Abisola Muftau

The construction industry stands as the biggest worldwide consumer of raw materials while producing the most waste among all industries, since the UK construction sector generates 62% of national waste. Organisations can address these challenges through Circular Procurement (CP), which implements Circular Economy (CE) principles by incorporating reuse and recycling and material efficiency into procurement processes. However, CP remains underexplored in construction scholarship and practice. The research uses sentiment analysis and topic modelling to study the integration of Artificial Intelligence (AI) in CP discourse. The research included 23 peer-reviewed articles, which were retrieved from Scopus and Google Scholar databases. The sentiment analysis showed a positive attitude toward AI procurement, but most of the content was neutral because it focused on description rather than emotional expression. The LDA model produced five themes, which included machine learning and circularity and uncertainty in market dynamics and AI in construction procurement and cost modelling techniques and advancing public procurement. The corpus shows that circular procurement appears in only 3% of the text, which indicates its early stage of development in this field. The results show that AI methods already simplify procurement and could be extended to embed CE principles more effectively in construction. The originality of this research lies in combining sentiment analysis with topic modelling to provide a dual perspective on both the tone and latent themes of the academic discourse. The research value comes from its demonstration of limited academic studies on AI-driven CP and its presentation of how existing AI procurement tools can be modified to support circularity. The research addresses current knowledge deficiencies while creating fundamental elements for sustainable procurement methods in construction projects.

Integrating circular procurement (CP) practices in the construction industry is vital to advancing sustainability and minimising the environmental impact of construction and demolition waste (CDW). Despite the substantial benefits, the implementation of circularity in procurement processes remains limited, particularly in terms of adopting artificial intelligence (AI) in the construction sector. The primary aim of this study is to explore the role of AI in supporting the implementation of circular procurement in the construction sector, with a specific focus on applications in the United Kingdom (UK) and Türkiye. The key objectives are to identify how AI technologies are currently applied to enhance circular procurement practices and to evaluate the opportunities and constraints associated with AI adoption by examining existing policy frameworks, data infrastructures, and technical capacities. Through a systematic literature review, the study identifies six key themes: circular economy policy and strategy, circular material design, circular construction, supply chain management, CDW management, and resource recovery. Based on the established themes, a comparative analysis is conducted between the UK and Türkiye, examining their positions regarding AI integration, highlighting overlapping patterns and unique challenges. AI presents considerable potential for advancing circular procurement, particularly through the development of data-driven decision-making, effective management protocols, resource optimisation, and improved supply chain management. The challenges encompass a lack of data for CDW monitoring, insufficient legislative frameworks, high upfront costs, a shortage of technical expertise, and ethical concerns about accountability. The findings emphasise the need for AI platforms that facilitate the adoption of circular construction practices, supporting the development of dynamic, responsive, and smart procurement strategies.

Urban waste minimisation remains constrained by fragmented governance, misaligned procurement, and uneven digital capacity, despite the circular economy’s prominence in policy agendas. A critical synthesis was undertaken to clarify how systems thinking and digital capabilities can be operationalised to overcome these barriers. A systematic review in Scopus (2014–2024) used four keyword blocks of systems thinking, digital technologies, circular economy/urban context, and waste management, producing 31 publications. Directed content analysis, guided by an a priori codebook with inductive refinement, identified mechanisms across design, operations, behaviour, markets, and policy. Causal loop diagrams (CLDs) were constructed to represent hypothesised feedback structures. Findings indicate three high-leverage domains: (i) upstream design and procurement, where BIM-enabled information quality and circular procurement generate a reinforcing loop (R1): better design information → less rework → lower schedule pressure → improved source separation → higher secondary material value → stronger procurement signals; (ii) digital operations, where data governance and interoperable standards catalyse R2: higher data quality → more reliable analytics → optimised routing/operations → reduced contamination → improved material value → reinvestment in data; and (iii) governance, where lowest-cost tendering and compliance burdens create a balancing loop (B1): cost pressure → late changes → rework → increased waste → short-term fixes that suppress prevention. Healthcare logistics exhibited a further balancing loop (B2) linked to risk and liability constraints. Implications include coupling city-scale circular assessments with sector-specific bundles (design-stage prevention, reverse logistics, automated control), underpinned by interoperable data standards, circular procurement, and digital assurance.