Streamlining construction and demolition waste minimisation at design stage: information-centric systematic literature review Open Access

The construction industry significantly contributes to economic growth and employment but also drives environmental challenges due to rapid urbanisation and resource consumption. It accounts for 40% of global raw materials use and energy consumption leading to resource depletion, pollution and greenhouse gas emissions (Illankoon and Vithanage, 2023). Additionally, it is the largest waste generator, producing 30% of global waste with over 35% ending up in landfills annually (Eze et al., 2024). These impacts not only intensify environmental degradation but also impose economic and social costs, including waste management expenses and health risk (Amaral et al., 2020; Hosseini et al., 2020).

Understanding the types of construction waste is essential for effective minimisation strategies. Lean construction identifies key wastes such as motion, inventory, waiting, overproduction, unused employee knowledge, transportation, and defects (Unnikrishnan and Sudhakumar, 2024). However, the interconnected nature of the two main types of waste, Construction and Demolition Waste (C&DW), necessitates integrated management strategies, as decisions made during construction directly affect both the volume and recycling potential of demolition waste (Nikmehr et al., 2021). During construction phases, waste generation occurs through material inefficiencies, design modifications, and inadequate planning, while demolition contributes substantially to overall waste streams at the end-of-life stage. Moreover, optimising material use throughout all construction stages, including demolition, provides a powerful sustainability strategy by enhancing resource efficiency and reducing environmental impact (WRAP, 2021)

Waste prevention is prioritised in sustainable management, as reflected in the European Union (EU) waste hierarchy; prevent, reuse, recycle, downcycle, landfill (Zhang et al., 2022). Yet, design inefficiencies contribute to 30% of on-site waste (Amaral et al., 2020) often due to poor planning, lack of procedures, and insufficient information for decision-making (Hassan and Alashwal, 2025). Addressing waste during the design phase offers the greatest potential for efficiency and minimal environmental impact (Ghafourian et al., 2016).

Designing out Waste (DoW) is a waste-specific strategy aligned with Circular Economy (CE) principles, focussing on minimising waste at early design stages through predictive and prescriptive approaches (Tong et al., 2024). In contrast, Design for X (DfX) is broader, addressing goals where “X” may refer to manufacturability, cost, quality, or sustainability (Narganes-Pineda et al., 2025). The Waste and Resources Action Programme (WRAP) outlines five DoW principles such as Design for reuse and recycle; Design for off-site construction; Design for material optimisation; Design for resource-efficient procurement; Design for Deconstruction’ and flexibility (WRAP, 2021). These principles are interconnected. The “Design for waste-efficient procurement” approach emphasises collaboration among project stakeholders. “Design for material optimisation” advocates for standardised, recyclable materials, while design for off-site construction promotes prefabricated modules over traditional on-site methods (Utrilla et al., 2020). “Design for Deconstruction” emphasises adaptability, reusability and recycling of materials, encouraging the recovery of materials for future projects (Kelly and Dowd, 2014).Effective decision-making mechanisms during design are crucial to minimise waste caused by design changes (Ajayi et al., 2016; Kelly and Dowd, 2014).

Existing C&DW tools such as SMARTWaste, SWMP, DoWT-B, and SmartAudit provide design guides and checklists but are often applied post-design, limiting waste mitigation at early-stages (Gupta et al., 2022; Mcneil-Ayuk and Jrade, 2024). To address this, tools are increasingly adopted to manage project information at early design stages in line with DoW principles, promoting material reuse and recycling (Tong et al., 2024). Building Information Modelling (BIM) is often identified as a tool to forecast potential waste and supports Design for Disassembly (DfD) (Akbarieh et al., 2020; Mcneil-Ayuk and Jrade, 2024; Susilowati et al., 2024; Dias et al. (2022). Other tools, such as Life Cycle Assessment (LCA), evaluate reusability, recyclability, and energy efficiency at the design phase (Gomis et al., 2023; Kahandawa et al., 2025). Several reviews examine the current state of C&DW minimisation, as shown in Table 1.

