Investigation of carbon footprint thresholds for designing climate neutral logistics warehouses in France at the early design stage

Reducing the environmental impact of logistics warehouses is a critical challenge, particularly during the early design phase when limited data is available to guide decision-making. This study aims to establish carbon footprint targets for logistics warehouses in alignment with climate neutrality objectives. Using Life Cycle Assessment (LCA) methodologies, the environmental impacts of 16 Lidl Company logistics warehouses in France were evaluated. A correlation analysis revealed that warehouse size and cold storage capacity are the strongest predictors of carbon footprint (Pearson coefficients of 0.78 and 0.68, respectively). Based on these relationships, a carbon footprint threshold function was developed using a linear regression model optimized by a Non-dominated Sorting Genetic Algorithm II (NSGA-II), achieving an error margin below 7%. The resulting model quantifies emissions per pallet space according to storage type, ranging from 1.11 TCO 2 eq/pallet for dry goods (high shelf) to 4.96 TCO 2 eq/pallet for fresh-produce block storage. These findings demonstrate that achieving carbon neutrality for logistics warehouses requires not only energy-efficient operations but also substantial reductions in embodied emissions through low-carbon materials and optimized design strategies. The predictive carbon footprint threshold function proposed here provides a robust, data-driven tool to guide the design of future industrial buildings aligned with national and international sustainability goals.