Integration of Thermal Comfort Modelling and Digital Technologies for Sustainable Circular Economy Practices in Built Environments
Keywords:
Thermal comfort, adaptive modeling, digital technologies, circular economy, occupant behaviorAbstract
The convergence of thermal comfort research and digital technologies presents novel opportunities to enhance sustainability within built environments while simultaneously enabling circular economy practices. Thermal comfort has been extensively studied through both static and adaptive models, reflecting the complex interplay between human physiology, clothing behavior, environmental parameters, and occupant preferences (Fanger, 1970; De Dear & Brager, 1998; De Carli et al., 2007). Recent advancements in predictive maintenance, Internet of Things (IoT), and machine learning enable real-time monitoring and adaptive control strategies to optimize indoor environmental conditions, reduce energy consumption, and support sustainable practices across building operations (Farhan et al., 2015; Nayak, 2021). Furthermore, circular economy frameworks emphasize resource efficiency, product longevity, and digital integration to influence consumer behavior toward sustainable consumption (Chaudhuri et al., 2022; Charnley et al., 2022). This research synthesizes theoretical and applied knowledge from thermal comfort modeling, occupant behavior, and digital-enabled circular practices to propose a holistic framework for sustainable building management. Emphasis is placed on the interaction between occupant clothing adaptation models, digital monitoring technologies, and circular economy strategies, highlighting the potential to align human comfort with energy efficiency and resource conservation objectives. The findings suggest that combining advanced data-driven modeling of occupant thermal behavior with digital technologies can significantly reduce environmental impacts while supporting the transition toward circular operational models in buildings. This study addresses critical knowledge gaps by linking thermal comfort theory, predictive digital frameworks, and circular economy applications in a comprehensive analytical context.
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