According to Table 1, these reviews highlight current literature, tools for measuring waste generation, and gaps in integrating DoW principles in designing out construction and demolition waste (DoC&DW) (Amaral et al., 2020; Majmundar and Ansari, 2018). Ghafourian et al. (2016) noted that C&DW management is not prioritised during the design stage, and few studies focus on waste minimisation at this phase (Mahinkanda et al., 2023; Tong et al., 2024). Recent studies by Nikmehr et al. (2021), Mrema et al. (2023) emphasise the role of BIM in DoC&DW including DfD, and its ability to manage waste during design and construction. BIM is central in limited studies exploring DoW at the design phase (Nawaz et al., 2023; Nikmehr et al., 2021). Integrating technologies like BIM and big data improves decision-making and collaboration (Mahinkanda et al., 2023). While Susilowati et al. (2024) provide a conceptual framework for “Designing Out Waste”, they do not address the tools or specific information requirements needed for implementation. Dias et al. (2022) stress the need for clear and comprehensive information. Existing reviews lack a thorough analysis of information needs in tools supporting DoC&DW aligned with DoW principles, hindering development of a holistic information architecture for waste management (Tong et al., 2024). The research question of this study is:

To address this gap, a systematic literature review (SLR) was conducted to explore existing tools and their potential information configurations to identify the information needs of the tools for DoC&DW. The IPO model (MacCuspie et al., 2014) offers a structured approach to understanding the information configuration of tools for DoC&DW and supports benchmarking to facilitate circular economy. Accordingly, this paper adapts the IPO model to develop a conceptual framework for the information needs of the tools for DoC&DW. The paper is organised as follows: Section 2 presents the SLR methodology; Section 3 shows descriptive and thematic analyses identifying IPO information in tools; Section 4 discusses BIM-enabled information needs; Section 5 develops the conceptual framework; and Section 6 concludes with future research directions.

This study employs a SLR to critically evaluate existing knowledge and uses content analysis to synthesise novel concepts and provides insights into literature patterns (Fink, 2019). Descriptive analysis, including keyword co-occurrence, was conducted using VOSviewer (Jin et al., 2019). The SLR follows the PRISMA framework, comprising four phases: identification, screening, eligibility, and inclusion, as illustrated in Figure 1.

Based on the defined scope in Section 1, a keyword search was conducted targeting tools for DoC&DW in Scopus and Web of Science which are commonly used, the largest repositories of engineering research (Ali et al., 2017; Mostafa et al., 2016) focussing on titles, abstracts, and keywords.

((“Design*” AND “Waste”) AND (“Deconstruct*” OR “reus*” OR “recycl*”)) AND ((“Technolog*” OR “Tool*” OR “Database*” OR “Prototype*” OR “Architecture*” OR “Platform*” OR “Model*” OR “Framework*” OR “Strateg*” OR “analytic*”)) AND ((“Construction” OR “Demolition”) AND (“Waste”))).

The search included peer-reviewed journal articles to ensure high-quality, relevant information from 2004 to 2024 to capture the evolution of existing tools for DoC&DW, including use of BIM, in embracing DoW principles. Although the initial search targeted publications from 2000, as identified by Tong et al. (2024) and Susilowati et al. (2024) as the period when early discussions on the DoW concept and decision support tools began, the first publication relevant to the specific scope of this review appeared in 2004. Publications from 2025 are still ongoing, thus, including them would not provide a complete overview. Limiting the scope to 2024 ensures a full, consistent dataset for a robust analysis.

The quality of the selected articles was ensured through inclusion and exclusion criteria. Duplicates, non-English publications, books, book chapters, conference papers, and review articles were excluded to reduce bias (Wijewickrama et al., 2021; Tong et al., 2024). Inclusion criteria focused on studies addressing tools for DoC&DW. Studies unrelated to DoW and C&DW other domains such as green logistics, supply chain management, disaster response, and sectors like automation, agriculture, and manufacturing were excluded, to improve the reliability and relevance of the synthesised evidence (Mahinkanda et al., 2023). Out of 778 abstracts, 99 were selected for full-text review, resulting in 46 final articles ( Appendix Table A1).

