Carbon Footprint Estimation of Conventional and Geosynthetic Reinforced Flexible Pavements

India’s vast road network requires an immense quantity of natural aggregates for constructing asphalt and granular layers. The processes involved, including quarrying, transportation, and hauling, contribute significantly to the carbon footprint, leading to direct environmental impacts. This extensive consumption underscores the pressing need for sustainable approaches to minimize the use of natural aggregates and mitigate the carbon footprint associated with road infrastructure development. Given the increasing scarcity of natural aggregate resources and the growing need to reduce environmental impacts, this study evaluates the benefits of incorporating geogrids to enhance pavement performance while lowering aggregate consumption and carbon emissions. This study aims to estimate the carbon footprint of flexible pavements by comparing conventional pavements and geosynthetic-reinforced (geogrid) under various subgrade conditions according to Indian Road Congress (IRC) 37 guidelines. The analysis considers different subgrade conditions with California Bearing Ratio (CBR) varying from 5 to 15%, traffic levels up to 50 million standard axles (MSA), and realistic Modulus Improvement Factor (MIF). The MIF values adopted in the study range from 1.75 to 3 for geogrid-reinforced base layers, depending on CBR conditions. The reductions in aggregate consumption and carbon footprint for geogrid-reinforced pavements (conventional) are estimated using realistic MIF values. Preliminary findings indicate that sustainable geosynthetic reinforcement can significantly curb carbon emissions by reducing the quantity of raw materials and transport energy requirements. This research contributes to sustainable construction practices by demonstrating the potential environmental benefits of using geogrids in pavement design, supporting the broader goals of reducing the natural aggregate consumption and carbon emissions in the road infrastructure sector.