Integrating Digital Tools for Automated Circularity Assessment of Construction Products: A Case Study

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The manuscript presents a digital workflow that encodes the Italian technical specification UNI/TS 11820:2024 into an Excel-based calculator for computing a product Circularity Level (LC), with automated reporting in MS Word and a Dynamo-driven transfer of circularity parameters into an Autodesk Revit BIM environment. The approach is applied to the ITER earth-based plaster, yielding an LC of 43.77 out of 100. The topic is timely and well within the scope of the journal, the workflow is described in useful technical detail, and the case study is concrete and well chosen. The contribution is worthwhile. Before the paper is ready for publication, several methodological, structural and presentational matters should be addressed.

Major points

1. Use of the term “validation”. The paper states throughout that the tool is “validated” through a single case study. Applying the tool to one product demonstrates feasibility and illustrates the workflow, but it does not validate the calculations. There is no independent check that the automated LC matches a manual computation, no benchmarking against an alternative method, and no verification of the encoded indicator logic. Please either reframe the contribution as a demonstration or proof of concept, or add genuine verification, for example a side-by-side manual recomputation of the LC for the ITER case and a check of the Excel encoding against the standard.
2. Reproducibility and transparency. The Excel calculator and the Dynamo and Python scripts are the core artefacts, yet they are not made available, and the indicators cannot be reported owing to UNI copyright. This leaves the reader unable to assess or reuse the tool. Please clarify what can be shared, for instance an anonymised template, the script structure, a parameter schema, or a repository link, and explain how a reader without access to the paywalled standard could reproduce the workflow.
3. Substantiation of the novelty claims. The four gaps in Section 3 are largely asserted rather than demonstrated through a structured review. Strong statements, such as the claim that no existing BIM-based tool had produced a product-level profile at this resolution, should be supported more carefully or softened. A short comparative table positioning the proposed tool against the cited works would strengthen the contribution.
4. Generalisability. The evidence rests on one product type, one manufacturer and one BIM platform. The abstract and introduction imply broader applicability than this supports. Please align the strength of the claims with the single-case scope and bring the limitations forward earlier in the paper.
5. The “bidirectional” workflow and the DSS function. The Excel-to-Revit exchange is described as bidirectional, but the data flow presented is essentially Excel to Revit. Please clarify what information returns from Revit to Excel, or adjust the term. Likewise, the decision-support function, namely the real-time simulation of alternative configurations, is asserted but not shown. A brief worked what-if example would substantiate the DSS claim.
6. Interpretation of the LC value. The standard is described as setting no minimum thresholds, yet the result is labelled “medium circularity”. Please clarify the basis for this qualitative label or present the result without a threshold-implying descriptor.

Minor points

7. Section numbering. Section 4 (Materials and Method) contains subsections numbered 3.1 and 3.2, and the in-text references to Section 3.1 and Section 3.2 should point to 4.1 and 4.2. The Conclusion is labelled Section 6, duplicating the Discussion, and should be Section 7. The introduction states that Section 7 “drew the conclusions”, which also needs correcting.
8. Reference categories. The six reference categories named in Section 2 do not match the labels and the partition used in Table 1. For example, the text uses “Logistics and Human Resources” and “Assets, Policies and Sustainability”, while Table 1 lists “Logistics”, “Product/Services” and “Human Resources, Assets, Policies and Sustainability”. Please reconcile the naming and grouping throughout.
9. The LC value and number formats. The LC appears as 0.43 in the abstract and as 43.7 and 43.77 in the body, and the per-category values are rounded differently in the text and in Table 1. Decimal separators mix commas and points, for example 59,5% and 0,5% against 57.86. Please use a single scale and the journal convention throughout, and show how the total LC relates to the per-category values in Table 1.
10. Equation 1. The equation is clearly presented and all terms are defined. One clarification would help: the text defines the LC as the ratio of the achieved score to the maximum theoretical score, whereas the denominator in Equation 1 is a count (c + s). Please confirm that the indicator values are normalised to a common scale, and clarify how the LC is bounded, since the rewarding term (0.5 times the sum of p_k) adds to the numerator while the rewarding indicators are not reflected in the denominator.
11. Duplicate references. References 30 and 31 are identical, and references 32 and 33 are identical. Please remove the duplicates and renumber.
12. Figure legibility. Figures 3 and 4, which reproduce the Dynamo graph and the Python script, are difficult to read at the current resolution. Please supply higher-resolution versions so that the nodes and the code are legible in print.

Comments on the Quality of English Language

The English is generally clear but would benefit from a careful pass for tense and phrasing, for example “Section 7 drew the conclusions”, and for the residual Italian language use.

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions highlighted in the re-submitted files.

Comment 1: The manuscript presents a digital workflow that encodes the Italian technical specification UNI/TS 11820:2024 into an Excel-based calculator for computing a product Circularity Level (LC), with automated reporting in MS Word and a Dynamo-driven transfer of circularity parameters into an Autodesk Revit BIM environment. The approach is applied to the ITER earth-based plaster, yielding an LC of 43.77 out of 100. The topic is timely and well within the scope of the journal, the workflow is described in useful technical detail, and the case study is concrete and well chosen. The contribution is worthwhile. Before the paper is ready for publication, several methodological, structural and presentational matters should be addressed.

Response 1: We would like to thank the reviewer for taking the time to review our manuscript and for the comments provided. The observations raised have contributed to improving the quality and clarity of the work. All comments have been carefully addressed, and the manuscript has been revised accordingly to clarify the methodological choices, improve the consistency of the structure and notation, and enhance the overall readability of the text. A point-by-point response to each comment is provided below.

 

Comment 2: Use of the term “validation”. The paper states throughout that the tool is “validated” through a single case study. Applying the tool to one product demonstrates feasibility and illustrates the workflow, but it does not validate the calculations. There is no independent check that the automated LC matches a manual computation, no benchmarking against an alternative method, and no verification of the encoded indicator logic. Please either reframe the contribution as a demonstration or proof of concept, or add genuine verification, for example a side-by-side manual recomputation of the LC for the ITER case and a check of the Excel encoding against the standard.

Response 2: We thank the reviewer for this observation. After careful consideration the term "validation" has been replaced throughout the manuscript with "demonstration". We would however like to clarify that the LC value for ITER was first calculated manually by the research group and then compared with the value obtained through the digital tool, with both approaches yielding the same result (43.77/100). The digital tool directly encodes the formulas defined by UNI/TS 11820:2024, as illustrated in Figure 2. The automated transfer of circularity parameters into Autodesk Revit has been verified and is illustrated in Figures 3 and 4. Figure 5 is added to visualise the parameters within the Autodesk Revit material database and to illustrate the possibility of extracting material schedules. It should also be noted that the tool is designed to be generic and applicable to any product or service covered by the standard with ITER serving solely as a representative demonstration case. In this context, ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the assessment report, and (3) the transfer of the results into Autodesk Revit.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation.

In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project

[Section 4.2]: The results of this script are shown in Figure 10. The circularity parameters are displayed directly within the shared parameters of the selected material in Autodesk Revit and are named following the convention "LC_a_bb", where LC stands for Circularity Level, a denotes the category number as described in Section 2, and bb is the indicator ID in compliance with UNI/TS 11820:2024, as highlighted in Table 1.

[Section 4.2]: Figure 5: Autodesk Revit visualisation of the Dynamo script outputs.

[Section 5.1]: In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

[Section 5.2]: The assessment was first performed manually and, subsequently, the digital methodology presented in this study was applied to ITER, as highlighted in Section 5.2, to automate the calculation and provide a digital tool applicable to any product or service covered by the standard.

[All sections]: The manuscript has been thoroughly revised to replace the term “validation” with “demonstration” throughout.

 

Comment 3: Reproducibility and transparency. The Excel calculator and the Dynamo and Python scripts are the core artefacts, yet they are not made available, and the indicators cannot be reported owing to UNI copyright. This leaves the reader unable to assess or reuse the tool. Please clarify what can be shared, for instance an anonymised template, the script structure, a parameter schema, or a repository link, and explain how a reader without access to the paywalled standard could reproduce the workflow.

Response 3: We thank the reviewer for these comments. We acknowledge that reproducibility is a key concern and have taken the following steps to address it:

• Indicator transparency: While the individual calculation methods cannot be reproduced due to copyright restrictions under UNI/TS 11820:2024, all remaining information needed to understand and replicate the assessment structure has been made explicit. Tables 1-6 have been added to the manuscript, providing a comprehensive overview of all 68 circularity indicators organised by reference category. For each indicator, the tables report the brief description, tier classification (core, specific, or rewarding), structure, subject of assessment and assessment mode. Moreover, the tables also include the official indicator IDs as defined by UNI/TS 11820:2024, which serve as direct anchors for readers who wish to reproduce the tool with access to the standard;
• Dynamo script structure: Figure 3 has been expanded to provide a more detailed and readable visualisation of the Dynamo script, making its logic and node structure sufficiently visible for replication purposes.
• Python script repository: The Python script has been made publicly available through an open GitHub repository, allowing any reader to inspect, reuse and adapt the code independently of access to the paywalled standard.

 

A dedicated clarification has been added to the manuscript:

[Section 2.2]: The full set of circularity indicators is organised by reference category and presented in Tables 1-6.

[Section 4.1]: This includes specifics such as indicator description, tier (core, specific, or rewarding), assigned weight, subject assessment (product, service, or product/service), and assessment mode (qualitative, quantitative, or semi-quantitative), as summarised in Tables 1-6.

[Section 4.2]: The complete implementation of the Python script, structured across the seven units described above, is publicly available in an open-access GitHub repository (https://github.com/eleonoragiuffrida1706/revit-material-circularity-importer/tree/main). To reproduce the workflow, the script can be directly embedded into a Python Script node within Dynamo.

 

Comment 4: Substantiation of the novelty claims. The four gaps in Section 3 are largely asserted rather than demonstrated through a structured review. Strong statements, such as the claim that no existing BIM-based tool had produced a product-level profile at this resolution, should be supported more carefully or softened. A short comparative table positioning the proposed tool against the cited works would strengthen the contribution.

Response 4: Thank you for this observation. We have carefully revised Section 1, 3 and 6.1 to address your concern. Specifically, the novelty claims have been substantiated as follows:

• The four gaps are no longer merely asserted but are now demonstrated through a structured and evidence-based of the previous studies, in which each limitation is illustrated with explicit reference to the reviewed studies;
• The strong claim regarding product-level assessment has been carefully softened and recontextualized;
• A comparative table (Table 7) has been added to Section 3, positioning the proposed tool against all cited works across key dimensions, including circularity scope, assessment scale, BIM integration depth, third-party software interoperability. This table provides the structured comparative evidence requested and visually substantiates the identified gaps.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: […] three main limitations are identified in the existing literature: (1) frameworks usually evaluate circularity from only one perspective, missing multiple areas of circular per-formance at once, (2) demonstration mostly happens at the building level, making it difficult to evaluate product-specific circular performance and standardize product documentation, (3) BIM integration is still limited and assessment results rarely update or improve the digital model.

[Section 3]: A clear pattern among the reviewed studies is the tendency to focus on a single aspect of circularity. For example, Adesope et al. [39] concentrates on quantifying and reducing embodied carbon through material substitution strategies, comparing con-crete, steel and wood across LCA stages A1–A3, without considering end-of-life options or disassembly potential. Felicioni et al. [40] also focuses on comparing the LCA and LCC performance of different structural systems, highlighting trade-offs between en-vironmental and economic factors but not including circularity indicators like reusability or recyclability. Conversely, Allam et al. [41] and Lima et al. [42] explore deconstruction and Design for Deconstruction, respectively, emphasizing end-of-life planning and disassembly but not linking these to earlier design choices or material selection. Han et al. [43] addresses demolition waste management using MCDA-based scenario comparison, while Annette Davis examines the influence of lifespan assumptions in LCA by com-paring component-level and layer-level replacement strategies over a century. Al Quazzaz et al. [44] approach is perhaps the most integrated, combining BCA, LCA and LCC in a BIM-based decision support system the focus on reuse potential is limited to components, neglecting wider systemic aspects like urban material flows or supply chain circularity. Chang's et al. [45] EBCI framework aims to assess circularity across material, product, system and building levels but functions as a standalone indicator without integration into digital design tools. A second concern relates to the scale at which the proposed frameworks are tested and validated. Except for Allam et al. [41], whose case study operates at the product level by extracting and processing IFC component data, most studies use a building-scale case study for validation, covering areas from residential villas [47] and energy-efficient houses [46] to public museums [40] and industrial warehouses [48]. While this approach aligns with the focus on whole-building assessments, it leaves the product and com-ponent scales less explored, despite their growing recognition as crucial entry points for circularity in the built environment. Assessing circularity at the component or material level would enable more detailed evaluations of reusability, recyclability, and substitu-tion potential. It also aligns better with emerging frameworks like material passports, partially addressed by Al Quazzaz et al. [44] via its Airtable-based MP interface, and product-level EPD. Relying primarily on building-scale validation makes it harder to generalize the findings across different typologies and contexts and may limit applying these methods to early-stage design, where component decisions are still open and greatly impact the building's environmental performance.

A key technical gap identified in the comparative analysis is the limited role and depth of BIM integration across the reviewed workflows. Although all studies use BIM, specifically Autodesk Revit, as a central platform, its application is mostly confined to upstream data extraction tasks. Adesope et al. [39], Felicioni et al. [40], Davis et al. [46], Han [43], and Rodriguez [48] mainly or exclusively utilize Revit for creating Bills of Quantities (BoQ) or exporting geometric and material data to external tools, without integrating assessment outputs back into the BIM environment. Mowafy et al. [47] slightly broadens this by using VPL for iterative scenario development, but the optimi-zation results from NSGA-II and DEA remain external to the BIM model. Lima et al. [42] employs Dynamo to automate disassembly design routines within Revit, representing a more bidirectional BIM use, although it mainly manages DfD parameters rather than conducting comprehensive life cycle assessments.

Most studies mention integration with third-party software, but this often involves manual file exchanges, such as IFC exports, CSV transfers, or XML outputs, limiting interoperability. For example, Allam et al. [41] framework follows a sequential data pipeline: from IFC to RDF, then to Knowledge Graph, CSV, XLS for Primavera P6, and finally XML for 4D simulation in Blender, with each step requiring a separate export. Rodriguez et al. [48] notes that neither Athena Impact Estimator nor SimaPro offers fully automated data transfer from Revit, indicating manual intervention remains essential in current BIM-LCA workflows. Additionally, none of the reviewed studies explicitly addresses integrating document management or archiving within the BIM environment, such as storing EPDs, assessment reports, or material passports in a directly linked and retrievable format. This gap is significant, especially given upcoming regulatory re-quirements like the EU Level(s) initiative and the CPR, which emphasize auditability, traceability and structured environmental data documentation.

The proposed tool addresses these gaps by operating at the product scale, struc-turing the circularity assessment across six reference categories aligned with UNI/TS 11820:2024, generating standardized LC Reports, and closing the BIM feedback loop via Dynamo scripts that populate BIM objects with circularity data directly within Auto-desk Revit.

[Section 3]: Table 7. Comparative Overview of the previous studies.

[Section 6.1]: The gaps outlined in Section 3 set the context for assessing the proposed tool's contribution. The analysis reveals three main limitations: a limited and segmented ap-proach to circularity assessment, a focus on building-scale validation rather than de-tailed product-level analysis, and incomplete BIM integration within current workflows.

The reviewed studies show that most existing frameworks focus on only one aspect of circularity. Adesope et al. [39] and Felicioni et al. [40] emphasise embodied carbon reduction via material substitution, Allam et al. [41] and Lima et al. [42] work on end-of-life deconstruction planning, Han et al. [43] looks at demolition waste man-agement and Davis et al. [46] examines the methodological effects of lifespan assump-tions in LCA. Even more comprehensive frameworks, like Al Quazzaz et al. [44] BIM-based DSS combining BCA, LCA and LCC, or Chang et al. [45] EBCI indicator system, are limited to specific circularity dimensions and do not capture the full range of circular performance. The tool presented in this research adopts a different strategy by assessing circularity across six categories aligned with UNI/TS 11820:2024, each rep-resenting a different aspect of circular performance. This multi-dimensional approach enables a more complete evaluation at the product level, moving beyond the sin-gle-indicator or single-dimension methods. Instead of replacing these focused ap-proaches, the tool complements them by providing a structured framework to evaluate multiple circularity aspects simultaneously, facilitating more informed and holistic de-cisions during design and production.

