The global pursuit of a sustainable energy future has ignited a keen interest in harnessing renewable energy resources. These resources, including solar, wind, hydroelectric, geothermal, tidal and biomass, offer a transformative solution to address critical challenges such as energy security, climate change, and resource depletion. Basra, strategically located in the Middle East, has historically been reliant on its oil sector for economic prosperity. However, as the world shifts towards sustainability, Basra’s energy landscape is required for a significant transformation. This paper reviews the immense potential of renewable energy sources in Basra. For instances, the long sunshine hours (∼9 hours per day) and rich agriculture biomass waste in Basra have suggested solar and bioenergy as the efficient alternative renewable energy source. The implementation of wind and geothermal energy, however, are restricted due to the relative low average wind speed (∼5 m/s) and limited geothermal gradient in this area. In addition, the challenges that must be overcome to achieve a sustainable energy transition is also emphasized. The successful implementations of renewable energy in regions similar to Basra, was also highlighted. Additionally, the paper discusses government policies, future prospects, and the potential impact on renewable energy development in Basra. This comprehensive analysis will interest readers in academia and government sectors, providing a holistic view of Basra’s journey towards a sustainable energy future.

Traditional Pavement Condition Index (PCI) assessments are highly resource-intensive, demanding substantial time and labor while generating significant carbon emissions through extensive field operations. To address these sustainability challenges, this research presents an innovative methodology utilizing Gene Expression Programming (GEP) to determine PCI values based on International Roughness Index (IRI) measurements from Iraqi road networks, offering an environmentally conscious and resource-efficient approach to pavement management. The study incorporated 401 samples of IRI and PCI data through comprehensive visual inspection procedures. The developed GEP model exhibited exceptional predictive performance, with coefficient of determination (R²) values achieving 0.821 for training, 0.858 for validation, and 0.8233 overall, successfully accounting for approximately 82–85% of PCI variance. Prediction accuracy remained robust with Mean Absolute Error (MAE) values of 12–13 units and Root Mean Square Error (RMSE) values of 11.209 and 11.00 for training and validation sets, respectively. The lower validation RMSE suggests effective generalization without overfitting. Strong correlations between predicted and measured values exceeded 0.90, with acceptable relative absolute error values ranging from 0.403 to 0.387, confirming model effectiveness. Comparative analysis reveals GEP outperforms alternative regression methods in generalization capacity, particularly in real-world applications. This sustainable methodology represents a cost-effective alternative to conventional PCI evaluation, significantly reducing environmental impact through decreased field operations, lower fuel consumption, and minimized traffic disruption. By streamlining pavement management while maintaining assessment reliability and accuracy, this approach supports environmentally responsible transportation systems and aligns contemporary sustainability goals in infrastructure management.