船舶碳排放的预测研究在挖掘碳排放时空分布特征、揭示排放时空演化规律、制订和调整脱碳政策等方面具有重要的理论与实践意义。在大数据背景下，船舶碳排放测算和预测研究成果显著。然而，船舶航行状态辨识的准确性依旧制约船舶碳排放测算精度，而且，排放预测多集中于揭示时间维度的演化规律，尚未考虑兼顾时空双重维度的系统预测。为解决船舶碳排放测算中航行状态辨识误判率高，以及传统碳排放预测方法难以兼顾空间相关性等问题，首先，本文以轨迹段为基本单元，在综合考虑航行状态持续时间，地理活动范围和航向变化等多维运动特征的基础上，提出了一种时空轨迹搜索算法，可实现航行状态的精准辨识。其次，本文引入了深度预测学习领域的ConvLSTM方法，构建了一种面向船舶碳排放的时空预测模型，并提出一种基于GIS空间识别方法的时空数据集构建方法。该模型不仅可预测船舶碳排放在时间维度上的演化规律，还可通过卷积运算，充分挖掘碳排放在空间上的局部依赖关系。最后，以我国渤海海域为例，基于船舶AIS数据，开展了船舶航行状态辨识和碳排放预测实验。结果表明，本文提出的航行状态辨识算法准确率达90%以上，有效解决了依据实时航速和主机负载划分航行状态的误判问题。此外，本文提出的船舶碳排放测算模型可在排放数据集上稳定收敛，且能够准确预测排放的热点区域。

    Maritime transport is the backbone of international trade and global economy, but ocean-going ships are also causing increasing emissions problems. With the advent of the big data era, ship emission governance has also entered a new stage. The traditional measurement and prediction methods of ship carbon emissions have a number of challenges, including poor data quality, single dimension, limited precision and rough resolution. As a result, it is impossible to establish an adequate picture of the actual emissions produced by ships, and it is also difficult to predict the evolution trend of emissions in time and space dimensions, posing significant difficulties in the governance of carbon emissions from ships. At the moment, the International Maritime Organization (IMO) and China attach great importance to the decarbonization of ships, and have successively proposed and restated the 2050 shipping decarbonization target, as well as the "dual carbon" goal. The decarbonization of ships is becoming increasingly severe, and it is urgent to further accelerate the decarbonization process of ships.Based on the above background, in order to further improve China′s carbon emission measurement system and prediction theory, as well as increase the accuracy of ship carbon emission measurement and prediction, this study relies on the massive shipping big data resources, employs big data analysis technology and artificial intelligence theory, and conducts an in-depth research on the identification of navigation status in ship carbon emission measurement and the issue of ignoring spatial correlation in ship carbon emission prediction. The aim is to propose a novel navigation status identification method and a ship carbon emission prediction framework in the context of big data.The innovations of this study are mainly reflected in the following aspects: firstly, the traditional navigation status classification method only takes into account two factors: ship speed over ground and main engine load, and the error rate is considerable. In order to improve the accuracy of navigation status identification, a novel spatiotemporal trajectory probe algorithm is proposed based on a thorough examination of multidimensional motion characteristics, such as the duration of navigation statuses, the geographical coverage of ship activities, and the heading changes. It considers a trajectory segment as the base unit and can achieve high accuracy of navigation status identification. Secondly, existing ship carbon emission prediction methods mainly adopt time series data, which means they can only predict the time evolution pattern of carbon emission, but cannot reflect the spatial correlation. In order to realize multiple prediction of carbon emissions in space-time dimensions, a novel spatiotemporal prediction model for ship carbon emissions is proposed in this study, which applies the ConvLSTM method in the field of deep predictive learning. A spatiotemporal dataset creating approach based on GIS identity analysis is also provided. The model can not only predict the temporal evolution of ship carbon emissions, but also properly account for the spatial dependency of carbon emissions using convolution operations.Taking China′s Bohai Sea as an example, a verification experiment for the proposed novel navigation status identification algorithm and ship carbon emission prediction framework is designed in this study. The results show that, firstly, after incorporating more ship motion features, the spatiotemporal trajectory probe algorithm proposed in this study can accurately identify various navigation status with an accuracy rate of more than 90% when compared to traditional algorithms, which effectively improves the accuracy of ship carbon emission measurement. Secondly, the ship carbon emission prediction framework based on the ConvLSTM model combines the advantages of convolutional neural networks (CNN) in processing spatial relationships and recurrent neural networks (RNN) in dealing with time series problems, which and can converge stably on spatio-temporal slice datasets constructed based on GIS identity analysis. Furthermore, it can accurately predict the hotspots of ship emissions, enriching the theory of ship carbon emission prediction driven by deep learning technology.Based on the "dual carbon" goal of China, this study employs the emerging big data analysis technology and artificial intelligence algorithms to improve the carbon emission measurement system of ships, providing a new solution for the prediction of spatial and temporal multidimensional ship carbon emissions, which aids in the exploration of the characteristics of spatial and temporal distribution of carbon emission and the law of spatial evolution, and provides a theoretical basis for the formulation and adjustment of decarbonization policy.