Optimising the environmental impact of Iran’s municipal roads using clustering techniques Available to Purchase

Assessing environmental impacts and prioritizing projects that minimize ecological harm is essential, especially in regions characterized by diverse climates and geographical features. This study presents a two-phase methodology aimed at optimizing environmental parameter coefficients for asphalt paving projects undertaken by municipalities in Iran. In the first phase, the Genetic Optimization Algorithm is employed to identify, categorize, and cluster coefficients associated with key environmental parameters. The second phase involves the development of a comprehensive environmental index that ranks proposed projects based on the derived coefficients, providing a systematic approach to environmentally conscious decision-making. The results indicate that water resource pollution is the most critical concern prior to project implementation, with a coefficient of 3.59. During the implementation phase, noise pollution emerges as the most significant factor (coefficient 5.89), while ecosystem damage is most pronounced during land use changes (coefficient 5.25). Soil pollution (coefficient 5.81) and local climate damage (coefficient 5.67) are dominant during the maintenance and operational phases, respectively. These findings provide practical insights for prioritizing road infrastructure projects, benefiting both urban and rural planning efforts.