The Role of Artificial Intelligence in Environmental Sustainability with an Emphasis on Construction Waste Management

Document Type : Research Paper

Authors

Department of Civil Engineering, Faculty of Engineering, Hakim Sabzevari University, Sabzevar, Iran

Abstract

Objective: This study examines the dual role of artificial intelligence (AI) in environmental sustainability by analyzing how AI-based resource efficiency, AI-related energy consumption, and AI-enabled environmental monitoring influence the reduction of construction and demolition waste (CDW). Addressing a gap in the literature regarding the lack of an integrated empirical model, this research develops and tests a comprehensive framework using Partial Least Squares Structural Equation Modeling (PLS-SEM).
Method: Data were collected using a structured questionnaire rated on a five-point Likert scale, completed by 233 experts from municipal waste management organizations, licensed CDW contractors, and engineering firms in Isfahan, Iran. The measurement model was evaluated for reliability and validity, and the structural model was assessed through path coefficients, significance values, and predictive metrics. Four hypotheses were tested to capture both the positive and negative pathways through which AI affects environmental performance.
Results: The findings showed that AI-based resource efficiency significantly improved CDW reduction (β = 0.48, p < 0.001), while AI-driven environmental monitoring exerted the strongest positive effect (β = 0.52, p < 0.001). Conversely, AI-related energy consumption negatively affected sustainability outcomes (β = −0.28, p < 0.05), emphasizing the environmental cost of high computational demand. Integration of AI into industrial processes also contributed to reduced resource depletion (β = 0.45, p < 0.001), confirming the robustness of the model.
Conclusions: According to the results, AI has considerable potential to enhance environmental sustainability through improved resource management and real-time environmental monitoring; however, its high energy consumption poses a major challenge to achieving net environmental gains. Developing energy-efficient AI models and integrating renewable energy sources into AI operations are essential steps toward balancing benefits and costs. The proposed model offers a unified empirical foundation for policymakers and practitioners to support responsible and sustainable AI adoption in CDW management.

Keywords

Main Subjects

References

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Journal of Environmental Studies
Volume 51, Issue 3
December 2025
Pages 351-369

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