Research on Multi-Objective Energy Efficiency Optimization Method of Ships Considering Carbon Tax

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.

Automated summary

This study classified the navigation environment between Wuhan and Shanghai in China using an improved K-means algorithm and established a fuel consumption prediction model considering navigation environment factors. Speed optimization models with multiple different optimization objectives were constructed and tested based on an artificial fish swarm algorithm. Sensitivity analysis focused on navigation time, fuel price, charter rate, free carbon credits, and carbon tax rate. The results showed that optimization could reduce total shipping cost and CO2 emissions by 0.94% and 0.38% respectively. With a carbon tax policy, the optimized result was close to the compromised solution of multi-objective optimization, providing a useful reference for policymakers.