Investigating Barriers to the Adoption of Energy Management Practices for Sustainable Construction Projects: SEM and ANN Approaches
Publish place: Civil Engineering Journal، Vol: 10، Issue: 4
Publish Year: 1403
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_CEJ-10-4_015
تاریخ نمایه سازی: 1 خرداد 1403
Abstract:
This research addresses the critical challenges hindering the integration of Energy Management Practices (EMPs) within the construction industry, impeding its progress toward sustainability. Recognizing the pivotal role of EMPs in fostering sustainable practices, this study aims to fill a notable research gap by conducting a meticulous survey involving ۱۰۰ industry professionals. Through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Artificial Neural Network (ANN) analyses, this study provides a comprehensive exploration of the intricate barriers and their interrelated dynamics within the construction sector. The findings reveal significant financial obstacles, including higher initial costs and limited financing options, underscoring the need for interventions to alleviate financial constraints. Additionally, policy and regulatory challenges, such as limited government incentives and shifting energy management rules, are identified, highlighting the necessity for stable and supportive regulatory environments to foster EMP adoptions. This research provides unique insights into the barriers hindering EMP adoption within the construction sector. The implications of this study extend beyond EMP adoption, offering a foundation for advancing sustainable practices in the construction industry. The insights gained can inform both academic research and practical decision-making, contributing to the ongoing discourse on sustainability in construction. Doi: ۱۰.۲۸۹۹۱/CEJ-۲۰۲۴-۰۱۰-۰۴-۰۱۵ Full Text: PDF
Keywords:
Barriers , Energy Management Practices (EMP) , Construction Industry , Overall Sustainable Success (OSS) , Partial Least Squares Structural Equation Modeling (PLS-SEM) , Artificial Neural Network (ANN).
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