband drives growth in low-income countries (Sudan, Kenya). The moderation by human capital is large: the marginal impact of internet penetration more than doubles once average education exceeds six years. However, we note that this threshold is illustrative, based on the distribution of the sample, and not necessarily a policy cutoff. The results have implications for SDG 4 (quality education), SDG 9 (infrastructure and innovation) and SDG 10 (inequality reduction). However, with our purposively selected five-country sample, these results are better considered as case-based evidence rather than statistically representative of all developing economies. The stark digital divide is evident in the 87% internet penetration in MENA countries compared to 44% in Sub-Saharan Africa and underscores the need for context-specific policy approaches. We suggest that low-income countries focus on expanding mobile broadband and middle-income countries make complementary investments in internet infrastructure and human capital, but emphasize that these policy suggestions are indicative rather than conclusive.