<![CDATA[The oil and gas industry is facing increasing pressure to reduce carbon dioxide (CO₂) emissions while maintaining operational efficiency and regulatory compliance. However, conventional cloud-based emissions monitoring systems often suffer from latency, bandwidth limitations, and reduced reliability in remote operational environments. This study aims to explore the role and implementation of edge computing as an enabling technology for real-time CO₂ emissions monitoring in oil and gas facilities. The architecture of the proposed system integrates the Internet of Things (IoT) gas sensors, local data processing units, and cloud platforms for long-term analytics and reporting. The system is designed to process the emissions data closer to the source with the aim of significantly reducing latency, improving measurement reliability, and enhancing responsiveness to abnormal emissions events. The performance was subsequently evaluated through a distributed monitoring test scenario that simulated multiple emissions monitoring points under intermittent network connectivity conditions often experienced in remote oil and gas operations. The focus was on key performance indicators including data latency, bandwidth utilization, and system availability. The results showed that the edge-enabled architecture reduced average data latency from approximately 3.8 s to 0.9 s and data transmission volume by an estimated 79% through local preprocessing while maintaining monitoring availability above 96% during network disruptions. The trend reflected the ability of edge computing to provide a scalable and robust solution for continuous CO₂ emissions monitoring, particularly in geographically distributed and harsh operational environments often associated with oil and gas operations.]]>
