Impact of AI-Based Route Optimization on Carbon Emission Reduction in Urban Construction Logistics: The Mediating Role of Vehicle Load Optimization

The purpose of this study is to evaluate the impact of AI-based route optimization on carbon emission reduction in urban construction logistics, with a particular focus on the mediating role of vehicle load optimization.The study adopts a cross-sectional quantitative research approach, targeting three urban locations in Ghana. The stratified random sampling was adopted to select 405 participants who answered a 5-point likert scale self-administered structured questionnaire. The data was entered and cleaned into SPSS. The descriptives, reliability and correlations were analyzed. The study further used the linear regression and mediation analysis to investigate the relationship between the study variables.The study findings showed that AI-based route optimization had a positive significant effect on carbon emission reduction and vehicle load optimization. Vehicle load optimization had a positive significant effect on carbon emissions reduction. Furthermore, the findings pointed to a partial mediation of AI-based route optimization on carbon emission reduction through vehicle load optimization.Ghanaian policymakers should invest in AI technologies and endorse vehicle load optimization solutions to meet carbon reduction targets, enhancing urban logistical efficiency. Future research should use longitudinal design, and focusing on rural construction logistics.