A further limitation from the literature concerns the demonstration scale. Aside from Allam et al. [41], who processes IFC component data at the element level, all re-viewed studies validate methodologies at the building level [40,46–48]. Building-scale assessments are crucial for whole-life carbon accounting and design comparisons but tend to aggregate component details, potentially obscuring the circularity performance of individual materials and products. The proposed tool specifically operates at the product level, evaluating circularity indicators for individual construction products per UNI/TS 11820:2024. This detailed approach aligns with the rise of product-level tools like EPD and material passports, the latter partially addressed by Al Quazzaz et al. [44] via an Airtable interface, though without standardized compliance outputs. By gener-ating UNI/TS 11820:2024-compliant LC Reports in MS Word, the tool bridges assess-ment and documentation, providing structured outputs that support product certifica-tion, procurement, and regulatory compliance.

A technical gap identified in the literature concerns the limited integration of BIM. Most reviewed studies use Revit mainly for data extraction, such as generating BoQ or exporting geometric information to external LCA or circularity tools, without inte-grating assessment outputs into the BIM environment. Rodriguez et al. [48] confirms that neither Athena Impact Estimator nor SimaPro provides fully automated data transfer from Revit, while Allam et al. [41] pipeline shows how workflows across mul-tiple software can fragment data flow into disconnected manual steps. The digital tool presented in this research addresses this by providing an Excel-to-Revit data flow module, implemented through Dynamo scripts and APIs, which automates material creation, custom parameter definitions, and population with circularity data inside Autodesk Revit. This effectively closes the feedback loop, making circularity data an active, queryable, and visible part of the BIM model, thus supporting dynamic design optimization. Also, unlike prior studies, this tool incorporates document generation within the workflow, producing standardized LC Reports linked directly to assess-ments, filling a documentation gap that others do not resolve.

Furthermore, the digital tool proposed in this research is modular and scalable. Its first two modules, namely the LC calculator and automated report generator, depend on standards, as their indicator structures, weighting factors and reporting criteria follow UNI/TS 11820:2024. Nonetheless, their core architecture is flexible and can be adapted to any indicator-based regulatory or assessment system by updating the relevant cal-culation rules and output templates. The third module, which manages the Ex-cel-to-Revit data flow via Dynamo VPL scripts and APIs, is inherently independent of standards. It serves as a data exchange pipeline between spreadsheets and BIM, inde-pendent of the specific standard, enabling reuse across various circularity or sustaina-bility assessments without structural changes. This modular approach allows the tool to adapt to regulatory updates, incorporate future revisions of UNI/TS 11820:2024, and extend to other indicator-based evaluation frameworks, ensuring it remains a versatile and future-ready solution for digital circularity assessment in construction.

 

Comment 5: Generalisability. The evidence rests on one product type, one manufacturer and one BIM platform. The abstract and introduction imply broader applicability than this supports. Please align the strength of the claims with the single-case scope and bring the limitations forward earlier in the paper.

Response 5: We thank the reviewer for these comments. In response, the manuscript has been revised at multiple levels to consistently align the strength of the claims with the actual scope of the demonstration. The limitation related to the single product type, manufacturer and BIM platform is now brought forward earlier in the paper and further validation across diverse material categories, production contexts and software environments is framed as an avenue for future research. However, it should nonetheless be clarified that the tool's applicability across different product and service categories is not an empirical claim derived from the case study, but rather a structural property inherited from the standard itself, which is designed to be generic in scope. The ITER project is therefore not intended as a basis for generalisation, but as a representative case through which the threefold automated procedure of the tool is illustrated. Additionally, the term "validation" has been replaced with "demonstration" throughout the manuscript, so that the language more accurately reflects the illustrative nature of the study.

 

A dedicated clarification has been added to the manuscript:

[Abstract]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation. The workflow is demonstrated through its application to a single-case, namely the ITER Project […]

[Section 1]: In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project […] However, the present demonstration is grounded in a single BIM-based case study. Future research could extend the validation of the workflow to diverse material categories, production contexts and software environments, further consolidating its scalability.

[Section 6.1]: First, the present demonstration is grounded in a single BIM-based case study, encompassing one product type and one manufacturer. Although this case is selected for its complexity and representativeness, the computational correctness of the formulae is guaranteed by the standard itself; what a single case cannot rule out are implementation errors in untested scenarios. Therefore, the generalisability of the findings to other construction product categories and organisational setups cannot be fully assumed.

[Section 6.2]: Firstly, the demonstration can be expanded to cover additional construction product categories like cement-based products, insulation materials or prefabricated components, as well as different organisational settings. This will help identify potential implementation issues in untested scenarios and enhance the transferability and scalability of the workflow across various materials and software environments.

[All sections]: The manuscript has been thoroughly revised to replace the term “validation” with “demonstration” throughout.

 

Comment 6: The “bidirectional” workflow and the DSS function. The Excel-to-Revit exchange is described as bidirectional, but the data flow presented is essentially Excel to Revit. Please clarify what information returns from Revit to Excel, or adjust the term. Likewise, the decision-support function, namely the real-time simulation of alternative configurations, is asserted but not shown. A brief worked what-if example would substantiate the DSS claim.

Response 6: We thank the reviewer for these comments. Both points have been addressed in the revised manuscript. Regarding the first, the term "bidirectional" has been removed following a careful revision of the manuscript, as it was recognised that the workflow is more accurately described as interoperable rather than bidirectional. The terminology has been adjusted accordingly throughout the text. Regarding the second, a what-if scenario has been introduced to substantiate the DSS claim. Specifically, a set of organisation-level indicators reflecting improvements in the manufacturer's operational practices is reported in Table 10 and applied without modifying the product formulation, thereby increasing the aggregate LC value from 43.77 to 56.27. This example illustrates the tool's capacity to simulate alternative configurations in real time and support informed decision-making at both the product and organisational level.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

[Section 5.2]: In this context, a what-if scenario involves adding a set of organisation-level indicators not included in the baseline assessment highlighted in Table 10. Specifically, as reported in Table 10, five indicators are introduced to reflect improvements in the manufacturer's operational practices: the proportion of energy from renewable or recovery sources (Indicator 13), the civil buildings' energy performance class (Indicator 16), the rate of special waste sent to material recovery facilities (Indicator 20), the inclusion of circular economy clauses in logistics operator selection (Indicator 25) and the share of assets and infrastructure designed for circular end-of-life management (Indicator 61). These indicators are scored from 0.80 to 1.00, indicating that the manufacturer adopts best-practice standards in energy procurement, waste management, logistics, and asset governance. These interventions do not require changes to the product's material composition or formulation, instead they focus solely on organisational measures. The LC value increases from 43.77 to 56.27, about 29%, indicating that circularity performance depends not only on the product's material properties but also on the operational context in which it is produced.

 

Comment 7: Interpretation of the LC value. The standard is described as setting no minimum thresholds, yet the result is labelled “medium circularity”. Please clarify the basis for this qualitative label or present the result without a threshold-implying descriptor.

Response 7: Thank you for this observation. The reviewer is correct that UNI/TS 11820:2024 does not define minimum thresholds for the LC value. The label "medium circularity" reflected the authors' own interpretive framing based on the 0-100 range of the index, rather than any normative classification. To avoid implying a threshold-based assessment that the standard does not support, the qualitative descriptor has been removed from the manuscript, and the result is now presented only as a numerical value.

 

 

Comment 8: Section numbering. Section 4 (Materials and Method) contains subsections numbered 3.1 and 3.2, and the in-text references to Section 3.1 and Section 3.2 should point to 4.1 and 4.2. The Conclusion is labelled Section 6, duplicating the Discussion, and should be Section 7. The introduction states that Section 7 “drew the conclusions”, which also needs correcting.

Response 8: We thank the reviewer for pointing out issues concerning the section numbering. We have carefully addressed all the issues raised:

• The subsections within Section 4 (Materials and Methods) are renumbered from 3.1 and 3.2 to 4.1 and 4.2, and all in-text references are updated accordingly;
• The Conclusion section, previously mislabelled as Section 6 (duplicating the Discussion), is corrected to Section 7;
• The sentence in the Introduction referring to Section 7 is updated to: "Section 7 draws the conclusions".

All changes are incorporated in the revised manuscript.

 

Comment 9: Reference categories. The six reference categories named in Section 2 do not match the labels and the partition used in Table 1. For example, the text uses “Logistics and Human Resources” and “Assets, Policies and Sustainability”, while Table 1 lists “Logistics”, “Product/Services” and “Human Resources, Assets, Policies and Sustainability”. Please reconcile the naming and grouping throughout.

Response 9: We thank the reviewer for highlighting this point. Section 2 has been revised to ensure that the category names and groupings are fully aligned with those used in Table 1. The six reference categories are now explicitly listed as follows: (1) Material Resources, Products and Services, (2) Energy and Water Resources, (3) Waste and Emissions, (4) Logistics, (5) Product/Services and (6) Human Resources, Assets, Policies and Sustainability. These labels are now consistent throughout the manuscript.

 

Comment 10: The LC value and number formats. The LC appears as 0.43 in the abstract and as 43.7 and 43.77 in the body, and the per-category values are rounded differently in the text and in Table 1. Decimal separators mix commas and points, for example 59,5% and 0,5% against 57.86. Please use a single scale and the journal convention throughout, and show how the total LC relates to the per-category values in Table 1.

Response 10: Thank you for this observation. The inconsistencies in the LC value scale and number formatting have been addressed throughout the revised manuscript. All values are now expressed on a consistent scale and formatted in accordance with the journal's editorial guidelines. Additionally, two dedicated tables (Table 8 and Table 9) have been added to enhance transparency, reporting respectively the indicators included in and excluded from the LC assessment. Regarding the relationship between the per-category values and the overall LC, a clarification has been added to the manuscript. The per-category LC values reported in Table 9 represent the average scores of the indicators within each reference category. The overall LC value of 43.77 is not the arithmetic mean of these per-category values; rather, it is calculated across all indicators together as per Eq. 1, which assigns different weights depending on whether an indicator is classified as core, specific or rewarding.

 

[Section 5.2]: As shown in Table 8, a group of the excluded indicators falls within the industrial symbiosis cluster, reflecting the manufacturer's current lack of integration into symbiotic networks for the exchange of material, water and energy flows. Several exclusions also concern the end-of-life and service dimension, as the manufacturer does not yet offer extended warranty schemes, repair support documentation, or Product-as-a-Service models. Furthermore, the absence of a carbon footprint assessment on incoming material resources and of a product tracking system points to gaps in the monitoring and traceability infrastructure. Taken together, these exclusions delineate a clear profile of the organisational and operational boundaries of the current production model, and implicitly identify the areas where targeted interventions could most effectively con-tribute to improving the aggregate LC value in future iterations of the assessment.

[Section 5.2]: Table 8. Organisation- and product-level indicators excluded from the LC assessment of the ITER plasters, with the corresponding reasons for non-applicability.

[Section 5.2]: As shown in Table 8, the excluded indicators fall into two distinct tiers with different implications for the LC calculation. The 15 Specific indicators that are excluded are considered technically not relevant to the product and organisational context of the ITER case study. Therefore, they are removed from both the numerator and denomi-nator of Eq. 1, following UNI/TS 11820:2024 guidelines. This removal is based on ob-jective evidence obtained through structured surveys, material composition datasheets, production process documentation and manufacturer declarations. The 12 Rewarding indicators that are excluded are also deemed technically irrelevant to the current or-ganisational context. Since these indicators are excluded from the denominator of Eq. 1 by definition, their omission does not artificially increase the final LC value.

[Section 5.2]: The per-category LC values shown in Table 9 represent the average scores of the indicators within each reference category and the individual indicator scores. The overall LC value of 43.77 is not the arithmetic mean of these per-category values, rather, it is calculated across all indicators together as per Eq. 1, which assigns different weights based on whether an indicator is core, specific or rewarding.

[Section 5.2]: Table 9. Indicators included in the LC assessment of the ITER plasters, classified by reference category, with individual indicator scores, per-category average values and the resulting total weighted LC score.

 

Comment 11: Equation 1. The equation is clearly presented and all terms are defined. One clarification would help: the text defines the LC as the ratio of the achieved score to the maximum theoretical score, whereas the denominator in Equation 1 is a count (c + s). Please confirm that the indicator values are normalised to a common scale, and clarify how the LC is bounded, since the rewarding term (0.5 times the sum of p_k) adds to the numerator while the rewarding indicators are not reflected in the denominator.

Response 11: We thank the reviewer for this observation. To address both points raised, we confirm that all indicator values are normalised to a 0-1 scale, as defined by UNI/TS 11820:2024, so that the denominator (c + s) represents the maximum theoretical score. We also clarify that UNI/TS 11820:2024 excludes rewarding indicators from the denominator because they represent optional best practices beyond the minimum requirements, and their contribution is discounted by a coefficient of 0.5 to ensure the LC remains bounded between 0 and 100.

 

A dedicated clarification has been added to the manuscript:

[Section 2.2]: Each indicator score ranges from 0 to 1: binary qualitative indicators are scored 1 when the condition is met and 0 when it isn't, while quantitative and semi-quantitative indicators are assigned values according to predefined formulas outlined in the standard.

[Section 2.2]: The full set of circularity indicators is organised by reference category and presented in Tables 1-6.

[Section 2.2]: Tables 1-6.

[Section 2.2]: Indicators that are deemed technically non-applicable to a given product or organisational context are excluded from both the numerator and the denominator of the LC formula, provided that the non-applicability is supported by documented justification and objective evidence.

[Section 4.1]: All indicator values are normalised on a 0 to 1 scale, where 1 indicates full compliance or optimal performance and 0 indicates non-compliance or the absence of the practice. This normalisation is built into the calculation logic specified by UNI/TS 11820:2024 and is applied across quantitative, semi-quantitative and qualitative indicators. Consequently, the denominator (c + s) represents the maximum possible score from core and specific indicators alone, making the LC value range between 0 and 100 when expressed as a percentage. UNI/TS 11820:2024 excludes rewarding indicators from the denominator because they denote optional best practices that surpass the minimum standards. Their influence is further reduced by a coefficient of 0.5, as highlighted in Section 2, indicating they carry half the weight of core and specific indicators. This setup ensures that rewarding indicators can only raise the LC score above the baseline established by core and specific indicators.

 

Comment 12: Duplicate references. References 30 and 31 are identical, and references 32 and 33 are identical. Please remove the duplicates and renumber.

Response 12: Thank you for pointing this out. The duplicate references have been removed and the reference list has been renumbered accordingly throughout the manuscript.

 

Comment 13: Figure legibility. Figures 3 and 4, which reproduce the Dynamo graph and the Python script, are difficult to read at the current resolution. Please supply higher-resolution versions so that the nodes and the code are legible in print.

Response 12: We thank the reviewer for this comment. Both figures have been updated in the revised manuscript. Figure 3 has been replaced with a higher-resolution and larger version, ensuring that the Dynamo nodes are fully legible in print. Figure 4 has likewise been replaced with a higher-resolution version. In addition, to further improve accessibility and allow readers to inspect the Python script in full detail, a link to the corresponding repository has been added to the manuscript.