The selected articles were subjected to descriptive and thematic analysis. Descriptive analysis examined publication year, co-occurrence of keywords, while thematic analysis provided an in-depth review of key topics (Liberati et al., 2009). To minimise potential bias and enhance reliability in the thematic analysis, the selected articles were independently reviewed and analysed by the first author, followed by collaborative discussions among all authors to refine and finalise the themes and content aligned with Wijewickrama et al. (2021) to ensure the robustness and reliability of their analysis.

The descriptive analysis categorises selected articles by publication year and keywords. Figure 2 illustrates the distribution of studies on tools for DoC&DW from 2004 to 2024.

Figure 2 shows a gradual rise in publications, with peaks in certain years. Between 2004 and 2016, research was limited, focussing on waste minimisation without modelling perspectives. From 2017 onward, publications increased, with over 80% published in the last decade. The highest peak was in 2019, reflecting growing interest in DoW and BIM. These trends highlight information needs of tools for DoC&DW, supporting sustainable waste reduction.

Keywordsco-occurrence analysis (Figure 3) was performed using “Author Keywords” and “Fractional Counting” via VOSViewer, which provided key insights into research themes (Hosseini et al., 2018; van Eck and Waltman, 2014).

The keyword occurrence threshold was set at two, identifying 25 of 124 keywords. Similar terms like “BIM” and “Building Information Modelling” were merged, resulting in 21 final keywords. In Figure 3, node size, distance, and connecting lines indicate the most frequent topics, including BIM, Waste Management, DfD, and C&DW. Nodes colours divide the keywords into clusters:

1. Aspects of DoC&DW: strategies like “DfD”, LCA, and material reuse.

2. Basic waste management strategies and mechanisms: from disposal to recycling, reuse, and reduction, with recycling as the primary diversion

3. C&DW minimisation: links between construction activities and waste, emphasising demolition waste prediction.

4. Emerging technologies: BIM and BIM and big data to enhance efficiency via design review, 3D coordination, quantity take-off, and analytics.

The thematic analysis uses the IPO model (MacCuspie et al., 2014) to classify and synthesise the information needs of tools for DoC&DW, organising findings under three stages: Inputs, Processes, and outputs.

Inputs are characterised as requirements from the environment (Bushnell, 1990). Table 2 outlines input classifications, types of data, and potential sources.

“Design specifications” consistently emerge as critical inputs, encompassing project-level attributes such as number of floors, gross floor area, building height, and typology; and element-level attributes such as dimensions, density, material type, lifespan. These underpin waste estimation and recovery, with key sources being architectural/structural drawing, BIM models, and structural calculations (Kunieda et al., 2019). “Material properties” align closely with design specifications. Akinade and Oyedele (2019) identified parameters such as thickness, length, width, and surface area, while Haeusler et al. (2021) noted sources like manufacturer databases and BIM. Keulemans and Adams (2024) demonstrated BIM metadata can enhance waste visualisation by adding historical, ecological, and material quality information to support contextual and sustainability insights. “Economic indicators” though less studied include costs of treatment, transport, and disposal of C&DW (Akinade and Oyedele, 2019) sourced from cost databases and market records which allow for the integration of financial feasibility into tools for DoC&DW (Yeheyis et al., 2013). “C&DW information,” spans waste typology, volumes, and generation data, often derived from audits and historical records but limited by post-design uncertainties (Bilal et al., 2016; Zubair et al., 2024). “Environmental impact categories” and “Life cycle parameters” draw on databases or Environmental Product Declarations, which assess the environmental impact of construction materials. “Social indicators” reflect stakeholder acceptance, participation, and safety in DoC&DW approaches, typically gathered from stakeholder consultations and regulatory requirements (Sanchez et al., 2021; Lu and Yuan, 2010).

