Resumen
Various measures have been taken to improve ship energy efficiency while decreasing CO2 emissions. In this work, the navigation environment between Wuhan and Shanghai in China has been classified based on an improved K-means algorithm in order to realize route division. A fuel consumption prediction model considering the navigation environment factors has been established. Consequently, speed optimization models with multiple different optimization objectives have been constructed and tested based on an actual case using an artificial fish swarm algorithm. Finally, sensitivity analysis has been carried out focusing on the navigation time, fuel price, charter rate, free carbon credits, and carbon tax rate. The results show that the total shipping cost and CO2 emissions could be reduced by 0.94% and 0.38%, respectively, after the optimization. Considering a carbon tax policy with a tax rate of roughly 1300 RMB/t, the optimization result (including the carbon tax cost) is close to the compromised solution of multi-objective optimization, and the corresponding carbon tax rate can provide a useful reference for policymakers.