A Framework of Adoption Factors for Successful IoT Implementation in Greenhouse Gas Emission Monitoring in the UAE

Abstract

This study presents the development of a framework that identifies and evaluates the key adoption factors for successfully deploying Internet of Things (IoT) technologies in the monitoring of greenhouse gas emissions within the United Arab Emirates. A unique aspect of this research lies in its focus on a specific sector, involving 384 employees from the UAE’s Department of Hazard Forecasting, Monitoring, and Control (HFMC), who are directly engaged with IoT-based emissions monitoring. The study employs a robust methodological approach, using Partial Least Squares (PLS) and Structural Equation Modelling (SEM) with SmartPLS software to analyze both the measurement and structural components of the model. The findings reveal that Interoperability and Compatibility (IC) is the most influential factor in greenhouse gas monitoring and utilization (GMAU), followed by Data Analytics and Processing (DAP) and Data Security and Privacy (DSP). Interestingly, Sensor Accuracy and Calibration (SAAC) and Connectivity and Network Infrastructure (CNI) were found to have negligible impacts. This study underscores the crucial importance of advanced data analytics capabilities and stringent data security measures in ensuring the effectiveness of IoT in emissions monitoring. Furthermore, it highlights that enhancing IC significantly boosts monitoring efficiency, providing novel insights into the factors that drive IoT adoption for environmental monitoring. The study’s findings also demonstrate that the proposed framework has strong predictive relevance, as evidenced by Q² values exceeding 0.35, which further reinforces its practical applicability. This research contributes novel insights into the deployment of IoT for environmental monitoring, offering a comprehensive guide for improving emissions tracking in the UAE.