An ESG-Oriented Carbon Emission Monitoring System for Industrial Parks: UAV Remote Sensing and IoT Sensor Fusion Method Research

Abstract

The increasing emphasis on environmental, social, and governance (ESG) frameworks has highlighted the need for effective carbon emission monitoring systems, particularly in industrial parks that contribute significantly to environmental degradation. Although various monitoring methods exist, there remains a notable gap in integrating unmanned aerial vehicle (UAV)-based remote sensing with Internet of Things (IoT) sensor technologies for comprehensive, real-time carbon emission monitoring in complex industrial settings. This study proposes an integrated UAV–IoT system that combines high-resolution spatial data from UAV platforms with continuous in situ measurements from distributed IoT sensors. The system architecture, data fusion workflow, and communication protocols are designed to support real-time acquisition, transmission, and processing of multi-source environmental data. A case study conducted in an industrial park demonstrates that the integrated system substantially outperforms traditional fixed-point monitoring approaches in terms of spatial coverage, detection sensitivity, and timeliness of data delivery. UAV-based mapping effectively identifies emission hotspots and plume dispersion patterns, while IoT sensors capture localized concentration dynamics and relevant meteorological parameters. The fused dataset enables more accurate characterization of emission profiles and supports early warning and rapid response. The findings contribute to ESG-oriented decision-making by providing an operational, scalable solution for carbon emission monitoring, supporting low-carbon governance, regulatory compliance, and continuous improvement of sustainability performance in industrial parks and similar industrial clusters.