Mostly employed information processing systems for tools supporting DoC&DW are BIM-enabled (70%). BIM-enabled tools rely on BIM objects (digital representations of building components such as walls, doors, and beams) embedded with geometric and parametric data (material properties, dimensions and lifecycle performance), and parametric modelling. Parametric modelling enabling real-time, rule-based design analysis and adjustments. These operate across a multi-layered architecture:

Data storage layer: Collects and organises scattered datasets, ensuring accessibility and machine readability. Features include integration and classification systems, real-time data findability, handling of large datasets, and semantic search (Bilal et al., 2016; Akanbi et al., 2019). Semantic layer: Provides access to databases for upper layers. Ontologies enhance this layer by structuring domain knowledge, describing data sources, and enabling advanced queries (Lu et al., 2017). Analytics and functional model layer: Execute core functions such as Whole Life Performance Analytics, Deconstruction Analytics, DfD and visualisation. Algorithms within this layer assess reuse potential, recyclability, and optimal deconstruction strategy (Akanbi et al., 2019; Bilal et al., 2017). Application layer: Provides web/mobile-based user interfaces for visualisation, reporting, and waste analysis (Akanbi et al., 2019; Chileshe et al., 2019), to identify waste hotspots and optimise reprocessing opportunities.

In practice, BIM-enabled tools often use platforms such as Revit, plug-ins to support DoC&DW across project stages, e.g: optimising reinforcement bars, planning off-site construction, and facilitating reuse and deconstruction strategies (Akinade and Oyedele, 2019; Han et al., 2024; Khondoker, 2021; Porwal and Hewage, 2012; Yuan et al., 2022). Llatas et al. (2022) and Zubair et al. (2024) demonstrate the use of BIM-enabled LCA to assess environmental impacts of construction waste and building projects. Both studies show how integrating BIM (and GIS, in Zubair et al., 2024) can support early-stage design decisions, optimise waste management, and provide a holistic evaluation of sustainability in construction.

Mathematical modelling was employed by 15%, which supports assessment scores, waste reduction, and C&DW recycling optimisation (Ekanayake and Ofori, 2004; Sassi, 2008; Hiete et al., 2011; Llatas and Osmani, 2016; Seeboo, 2022). For example, Akanbi et al. (2019) introduced Disassembly and Deconstruction Analytics System (D-DAS) to evaluate building components for reuse and recycling. Building on this foundation, Mohammed et al. (2024) integrated a mathematical model within BIM for steel structures, introducing deconstructability and salvage metrics considering deterioration and reuse potential. Ekanayake and Ofori (2004) applied multi-attribute value techniques to create a waste assessment score, while Llatas and Osmani (2016), developed a waste reduction model simulating DoC&DW. A few used “simulations” (9%) process to enhance demolition efficiency and material sortability (Kunieda et al., 2019; Eckelman et al., 2018; Sadafi et al., 2012). Sadafi et al. (2012) integrated simulation with LCA for DfD structural system.

The outputs of the selected studies were categorised based on their nature of the output and alignment with waste management hierarchy. Table 3 illustrates output, with 48% focussing on analysing DoC&DW principles, including deconstructability, recovery potential, health impacts, and demolition strategies (Akinade and Oyedele, 2019; Ge et al., 2017; Zubair et al., 2024).

Quantitative Measures (35%) emphasise numerical data to validate DoC&DW (Bilal et al., 2016; Ge et al., 2017). For example, Akanbi et al. (2019) and Mohammed et al. (2024), evaluated salvage performance, quantifying the potential for component reuse at the end of life. Quality of Material (13%) assesses material condition affecting end-of-life impact, while 3% focus on BIM-enabled material/component banks/databases (Jayasinghe and Waldmann, 2020; Keulemans and Adams, 2024; Lima et al., 2023).

