Abstract
Market-based measures, like the carbon cap-and-trade (CCT) scheme, can effectively reduce emissions and promote green technology by internalizing the external costs in the shipping industry. Under the CCT scheme, the government sets the quantity commitment of carbon emissions for a unit of shipping work, and the excessive quantity must be traded from the inner shipping market. To cope with carbon regulation, carriers can adjust their ship deployment plan and adopt new engine technologies to support low-carbon fuels. This paper proposes a bi-level programming model to investigate the effects of the CCT scheme on the decisions of carriers in a competitive inland container shipping network. At the upper level, carriers adjust their own ship deployment plan and the choice of technology and fuel type. At the lower level, the shippers between different origins and destinations choose the shipping lines with the given ship deployment plans of carriers. Two kinds of equilibrium are considered in the proposed problem: the competitive equilibrium of carriers on freight shipment in the inland shipping network and the trading equilibrium of carbon quotas. The former equilibrium can be captured by a traffic assignment model in a similar transit transportation network, and the latter equilibrium follows the idea of the demand–supply equilibrium of carbon quotas in the traditional market. The two equilibria interact since the freight distribution affects the shipping revenue of each ship and the trading equilibrium is related to the total cost of the ship. The properties of the optimal choice of technology and fuel type are theoretically studied. We find that the optimal choice of fuel technology for each ship assigned to any shipping line at any given CCT must be Pareto optimal in the sense of cost-related and emission-relative measures. Based on the result, the optimal solution of the proposed model can be calculated by a sampling-based method. Numerical examples based on the Yangtze River are further adopted to illustrate the proposed model and conclusions.
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