Climate change makes it necessary to implement precise carbon dioxide reduction measures, and establishing a port carbon dioxide emission inventory has become a key step in port management.However, the establishment of a port carbon emission inventory, while crucial for the execution of environmental policies, currently lacks effective monitoring and path planning schemes to support this process. To address these issues, we propose a path planning scheme for port-based carbon emission monitoring. The scheme analyzes the factors affecting the effectiveness of the scheme and establishes a hierarchy of these factors to optimize the monitoring path. Furthermore, a multi-objective path planning mathematical model is established in this article, introducing the optimal goals of economic and environmental costs to achieve the overall path planning objectives. Lastly, this paper focuses on the initial path planning problem of drone monitoring and proposes an improved A* algorithm (IEHA). The algorithm improves the search method of child nodes by eliminating nodes that collide with obstacles, thereby reducing the threat of path collisions. At the same time, the evaluation function is improved by introducing the average value and Gaussian distribution probability function into the heuristic function, enhancing the reliability of the evaluation function, reducing the length of the optimal path, and improving operational efficiency. In the simulation experiment results, the proposed algorithm outperforms A*, ACO, and IEA algorithms in optimizing path length, reducing collision points to ensure path safety, and robustness. This indicates that the proposed algorithm can effectively improve the path planning performance of drones in port carbon dioxide emission monitoring, providing an effective technical means for port carbon dioxide emission monitoring.

Bulk carriers play a critical role in global dry bulk transportation, and their safe operation is closely related to commodity supply chains, port continuity, and maritime governance. However, bulk carrier accidents are unevenly distributed across maritime space, and existing maritime blackspot studies are often limited by single-scale density estimation, unconstrained planar smoothing, and insufficient consideration of temporal persistence. These limitations make it difficult to distinguish robust accident-prone waters from scale-sensitive or temporally unstable hotspots. To address this problem, this study proposes a constrained multiscale consensus framework for identifying and interpreting global bulk carrier accident blackspots. The framework first screens and standardizes global maritime accident records to extract valid bulk carrier accident samples. It then constructs an ocean-constrained equal-area analysis grid and estimates severity-weighted accident intensity under multiple Gaussian smoothing bandwidths. Scale-specific hotspots are further extracted through threshold-based segmentation and minimum-area filtering, and a consensus persistence rule is developed to classify core, secondary, and transition blackspots. Finally, threshold sensitivity analysis, bootstrap resampling, time-window comparison, lifecycle classification, accident-type stratification, and severity-weighted versus frequency-only comparison are conducted to evaluate the robustness and interpretability of the identified blackspots. Based on 38,139 raw accident records, the empirical analysis retained 1441 cleaned bulk carrier accidents from 2015 to 2023 and identified 87 core consensus blackspots, covering approximately 8.06 million km² and containing 863 accidents. These blackspots are mainly concentrated in major coastal shipping regions, and the proposed framework provides a reproducible and geographically constrained basis for global maritime blackspot identification.