 

A dedicated clarification has been added to the manuscript:

[Section 4.2]: The complete implementation of the Python script, structured across the seven units described above, is publicly available in an open-access GitHub repository (https://github.com/eleonoragiuffrida1706/revit-material-circularity-importer/tree/main). To reproduce the workflow, the script can be directly embedded into a Python Script node within Dynamo.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

1. The novelty of the study should be more clearly distinguished from existing BIM–LCA, BIM–circularity, and digital product passport workflows.
   The manuscript claims to address four gaps: inclusion of organisational factors, workflow automation, integration of an official circularity standard, and product-level assessment. These points are relevant, but the current discussion remains somewhat descriptive. The authors should provide a more critical comparison with existing tools and frameworks, especially BIM-based environmental assessment, circular material passport platforms, digital product passport approaches, and Level(s)-aligned tools. A concise comparative table would help demonstrate what is technically new in the proposed workflow beyond combining Excel, Word, Dynamo, and Revit.
2. The calculation procedure for the Circularity Level should be made more transparent despite copyright restrictions.
   The authors state that detailed indicator specifications cannot be reported because of UNI/TS 11820:2024 copyright restrictions. This is understandable, but the manuscript still needs sufficient methodological transparency for scientific reproducibility. The authors should provide a non-copyrighted summary of the calculation logic, including indicator classification, scoring ranges, weighting principles, treatment of non-applicable indicators, and aggregation rules. Without this information, readers cannot adequately evaluate whether the LC score is correctly calculated or whether the tool can be reproduced independently.
3. The handling of non-applicable indicators requires stronger justification.
   In the ITER case study, only 41 of the 68 indicators are retained, including 10 core, 30 specific, and 1 rewarding indicator. The exclusion of 27 indicators has a substantial influence on the final LC value. The manuscript lists general reasons for exclusion, such as the absence of critical raw materials, transport-related indicators outside the project scope, and lack of EPDs. However, the authors should provide a more systematic table showing each excluded indicator group, the reason for exclusion, the evidence used, and whether exclusion affects the denominator of the LC equation. This is essential because different applicability decisions could significantly change the final score.
4. The case-study data supporting the LC score should be reported in greater detail.
   The final LC value of 43.77 is central to the manuscript, but the underlying numerical evidence is insufficiently presented. Table 1 reports category-level scores, such as 70.00 for Product/Services, 57.86 for Material Resources, 0.00 for Energy and Water Resources, 16.67 for Waste and Emissions, and 17.50 for Logistics. However, the reader cannot see which specific inputs produced these values. The authors should include a supplementary table with anonymised indicator-level scores, supporting data sources, and evidence types. This would substantially improve transparency and allow readers to understand how the medium circularity level was obtained.
5. The validation strategy is too limited for a tool-oriented manuscript.
   The proposed tool is validated using only one case study, namely the ITER earthen plaster. While this is a useful demonstration, it does not sufficiently prove general applicability across different construction products, product categories, or organisational contexts. The authors should either add at least one additional contrasting case study, such as a cement-based product, insulation material, or prefabricated component, or explicitly reposition the manuscript as a proof-of-concept rather than a validated general-purpose tool. At minimum, a sensitivity analysis should be added to show how changes in key inputs affect the LC value.
6. The reliability of survey-based input data should be critically addressed.
   The manuscript states that data were collected through structured surveys administered to researchers and the manufacturer. This approach is practical, but it introduces potential bias, uncertainty, and inconsistency, especially when the responses are not independently verified. Since UNI/TS 11820:2024 is intended to support formal circularity claims, the authors should clarify how evidence was checked, what documentation was available, and how missing or uncertain information was handled. A data-quality scoring system or uncertainty classification would make the workflow more robust.
7. The BIM integration is described technically, but its added value should be demonstrated more convincingly.
   The Dynamo and Revit workflow is described in detail, including material creation, shared parameter generation, and population of circularity values. However, the manuscript does not sufficiently demonstrate how BIM integration improves decision-making compared with simply storing results in Excel or a PDF/Word report. The authors should include a clearer example of how a designer would use the embedded circularity parameters in Revit, for example material filtering, comparison of alternative products, schedule generation, model-based reporting, or connection with future digital twin data.
8. The discussion of low-performing categories should be deepened and linked to actionable improvement scenarios.
   The ITER case study shows very low scores for Energy and Water Resources, Waste and Emissions, and Logistics. These results are important because they reveal that a product based on local and recyclable materials may still have limited organisational circularity. The authors briefly mention that changes in production methods and facility equipment could improve the overall LC value, but no quantitative improvement scenarios are provided. The manuscript would be stronger if it simulated practical interventions, such as renewable electricity adoption, improved waste recovery, EPD development, low-emission logistics, or sustainability certification, and reported the corresponding change in LC score.
9. The manuscript should better integrate circularity assessment with environmental performance.
   The ITER project is described as involving LCA and LCC in another phase, but the present manuscript focuses mainly on circularity indicators. Circularity and environmental benefit are related but not equivalent. For example, high recyclability or local sourcing does not automatically guarantee lower embodied carbon or reduced life-cycle impacts. The authors should discuss this distinction more explicitly and clarify whether the LC score is intended to complement, not replace, LCA-based environmental assessment. If possible, even a brief comparison with available LCA/LCC findings from the ITER project would improve the scientific value of the case study.
10. The figures and manuscript structure require editorial improvement.
    The workflow figures are useful, but some screenshots are difficult to read and contain small text. Figures 2–4 should be improved in resolution and simplified so that the main methodological steps are understandable without zooming. There is also a section-numbering inconsistency: the manuscript introduces Section 4 as Materials and Method, but subsections are labelled 3.1 and 3.2. The authors should correct this throughout the text. In addition, several sentences are overly long and should be revised for clarity, especially in the methodology and discussion sections.

Overall assessment:
The manuscript addresses an important topic and proposes a potentially useful digital workflow for automated circularity assessment of construction products. Its strengths include the use of an official circularity standard, product-level focus, BIM integration, and a real case study. However, the current version is closer to a proof-of-concept than a fully validated tool. The authors should strengthen methodological transparency, provide more detailed case-study data, justify indicator applicability decisions, expand validation or sensitivity analysis, and better demonstrate the practical value of BIM integration. After major revision, the manuscript could make a meaningful contribution to digital circularity assessment in the construction sector.

 

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions highlighted in the re-submitted files.

Comment 1: The novelty of the study should be more clearly distinguished from existing BIM–LCA, BIM–circularity, and digital product passport workflows.

The manuscript claims to address four gaps: inclusion of organisational factors, workflow automation, integration of an official circularity standard, and product-level assessment. These points are relevant, but the current discussion remains somewhat descriptive. The authors should provide a more critical comparison with existing tools and frameworks, especially BIM-based environmental assessment, circular material passport platforms, digital product passport approaches, and Level(s)-aligned tools. A concise comparative table would help demonstrate what is technically new in the proposed workflow beyond combining Excel, Word, Dynamo, and Revit.

Response 1: Thank you for this observation. We fully agree that the novelty of the proposed tool required a more rigorous and critical positioning against existing workflows, and we have revised the manuscript accordingly. Specifically, the following interventions have been made:

• A structured comparative analysis of the previous studies has been developed in Section 3. Rather than describing each study in isolation, the revised section explicitly traces the specific limitations of each work with respect to circularity scope, assessment scale, BIM integration depth, third-party software interoperability, and documentation output;
• The gaps are no longer presented descriptively but are substantiated through explicit cross-referencing of the reviewed studies, demonstrating that the combination of product-scale assessment, multi-dimensional circularity evaluation across six UNI/TS 11820:2024-aligned categories, automated report generation, and bidirectional BIM integration via Dynamo scripts represents a technically distinct contribution rather than a recombination of existing approaches;
• Table 7 has been added as a concise comparative table positioning the proposed tool against all cited works. This table provides structured visual evidence of what is technically new in the proposed workflow, addressing directly your request for a more rigorous comparative demonstration.
• The discussion in Section 6.1 has been expanded to explicitly compare the proposed tool against the closest existing approaches clarifying how the proposed workflow differs in terms of standard compliance, automation depth, documentation integration and BIM feedback loop closure.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: […] three main limitations are identified in the existing literature: (1) frameworks usually evaluate circularity from only one perspective, missing multiple areas of circular per-formance at once, (2) demonstration mostly happens at the building level, making it difficult to evaluate product-specific circular performance and standardize product documentation, (3) BIM integration is still limited and assessment results rarely update or improve the digital model.

[Section 3]: A clear pattern among the reviewed studies is the tendency to focus on a single aspect of circularity. For example, Adesope et al. [39] concentrates on quantifying and reducing embodied carbon through material substitution strategies, comparing con-crete, steel and wood across LCA stages A1–A3, without considering end-of-life options or disassembly potential. Felicioni et al. [40] also focuses on comparing the LCA and LCC performance of different structural systems, highlighting trade-offs between en-vironmental and economic factors but not including circularity indicators like reusability or recyclability. Conversely, Allam et al. [41] and Lima et al. [42] explore deconstruction and Design for Deconstruction, respectively, emphasizing end-of-life planning and disassembly but not linking these to earlier design choices or material selection. Han et al. [43] addresses demolition waste management using MCDA-based scenario comparison, while Annette Davis examines the influence of lifespan assumptions in LCA by com-paring component-level and layer-level replacement strategies over a century. Al Quazzaz et al. [44] approach is perhaps the most integrated, combining BCA, LCA and LCC in a BIM-based decision support system the focus on reuse potential is limited to components, neglecting wider systemic aspects like urban material flows or supply chain circularity. Chang's et al. [45] EBCI framework aims to assess circularity across material, product, system and building levels but functions as a standalone indicator without integration into digital design tools. A second concern relates to the scale at which the proposed frameworks are tested and validated. Except for Allam et al. [41], whose case study operates at the product level by extracting and processing IFC component data, most studies use a building-scale case study for validation, covering areas from residential villas [47] and energy-efficient houses [46] to public museums [40] and industrial warehouses [48]. While this approach aligns with the focus on whole-building assessments, it leaves the product and com-ponent scales less explored, despite their growing recognition as crucial entry points for circularity in the built environment. Assessing circularity at the component or material level would enable more detailed evaluations of reusability, recyclability, and substitu-tion potential. It also aligns better with emerging frameworks like material passports, partially addressed by Al Quazzaz et al. [44] via its Airtable-based MP interface, and product-level EPD. Relying primarily on building-scale validation makes it harder to generalize the findings across different typologies and contexts and may limit applying these methods to early-stage design, where component decisions are still open and greatly impact the building's environmental performance.

A key technical gap identified in the comparative analysis is the limited role and depth of BIM integration across the reviewed workflows. Although all studies use BIM, specifically Autodesk Revit, as a central platform, its application is mostly confined to upstream data extraction tasks. Adesope et al. [39], Felicioni et al. [40], Davis et al. [46], Han [43], and Rodriguez [48] mainly or exclusively utilize Revit for creating Bills of Quantities (BoQ) or exporting geometric and material data to external tools, without integrating assessment outputs back into the BIM environment. Mowafy et al. [47] slightly broadens this by using VPL for iterative scenario development, but the optimi-zation results from NSGA-II and DEA remain external to the BIM model. Lima et al. [42] employs Dynamo to automate disassembly design routines within Revit, representing a more bidirectional BIM use, although it mainly manages DfD parameters rather than conducting comprehensive life cycle assessments.

Most studies mention integration with third-party software, but this often involves manual file exchanges, such as IFC exports, CSV transfers, or XML outputs, limiting interoperability. For example, Allam et al. [41] framework follows a sequential data pipeline: from IFC to RDF, then to Knowledge Graph, CSV, XLS for Primavera P6, and finally XML for 4D simulation in Blender, with each step requiring a separate export. Rodriguez et al. [48] notes that neither Athena Impact Estimator nor SimaPro offers fully automated data transfer from Revit, indicating manual intervention remains essential in current BIM-LCA workflows. Additionally, none of the reviewed studies explicitly addresses integrating document management or archiving within the BIM environment, such as storing EPDs, assessment reports, or material passports in a directly linked and retrievable format. This gap is significant, especially given upcoming regulatory re-quirements like the EU Level(s) initiative and the CPR, which emphasize auditability, traceability and structured environmental data documentation.

The proposed tool addresses these gaps by operating at the product scale, struc-turing the circularity assessment across six reference categories aligned with UNI/TS 11820:2024, generating standardized LC Reports, and closing the BIM feedback loop via Dynamo scripts that populate BIM objects with circularity data directly within Auto-desk Revit.

[Section 3]: Table 7. Comparative Overview of the previous studies.

[Section 6.1]: The gaps outlined in Section 3 set the context for assessing the proposed tool's contribution. The analysis reveals three main limitations: a limited and segmented ap-proach to circularity assessment, a focus on building-scale validation rather than de-tailed product-level analysis, and incomplete BIM integration within current workflows.

The reviewed studies show that most existing frameworks focus on only one aspect of circularity. Adesope et al. [39] and Felicioni et al. [40] emphasise embodied carbon reduction via material substitution, Allam et al. [41] and Lima et al. [42] work on end-of-life deconstruction planning, Han et al. [43] looks at demolition waste man-agement and Davis et al. [46] examines the methodological effects of lifespan assump-tions in LCA. Even more comprehensive frameworks, like Al Quazzaz et al. [44] BIM-based DSS combining BCA, LCA and LCC, or Chang et al. [45] EBCI indicator system, are limited to specific circularity dimensions and do not capture the full range of circular performance. The tool presented in this research adopts a different strategy by assessing circularity across six categories aligned with UNI/TS 11820:2024, each rep-resenting a different aspect of circular performance. This multi-dimensional approach enables a more complete evaluation at the product level, moving beyond the sin-gle-indicator or single-dimension methods. Instead of replacing these focused ap-proaches, the tool complements them by providing a structured framework to evaluate multiple circularity aspects simultaneously, facilitating more informed and holistic de-cisions during design and production.

A further limitation from the literature concerns the demonstration scale. Aside from Allam et al. [41], who processes IFC component data at the element level, all re-viewed studies validate methodologies at the building level [40,46–48]. Building-scale assessments are crucial for whole-life carbon accounting and design comparisons but tend to aggregate component details, potentially obscuring the circularity performance of individual materials and products. The proposed tool specifically operates at the product level, evaluating circularity indicators for individual construction products per UNI/TS 11820:2024. This detailed approach aligns with the rise of product-level tools like EPD and material passports, the latter partially addressed by Al Quazzaz et al. [44] via an Airtable interface, though without standardized compliance outputs. By gener-ating UNI/TS 11820:2024-compliant LC Reports in MS Word, the tool bridges assess-ment and documentation, providing structured outputs that support product certifica-tion, procurement, and regulatory compliance.

A technical gap identified in the literature concerns the limited integration of BIM. Most reviewed studies use Revit mainly for data extraction, such as generating BoQ or exporting geometric information to external LCA or circularity tools, without integrating assessment outputs into the BIM environment. Rodriguez et al. [48] confirms that neither Athena Impact Estimator nor SimaPro provides fully automated data transfer from Revit, while Allam et al. [41] pipeline shows how workflows across mul-tiple software can fragment data flow into disconnected manual steps. The digital tool presented in this research addresses this by providing an Excel-to-Revit data flow module, implemented through Dynamo scripts and APIs, which automates material creation, custom parameter definitions, and population with circularity data inside Autodesk Revit. This effectively closes the feedback loop, making circularity data an active, queryable, and visible part of the BIM model, thus supporting dynamic design optimization. Also, unlike prior studies, this tool incorporates document generation within the workflow, producing standardized LC Reports linked directly to assess-ments, filling a documentation gap that others do not resolve.

Furthermore, the digital tool proposed in this research is modular and scalable. Its first two modules, namely the LC calculator and automated report generator, depend on standards, as their indicator structures, weighting factors and reporting criteria follow UNI/TS 11820:2024. Nonetheless, their core architecture is flexible and can be adapted to any indicator-based regulatory or assessment system by updating the relevant cal-culation rules and output templates. The third module, which manages the Ex-cel-to-Revit data flow via Dynamo VPL scripts and APIs, is inherently independent of standards. It serves as a data exchange pipeline between spreadsheets and BIM, inde-pendent of the specific standard, enabling reuse across various circularity or sustaina-bility assessments without structural changes. This modular approach allows the tool to adapt to regulatory updates, incorporate future revisions of UNI/TS 11820:2024, and extend to other indicator-based evaluation frameworks, ensuring it remains a versatile and future-ready solution for digital circularity assessment in construction.

 

Comment 2: The calculation procedure for the Circularity Level should be made more transparent despite copyright restrictions.