Table 3 shows that Reuse (36%) and Reduce (29%) are the primary focus area, reflecting principles like Design for off-site construction and DfD to minimise waste and extend material life (Lu et al., 2017; Mei et al., 2021; Xiao et al., 2023; Keulemans and Adams, 2024). Recycling (22%) highlights the efforts for design for recyclability (Akinade and Oyedele, 2019; Llatas et al., 2022; Xia et al., 2020). Few studies combined reduce, reuse, and recycle strategies for a holistic DoC&DW approach DoC&DW (Seeboo, 2022; Basta et al., 2020; Mayer and Bechthold, 2017).

The descriptive analysis reveals significant insights into the evolution and maturation of DoC&DW research over the past 2 decades. Between 2004–2016, research activity on C&DW was limited and intermittent, suggesting that early approaches were largely reactive rather than implementing proactive design strategies (Ajayi et al., 2016; Yuan and Shen, 2011). Keyword network analysis reinforces this evolution, showing a transition from isolated waste management approaches to integrated digital solutions incorporating BIM, big data analytics, and lifecycle thinking (Susilowati et al., 2024; Lins et al., 2024). This trend also reflects the transition of the construction industry from a linear “take-make-dispose” model to a circular approach, embedding waste prevention into the design process (Gomis et al., 2023).

Based on the findings, Table 4 maps the IPO information to DoW principles. Each DoW principle has specific inputs and outputs with a common process at the semantic, analytics and functional model layers. However, depending on the purpose and application of the tool, the process may have a unique architecture.

The mapping reveals distinct information architectures for different DoW principles, indicating that effective tool development requires principle-specific approaches rather than generic solutions. Tools focused on resource-efficient procurement demonstrate the most comprehensive input requirements, utilising life-cycle parameters, material properties, and design specifications to optimise material procurement (Ajayi and Oyedele, 2018). Procurement-focused tools require the highest level of information integration, reflecting the complexity of balancing cost, environmental impact, and availability factors in material selection processes (Tong et al., 2024). For waste-efficient materials, material and design specifications as primary inputs reflect the geometric nature of cutting pattern optimisation and challenges in quantity estimation (Tong et al., 2024). This shows that geometric precision is more critical than lifecycle data, favouring specialised tools (Lins et al., 2024).

Design for reuse and recycling demonstrates unique reliance on C&DW information and social indicators, distinguishing it from other DoW principles through emphasis on stakeholder perspectives and historical waste data (Yeheyis et al., 2013). This indicates that reuse strategies are inherently more social and contextual than purely technical. This highlights the requirement of tools for DoW to integrate community acceptance, regulatory compliance, and cultural factors alongside technical feasibility assessments (Lins et al., 2024; Tong et al., 2024).

Outputs related to materials recovery and alternative design options are generated by creating material or component banks with reliable traceability. Certification ensures reused materials meet standards and perform effectively in new projects (Jayasinghe and Waldmann, 2020; Bertin et al., 2020). Off-site construction tools emphasise material properties, design specifications, and economic factors leading to improved prefabrication and compliance with codes. While prefabrication minimises waste barriers such as transportation cost limit adoption (Wang et al., 2015) which must be addressed to enhance effectiveness. DfD tools rely on design specifications and BIM models, to produce outputs like deconstruction sequences, where selecting materials that facilitate disassembly is crucial (Akinade et al., 2015). Tools related to design for materials consider key information including material properties, design specifications, and life-cycle parameters to optimise material selection, ensuring sustainability and efficiency and identify hazardous materials to reduce health and environmental risks.

BIM plays a crucial role in simulating the building design and fostering collaboration among stakeholders by integrating design, elements, quantities, and material information (Hassan and Alashwal, 2025). Effective information management at the design stage is crucial, as it feeds into multiple sources throughout the project lifecycle (Tedjosaputro, 2024). BIM-enabled information management is widely recognised for implementing the DoC&DW (Akanbi et al., 2019; Khondoker, 2021; Tong et al., 2024). BIM core and auxiliary features support the development of a plug-in for managing C&DW (Bilal et al., 2016). Table 5 maps key BIM features to layers in a BIM-enabled DoC&DW architecture.

