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.

Nowadays, more and more attention is being paid to environmental issues due to the development of road transportation, particularly the construction of arterial roads. Despite the existence of diverse methods to determine convenient criteria for their assessment, determining the projects with the least harmful effects on the environment and ranking them for purposes of budget allocation and prioritization are remarkably important. The case is more highlighted in regions where roads go through diverse areas with different climatic and geographical distributions. In the present study, a new method consisting of two phases was proposed to determine the optimal coefficient of environmental parameters in road construction parameters. In the first phase, the Genetic Optimization Algorithm was implemented to determine convenient coefficients for the relevant parameters. During this stage, similar coefficients were clustered together. In the second phase, an environmental index for various projects was determined based on the obtained results, and the proposed projects were ranked based on that. According to the results obtained concerning environmental parameters during the pre-implementation stage, polluting water resources was the most influential parameter, with a coefficient determined at 3.59. Moreover, the most significant parameter during the implementation was noise pollution, with a coefficient of 5.89, while damaging the ecosystem was the most significant one during the stage of land use change (5.25). Furthermore, soil pollution was the most remarkable parameter during the stage of maintenance (5.81), while damaging the local climate pollution was the most important one during the stage of road implementation (5.67). The above findings can be helpful for researchers in road construction projects.