Exploring Carbon Emission Reduction in Inland Port Ship Based on a Multi-Scenario Model

Abstract

Assessing carbon emission reduction potential is vital for achieving carbon peak and neutrality in the maritime sector. In this study, we proposed a universal framework for assessing the effectiveness of different measures on carbon emission reduction from ships, including port and ship electrification (PSE), ship speed optimization (SSO), and clean fuel substitution (CFS). Firstly, the projection method of future ship traffic flows and activity levels relies on a neural network, and the ARIMA model was proposed. Then, the potential of various emission reduction measures was detailed and analyzed under different intensity scenarios. The proposed model was applied to Wuhan port, the results indicate that CFS is the most effective for long-term decarbonization, potentially achieving a carbon peak by 2025 under an aggressive scenario. For the short to medium term, PSE is favored due to technical maturity. SSO primarily delays emissions growth, making it a suitable auxiliary measure. These findings guide emission reduction strategies for ports, fostering green and sustainable shipping development.