The authors state that detailed indicator specifications cannot be reported because of UNI/TS 11820:2024 copyright restrictions. This is understandable, but the manuscript still needs sufficient methodological transparency for scientific reproducibility. The authors should provide a non-copyrighted summary of the calculation logic, including indicator classification, scoring ranges, weighting principles, treatment of non-applicable indicators, and aggregation rules. Without this information, readers cannot adequately evaluate whether the LC score is correctly calculated or whether the tool can be reproduced independently.

Response 2: Thank you for this observation. The manuscript has been revised to improve methodological clarity while respecting the copyright restrictions of UNI/TS 11820:2024. The weighting principles are now explicitly outlined, indicating that core and specific indicators have a weight of 1, while rewarding indicators are given a coefficient of 0.5. The scoring system is described, detailing that each indicator is scored from 0 to 1, with binary qualitative indicators scored as 1 when the condition is met and 0 otherwise. Quantitative and semi-quantitative indicators are scored based on formulas specified in the standard. The handling of technically non-applicable indicators is clarified; such indicators are excluded from both numerator and denominator of the LC formula when justified. The aggregation logic for the LC formula is further explained, noting that all indicator values are normalised to a 0-1 scale, the denominator reflects the maximum possible score from core and specific indicators, and rewarding indicators can only increase the LC score, not penalise organisations. Lastly, six tables (Table 1-6) have been added to give a detailed overview of all 68 indicators defined by the standard, organized by reference category and characterised by four dimensions: tier structure, subject of assessment, assessment mode, and indicator ID, the latter allowing direct cross-reference with the standard without reproducing copyrighted content.

 

A dedicated clarification has been added to the manuscript:

[Section 2.2]: UNI/TS 11820:2024 also assigns a specific weight to each indicator type for calculating the final score: 1 for core and specific indicators, and 0.5 for rewarding indicators.

[Section 2.2]: Each indicator score ranges from 0 to 1: binary qualitative indicators are scored 1 when the condition is met and 0 when it isn't, while quantitative and semi-quantitative indicators are assigned values according to predefined formulas outlined in the standard.

[Section 2.2]: The full set of circularity indicators is organised by reference category and presented in Tables 1-6.

[Section 2.2]: Tables 1-6.

[Section 2.2]: Indicators that are deemed technically non-applicable to a given product or organisational context are excluded from both the numerator and the denominator of the LC formula, provided that the non-applicability is supported by documented justification and objective evidence.

[Section 4.1]: All indicator values are normalised on a 0 to 1 scale, where 1 indicates full compliance or optimal performance and 0 indicates non-compliance or the absence of the practice. This normalisation is built into the calculation logic specified by UNI/TS 11820:2024 and is applied across quantitative, semi-quantitative and qualitative indicators. Consequently, the denominator (c + s) represents the maximum possible score from core and specific indicators alone, making the LC value range between 0 and 100 when expressed as a percentage. UNI/TS 11820:2024 excludes rewarding indicators from the denominator because they denote optional best practices that surpass the minimum standards. Their influence is further reduced by a coefficient of 0.5, as highlighted in Section 2, indicating they carry half the weight of core and specific indicators. This setup ensures that rewarding indicators can only raise the LC score above the baseline established by core and specific indicators.

 

Comment 3: The handling of non-applicable indicators requires stronger justification.

In the ITER case study, only 41 of the 68 indicators are retained, including 10 core, 30 specific, and 1 rewarding indicator. The exclusion of 27 indicators has a substantial influence on the final LC value. The manuscript lists general reasons for exclusion, such as the absence of critical raw materials, transport-related indicators outside the project scope, and lack of EPDs. However, the authors should provide a more systematic table showing each excluded indicator group, the reason for exclusion, the evidence used, and whether exclusion affects the denominator of the LC equation. This is essential because different applicability decisions could significantly change the final score.

Response 3: We thank the reviewer for this observation. The manuscript has been revised to provide a more systematic and transparent treatment of the excluded indicators. A dedicated table (Table 8) has been added reporting all 27 excluded indicators, organised by tier structure and accompanied by the specific reason for non-applicability of each. The evidence base underlying each exclusion decision is also clarified, drawing on objective sources including structured surveys administered to both the manufacturer and the researchers involved in product development, material composition datasheets, production process documentation and manufacturer declarations. Furthermore, the differential impact of the exclusions on the denominator of Eq. 1 is now explicitly addressed. The 15 excluded Specific indicators are removed from both the numerator and the denominator of the LC formula in accordance with UNI/TS 11820:2024, as their non-applicability is grounded in the specific product and organisational context of the case study. The 12 excluded Rewarding indicators, by contrast, are excluded from the denominator by definition, regardless of whether they are compiled or not, their non-compilation therefore does not artificially inflate the final LC value.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: As shown in Table 8, a group of the excluded indicators falls within the industrial symbiosis cluster, reflecting the manufacturer's current lack of integration into symbiotic networks for the exchange of material, water and energy flows. Several exclusions also concern the end-of-life and service dimension, as the manufacturer does not yet offer extended warranty schemes, repair support documentation, or Product-as-a-Service models. Furthermore, the absence of a carbon footprint assessment on incoming material resources and of a product tracking system points to gaps in the monitoring and traceability infrastructure. Taken together, these exclusions delineate a clear profile of the organisational and operational boundaries of the current production model, and implicitly identify the areas where targeted interventions could most effectively contribute to improving the aggregate LC value in future iterations of the assessment.

[Section 5.2]: Table 8. Organisation- and product-level indicators excluded from the LC assessment of the ITER plasters, with the corresponding reasons for non-applicability.

[Section 5.2]: As shown in Table 8, the excluded indicators fall into two distinct tiers with different implications for the LC calculation. The 15 Specific indicators that are excluded are considered technically not relevant to the product and organisational context of the ITER case study. Therefore, they are removed from both the numerator and denominator of Eq. 1, following UNI/TS 11820:2024 guidelines. This removal is based on ob-jective evidence obtained through structured surveys, material composition datasheets, production process documentation and manufacturer declarations. The 12 Rewarding indicators that are excluded are also deemed technically irrelevant to the current or-ganisational context. Since these indicators are excluded from the denominator of Eq. 1 by definition, their omission does not artificially increase the final LC value.

 

Comment 4: The case-study data supporting the LC score should be reported in greater detail.

The final LC value of 43.77 is central to the manuscript, but the underlying numerical evidence is insufficiently presented. Table 1 reports category-level scores, such as 70.00 for Product/Services, 57.86 for Material Resources, 0.00 for Energy and Water Resources, 16.67 for Waste and Emissions, and 17.50 for Logistics. However, the reader cannot see which specific inputs produced these values. The authors should include a supplementary table with anonymised indicator-level scores, supporting data sources, and evidence types. This would substantially improve transparency and allow readers to understand how the medium circularity level was obtained.

Response 4: We appreciate the reviewer's attention to this aspect. A dedicated table (Table 9) has been added reporting the individual indicator scores for all 41 applicable indicators, organised by reference category, alongside the per-category average values and the resulting total weighted LC score of 43.77. It is further clarified that the per-category values represent the average scores of the indicators within each reference category, and that the overall LC value is not the arithmetic mean of these per-category figures but is calculated across all indicators together as per Eq. 1, which assigns different weights depending on whether an indicator is classified as core, specific or rewarding. The indicator IDs reported in the table serve as direct references to the corresponding entries in UNI/TS 11820:2024, enabling readers to retrieve the detailed calculation procedure for each individual indicator without the need to reproduce copyrighted content in the manuscript. Finally, the data sources underlying all indicator values are explicitly stated: all scores are derived from structured surveys administered to both the manufacturer and the researchers involved in the development of the ITER plasters, corroborated where applicable by material composition datasheets, production process documentation and manufacturer declarations.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: The per-category LC values shown in Table 9 represent the average scores of the indicators within each reference category and the individual indicator scores. The overall LC value of 43.77 is not the arithmetic mean of these per-category values; rather, it is calculated across all indicators together as per Eq. 1, which assigns different weights based on whether an indicator is core, specific or rewarding.

[Section 5.2]: Table 9. Indicators included in the LC assessment of the ITER plasters, classified by reference category, with individual indicator scores, per-category average values and the resulting total weighted LC score.

[Section 5.2]: All indicator values are based on structured surveys conducted with the manufacturer and researchers involved in developing the ITER plasters. These are supported by material composition datasheets, production process documentation and manufacturer declarations where applicable.

 

Comment 5: The validation strategy is too limited for a tool-oriented manuscript.

The proposed tool is validated using only one case study, namely the ITER earthen plaster. While this is a useful demonstration, it does not sufficiently prove general applicability across different construction products, product categories, or organisational contexts. The authors should either add at least one additional contrasting case study, such as a cement-based product, insulation material, or prefabricated component, or explicitly reposition the manuscript as a proof-of-concept rather than a validated general-purpose tool. At minimum, a sensitivity analysis should be added to show how changes in key inputs affect the LC value.

Response 5: We thank the reviewer for this observation. Following the reviewer's suggestion, the term "validation" has been replaced throughout with "demonstration", explicitly repositioning the manuscript.

However, we would like to clarify two aspects that partially address the reviewer's concern. First, the LC value for ITER was calculated both manually by the research group and through the digital tool, with both approaches yielding the same result of 43.77. This cross-check confirms the computational correctness of the tool's implementation of the formulas defined by UNI/TS 11820:2024. Second, the tool's generalisability at the normative level is not an empirical claim derived from the case study, but a structural property inherited from the standard itself, which is explicitly designed to be applicable to any industrial product or process. ITER therefore serves as a representative demonstration case, selected for its complexity and multi-component nature, rather than as the sole basis for a generalisation claim.

Regarding the sensitivity analysis, a what-if scenario has been introduced in Section 5.2, in which a set of organisation-level indicators, highlighted in Table 10, reflecting improvements in operational practices is applied without modifying the product formulation. The resulting increase in the aggregate LC value from 43.77 to 56.27 (approximately 29%) illustrates how changes in key inputs affect the final score and demonstrates the tool's capacity to support real-time simulation of alternative configurations.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation.

In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project

[Section 1]: In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project […] However, the present demonstration is grounded in a single BIM-based case study. Future research could extend the validation of the workflow to diverse material categories, production contexts and software environments, further consolidating its scalability.

[Section 5.1]: In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

[Section 5.2]: The assessment was first performed manually and, subsequently, the digital methodology presented in this study was applied to ITER, as highlighted in Section 5.2, to automate the calculation and provide a digital tool applicable to any product or service covered by the standard.

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

[Section 5.2]: In this context, a what-if scenario involves adding a set of organisation-level indicators not included in the baseline assessment highlighted in Table 10. Specifically, as reported in Table 10, five indicators are introduced to reflect improvements in the manufacturer's operational practices: the proportion of energy from renewable or recovery sources (Indicator 13), the civil buildings' energy performance class (Indicator 16), the rate of special waste sent to material recovery facilities (Indicator 20), the inclusion of circular economy clauses in logistics operator selection (Indicator 25) and the share of assets and infrastructure designed for circular end-of-life management (Indicator 61). These indicators are scored from 0.80 to 1.00, indicating that the manufacturer adopts best-practice standards in energy procurement, waste management, logistics, and asset governance. These interventions do not require changes to the product's material composition or formulation, instead they focus solely on organisational measures. The LC value increases from 43.77 to 56.27, about 29%, indicating that circularity performance depends not only on the product's material properties but also on the operational context in which it is produced.

[Section 6.1]: First, the present demonstration is grounded in a single BIM-based case study, encompassing one product type and one manufacturer. Although this case is selected for its complexity and representativeness, the computational correctness of the formulae is guaranteed by the standard itself; what a single case cannot rule out are implementation errors in untested scenarios. Therefore, the generalisability of the findings to other construction product categories and organisational setups cannot be fully assumed.

[Section 6.2]: Firstly, the demonstration can be expanded to cover additional construction product categories like cement-based products, insulation materials or prefabricated components, as well as different organisational settings. This will help identify potential implementation issues in untested scenarios and enhance the transferability and scalability of the workflow across various materials and software environments.

[All sections]: The manuscript has been thoroughly revised to replace the term “validation” with “demonstration” throughout.

 

Comment 6: The reliability of survey-based input data should be critically addressed.

The manuscript states that data were collected through structured surveys administered to researchers and the manufacturer. This approach is practical, but it introduces potential bias, uncertainty, and inconsistency, especially when the responses are not independently verified. Since UNI/TS 11820:2024 is intended to support formal circularity claims, the authors should clarify how evidence was checked, what documentation was available, and how missing or uncertain information was handled. A data-quality scoring system or uncertainty classification would make the workflow more robust.

Response 6: Thank you for this observation. The limitations associated with the survey-based data collection approach, including the reliance on self-reported data and the absence of third-party verification, are now explicitly acknowledged in the limitations section of the revised manuscript. To improve transparency, two dedicated tables have been included: one reporting all 41 applicable indicators with their individual scores, and another documenting reasons for the non-applicability of all 27 excluded indicators. The indicator IDs reported in both tables serve as direct references to the corresponding entries in UNI/TS 11820:2024, enabling readers to cross-check each input against the standard's requirements. Furthermore, the development of a structured data-quality classification system, enabling users to tag each input according to its evidence level, has been included among the directions for future research.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: As shown in Table 8, a group of the excluded indicators falls within the industrial symbiosis cluster, reflecting the manufacturer's current lack of integration into symbiotic networks for the exchange of material, water and energy flows. Several exclusions also concern the end-of-life and service dimension, as the manufacturer does not yet offer extended warranty schemes, repair support documentation, or Product-as-a-Service models. Furthermore, the absence of a carbon footprint assessment on incoming material resources and of a product tracking system points to gaps in the monitoring and traceability infrastructure. Taken together, these exclusions delineate a clear profile of the organisational and operational boundaries of the current production model, and implicitly identify the areas where targeted interventions could most effectively contribute to improving the aggregate LC value in future iterations of the assessment.

[Section 5.2]: Table 8. Organisation- and product-level indicators excluded from the LC assessment of the ITER plasters, with the corresponding reasons for non-applicability.

[Section 5.2]: The per-category LC values shown in Table 9 represent the average scores of the indicators within each reference category and the individual indicator scores. The overall LC value of 43.77 is not the arithmetic mean of these per-category values; rather, it is calculated across all indicators together as per Eq. 1, which assigns different weights based on whether an indicator is core, specific or rewarding.

[Section 5.2]: Table 9. Indicators included in the LC assessment of the ITER plasters, classified by reference category, with individual indicator scores, per-category average values and the resulting total weighted LC score.

[Section 6.1]: Furthermore, data is gathered through structured surveys administered to researchers and the manufacturer. This approach aligns with the UNI/TS 11820:2024 indicator structure but depends on self-reported data, which relies on respondents' knowledge of the standard's requirements. The lack of third-party verification of this data is acknowledged as a limitation in fully meeting the compliance pathway outlined by the standard for claim-based assessments

[Section 6.2]: Secondly, the reliability of survey-based data collection could be improved by creating a structured data-quality classification system. This would allow users to tag inputs based on evidence level and gradually incorporate third-party verified sources, such as Environmental Product Declarations (EPDs), moving the tool closer to the formal compliance pathway described in standards for claim-based assessments.

[Section 7]:  In this regard, the development of a structured data-quality classification system, enabling users to tag each input according to its evidence level, would further strengthen the robustness of the assessment workflow and bring it closer to the compliance pathway outlined by the standard for claim-based assessments.

 

 

Comment 7: The BIM integration is described technically, but its added value should be demonstrated more convincingly.

The Dynamo and Revit workflow is described in detail, including material creation, shared parameter generation, and population of circularity values. However, the manuscript does not sufficiently demonstrate how BIM integration improves decision-making compared with simply storing results in Excel or a PDF/Word report. The authors should include a clearer example of how a designer would use the embedded circularity parameters in Revit, for example material filtering, comparison of alternative products, schedule generation, model-based reporting, or connection with future digital twin data.

