## 2 Problem Description  
The main issue investigated in this study is the monitoring of carbon dioxide emission data on the land side of ports. The aim is to offer ports with more precise carbon dioxide emission inventories (CEIs). The discussion is structured as follows: Initially, our study introduces the UAV monitoring path planning scheme proposed for monitoring carbon dioxide emissions from heavy vehicles on the land side of ports, as illustrated in Figure 1. Carbon dioxide emission data monitoring stations are established within the port area, and a fleet of UAVs is deployed to conduct real-time monitoring of carbon dioxide emissions from the distributed heavy vehicle groups within the port. UAVs execute monitoring tasks based on the assignments from the monitoring station. During the initial monitoring phase, each UAV is assigned a specific destination and proceeds to these destinations safely and efficiently to perform the monitoring as directed. In the second phase, based on the completion status of UAV A's tasks, a secondary assignment is considered. If UAV A's battery level falls below the minimum required for operation, and if another UAV B with sufficient battery life is available to meet the monitoring requirements, a secondary dispatch for the monitoring task can be made nearby. Should there be no available idle UAVs in the current system, a new UAV C is dispatched by the port monitoring station to undertake new monitoring tasks, while the original UAV A returns to the station for battery recharge. Throughout this process, UAVs maintain data integration with the port carbon dioxide emission monitoring station, ensuring real-time surveillance, task dispatch, and data collection and organization. The selection of UAVs equipped with gas sensors (pollution gas sensors) for carbon dioxide emission monitoring of heavy vehicles in port landslide areas is based on their comprehensive advantages in flexibility, cost-effectiveness, real-time data transmission, and safety. UAVs can respond quickly, flexibly adjust monitoring routes, and transmit high-precision data in real time, making them particularly suitable for short-term, high spatiotemporal resolution monitoring tasks in complex environments like ports. In comparison, ground sensor monitoring is suitable for long-term, stable monitoring tasks but is costly and has limited coverage. Satellite monitoring, while covering a large area, has low data precision and cannot meet the detailed monitoring needs of specific port areas. Therefore, UAV monitoring has unique advantages and application value in port carbon dioxide emission monitoring [22; 23; 24]. Secondly, the DEMATEL-ISM analysis is conducted to assess the influencing factors on the accuracy of the final carbon dioxide emission inventory (CEI), to guide the subsequent research focus of this paper [25; 26]. Finally, addressing the key issues identified through  
Figure 1: The path planning scheme for monitoring carbon dioxide emissions from heavy vehicles on the land side of ports. the analysis of influencing factors, we explore how to devise the initial UAV monitoring path for carbon dioxide emissions from heavy trucks in ports, while also considering UAV monitoring safety and path optimization. The DEMATEL-ISM analysis of the influencing factors in the entire monitoring scheme on the accuracy of the finally formed carbon dioxide emission inventory (CEI) proceeds as follows:  
In the study of port carbon dioxide emission monitoring, keywords such as \"port carbon dioxide emission\", \"carbon dioxide emission monitoring\", \"direct carbon dioxide emission\", \"indirect carbon dioxide emission\", \"monitoring path planning\", \"drone path planning\", and \"path planning influencing factors\" were utilized in the research conducted on China Knowledge Network and Wanfang Data. By extensively reading and analyzing influential journals and papers, and consulting with experienced logistics and transportation industry experts, a comprehensive list of factors influencing the monitoring process was identified.