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Economic Signals and Artificial Intelligence for Sustainable Transport and Low-Carbon Logistics Systems

Turdiev Abdullo SagdullaevichAlfraganus University, Tashkent, UzbekistanS. AarthiMarwadi University, Rajkot, IndiaR. N. RavikumarMarwadi University, Rajkot, IndiaShoyimkulov AsrorTermez University of Economics and Service, Termez, UzbekistanJabbarov Umarbek
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Supply chains involve transport and logistics systems that are significant sources of global carbon emission and thus their decarbonization is a key sustainability issue. This chapter discusses how economic indicators and Artificial Intelligence (AI) may be combined to promote low-carbon transport and logistic systems. It takes a conceptual and analytical approach based on recent case studies on urban transport and freight logistics. The paper shows that the optimization through AI, when accompanied by the emissions-based pricing and incentives, could be an effective way of enhancing the efficiency of the supply chain and minimizing logistics-related emissions. The chapter makes a contribution because it suggests a single framework that interconnects economic indicators, AI-based decision-making, and regulatory policies. The results indicate that efficient transport decarbonization involves collaborative signal planning to provide sustainable, efficient and scalable mobility and logistics networks.

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