Response 7: We thank the reviewer for this remark. An added figure (Figure 9) illustrates the practical use of the circularity workflow in Autodesk Revit, showing how the material database is customized with UNI/TS 11820:2024-compliant shared parameters, enabling filtering of materials in use and automatic extraction of circularity-based schedules and quantity take-offs. These features help designers filter, compare and analyze materials by LC score directly within the BIM environment, facilitating critical material choices and substitution scenarios during design. Furthermore, the revised manuscript demonstrates that the BIM model enriched with circularity parameters can be exported in IFC format, with the circularity indicators correctly mapped as custom IfcPropertySets. This confirms that the embedded circularity data is accessible through any IFC-compliant software in an open, platform-independent format, enabling the sharing of verified circularity information across the full project supply chain. Additionally, the future research section has been expanded to discuss potential improvements through AI, data processing libraries, workflow automation tools such as N8N and autonomous AI agents, aiming to develop a proactive decision-support system for ongoing circularity monitoring throughout the product lifecycle.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: As highlighted in Figure 9, the material database is customised using the generated circularity data, with Autodesk Revit parameters automatically populated. Specifically, each material includes a set of shared parameters that represent the indicator structure of UNI/TS 11820:2024. This parametric enhancement allows the BIM model to serve as both a geometric representation and a structured repository of circularity data at the material level. Additionally, Revit's native scheduling feature can automatically generate circularity schedules, enabling designers to filter, sort and compare materials by LC score. This facilitates better material selection during design, allowing the identification of low-performing components and the exploration of substitution scenarios directly within the BIM environment.

[Section 5.2]: Furthermore, the BIM model with added circularity parameters is exported in Industry Foundation Classes (IFC) format. This confirms that the circularity indicators are properly assigned as custom IfcPropertySets linked to the relevant materials and building components. As a result, the embedded circularity data can be accessed and viewed using any IFC-compliant software, without needing the original Revit model. This enables the sharing of verified circularity information across the entire project supply chain in a platform-independent and open format.

[Section 5.2]: Figure 9. BIM integration of circularity data (customisation of the material database with UNI/TS 11820:2024-compliant shared parameters and extraction of circularity-based material schedules and quantity take-offs in Autodesk Revit) and IFC export.

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

[Section 6.2]: Thirdly, the circularity parameters transferred to Autodesk Revit are used, as discussed in Section 5.2, to facilitate filtering and comparing materials across different product configurations. They also support the automatic creation of schedules that report circularity performance at the component or assembly level, showcasing the benefits of BIM integration over static reporting methods. Future developments aim to further improve these features by integrating Artificial Intelligence (AI) and. For ex-ample, data analysis libraries such as NumPy, Pandas, and Matplotlib are designed to enable automated processing and visualisation of large volumes of circularity data across multiple products and scenarios. The use of deep learning frameworks such as PyTorch is expected to aid the development of predictive models that can automatically identify the best indicator combinations to maximise the LC score. Workflow automa-tion tools like N8N are also considered to connect the system with external data sources, product databases and BIM platforms, supporting continuous updates of circularity parameters. Also, the strucutre of autonomous AI agents is envisioned to transform the system from a DSS into a proactive system that offers adaptive recommendations for circularity optimization throughout the entire product lifecycle.

 

 

Comment 8: The discussion of low-performing categories should be deepened and linked to actionable improvement scenarios.

The ITER case study shows very low scores for Energy and Water Resources, Waste and Emissions, and Logistics. These results are important because they reveal that a product based on local and recyclable materials may still have limited organisational circularity. The authors briefly mention that changes in production methods and facility equipment could improve the overall LC value, but no quantitative improvement scenarios are provided. The manuscript would be stronger if it simulated practical interventions, such as renewable electricity adoption, improved waste recovery, EPD development, low-emission logistics, or sustainability certification, and reported the corresponding change in LC score.

Response 8: We thank the reviewer for these comments. To address this point, a what-if scenario has been introduced to substantiate the DSS claim and strengthen the manuscript. Specifically, a set of organisation-level indicators reflecting improvements in the manufacturer's operational practices is reported in Table 10 and applied without modifying the product formulation, thereby increasing the aggregate LC value from 43.77 to 56.27. This example illustrates the tool's capacity to simulate alternative configurations in real time and support informed decision-making at both the product and organisational level.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

[Section 5.2]: In this context, a what-if scenario involves adding a set of organisation-level indicators not included in the baseline assessment highlighted in Table x. Specifically, as reported in Table x, five indicators are introduced to reflect improvements in the manufacturer's operational practices: the proportion of energy from renewable or recovery sources (Indicator 13), the civil buildings' energy performance class (Indicator 16), the rate of special waste sent to material recovery facilities (Indicator 20), the inclusion of circular economy clauses in logistics operator selection (Indicator 25) and the share of assets and infrastructure designed for circular end-of-life management (Indicator 61). These indicators are scored from 0.80 to 1.00, indicating that the manufacturer adopts best-practice standards in energy procurement, waste management, logistics, and asset governance. These interventions do not require changes to the product's material composition or formulation, instead they focus solely on organisational measures. The LC value increases from 43.77 to 56.27, about 29%, indicating that circularity performance depends not only on the product's material properties but also on the operational context in which it is produced.

 

Comment 9: The manuscript should better integrate circularity assessment with environmental performance.

The ITER project is described as involving LCA and LCC in another phase, but the present manuscript focuses mainly on circularity indicators. Circularity and environmental benefit are related but not equivalent. For example, high recyclability or local sourcing does not automatically guarantee lower embodied carbon or reduced life-cycle impacts. The authors should discuss this distinction more explicitly and clarify whether the LC score is intended to complement, not replace, LCA-based environmental assessment. If possible, even a brief comparison with available LCA/LCC findings from the ITER project would improve the scientific value of the case study.

Response 9: Thank you for this observation. We fully agree that circularity performance and environmental performance are related but distinct dimensions of sustainability, and that a high LC score does not automatically imply favourable life-cycle environmental outcomes. Regarding the comparison with LCA/LCC findings from the ITER project, we note that the plasters are currently undergoing a dedicated LCA study, however, as this work is still in the publication process, its results cannot be reported in the present manuscript. To address the reviewer's note, a clarification has been added in Section 5.1, where it is now stated that the LC score and the LCA-based assessment capture complementary but distinct dimensions of sustainability and that the two frameworks should be read in conjunction to provide a complete picture of a product's sustainability profile.

 

A dedicated clarification has been added to the manuscript:

[Section 5.1]: The present research contributes to this broader assessment framework by computing the LC of the ITER plasters through the automated digital tool presented in Section 4. The LC score and the LCA-based assessment capture complementary but distinct di-mensions of sustainability: while the former measures the degree of circular economy alignment at both product and organisational level, the latter quantifies environmental burdens and impacts across the full life cycle. The two frameworks should therefore be read in conjunction to provide a complete picture of a product's sustainability profile. In the final demonstration phase, compliance with reference standards is verified. […] In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

 

Comment 10: The figures and manuscript structure require editorial improvement.

The workflow figures are useful, but some screenshots are difficult to read and contain small text. Figures 2–4 should be improved in resolution and simplified so that the main methodological steps are understandable without zooming. There is also a section-numbering inconsistency: the manuscript introduces Section 4 as Materials and Method, but subsections are labelled 3.1 and 3.2. The authors should correct this throughout the text. In addition, several sentences are overly long and should be revised for clarity, especially in the methodology and discussion sections.

Response 10: We thank the reviewer for these comments. All three points have been addressed in the revised manuscript. Regarding figure legibility, Figures 2 and 4 have been replaced with higher-resolution versions. Figure 3 has also been enlarged to improve the readability of the Dynamo nodes, and a link to the repository containing the Python script has been added to further support accessibility. Regarding the section-numbering inconsistency:

• The subsections within Section 4 (Materials and Methods) are renumbered from 3.1 and 3.2 to 4.1 and 4.2, and all in-text references are updated accordingly;
• The Conclusion section, previously mislabelled as Section 6 (duplicating the Discussion), is corrected to Section 7;
• The sentence in the Introduction referring to Section 7 is updated to: "Section 7 draws the conclusions;
• all in-text references have been updated accordingly throughout the manuscript.

Regarding sentence clarity, the methodology and discussion sections have been carefully revised to shorten and simplify overly long sentences, improving overall readability.

 

A dedicated clarification has been added to the manuscript:

[Section 4.2]: The complete implementation of the Python script, structured across the seven units described above, is publicly available in an open-access GitHub repository (https://github.com/eleonoragiuffrida1706/revit-material-circularity-importer/tree/main). To reproduce the workflow, the script can be directly embedded into a Python Script node within Dynamo.

 

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

1.The proposed digital tool seems merely a basic integration of available commercial software features:. All components including Excel automation, Dynamo parameter mapping and BIM material creation have already been well-documented in existing literature and industry practice. Where are the new algorithms, data structures or workflow paradigms?

2. The core contribution is claimed to be the encoding of UNI/TS  into a digital tool. However, it is a technical implementation task rather than academic research. Authors do not  extend or validate the standard itself. Authors merely translate the rules into spreadsheet formulas, which provides no new insights into circularity assessment methodology.

3. The tool does not solve critical pain points in circularity assessment, such as interoperability across BIM platforms, integration with third-party verified data, dynamic lifecycle updates or cross-standard alignment. It produces static, one-time assessments that cannot adapt to changing product data or regulatory requirements.

4. Authors state that details of the 68 UNI/TS 11820:2024 indicators cannot be disclosed due to copyright restrictions, which makes the research completely non-reproducible. How to verify the accuracy of the calculator’s implementation, test its performance on different products or build upon the work?

5. All input data relies on self-reported surveys from the research team and a single manufacturer, which could introduce significant bias and undermines the reliability of the resulting LC score. Authors directly adopt the indicator weights from UNI/TS? No justification is provided for why these weights are suitable for earthen plasters or other building materials.

6. The workflow is exclusively tied to Autodesk Revit and does not support open standards such as Industry Foundation Classes, which limits the practical utility in the global AEC industry, where multiple BIM platforms are used. The bidirectional data flow claim appears somehow misleading, since only Excel-to-Revit transfer is demonstrated, with no Revit-to-Excel update capability.

7. The tool is validated only on the ITER earthen plaster project. No testing is performed on other construction product categories like concrete, steel, insulation or different manufacturing contexts, so the tool’s generalizability is unproven. Moreover, authors do not compare the tool’s performance with any existing circularity assessment tools or manual calculation methods. How to prove that the tool produces more accurate, faster or more reliable results than established alternatives?

 

8. The results merely presents the LC score and category-wise breakdown without any critical analysis. Why the Energy and Water Resources category scored 0 beyond stating the lack of renewable energy? How to quantify the potential impact of specific improvement measures?

9. In the discussion, authors claim to have addressed four literature gaps, but none are convincingly resolved. Organizational factors are included only because UNI/TS 11820 requires them. There is no new framework for integrating organizational and product-level data. Workflow automation is partial and relies on manual data entry. Standard digitalization is limited to a single local standard and product-level assessment is demonstrated for one product only.

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions highlighted in the re-submitted files.

Comment 1: The proposed digital tool seems merely a basic integration of available commercial software features:. All components including Excel automation, Dynamo parameter mapping and BIM material creation have already been well-documented in existing literature and industry practice. Where are the new algorithms, data structures or workflow paradigms?

Response 1: Thank you for raising this point, which gives us the opportunity to clarify the technical contribution of the proposed tool more precisely. We would like to draw the Reviewer's attention to the fact that the novelty of the proposed tool does not reside in the invention of new software components, but rather in the design of a structured, standard-compliant and modular workflow architecture that integrates these components in an innovative way. Specifically, the following technically distinct contributions are highlighted:

• Standard-compliant automation: The LC calculator and automated report generator implement the full indicator structure, weighting factors, and reporting criteria defined by UNI/TS 11820:2024, translating a normative framework into a computable and automated workflow. This is not a generic Excel automation but a rule-based calculation engine whose logic is directly derived from and traceable to a regulatory document, producing legally and technically compliant LC Reports in MS Word without manual intervention.
• Multi-dimensional product-level circularity assessment: The tool evaluates circularity across six reference categories, each addressing a distinct area of circular performance, at the product scale. As demonstrated in the revised Section 3 and Table 7, no existing BIM-based framework in the reviewed literature simultaneously operates at this scale and across this breadth of circularity dimensions.
• Bidirectional BIM integration as a workflow paradigm shift: While Dynamo scripting and parameter mapping are individually documented techniques, their deployment in this research serves a specific function: closing the feedback loop between a standard-compliant circularity assessment conducted outside BIM and the BIM model itself. The Dynamo scripts automate not only parameter population but also material creation and custom parameter definition, effectively transforming static circularity assessment outputs into queryable, model-embedded data. This represents a workflow paradigm in which circularity data becomes a living attribute of the digital building model, rather than remaining in an external report.
• Modularity and scalability as an architectural contribution: The three-module architecture is explicitly designed so that the standard-dependent modules (the calculator and report generator) can be updated independently of the standard-independent BIM integration module. This separation of concerns is a deliberate architectural decision that ensures the tool remains adaptable to regulatory updates and extensible to other indicator-based frameworks without structural redesign.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation. In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project.

[Section 1]: […] three main limitations are identified in the existing literature: (1) frameworks usually evaluate circularity from only one perspective, missing multiple areas of circular per-formance at once, (2) demonstration mostly happens at the building level, making it difficult to evaluate product-specific circular performance and standardize product documentation, (3) BIM integration is still limited and assessment results rarely update or improve the digital model.

[Section 3]: A clear pattern among the reviewed studies is the tendency to focus on a single aspect of circularity. For example, Adesope et al. [39] concentrates on quantifying and reducing embodied carbon through material substitution strategies, comparing con-crete, steel and wood across LCA stages A1–A3, without considering end-of-life options or disassembly potential. Felicioni et al. [40] also focuses on comparing the LCA and LCC performance of different structural systems, highlighting trade-offs between en-vironmental and economic factors but not including circularity indicators like reusability or recyclability. Conversely, Allam et al. [41] and Lima et al. [42] explore deconstruction and Design for Deconstruction, respectively, emphasizing end-of-life planning and disassembly but not linking these to earlier design choices or material selection. Han et al. [43] addresses demolition waste management using MCDA-based scenario comparison, while Annette Davis examines the influence of lifespan assumptions in LCA by com-paring component-level and layer-level replacement strategies over a century. Al Quazzaz et al. [44] approach is perhaps the most integrated, combining BCA, LCA and LCC in a BIM-based decision support system the focus on reuse potential is limited to components, neglecting wider systemic aspects like urban material flows or supply chain circularity. Chang's et al. [45] EBCI framework aims to assess circularity across material, product, system and building levels but functions as a standalone indicator without integration into digital design tools. A second concern relates to the scale at which the proposed frameworks are tested and validated. Except for Allam et al. [41], whose case study operates at the product level by extracting and processing IFC component data, most studies use a building-scale case study for validation, covering areas from residential villas [47] and energy-efficient houses [46] to public museums [40] and industrial warehouses [48]. While this approach aligns with the focus on whole-building assessments, it leaves the product and com-ponent scales less explored, despite their growing recognition as crucial entry points for circularity in the built environment. Assessing circularity at the component or material level would enable more detailed evaluations of reusability, recyclability, and substitu-tion potential. It also aligns better with emerging frameworks like material passports, partially addressed by Al Quazzaz et al. [44] via its Airtable-based MP interface, and product-level EPD. Relying primarily on building-scale validation makes it harder to generalize the findings across different typologies and contexts and may limit applying these methods to early-stage design, where component decisions are still open and greatly impact the building's environmental performance.

A key technical gap identified in the comparative analysis is the limited role and depth of BIM integration across the reviewed workflows. Although all studies use BIM, specifically Autodesk Revit, as a central platform, its application is mostly confined to upstream data extraction tasks. Adesope et al. [39], Felicioni et al. [40], Davis et al. [46], Han [43], and Rodriguez [48] mainly or exclusively utilize Revit for creating Bills of Quantities (BoQ) or exporting geometric and material data to external tools, without integrating assessment outputs back into the BIM environment. Mowafy et al. [47] slightly broadens this by using VPL for iterative scenario development, but the optimi-zation results from NSGA-II and DEA remain external to the BIM model. Lima et al. [42] employs Dynamo to automate disassembly design routines within Revit, representing a more bidirectional BIM use, although it mainly manages DfD parameters rather than conducting comprehensive life cycle assessments.

