Advancing Sustainable Construction in Nigeria: Insights from Structural Equation Modeling - Unegbu, Hyginus Chidiebere Onyekachi; Yawas, D. S.; Alabi, A. A. (Jurnal Pengabdian KOLABORATIF, 2025)
The construction industry is a major driver of economic growth but remains one of the largest contributors to environmental degradation, resource depletion, and carbon emissions. This study examines the key determinants influencing the adoption of sustainable construction practices in Nigeria, focusing on environmental, economic, social, policy, and technological factors. A cross-sectional survey was conducted with 400 construction professionals, and data were analysed using Structural Equation Modelling (SEM) to establish causal relationships among the identified variables. The findings reveal that policy regulation (β = 0.37, p < 0.001), environmental factors (β = 0.42, p < 0.001), and climate change awareness (β = 0.38, p < 0.001) are the strongest predictors of sustainability adoption. The study also establishes that stakeholder collaboration mediates the relationship between policy regulation and sustainability adoption, while market demand for green buildings moderates the influence of economic feasibility. Despite increasing awareness of the benefits of sustainability, barriers such as high initial costs, weak regulatory enforcement, limited access to green financing, and slow technological adoption continue to hinder widespread implementation. The study underscores the need for robust policy enforcement, financial incentives, capacity-building programs, and the integration of smart construction technologies to drive sustainability adoption in Nigeria’s built environment. The findings provide valuable insights for policymakers, industry practitioners, and researchers, offering a strategic framework to accelerate the transition toward climate-resilient, energy-efficient, and economically viable construction practices.
Keywords: climate resilience; green building adoption; Nigeria’s built environment; structural equation modelling; sustainable construction