The four-layered BIM architecture represents a significant advancement from traditional CAD-based approaches by enabling systematic information processing rather than mere geometric representation. According to Mcneil-Ayuk and Jrade (2024), this architecture facilitates detailed information exchange and interactions between BIM features and DoW principles, providing a structured approach to waste minimisation decision-making. It enables comprehensive information integration across multiple project phases and stakeholder groups (Lins et al., 2024). BIM core features primarily relate to the application layer, enabling plug-in software for waste minimisation and prediction via the Application Programming Interface (API) platform. However, the predominant focus on application-layer functionality indicates that current tools operate as add-ons rather than integrated solutions, potentially limiting their effectiveness in influencing fundamental design decisions (Bilal et al., 2016).

Intelligent modelling supports the semantic layer by providing a unified data platform for external sources enabling integration of diverse data types and sources required for comprehensive waste analysis (Akanbi et al., 2019). Waste analytics integrate waste intelligence and innovative technologies such as GPS and Radio Frequency Identification tags to automate the collection and analysis, reinforcing DoC&DW strategies (Akinade et al., 2018). Parametric modelling within a BIM library demonstrates the most sophisticated approach to waste reduction by enabling rule-based design modifications that automatically optimise waste generation patterns. This capability facilitates the assessment of design elements based on cost, energy consumption, and waste reduction simultaneously, representing the integration of sustainability considerations into design processes (Lu et al., 2017). The analysis reveals that parametric approaches are most effective when combined with comprehensive databases containing project-specific waste generation data, rather than relying on generic industry averages, suggesting that tool effectiveness is directly related to data quality and contextual relevance (Akanbi et al., 2019).

BIM auxiliary features such as design, visualisation, lifestyle considerations, technology, interoperability, and cost-benefit analysis enhance waste analytics and functional modelling by supporting core features of parametric modelling and intelligent modelling (Bertin et al., 2020; Ge et al., 2017). Interoperability facilitates information exchange, enabling effective assessments at the functional layer and ensuring BIM (Revit) platforms remain accessible through API integration (Tong et al., 2024; Lu et al., 2017). The application layer extends BIM functionality through plug-in, leveraged through the analytics and functional model layer. These plug-ins serve as visualisation platforms, assisting designers during the building design process (Akanbi et al., 2019; Bilal et al., 2016; Chileshe et al., 2019).

The SLR confirmed the significance of BIM as a tool for DoC&DW. Bilal et al. (2016) highlighted the reliance of BIM on large datasets makes it highly compatible with big data technologies. This capability allows designers to anticipate waste generation from materials or construction methods improving decision-making. To illustrate this, a conceptual framework (See Figure 4) applies the IPO model to systematically map the information needs of tools for DoC&DW.

The framework aligns information needs with DoW principles, demonstrating how a BIM-enabled architecture supports designers in minimising waste and improving resource efficiency at the early design stage (Tong et al., 2024). In Figure 4, solid arrows indicate information flow, while dashed lines map information needs against DoW principles.

The framework presents a flow diagram, where the critical flow of information begins with inputs linked to DoW principle, including material properties, design specifications, C&DW information, life-cycle parameters, environmental, economic and social indicators. Dias et al. (2022) stressed the need for comprehensive environmental information for effective DoC&DW. Since the framework is BIM-enabled, input information must align with BIM features (Table 5) such as location, gross and net floor area and building typology (Dias et al., 2022; Bilal et al., 2016).

