High Spatio-temporal Resolution CO2 Emission Estimation Model for Chinese Crude Oil Maritime Transportation

With the accelerating pace of the construction of the 21st Century Maritime Silk Road, China's maritime trade volume has been increasing, leading to the rapid growth of CO₂ emissions from ships. To achieve the "dual-carbon" goal and develop green shipping, it is necessary to understand the current situation of CO₂ emission from ships and provide data support for emission reduction efforts. This study focuses on the trajectory of oil tankers, employing a high-resolution ship CO₂ emission estimation method to reveal the spatial and temporal characteristics of China's crude oil maritime CO₂ emissions and the influencing factors of these emissions. This research constructs a high-resolution ship CO₂ emission estimation framework consisting of the three parts of “ship behavior semantic mining - CO₂ emission model construction - multi-level spatial and temporal analysis”. The study discards the rough classification method based solely on ship speed and achieves a fine classification of ship working conditions through multi-source data fusion and semantic reasoning based on Bayesian networks. The ship CO₂ emission estimation model considers multiple factors, including geographic scenario, ship type, fuel type, engine type, activity port, and activity time. By estimating the CO₂ emissions of Chinese oil tankers from 2014 to 2017, this study analyzes the temporal and spatial characteristics. These characteristics are analyzed across different spatial scales, such as global and port levels, and temporal scales, such as yearly, quarterly, monthly, and daily. The results show that China's crude oil maritime CO₂ emissions face significant pressure for reduction. Emissions can be mitigated through ship equipment modification, ship production planning, and port power supply improvements. This study contributes to a more comprehensive understanding of China's crude oil maritime CO₂ emissions and provides a decision-making reference for targeted emission reduction initiatives. Additionally, the methodology of this study is generalizable and can be applied to study CO₂ emissions from different types of ships in other countries or regions.