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To address the issue of intelligent ship route planning, a ship planning method based on the improved D* Lite algorithm is proposed. Firstly, a navigation environment grid map is constructed using the acquired meteorological and hydrological datasets. The grids are divided into navigable and non-navigable according to navigation requirements, and a route planning model is built. Secondly, the heuristic function and the path function of the D* Lite algorithm are improved. The heuristic function is optimized and weighted, and a risk factor is introduced into the path function to enhance efficiency of path planning while maintaining a safe distance between the planned route and obstacles. Finally, by dynamically adjusting the search step length and the selectable directions of the D* Lite algorithm, the number of waypoints is reduced, and the voyage of the planned route is shortened, resulting in a smooth and collision-free route of ships. The effectiveness of the proposed algorithm is verified through three sets of simulation experiments. The simulation results show that the proposed method in this paper is more suitable for ship route planning and ship maneuvering in practice and can effectively avoid non-navigable grids while optimizing path length, path smoothness, and computation time, making the routes more aligned with actual navigation tasks.

China’s ambitious measures for developing a low-carbon economy led to the “double carbon” target initiation. Under this national goal, reaching peak carbon emissions by 2030 is desired. This should not come at the cost of economic growth; which means carbon emissions can be reduced while economic growth can be achieved simultaneously. To realise this strategic reform, the first pilot ecological civilisation zone in Fujian Province of China was initiated; its outcome is set to be the responsible case for such initiations aiming at increased low-carbon economy development. Therefore, it is essential to investigate the relationship between carbon emissions and economic growth based on the evidence. Hence, we applied a model that combines the Tapio and Coupled coordination. Combining the Tapio and the coupled coordination models allows us to analyse carbon emissions and economic growth in Fujian Province over 20 years, i.e., 2001–2020. First, we divided the urban-rural integration process into four stages following China’s Five-Year Plan (FYP): T1 (2001–2005), T2 (2006–2010), T3 (2011–2015), and T4 (2016–2020). Second, ArcGIS mapping was used to represent the spatial evolution pattern of low-carbon economic development in Fujian Province. We observed that the low-carbon economy in Fujian Province had reached a point where the economic growth rate has already exceeded its carbon emission growth rate and is currently in a weak decoupling state. In addition, there observed a bifurcation pattern between carbon emissions and economic growth, especially in the coastal cities that are out of balance, whereas the inland cities are being coordinated. Overall, it is observed that the concept of ecological civilisation is crucial for China to achieve the “double carbon goal,” and it is high time to create accelerating measures that guide the integration of urban and rural areas in the future with appropriate infrastructure.