Most studies mention integration with third-party software, but this often involves manual file exchanges, such as IFC exports, CSV transfers, or XML outputs, limiting interoperability. For example, Allam et al. [41] framework follows a sequential data pipeline: from IFC to RDF, then to Knowledge Graph, CSV, XLS for Primavera P6, and finally XML for 4D simulation in Blender, with each step requiring a separate export. Rodriguez et al. [48] notes that neither Athena Impact Estimator nor SimaPro offers fully automated data transfer from Revit, indicating manual intervention remains essential in current BIM-LCA workflows. Additionally, none of the reviewed studies explicitly addresses integrating document management or archiving within the BIM environment, such as storing EPDs, assessment reports, or material passports in a directly linked and retrievable format. This gap is significant, especially given upcoming regulatory re-quirements like the EU Level(s) initiative and the CPR, which emphasize auditability, traceability and structured environmental data documentation.

The proposed tool addresses these gaps by operating at the product scale, struc-turing the circularity assessment across six reference categories aligned with UNI/TS 11820:2024, generating standardized LC Reports, and closing the BIM feedback loop via Dynamo scripts that populate BIM objects with circularity data directly within Auto-desk Revit.

[Section 3]: Table 7. Comparative Overview of the previous studies.

[Section 6.1]: The gaps outlined in Section 3 set the context for assessing the proposed tool's contribution. The analysis reveals three main limitations: a limited and segmented ap-proach to circularity assessment, a focus on building-scale validation rather than de-tailed product-level analysis, and incomplete BIM integration within current workflows.

The reviewed studies show that most existing frameworks focus on only one aspect of circularity. Adesope et al. [39] and Felicioni et al. [40] emphasise embodied carbon reduction via material substitution, Allam et al. [41] and Lima et al. [42] work on end-of-life deconstruction planning, Han et al. [43] looks at demolition waste man-agement and Davis et al. [46] examines the methodological effects of lifespan assump-tions in LCA. Even more comprehensive frameworks, like Al Quazzaz et al. [44] BIM-based DSS combining BCA, LCA and LCC, or Chang et al. [45] EBCI indicator system, are limited to specific circularity dimensions and do not capture the full range of circular performance. The tool presented in this research adopts a different strategy by assessing circularity across six categories aligned with UNI/TS 11820:2024, each rep-resenting a different aspect of circular performance. This multi-dimensional approach enables a more complete evaluation at the product level, moving beyond the sin-gle-indicator or single-dimension methods. Instead of replacing these focused ap-proaches, the tool complements them by providing a structured framework to evaluate multiple circularity aspects simultaneously, facilitating more informed and holistic de-cisions during design and production.

A further limitation from the literature concerns the demonstration scale. Aside from Allam et al. [41], who processes IFC component data at the element level, all re-viewed studies validate methodologies at the building level [40,46–48]. Building-scale assessments are crucial for whole-life carbon accounting and design comparisons but tend to aggregate component details, potentially obscuring the circularity performance of individual materials and products. The proposed tool specifically operates at the product level, evaluating circularity indicators for individual construction products per UNI/TS 11820:2024. This detailed approach aligns with the rise of product-level tools like EPD and material passports, the latter partially addressed by Al Quazzaz et al. [44] via an Airtable interface, though without standardized compliance outputs. By gener-ating UNI/TS 11820:2024-compliant LC Reports in MS Word, the tool bridges assess-ment and documentation, providing structured outputs that support product certifica-tion, procurement, and regulatory compliance.

A technical gap identified in the literature concerns the limited integration of BIM. Most reviewed studies use Revit mainly for data extraction, such as generating BoQ or exporting geometric information to external LCA or circularity tools, without inte-grating assessment outputs into the BIM environment. Rodriguez et al. [48] confirms that neither Athena Impact Estimator nor SimaPro provides fully automated data transfer from Revit, while Allam et al. [41] pipeline shows how workflows across mul-tiple software can fragment data flow into disconnected manual steps. The digital tool presented in this research addresses this by providing an Excel-to-Revit data flow module, implemented through Dynamo scripts and APIs, which automates material creation, custom parameter definitions, and population with circularity data inside Autodesk Revit. This effectively closes the feedback loop, making circularity data an active, queryable, and visible part of the BIM model, thus supporting dynamic design optimization. Also, unlike prior studies, this tool incorporates document generation within the workflow, producing standardized LC Reports linked directly to assess-ments, filling a documentation gap that others do not resolve.

Furthermore, the digital tool proposed in this research is modular and scalable. Its first two modules, namely the LC calculator and automated report generator, depend on standards, as their indicator structures, weighting factors and reporting criteria follow UNI/TS 11820:2024. Nonetheless, their core architecture is flexible and can be adapted to any indicator-based regulatory or assessment system by updating the relevant cal-culation rules and output templates. The third module, which manages the Ex-cel-to-Revit data flow via Dynamo VPL scripts and APIs, is inherently independent of standards. It serves as a data exchange pipeline between spreadsheets and BIM, inde-pendent of the specific standard, enabling reuse across various circularity or sustaina-bility assessments without structural changes. This modular approach allows the tool to adapt to regulatory updates, incorporate future revisions of UNI/TS 11820:2024, and extend to other indicator-based evaluation frameworks, ensuring it remains a versatile and future-ready solution for digital circularity assessment in construction.

 

Comment 2: The core contribution is claimed to be the encoding of UNI/TS  into a digital tool. However, it is a technical implementation task rather than academic research. Authors do not  extend or validate the standard itself. Authors merely translate the rules into spreadsheet formulas, which provides no new insights into circularity assessment methodology.

Response 2: Thank you for this challenging observation, which encourages us to articulate the research contribution more precisely. We respectfully offer the following clarifications.

We acknowledge that the encoding of UNI/TS 11820:2024 into a computable workflow is, in part, an implementation task. However, we would like to draw the Reviewer's attention to the fact that the research contribution extends beyond the translation of normative rules into spreadsheet formulas in several technically and methodologically significant ways. Specifically:

• UNI/TS 11820:2024 is applied in this research as a structured framework. The standard provides the indicator structure and weighting logic, but the research contribution lies in demonstrating how this structure can be operationalised within a modular, scalable, and interoperable digital workflow that connects regulatory compliance with design decision-making;
• The what-if scenario presented in Section 5.2 demonstrates that circularity performance, as measured by the LC value, is not solely determined by the material composition of a product, but is substantially influenced by organisational-level factors such as energy procurement from renewable sources, waste management practices, logistics governance and asset design for end-of-life circularity. The LC value increases from 43.77 to 56.27 (approximately 29%) through the introduction of five organisation-level indicators, without any change to the product's material formulation. This finding provides a concrete and quantified insight into the multidimensional and context-dependent nature of circularity assessment, demonstrating that product-level circularity cannot be fully captured by material properties alone.
• The third module of the tool (the Excel-to-Revit data flow implemented via Dynamo scripts) contributes a workflow paradigm in which circularity data, generated through a standard-compliant assessment, becomes a living, queryable and visually accessible attribute of the BIM model. As demonstrated in Section 5.2 and Figure 6, the parametric enhancement of Revit's material database enables designers to filter, sort and compare materials by LC score directly within the BIM environment, and to generate circularity-based material schedules and quantity take-offs using Revit's native scheduling features. This transforms circularity assessment from a static compliance task into a dynamic design optimisation tool, reframing how regulatory standards can be integrated into digital design practice and contributing to both research and professional methodology.
• Scalability and adaptability as architectural research contributions. The modular architecture of the tool, in which the standard-dependent modules are explicitly decoupled from the standard-independent BIM integration module, represents a deliberate design decision with broader implications for how digital circularity tools can be built to remain adaptable to regulatory evolution. This architectural principle is not prescribed by the standard and represents an original contribution to the design of digital assessment workflows in the construction sector.

 

In addition to the clarifications already provided in response to Comment 1, a dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation. In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project.

[Section 5.1]: In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

[Section 5.2]: In this context, a what-if scenario involves adding a set of organization-level indicators not included in the baseline assessment highlighted in Table 10. Specifically, as reported in Table 10, five indicators are introduced to reflect improvements in the manufacturer's operational practices: the proportion of energy from renewable or recovery sources (Indicator 13), the civil buildings' energy performance class (Indicator 16), the rate of special waste sent to material recovery facilities (Indicator 20), the inclusion of circular economy clauses in logistics operator selection (Indicator 25) and the share of assets and infrastructure designed for circular end-of-life management (Indicator 61).

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

[Section 5.2]: These indicators are scored from 0.80 to 1.00, indicating that the manufacturer adopts best-practice standards in energy procurement, waste management, logistics, and asset governance. These interventions do not require changes to the product's material composition or formulation, instead they focus solely on organisational measures. The LC value increases from 43.77 to 56.27, about 29%, indicating that circularity performance depends not only on the product's material properties but also on the operational context in which it is produced.

[Section 5.2]: As highlighted in Figure 9, the material database is customised using the generated circularity data, with Autodesk Revit parameters automatically populated. Specifically, each material includes a set of shared parameters that represent the indicator structure of UNI/TS 11820:2024. This parametric enhancement allows the BIM model to serve as both a geometric representation and a structured repository of circularity data at the material level. Additionally, Revit's native scheduling feature can automatically gener-ate circularity schedules, enabling designers to filter, sort and compare materials by LC score. This facilitates better material selection during design, allowing the identification of low-performing components and the exploration of substitution scenarios directly within the BIM environment.

[Section 5.2]: Figure 9. BIM integration of circularity data (customisation of the material database with UNI/TS 11820:2024-compliant shared parameters and extraction of circularity-based material schedules and quantity take-offs in Autodesk Revit) and IFC export.

 

Comment 3: The tool does not solve critical pain points in circularity assessment, such as interoperability across BIM platforms, integration with third-party verified data, dynamic lifecycle updates or cross-standard alignment. It produces static, one-time assessments that cannot adapt to changing product data or regulatory requirements.

Response 3: We appreciate this observation and recognize the genuine challenges highlighted. Concerning interoperability among BIM platforms, the revised manuscript explains that the BIM model with added circularity parameters is exported in IFC format, ensuring the circularity indicators are properly mapped as custom IfcPropertySets linked to relevant materials and building components. This guarantees that the circularity data is available in an open format usable by any IFC-compliant software, without needing access to the original Revit model (Figure 9). Regarding the tool's nature, although the first two modules depend on UNI/TS 11820:2024, their structure is adaptable to any indicator-based regulatory framework by updating calculation rules and output templates. The third module, involving Excel-to-Revit data transfer through Dynamo VPL scripts and APIs, is inherently norm-neutral and can be reused in different assessment contexts without structural changes, ensuring future regulatory updates can be accommodated. To further support this claim and enhance the reproducibility and reusability of the workflow, the Python script node developed for the Dynamo-based data exchange has been made publicly available in a dedicated repository. On cross-standard alignment, the manuscript now clarifies that since UNI/TS 11820:2024 aligns with the ISO/TC 323 family and the EU Level(s) framework, the tool indirectly inherits compatibility with these broader international and European contexts. Explicit cross-standard integration remains a goal for future research. Lastly, regarding third-party verified data, reliance on self-reported survey data is acknowledged as a current limitation. Future development priorities include integrating third-party verified sources like EPDs and establishing a structured data-quality classification system.

 

A dedicated clarification has been added to the manuscript:

[Section 4]: The tool is specifically implemented for Autodesk Revit, the most widely adopted BIM software in the construction industry (see Section 3) and, as highlighted in Figure 1, is composed of interdependent modules in which the output of each module serves as the direct input for the subsequent one, ensuring an automated process.

[Section 4]: Interoperability across the data flow is ensured by the combined use of VPL scripts implemented in Dynamo and APIs, which enable structured, reliable data exchange between the Excel environment and the Autodesk Revit platform without manual intervention.

[Section 4.2]: The complete implementation of the Python script, structured across the seven units described above, is publicly available in an open-access GitHub repository (https://github.com/eleonoragiuffrida1706/revit-material-circularity-importer/tree/main). To reproduce the workflow, the script can be directly embedded into a Python Script node within Dynamo.

[Section 5.2]: Figure 9. BIM integration of circularity data (customisation of the material database with UNI/TS 11820:2024-compliant shared parameters and extraction of circularity-based material schedules and quantity take-offs in Autodesk Revit) and IFC export.

[Section 5.2]: Furthermore, the BIM model with added circularity parameters is exported in Industry Foundation Classes (IFC) format. This confirms that the circularity indicators are properly assigned as custom IfcPropertySingleValue linked to the relevant materials and building components. As a result, the embedded circularity data can be accessed and viewed using any IFC-compliant software, without needing the original Revit model. This enables the sharing of verified circularity information across the entire project supply chain in a platform-independent and open format.

[Section 6.1]: Furthermore, the digital tool proposed in this research is modular and scalable. Its first two modules, namely the LC calculator and automated report generator, depend on standards, as their indicator structures, weighting factors and reporting criteria follow UNI/TS 11820:2024. Nonetheless, their core architecture is flexible and can be adapted to any indicator-based regulatory or assessment system by updating the relevant calculation rules and output templates. The third module, which manages the Excel-to-Revit data flow via Dynamo VPL scripts and APIs, is inherently independent of standards. It serves as a data exchange pipeline between spreadsheets and BIM, independent of the specific standard, enabling reuse across various circularity or sustainability assessments without structural changes. This modular approach allows the tool to adapt to regulatory updates, incorporate future revisions of UNI/TS 11820:2024, and extend to other indicator-based evaluation frameworks, ensuring it remains a versatile and future-ready solution for digital circularity assessment in construction.

[Section 6.1]: Furthermore, data is gathered through structured surveys administered to researchers and the manufacturer. This approach aligns with the UNI/TS 11820:2024 indicator structure but depends on self-reported data, which relies on respondents' knowledge of the standard's requirements. The lack of third-party verification of this data is acknowledged as a limitation in fully meeting the compliance pathway outlined by the standard for claim-based assessments

[Section 6.2]: The reliability of survey-based data collection could be improved by creating a structured data-quality classification system. This would allow users to tag inputs by evidence level and gradually incorporate third-party-verified sources, such as Environmental Product Declarations (EPDs), thereby moving the tool closer to the formal compliance pathway described in standards for claim-based assessments.

[Section 6.2]: Additionally, since UNI/TS 11820:2024 aligns closely with the ISO/TC 323 family and the EU Level(s) framework, as noted in Section 2, the tool is compatible with these international and European standards. Although this cross-standard alignment is not yet explicitly integrated into the current version, it offers a future route for future updates. This could involve expanding the modular architecture to include indicator structures and reporting requirements from other circularity assessment frameworks, thereby enhancing the tool's applicability and regulatory scope

[Section 7]:  In this regard, the development of a structured data-quality classification system, enabling users to tag each input according to its evidence level, would further strengthen the robustness of the assessment workflow and bring it closer to the compliance pathway outlined by the standard for claim-based assessments.

 

Comment 4: Authors state that details of the 68 UNI/TS 11820:2024 indicators cannot be disclosed due to copyright restrictions, which makes the research completely non-reproducible. How to verify the accuracy of the calculator’s implementation, test its performance on different products or build upon the work?

Response 4: We thank the reviewer for this observation. In this regard, we have taken several concrete steps in the revised manuscript to maximise transparency within these constraints.

Regarding the calculation methodology, Section 2.2 now provides a self-contained description of the LC calculation logic, including the classification of indicators into core, specific and rewarding tiers, the scoring ranges for binary, quantitative and semi-quantitative indicators, the weighting principles applied to each class, the treatment of technically non-applicable indicator and the aggregation rules underlying Eq. 1. Furthermore, six tables have been added reporting all 68 indicators organised by reference category, tier structure, subject of assessment and assessment mode, with indicator IDs enabling direct cross-referencing with the standard.

Regarding the case study, full transparency on the ITER assessment is now provided through three dedicated tables: (1) the first one reporting all 41 applicable indicators with individual scores, per-category values and the total weighted LC score, (2) the second one reporting all 27 excluded indicators with documented reasons for non-applicability, and (3) the last one reporting the what-if scenario with the resulting increase in LC from 43.77 to 56.27.

