## 2 Problem Description
These include Initial Resource Allocation Efficiency, Initial Path Algorithm Quality, Initial Path Modeling Accuracy, Monitoring Task Priority, Re-planning Theory Compatibility, Re-planning Monitoring Algorithm Quality, Re-planning Response Timeliness, Compliance of Inspection Task Execution, Monitoring Data Processing Standardization, Communication Link Stability, Monitoring Data Real-time Availability, Monitoring Target Value, Charging Facilities and Maintenance Equipment, UAV Equipment Status, and Number of Heavy Truck Area Sets, totaling 15 influencing factors. By identifying system elements, constructing the direct influence matrix, normalizing the influence matrix, calculating the comprehensive influence matrix, and evaluating the degree of influence and the degree of being influenced for each element, the causal diagram is constructed. This process is followed by determining the overall influence matrix and the reachability matrix, culminating in the delineation of the system elements' hierarchy and the construction of the hierarchical structure to reveal the interrelationships and the distribution of hierarchy among the elements of the complex system [27]. The DEMATEL-ISM analysis presents the center degree and cause degree in Figure 2. The influence degree and influenced degree are depicted in Figure 3. The hierarchical relationship is illustrated in Figure 4.  
Based on the outcomes of the DEMATEL-ISM analysis, monitoring task priority, initial resource allocation efficiency, initial path modeling accuracy, UAV equipment status, and the strengths and weaknesses of the initial path algorithm are identified as causal factors. These five factors are recognized as significant influencers within the research system, specifically concerning the optimization of UAV monitoring paths for heavy vehicle carbon dioxide emissions in port areas, and they exert an impact on other factors, necessitating enhanced focus on these elements during the research. Monitoring task priority, initial resource allocation efficiency, re-planning response timeliness, initial path modeling accuracy,  
Figure 2: DEMATEL centrality-impact diagram. re-planning theory fitness, re-planning response timeliness, and real-time monitoring data are the seven key indirect influencing factors on the research system, concerning the UAV monitoring of heavy vehicle carbon dioxide emissions in port areas. Initial resource allocation efficiency, re-planning theory adaptability, initial path algorithm effectiveness, initial path modeling precision, and compliance with detection task execution are intermediate tier factors that are closely associated with others and require focused attention. Taking into account the key insights, the influencing factors that warrant attention exhibit a hierarchical and progressive influence, ranging from the preliminary preparation of the research problem, including the condition of UAV equipment, to subsequent stages involving initial path planning elements such as initial resource allocation efficiency, initial path algorithm strengths and weaknesses, and initial path modeling accuracy, and finally to the re-planning phase. Accordingly, integrating the aforementioned data analysis and outcome assessment, building on prior research, affirms that, within the research problem at hand, the efficiency of resource allocation, modeling, and algorithmic problem-solving during the initial path planning stage are pivotal to the system's functionality. In light of this finding, this study proceeds to advance the initial path planning efforts within the  
Figure 4: ISM analytics impact factor hierarchy chart. Figure 3: DEMATEL influence-impact diagram. dynamic UAV monitoring for optimizing the carbon dioxide emission paths of heavy trucks in port areas, aiming to minimize the overall monitoring cost.