An Investigation into effective energy behavior control strategies in the apparel manufacturing sector

Abstract

Energy consumption in the apparel industry is high, driven by intense usage of Heating, Ventilation, and Air Conditioning (HVAC) systems, lighting, and machines. Given its intensive nature and high carbon footprint, implementing efficient energy behavioural control strategies is crucial for advancing sustainability goals. This study identifies, evaluates, and rates thirteen strategies spanning technological, managerial, legal, and social domains through a comprehensive literature analysis and expert validation using the Relative Importance Index (RII) technique. Findings demonstrate a high preference for technologically driven interventions such as Artificial Intelligence (AI) and machine learning based predictive control, automation and smart controls, and smart metering systems, which enable precision, scalability, and real-time adaptability. Mid-tier tactics like incentive programs, dashboards, and training were regarded vital, while behaviour focused, legal, and social initiatives were ranked less effective due to perceived complexity, longer return scopes, and lower direct impact. The study underlines the necessity of a hybrid implementation approach that combines high impact technology with structured behavioural support mechanisms to foster sustainable energy practices. Limitations include potential sample bias in expert replies and a focus on large-scale manufacturing contexts, which may not completely represent issues experienced by small and medium enterprises (SMEs). Future studies should explore contextual barriers to strategy adoption across different organizational sizes and cultures and analyse the long-term performance of integrated technical behavioural models.