 

A dedicated clarification has been added to the manuscript:

[Section 2.2]: UNI/TS 11820:2024 also assigns a specific weight to each indicator type for calculating the final score: 1 for core and specific indicators, and 0.5 for rewarding indicators.

[Section 2.2]: Each indicator score ranges from 0 to 1: binary qualitative indicators are scored 1 when the condition is met and 0 when it isn't, while quantitative and semi-quantitative indicators are assigned values according to predefined formulas outlined in the standard.

[Section 2.2]: The full set of circularity indicators is organised by reference category and presented in Tables 1-6.

[Section 2.2]: Tables 1-6.

[Section 2.2]: Indicators that are deemed technically non-applicable to a given product or organisational context are excluded from both the numerator and the denominator of the LC formula, provided that the non-applicability is supported by documented justification and objective evidence.

[Section 4.1]: All indicator values are normalised on a 0 to 1 scale, where 1 indicates full compliance or optimal performance and 0 indicates non-compliance or the absence of the practice. This normalisation is built into the calculation logic specified by UNI/TS 11820:2024 and is applied across quantitative, semi-quantitative and qualitative indicators. Consequently, the denominator (c + s) represents the maximum possible score from core and specific indicators alone, making the LC value range between 0 and 100 when expressed as a percentage. UNI/TS 11820:2024 excludes rewarding indicators from the denominator because they denote optional best practices that surpass the minimum standards. Their influence is further reduced by a coefficient of 0.5, as highlighted in Section 2, indicating they carry half the weight of core and specific indicators. This setup ensures that re-warding indicators can only raise the LC score above the baseline established by core and specific indicators.

[Section 5.2]: As shown in Table 8, a group of the excluded indicators falls within the industrial symbiosis cluster, reflecting the manufacturer's current lack of integration into symbiotic networks for the exchange of material, water and energy flows. Several exclusions also concern the end-of-life and service dimension, as the manufacturer does not yet offer extended warranty schemes, repair support documentation, or Product-as-a-Service models. Furthermore, the absence of a carbon footprint assessment on incoming material resources and of a product tracking system points to gaps in the monitoring and traceability infrastructure. Taken together, these exclusions delineate a clear profile of the organisational and operational boundaries of the current production model, and implicitly identify the areas where targeted interventions could most effectively contribute to improving the aggregate LC value in future iterations of the assessment.

[Section 5.2]: Table 8. Organisation- and product-level indicators excluded from the LC assessment of the ITER plasters, with the corresponding reasons for non-applicability.

[Section 5.2]: The per-category LC values shown in Table 9 represent the average scores of the indicators within each reference category and the individual indicator scores. The overall LC value of 43.77 is not the arithmetic mean of these per-category values; rather, it is calculated across all indicators together as per Eq. 1, which assigns different weights based on whether an indicator is core, specific or rewarding.

[Section 5.2]: Table 9. Indicators included in the LC assessment of the ITER plasters, classified by reference category, with individual indicator scores, per-category average values and the resulting total weighted LC score.

[Section 5.2]: In this context, a what-if scenario involves adding a set of organisation-level indicators not included in the baseline assessment highlighted in Table 10. Specifically, as reported in Table x, five indicators are introduced to reflect improvements in the manufacturer's operational practices: the proportion of energy from renewable or recovery sources (Indicator 13), the civil buildings' energy performance class (Indicator 16), the rate of special waste sent to material recovery facilities (Indicator 20), the inclusion of circular economy clauses in logistics operator selection (Indicator 25) and the share of assets and infrastructure designed for circular end-of-life management (Indicator 61). These indicators are scored from 0.80 to 1.00, indicating that the manufacturer adopts best-practice standards in energy procurement, waste management, logistics, and asset governance. These interventions do not require changes to the product's material composition or formulation, instead they focus solely on organisational measures. The LC value increases from 43.77 to 56.27, about 29%, indicating that circularity performance depends not only on the product's material properties but also on the operational context in which it is produced.

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

 

Comment 5: All input data relies on self-reported surveys from the research team and a single manufacturer, which could introduce significant bias and undermines the reliability of the resulting LC score.

Response 5: We thank the reviewer for pointing this out. The reliance on self-reported survey data administered to a single manufacturer and the research team is now explicitly acknowledged as a limitation in the revised manuscript. To improve transparency, two dedicated tables have been included: one reporting all 41 applicable indicators with their individual scores, and another documenting reasons for the non-applicability of all 27 excluded indicators. The indicator IDs reported in both tables serve as direct references to the corresponding entries in UNI/TS 11820:2024, enabling readers to cross-check each input against the standard's requirements. Furthermore, the integration of third-party-verified data sources, such as EPDs, and the development of a structured data-quality classification system are identified as priority directions for future research.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: As shown in Table 8, a group of the excluded indicators falls within the industrial symbiosis cluster, reflecting the manufacturer's current lack of integration into symbiotic networks for the exchange of material, water and energy flows. Several exclusions also concern the end-of-life and service dimension, as the manufacturer does not yet offer extended warranty schemes, repair support documentation, or Product-as-a-Service models. Furthermore, the absence of a carbon footprint assessment on incoming material resources and of a product tracking system points to gaps in the monitoring and traceability infrastructure. Taken together, these exclusions delineate a clear profile of the organisational and operational boundaries of the current production model, and implicitly identify the areas where targeted interventions could most effectively contribute to improving the aggregate LC value in future iterations of the assessment.

[Section 5.2]: Table 8. Organisation- and product-level indicators excluded from the LC assessment of the ITER plasters, with the corresponding reasons for non-applicability.

[Section 5.2]: The per-category LC values shown in Table 9 represent the average scores of the indicators within each reference category and the individual indicator scores. The overall LC value of 43.77 is not the arithmetic mean of these per-category values; rather, it is calculated across all indicators together as per Eq. 1, which assigns different weights based on whether an indicator is core, specific or rewarding.

[Section 5.2]: Table 9. Indicators included in the LC assessment of the ITER plasters, classified by reference category, with individual indicator scores, per-category average values and the resulting total weighted LC score.

[Section 6.1]: Furthermore, data is gathered through structured surveys administered to researchers and the manufacturer. This approach aligns with the UNI/TS 11820:2024 indicator structure but depends on self-reported data, which relies on respondents' knowledge of the standard's requirements. The lack of third-party verification of this data is acknowledged as a limitation in fully meeting the compliance pathway outlined by the standard for claim-based assessments

[Section 6.2]: Secondly, the reliability of survey-based data collection could be improved by creating a structured data-quality classification system. This would allow users to tag inputs based on evidence level and gradually incorporate third-party verified sources, such as Environmental Product Declarations (EPDs), moving the tool closer to the formal compliance pathway described in standards for claim-based assessments.

[Section 7]:  In this regard, the development of a structured data-quality classification system, enabling users to tag each input according to its evidence level, would further strengthen the robustness of the assessment workflow and bring it closer to the compliance pathway outlined by the standard for claim-based assessments.

 

 

Comment 6:  Authors directly adopt the indicator weights from UNI/TS? No justification is provided for why these weights are suitable for earthen plasters or other building materials.

Response 6: We thank the reviewer for raising this point.These aspects have now been made explicit in the revised manuscript. Specifically, it is now clarified that the indicator weights are defined directly by UNI/TS 11820:2024, which assigns a weight of 1 to core and specific indicators and a weight of 0.5 to rewarding indicators, and that these weights are incorporated into the LC calculation as specified by Eq. 1. It is also clarified that UNI/TS 11820:2024 is designed as a general-purpose standard applicable to any industrial product or process, and that ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool rather than as the basis for a generalisation claim.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. […] In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project […]

[Section 2.2]: UNI/TS 11820:2024 also assigns a specific weight to each indicator type for calculating the final score: 1 for core and specific indicators, and 0.5 for rewarding indicators.

[Section 4.1]: All indicator values are normalised on a 0 to 1 scale, where 1 indicates full compliance or optimal performance and 0 indicates non-compliance or the absence of the practice. This normalisation is built into the calculation logic specified by UNI/TS 11820:2024 and is applied across quantitative, semi-quantitative and qualitative indicators. Consequently, the denominator (c + s) represents the maximum possible score from core and specific indicators alone, making the LC value range between 0 and 100 when expressed as a percentage. UNI/TS 11820:2024 excludes rewarding indicators from the denominator because they denote optional best practices that surpass the minimum standards. Their influence is further reduced by a coefficient of 0.5, as highlighted in Section 2, indicating they carry half the weight of core and specific indicators. This setup ensures that rewarding indicators can only raise the LC score above the baseline established by core and specific indicators.

[Section 5.1]: In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

 

Comment 7: The workflow is exclusively tied to Autodesk Revit and does not support open standards such as Industry Foundation Classes, which limits the practical utility in the global AEC industry, where multiple BIM platforms are used. The bidirectional data flow claim appears somehow misleading, since only Excel-to-Revit transfer is demonstrated, with no Revit-to-Excel update capability.

Response 7: We thank the reviewer for these observations, both of which have been addressed in the revised manuscript. The manuscript now shows that the BIM model, enhanced with circularity parameters, can be exported in Industry Foundation Classes (IFC) format (Figure 9). The circularity indicators are correctly assigned as custom IfcPropertySingleValue linked to the appropriate materials and building components. In the current version, the embedded circularity data is accessible in an open format via  IFC-compliant software. This facilitates sharing verified circularity information across the entire project supply chain, regardless of the BIM platform used. The text now clearly states that Autodesk Revit is chosen as the platform because it is the most widely used BIM software in the construction field. It explains that data interoperability is achieved through VPL scripts in Dynamo and APIs, which allow for structured and reliable data exchanges without manual input. The added value of BIM integration is highlighted more explicitly (Figure 9), illustrating how circularity parameters enable material filtering, comparison of alternative product options, automated creation of circularity-based schedules and quantity take-offs, and selecting critical materials based on circularity.

Regarding the bidirectional data flow the term "bidirectional" has been removed after a careful revision, as the workflow is more accurately described as interoperable rather than bidirectional.

 

A dedicated clarification has been added to the manuscript:

[Section 4]: The tool is specifically implemented for Autodesk Revit, the most widely adopted BIM software in the construction industry (see Section 3) and, as highlighted in Figure 1, is composed of interdependent modules in which the output of each module serves as the direct input for the subsequent one, ensuring an automated process.

[Section 4]: Interoperability across the data flow is ensured by the combined use of VPL scripts implemented in Dynamo and APIs, which enable structured, reliable data exchange between the Excel environment and the Autodesk Revit platform without manual intervention.

[Section 5.2]: As highlighted in Figure 9, the material database is customised using the generated circularity data, with Autodesk Revit parameters automatically populated. Specifically, each material includes a set of shared parameters that represent the indicator structure of UNI/TS 11820:2024. This parametric enhancement allows the BIM model to serve as both a geometric representation and a structured repository of circularity data at the material level. Additionally, Revit's native scheduling feature can automatically generate circularity schedules, enabling designers to filter, sort and compare materials by LC score. This facilitates better material selection during design, allowing the identification of low-performing components and the exploration of substitution scenarios directly within the BIM environment.

[Section 5.2]: Furthermore, the BIM model with added circularity parameters is exported in Industry Foundation Classes (IFC) format. This confirms that the circularity indicators are properly assigned as custom IfcPropertySets linked to the relevant materials and building components. As a result, the embedded circularity data can be accessed and viewed using any IFC-compliant software, without needing the original Revit model. This enables the sharing of verified circularity information across the entire project supply chain in a platform-independent and open format.

[Section 5.2]: Figure 9. BIM integration of circularity data (customisation of the material database with UNI/TS 11820:2024-compliant shared parameters and extraction of circularity-based material schedules and quantity take-offs in Autodesk Revit) and IFC export.

 

Comment 8: The tool is validated only on the ITER earthen plaster project. No testing is performed on other construction product categories like concrete, steel, insulation or different manufacturing contexts, so the tool’s generalizability is unproven. Moreover, authors do not compare the tool’s performance with any existing circularity assessment tools or manual calculation methods. How to prove that the tool produces more accurate, faster or more reliable results than established alternatives?

Response 8: We appreciate the reviewer raising these points. Both aspects are addressed in the revised manuscript. This study's literature review on circularity assessment tools shows that no existing solution automates LC value calculation in line with UNI/TS 11820:2024 and transfers the data into Autodesk Revit. Without similar tools available, it was not possible to measure performance quantitatively. We recognise that testing the tool on only one case study limits its generalisability, which is now explicitly noted in the limitations section. The normative applicability of the tool isn't an empirical claim from the case study but a structural feature of UNI/TS 11820:2024, designed for any industrial product or process regardless of material type. ITER serves as a representative example chosen for its complexity, not as a basis for broad generalisation. Future research should extend the demonstration to other product categories such as cement-based items, insulation, and prefabricated components. On benchmarking, the manuscript explains that the LC assessment was first done manually by the research team and then duplicated with the digital tool, with both methods resulting in 43.77/100. This confirms the tool's formulas, as per UNI/TS 11820:2024, are implemented correctly.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation.

In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project

[Section 1]: In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project […] However, the present demonstration is grounded in a single BIM-based case study. Future research could extend the validation of the workflow to diverse material categories, production contexts and software environments, further consolidating its scalability.

[Section 5.1]: In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

[Section 5.2]: The assessment was first performed manually and, subsequently, the digital methodology presented in this study was applied to ITER, as highlighted in Section 5.2, to automate the calculation and provide a digital tool applicable to any product or service covered by the standard.

[Section 6.1]: The present demonstration is grounded in a single BIM-based case study, encompassing one product type and one manufacturer. Although this case is selected for its complexity and representativeness, the computational correctness of the formulae is guaranteed by the standard itself; what a single case cannot rule out are implementation errors in untested scenarios. Therefore, the generalisability of the findings to other construction product categories and organisational setups cannot be fully assumed.

[Section 6.2]: The demonstration can be expanded to cover additional construction product categories like cement-based products, insulation materials or prefabricated components, as well as different organisational settings. This will help identify potential implementation issues in untested scenarios and enhance the workflow's transferability and scalability across various materials and software environments.

 

Comment 9: The results merely presents the LC score and category-wise breakdown without any critical analysis. Why the Energy and Water Resources category scored 0 beyond stating the lack of renewable energy? How to quantify the potential impact of specific improvement measures?

Response 9: We thank the reviewer for pointing this out. The results section has been revised to provide a critical reading of the LC scores across all six reference categories, moving beyond a descriptive presentation of the numerical outcomes. For the categories, including Energy and Water Resources, the analysis now identifies the underlying structural and organisational factors driving the scores, distinguishes between areas of genuine circularity strength and areas where targeted interventions could yield measurable improvements and highlights the divergence between product-level and organisational-level circularity performance. Regarding the quantification of improvement measures, a what-if scenario has been introduced in which five organisation-level indicators are applied without modifying the product formulation, resulting in an increase in the LC value from 43.77 to 56.27, approximately 29%, concretely demonstrating the quantitative impact of targeted organisational interventions on the aggregate circularity score.

 

A dedicated clarification has been added to the manuscript:

[Section 5.2]: This strong performance indicates that the material design of the product aligns well with circular economy principles. It also shows that the formulation decisions made during the ITER project's development have resulted in tangible circularity benefits at the product level. […] The scores in this category show that the strategic and relational aspects of circularity are well developed, indicated by high scores in research partnership and circular product development. However, the operational and governance aspects are less advanced, especially in social impact assessment and sustainable mobility planning, which both score 0.00. […] This outcome is common among small and medium-sized construction material man-ufacturers, where renewable energy transition is limited by investment and grid issues, not a lack of sustainability commitment. This category offers the highest potential to improve LC value: with all three indicators at 0.00, even a partial shift to renewable energy, like a green supply contract, could activate this cluster and boost the overall LC score. […] Furthermore, the implications for policy and industry are emphasized: it indicates that circularity assessment frameworks focused solely on product-level properties may sys-tematically overstate the overall circularity performance of such manufacturers.

