Beyond buzzwords: NLP reveals common threads in sustainable and circular construction discourse
Aubakirov S. Pak A. Akhmetov I. Tleuken A. Varol H.A. Akzhalova A. Karaca F.
2025PeerJ Inc.
PeerJ Computer Science
2025#11
Circular economy and sustainability have both seen rapid growth in academic literature, often leading to ambiguity and the overuse of these terms. This obscures their true objectives and makes it challenging to discern their distinct intentions. Manually analyzing the vast body of recent publications to understand how these concepts connect to environmentally beneficial practices is laborious and timeconsuming. This study aims to compare and analyze existing literature on sustainable and circular construction using natural language processing (NLP) techniques to elucidate the similarities and overlaps between these concepts within the construction industry. To achieve this, we employed three NLP methods: (1) TextRank, a graph-based ranking algorithm that extracts key structural relationships between terms in a document; (2) term frequency-inverse document frequency, a statistical measure that identifies the most significant terms based on their frequency and uniqueness within the corpus; and (3) semantic annotation (Wikifier), a method that links text tokens to structured knowledge bases such as Wikipedia for better contextual understanding. These methods are used to analyze a dataset of 480 academic articles focusing on sustainability and circular economy in the construction sector. Our analysis revealed that circular construction is more specific and practical, emphasizing resource efficiency, waste management, and industry-specific processes, targeting the operational aspects of recycling and resource recovery. In contrast, sustainable construction encompasses a broader and more holistic scope, including urban planning, community development, and long-term environmental impacts. This study demonstrates how NLP methods can systematically disentangle closely related frameworks in construction literature, providing a replicable methodological framework for future data-driven investigations. By clarifying the distinctions and overlaps between the terms “circular construction” and “sustainable construction”, our research offers enhanced understanding for policymakers, industry practitioners, and academics aiming to integrate sustainable and circular principles effectively within the construction sector.
Artificial Intelligence , Circular economy , Computational Linguistics , Concept analysis , Construction , Natural Language and Speech , NLP , Semantic annotation , Sustainability , Text Mining , Textrank , TF-IDF
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Kazakh-British Technical University, Almaty, Kazakhstan
Institute of Information and Computational Technologies, Almaty, Kazakhstan
School of Engineering and Digital Sciences, Nazarbayev University, Astana, Kazakhstan
Department Industrial Engineering and Business Information Systems, University of Twente, Enschede, Netherlands
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan
Kazakh-British Technical University
Institute of Information and Computational Technologies
School of Engineering and Digital Sciences
Department Industrial Engineering and Business Information Systems
Institute of Smart Systems and Artificial Intelligence
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