The information processing system, designed based on core BIM functionalities and auxiliary features, ensures effective information handling and seamless integration, thereby reducing the technical limitations and compatibility issues encountered in real-world implementations (Bilal et al., 2016). The semantic layer plays a critical role in structuring and standardising raw data from diverse source (Akanbi et al., 2019). Although the analytics and functional layer enable predictive waste assessment, current implementations tend to emphasise prediction over prescription, offering waste estimates without corresponding design modification recommendations. This limitation reduces the potential of the framework to impact on actual design decisions, indicating that future developments should prioritise actionable guidance over analytical accuracy, particularly in relation to specific DoW principles. This holistic IPO-based framework helps address fragmentation in information needs in tools for DoC&DW. Without industry-wide data standards, the framework's interoperability benefits may remain theoretical; therefore, it should be developed as a BIM-enabled prototype empirically assessed and applied in practice to validate its impact (Dias et al., 2022).

This SLR follows PRISMA guidelines and employs descriptive and thematic analysis to investigate the information needs of the tools for DoC&DW using the IPO model. A total of 46 journal articles published since 2004 were analysed to identify the input, process and output information needs. The review highlights the increasing focus on tools for DoC&DW tools as a key approach to waste minimisation through DoW principles, revealing a paradigm shift from reactive waste management to proactive design intervention. Despite the variety of tools for BIM-enabled approaches stand out reflecting the recognition that effective waste minimisation requires comprehensive integration of information, automation, robust databases, and interoperability across project phases, stakeholders, and decision points. This challenges traditional project delivery methods that treat waste management as post-design rather than a core design driver. Therefore, this review proposes a conceptual framework, holistically mapping the information needs of the tools for DoC&DW based on IPO model. Findings are significant for both practical and theoretical applications, supporting integrated BIM operations to efficiently divert C&DW from landfills and promote sustainable waste minimisation.

The findings provide practical implications for Architects, Engineers, Designers, and Construction managers aiming to minimise C&DW at the design stage. By mapping information needs to the IPO model, BIM-enabled tools provide designers a streamlined approach incorporating waste management strategies into the conceptual design (Mcneil-Ayuk and Jrade, 2024) and actionable insights on material properties, waste generation potential, and reuse or recycling opportunities (Wijewickrama et al., 2021). This enables evidence-based decisions to reduce waste before construction. The framework demonstrates how specific DoW principles can be embedded in BIM-enabled tools. Practitioners can use this framework as a blueprint to develop future tools and ensure design choices align with circular economy objectives, improving resource efficiency and reducing environmental impacts (Tong et al., 2024). Software vendors can use this framework to create standardised platforms predicting, analysing, and managing C&DW at the design stage, while practitioners can customise the information inputs and process layers to suit specific project requirements, enhancing the applicability of BIM-enabled DoC&DW principles. Adoption of this approach can reduce environmental impacts, lower costs, and improve project sustainability.

This study builds on established theoretical frameworks to examine how tools for DoC&DW facilitate C&DW minimisation during design. By applying the IPO model, the study categorises the information needs of BIM-enabled tools, aligning them with DoW principles and highlighting the need for pilot tests or case studies to validate the model. The conceptual framework helps future BIM model designers understand the functions of each layer and guide researchers in developing BIM plug-in for DoC&DW (Tong et al., 2024). The study aligns with circular economy principles, advocating DoW and keeping materials in use. By aligning BIM-enabled DoC&DW tools with CE principles, study emphasises the importance of information configurations that support sustainable design and reduce C&DW through informed decision-making. While the study focuses on identifying information needs rather than evaluating the performance of the tool, the analysis reveals critical limitations in existing tools with theoretical implications. Current tools demonstrate fragmented data, poor interoperability between BIM platforms, and inadequate real-time decision support capabilities (Bilal et al., 2016; Akinade et al., 2018). These limitations highlight theoretical gaps in operationalising DoW principles within digital environments, particularly the disconnect between conceptual frameworks and practical implementation, which can be addressed in future research. Figure 5 outlines these limitations and potential research pathways.

Addressing them will strengthen the empirical foundation, expand the applicability of the IPO-based framework, and guide the development of more effective, data-driven, and context-sensitive tools for DoC&DW at the design stage.