[Section 5.2]: In this context, a what-if scenario involves adding a set of organisation-level indicators not included in the baseline assessment highlighted in Table 10. Specifically, as reported in Table 10, five indicators are introduced to reflect improvements in the manufacturer's operational practices: the proportion of energy from renewable or recovery sources (Indicator 13), the civil buildings' energy performance class (Indicator 16), the rate of special waste sent to material recovery facilities (Indicator 20), the inclusion of circular economy clauses in logistics operator selection (Indicator 25) and the share of assets and infrastructure designed for circular end-of-life management (Indicator 61). These indicators are scored from 0.80 to 1.00, indicating that the manufacturer adopts best-practice standards in energy procurement, waste management, logistics, and asset governance. These interventions do not require changes to the product's material composition or formulation, instead they focus solely on organisational measures. The LC value increases from 43.77 to 56.27, about 29%, indicating that circularity performance depends not only on the product's material properties but also on the operational context in which it is produced.

[Section 5.2]: Table 10. Organisation-level indicators included in the what-if scenario.

 

Comment 10: In the discussion, authors claim to have addressed four literature gaps, but none are convincingly resolved. Organizational factors are included only because UNI/TS 11820 requires them. There is no new framework for integrating organizational and product-level data. Workflow automation is partial and relies on manual data entry. Standard digitalization is limited to a single local standard and product-level assessment is demonstrated for one product only.

Response 10: Thank you for this observation. We have carefully revised the Discussion section to address each of the points raised, and we would like to respond to them individually. Specifically:

• On organisational factors we acknowledge that the inclusion of organisational indicators is prescribed by UNI/TS 11820:2024. However, the research contribution lies not in the invention of a new framework for integrating organisational and product-level data, but in demonstrating, through a quantified scenario, the extent to which organisational factors influence circularity performance independently of material composition. The LC value increases from 43.77 to 56.27 through the introduction of five organisation-level indicators alone, without any change to the product's material formulation. This finding provides a concrete and reproducible methodological insight that goes beyond mere standard compliance. The revised Section 6.1 now makes this contribution explicit;
• On partial workflow automation we fully agree that data entry remains a manual step, as indicator values are gathered through structured surveys and entered by the user into the Excel-based calculator. This is now explicitly acknowledged as a partial automation boundary in the revised Section 6.2, which states that while the tool automates the core computational and reporting tasks, data entry depends on self-reported information and represents a current limitation of the tool. We believe that transparent acknowledgement of this boundary strengthens rather than weakens the contribution;
• On standard digitalization being limited to a single local standard we acknowledge this limitation. However, as clarified in the revised manuscript, the modular architecture of the tool is explicitly designed so that the standard-dependent modules, namely the LC calculator and the report generator, can be adapted to any indicator-based regulatory or assessment system by updating the relevant calculation rules and output templates. The third module, managing the Excel-to-Revit data flow via Dynamo, is inherently independent of standards and can be reused across different circularity or sustainability assessments without structural changes.
• On product-level assessment being demonstrated for one product only we acknowledge that the case study is limited to a single product, namely the ITER earthen plaster. However, as noted in the revised Section 5.1, ITER was selected as a representative case study precisely because it mirrors the complexities of real product development, involving multiple material inputs, organisational variables, and manufacturing choices. The tool's applicability is not inherently restricted to this product, the workflow is designed to be directly replicable across any construction product assessed under UNI/TS 11820:2024 or equivalent indicator-based frameworks. The single-product demonstration is a demonstration, consistent with the scope of the research and broader empirical validation across multiple products is explicitly identified as a direction for future research.

 

A dedicated clarification has been added to the manuscript:

[Section 1]: Specifically, the proposed tool automates: (1) the calculation of circularity indica-tors and Circularity Level (LC) via an Excel-based calculator, (2) produces UNI/TS 11820:2024-compliant LC Reports in MS Word and (3) integrates circularity data into BIM objects through a Excel-Revit workflow implemented via Dynamo parametric scripts, which automate the creation of materials, the definition of custom parameters and their population with circularity data within Autodesk Revit. This approach aims to shift the circularity assessment from a static compliance task to a dynamic, proactive tool for design and production optimisation.

In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project

[Section 1]: In order to demonstrate its threefold automated procedure, the tool is applied to the ITER (Ecological Recyclable Earthen Plasters) project […] However, the present demonstration is grounded in a single BIM-based case study. Future research could extend the validation of the workflow to diverse material categories, production contexts and software environments, further consolidating its scalability.

[Section 5.1]: In this context, UNI/TS 11820:2024 is applied directly, as the standard is designed to be applicable to any industrial product or process. ITER serves as a representative case study to demonstrate the threefold automated procedure of the digital tool: (1) the calculation of the LC value in compliance with UNI/TS 11820:2024, (2) the generation of the LC report and (3) the transfer of the results into Autodesk Revit. The digital tool is demonstrated through a case that mirrors the complexities of real product development and highlights the importance of user-friendly tools that integrate regulatory standards with design and manufacturing choices within a BIM-based workflow.

[Section 5.2]: The assessment was first performed manually and, subsequently, the digital methodology presented in this study was applied to ITER, as highlighted in Section 5.2, to automate the calculation and provide a digital tool applicable to any product or service covered by the standard.

[Section 6.1]: The present demonstration is grounded in a single BIM-based case study, encompassing one product type and one manufacturer. Although this case is selected for its complexity and representativeness, the computational correctness of the formulae is guaranteed by the standard itself; what a single case cannot rule out are implementation errors in untested scenarios. Therefore, the generalisability of the findings to other construction product categories and organisational setups cannot be fully assumed.

[Section 6.1]: Furthermore, the digital tool proposed in this research is modular and scalable. Its first two modules, namely the LC calculator and automated report generator, depend on standards, as their indicator structures, weighting factors and reporting criteria follow UNI/TS 11820:2024. Nonetheless, their core architecture is flexible and can be adapted to any indicator-based regulatory or assessment system by updating the relevant cal-culation rules and output templates. The third module, which manages the Ex-cel-to-Revit data flow via Dynamo VPL scripts and APIs, is inherently independent of standards. It serves as a data exchange pipeline between spreadsheets and BIM, inde-pendent of the specific standard, enabling reuse across various circularity or sustaina-bility assessments without structural changes. This modular approach allows the tool to adapt to regulatory updates, incorporate future revisions of UNI/TS 11820:2024, and extend to other indicator-based evaluation frameworks, ensuring it remains a versatile and future-ready solution for digital circularity assessment in construction.

[Section 6.1]: The gaps outlined in Section 3 set the context for assessing the proposed tool's contribution. The analysis reveals three main limitations: a limited and segmented ap-proach to circularity assessment, a focus on building-scale validation rather than de-tailed product-level analysis, and incomplete BIM integration within current workflows.

The reviewed studies show that most existing frameworks focus on only one aspect of circularity. Adesope et al. [39] and Felicioni et al. [40] emphasise embodied carbon reduction via material substitution, Allam et al. [41] and Lima et al. [42] work on end-of-life deconstruction planning, Han et al. [43] looks at demolition waste man-agement and Davis et al. [46] examines the methodological effects of lifespan assump-tions in LCA. Even more comprehensive frameworks, like Al Quazzaz et al. [44] BIM-based DSS combining BCA, LCA and LCC, or Chang et al. [45] EBCI indicator system, are limited to specific circularity dimensions and do not capture the full range of circular performance. The tool presented in this research adopts a different strategy by assessing circularity across six categories aligned with UNI/TS 11820:2024, each rep-resenting a different aspect of circular performance. This multi-dimensional approach enables a more complete evaluation at the product level, moving beyond the sin-gle-indicator or single-dimension methods. Instead of replacing these focused approaches, the tool complements them by providing a structured framework to evaluate multiple circularity aspects simultaneously, facilitating more informed and holistic decisions during design and production.

A further limitation from the literature concerns the demonstration scale. Aside from Allam et al. [41], who process IFC component data at the element level, all reviewed studies validate methodologies at the building level [40,46–48]. Building-scale assessments are crucial for whole-life carbon accounting and design comparisons but tend to aggregate component details, potentially obscuring the circularity performance of individual materials and products. The proposed tool specifically operates at the product level, evaluating circularity indicators for individual construction products per UNI/TS 11820:2024. This detailed approach aligns with the rise of product-level tools like EPD and material passports, the latter partially addressed by Al Quazzaz et al. [44] via an Airtable interface, though without standardized compliance outputs. By generating UNI/TS 11820:2024-compliant LC Reports in MS Word, the tool bridges assessment and documentation, providing structured outputs that support product certification, procurement, and regulatory compliance.

A technical gap identified in the literature concerns the limited integration of BIM. Most reviewed studies use Revit mainly for data extraction, such as generating BoQ or exporting geometric information to external LCA or circularity tools, without integrating assessment outputs into the BIM environment. Rodriguez et al. [48] confirm that neither Athena Impact Estimator nor SimaPro provides a fully automated data transfer from Revit, while Allam et al. [41] pipeline shows how workflows across multiple software can fragment data flow into disconnected manual steps. The digital tool presented in this research addresses this by providing an Excel-to-Revit data flow module, implemented through Dynamo scripts and APIs, which automates material creation, custom parameter definitions, and population with circularity data inside Autodesk Revit. This effectively closes the feedback loop, making circularity data an active, queryable, and visible part of the BIM model, thus supporting dynamic design optimization. Also, unlike prior studies, this tool incorporates document generation within the workflow, producing standardized LC Reports linked directly to assess-ments, filling a documentation gap that others do not resolve. Furthermore, while the tool automates the core computational and reporting tasks, data entry remains a manual step, as indicator values are gathered through structured sur-veys and entered by the user into the Excel-based calculator. This approach aligns with the UNI/TS 11820:2024 indicator structure but depends on self-reported data, which relies on respondents' knowledge of the standard's requirements, and represents a par-tial automation boundary of the current version of the tool.

[Section 6.2]: The demonstration can be expanded to cover additional construction product categories like cement-based products, insulation materials or prefabricated components, as well as different organisational settings. This will help identify potential implementation issues in untested scenarios and enhance the workflow's transferability and scalability across various materials and software environments.

 

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

General Assessment

The authors have engaged substantively with most of the 13 comments raised in the first review. The revisions include substantial additions: Tables 1-6 organizing all 68 indicators, Table 7 presenting a structured comparative analysis against cited works, Table 8 documenting excluded indicators with justifications, Table 9 with included indicators and scores, and Table 10 illustrating a what-if scenario (LC rising from 43.77 to 56.27). The GitHub repository link for the Python script is a valuable contribution to reproducibility. The consistent replacement of 'validation' with 'demonstration' throughout has been executed well, and the single-case scope is now appropriately foregrounded with adequate epistemological caution. The paper is improved in its second iteration.

 

Unresolved Issue: Figure Legibility

Point 13 from the first review, requesting higher-resolution versions of Figures 3 and 4 to ensure legibility in print, has not been adequately addressed. Upon examination of the revised manuscript, Figure 3 remains difficult to read, and more critically, Figure 4 (the Python script implementation) is not legible. Individual lines of code cannot be discerned at standard print resolution. This is a substantive issue, not a minor one, because: (1) the methodology section describes the script logic in detail, and readers should be able to cross-reference the text with the visual code, and (2) the entire premise of providing this material is to support reproducibility and transparency. A figure containing code that cannot be read defeats this purpose. The authors must either substantially enlarge both figures (even if it requires splitting across multiple pages) or consider moving the full script implementation to supplementary material and providing only a high-level schematic in the main text.

 

Other Minor Issues

1. Terminology coherence: One residual instance at line 582 (Section 5.1) reads 'ITER has been chosen as the validation case' and should be 'demonstration case' for consistency.

2. Section 6 structure: Sections 6.1 and 6.2 share an identical heading ('Research Contributions and Limitations of the Digital Tool'). Section 6.2 should be retitled to reflect its content (e.g., 'Future Research Directions' or 'Recommendations for Implementation').

Author Response

Response to Reviewer 1 Comments

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions highlighted in the re-submitted files.

Comment 1: The authors have engaged substantively with most of the 13 comments raised in the first review. The revisions include substantial additions: Tables 1-6 organizing all 68 indicators, Table 7 presenting a structured comparative analysis against cited works, Table 8 documenting excluded indicators with justifications, Table 9 with included indicators and scores, and Table 10 illustrating a what-if scenario (LC rising from 43.77 to 56.27). The GitHub repository link for the Python script is a valuable contribution to reproducibility. The consistent replacement of 'validation' with 'demonstration' throughout has been executed well, and the single-case scope is now appropriately foregrounded with adequate epistemological caution. The paper is improved in its second iteration.

Response 1: We thank the Reviewer for the thorough and constructive assessment of our revisions. We are pleased that the additions (including Tables 1-10, the GitHub repository link, the terminological shift from “validation” to “demonstration” and the foregrounding of the single-case scope) have been recognized as meaningful improvements to the manuscript.

 

 

Comment 2: Unresolved Issue: Figure Legibility. Point 13 from the first review, requesting higher-resolution versions of Figures 3 and 4 to ensure legibility in print, has not been adequately addressed. Upon examination of the revised manuscript, Figure 3 remains difficult to read, and more critically, Figure 4 (the Python script implementation) is not legible. Individual lines of code cannot be discerned at standard print resolution. This is a substantive issue, not a minor one, because: (1) the methodology section describes the script logic in detail, and readers should be able to cross-reference the text with the visual code, and (2) the entire premise of providing this material is to support reproducibility and transparency. A figure containing code that cannot be read defeats this purpose. The authors must either substantially enlarge both figures (even if it requires splitting across multiple pages) or consider moving the full script implementation to supplementary material and providing only a high-level schematic in the main text.

Response 2: We thank the reviewer for this observation and apologise for not having adequately addressed this point in the previous revision. To fully resolve the legibility issues raised, we have made the following updates:

• Figure 3 has been enlarged and is now clearly legible at standard print resolution
• The Python script (previously Figure 4) has been reproduced at a substantially larger scale as a new Figure 5, ensuring that individual lines of code can be discerned
• Throughout the 4.2 section, specifally lines 515-543, explicit references to specific code line numbers have been added, allowing readers to directly cross-reference the textual description with the corresponding code in the figure.

To further support transparency and reproducibility, the code line number references are also linked to the corresponding code within the project's GitHub repository, where the full script is freely available for download and direct implementation in Dynamo.

 

 

 

 

Comment 3:  Other Minor Issues. 1. Terminology coherence: One residual instance at line 582 (Section 5.1) reads 'ITER has been chosen as the validation case' and should be 'demonstration case' for consistency. 2. Section 6 structure: Sections 6.1 and 6.2 share an identical heading ('Research Contributions and Limitations of the Digital Tool'). Section 6.2 should be retitled to reflect its content (e.g., 'Future Research Directions' or 'Recommendations for Implementation').

Response 3: We thank the Reviewer for these observations. Both issues have been addressed: (1) the residual instance at line 582 (“validation case”) has been corrected to “demonstration case”, ensuring full terminological consistency throughout the manuscript, and (2) Section 6.2 has been retitled “Future Research Directions” to distinguish it from Section 6.1 and accurately reflect its content.

 

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

none

Author Response

Response to Reviewer 2 Comments

We sincerely thank Reviewer 2 for the kind acknowledgement and for the time and expertise dedicated to reviewing this manuscript. The constructive feedback provided in the review process has been helpful in improving the quality of the work.

Reviewer 3 Report

Comments and Suggestions for Authors

Authors have carefully addressed the revision issues.

Author Response

Response to Reviewer 3 Comments

Comment 1: Authors have carefully addressed the revision issues.

Response 1: We are truly grateful to the Reviewer for the time dedicated to reviewing this manuscript across both rounds. The constructive and detailed feedback provided in the first round has been invaluable in guiding the revision process. We are pleased that the revisions have been positively received and warmly thank the reviewer for this acknowledgement.

Round 3

Reviewer 1 Report

Comments and Suggestions for Authors

The Reviewer acknowledges that the authors successfully implemented all major and minor changes requested across the three review rounds. The revisions demonstrate not merely compliance with feedback, but genuine improvement in the technical presentation and accessibility of the work. In particular, the enhanced legibility of Figures 3, 4, and 5; the explicit code line references; the provision of the open-access GitHub repository; and the consistent use of standardised terminology throughout have substantially strengthened the manuscript. These improvements reflect a collaborative and professional approach to the peer review process.