An Overview on LCA Integration in BIM: Tools, Applications, and Future Trends

Abstract

The integration of Life Cycle Assessment (LCA) into Building Information Modeling (BIM) processes is becoming increasingly important for enhancing the environmental performance of construction projects. This scoping review examines how LCA methods and environmental data are currently integrated into BIM workflows, focusing on automation, data standardization, and visualization strategies. We selected 43 peer-reviewed studies (January 2010–May 2025) via structured searches in five major academic databases. The review identifies five main types of BIM–LCA integration workflows; the most common approach involves exporting quantity data from BIM models to external LCA tools. More recent studies explore the use of artificial intelligence for improving automation and accuracy in data mapping between BIM objects and LCA databases. Key challenges include inconsistent levels of data granularity, a lack of harmonized EPD formats, and limited interoperability between BIM and LCA software environments. Visualization methods such as color-coded 3D models are used to support early-stage decision-making, although uncertainty representation remains limited. To address these issues, future research should focus on standardizing EPD data structures, enriching BIM objects with validated environmental information, and developing explainable AI solutions for automated classification and matching. These advancements would improve the reliability and usability of LCA in BIM-based design, contributing to more informed decisions in sustainable construction.

1. Introduction

In recent years, the construction industry has experienced growing pressure to reduce its environmental impact and align with international sustainability targets. Regulatory frameworks such as the European Green Deal and national environmental action plans increasingly emphasize the need for low-carbon, resource-efficient building practices.

Mitigating impacts and climate change requires a holistic approach that includes policy changes and provides more simplified methodologies to optimize decision-making by encouraging more sustainable choices [1]. Life Cycle Assessment (LCA) is a methodology for assessing the environmental impact of products and is used in several certification systems (GBRS), including LEED BREEM. Backes et al. [2] show that several countries, including the Netherlands, France, Sweden, Denmark, and Finland, are introducing policies to mandate LCA for new buildings. The “design phase”, due to its high degree of flexibility in compositional and technological choices, provides a significant opportunity to guide the selection of low-impact materials. Recent advancements in energy efficiency during the operational phase of buildings have reduced the relative importance of operational energy consumption, leading to increased attention on embodied energy in materials.

European research and policy efforts are increasingly focusing on evaluating embodied energy in construction materials throughout their life cycles. In this context, there is a growing need for tools and methodologies that enable greater control and verification of sustainability performance. It is precisely within this framework that the integration of Building Information Modeling (BIM) and Life Cycle Assessment (LCA) emerges as a valuable opportunity to automate, streamline, and enhance the reliability of environmental assessments from the early design stages.

To explore the integration of LCA evaluation processes with BIM methodology, a selection of scientific articles comparing their combined application was analyzed. Among the relevant standards considered are:

• ISO 14040:2006 [3]
• ISO 14044:2006 [4]
• EN 15978:2011 [5]
• ISO 19650-1:2018 [6]

The ISO 22057:2022 [7] standard was also consulted to support the application of BIM and the integration of Environmental Product Declarations (EPDs), and it proposes a methodological framework specifically for the management of EPDs within the BIM process. Specifically, EN 15978 presents the methodological approach to quantifying the environmental impacts of buildings. ISO 19650 presents a modular framework for asset information management in BIM. According to Guignone et al. [8], obvious commonalities exist between ISO 19650 and EN 15978—for example, the mobilization phase (E5), production phases (F6–F7), and project delivery phase (B8). These elements can be related to the elements in EN 15978: the product phase, the construction process phase, the use phase, and the end-of-life phase.

According to Guignone et al. [8], it is important that impact assessment takes place from the very beginning of project design even if it is an estimate. The assessment process must occur throughout the project life cycle, allowing for up-to-date decision-making. In the same vein, ref. [9] advocated the need for the use of BIM–LCA not only at a specific stage but also during project development as an integrated decision-making tool, considering the entire design process and the evolution of available information. Guignone et al. [8] state that during the planning phase, after the service order for the execution of a project or construction Phase E5 of ISO 19650, accounting can be made for environmental impacts related to the extraction of raw materials and transportation and production of construction materials. Here, it is important to consider that in the initial design phase, the choice of information management, and the level of development of the BIM model according to concept (LOD) is important for the precision of the assessment. The issue of LODs in the design stages added to the incompleteness of data turns out to be one of the major problems in the BIM–LCA methodology. In fact, Palumbo et al. [10] point out that generic LCA databases are often used in the early stages of design to conduct LCI, while the detailed stages require more accurate data and often the results of LCA assessments appear inaccurate. To address this issue, Palumbo et al. [10] point to the desirability of adopting EPDs, documents that provide accurate results and precise LCA-based analysis for building products. However, by integrating EPDs into the calculation, limitations are recognized when using EPDs in BIM elements at different levels of development (LODs) in the design stages, particularly in relation to the data consistency and system boundaries of LCA. The problem of data granularity in LCA assessments has been addressed by Cavalliere et al. [9], who used BIM and mixing LCA databases with different levels of detail to obtain correct results. However, this methodology has limitations in that it divides the building into functional elements by not adopting an information management strategy by material. Santos et al. [11] proposed the creation of BIM objects enriched with information from different design phases by associating simplified data and complete data. Durão et al. [12] propose, in research, the use of LCA environmental information (generic databases) for lower LODs and specific information (EPDs) for high LODs, achieving very good results in terms of accuracy. Additionally, Almeida et al. [13] look for ways that EPDs can be brought into a BIM process considering a more fluid and direct process, using ISO 19650:2018 as a basis for analysis. Almeida et al. [13] state that the difficulties identified include the standardization of processes and integration between tools. Palumbo et al. [10] suggest the possibility of determining an accurate range of generic values at the design stage, suggesting the integration of a default safety factor. A similar system has been applied by the German Sustainable Building Council [14], which has introduced a safety factor of 10 percent in case the EPD does not exactly match the building materials. Palumbo et al. [10] affirm that the use of EPDs as a data source in LCA is reliable, although the methodology has limitations, especially in the case of lower LODs. Appropriate use of a system of association between low and high LODs to consistent data makes it possible to obtain consistent results without mixing different granularities. Antón et al. [15] state that a sustainable design tool must be able to evaluate building performance against different criteria. At the same time, information must be integrated into the design framework to compare different alternatives. For this reason, according to Antón et al. [10], it is necessary to manage a huge amount of information that can only be solved by integrating different tools to evaluate the decision-making process.

In response to these challenges, the present literature review seeks to critically examine existing methodologies by analyzing a selection of recent case studies, with the aim of identifying effective and replicable approaches for the integration of LCA within BIM-based design processes. Particular emphasis is placed on the early design phase, where the limited availability and granularity of data represent a major barrier to accurate environmental assessment. Furthermore, this study investigates the emerging role of artificial intelligence as a promising tool to support data processing and classification and address persistent data gaps. By consolidating current research and practice, this review outlines potential directions for future development, aiming to contribute to the advancement of automated, reliable, and scalable BIM–LCA frameworks aligned with evolving sustainability policies and regulatory requirements.

2. Materials and Methods

The primary objective of this scoping review is to critically analyze the role of integrating Building Information Modeling (BIM) and Life Cycle Assessment (LCA) methodologies within the construction sector. The study aims to identify the benefits, operational challenges, and future perspectives associated with such integration in the context of sustainable design.

Specifically, the research is guided by the following research questions:

• What are the most commonly adopted workflows in the scientific literature for integrating LCA evaluations into BIM processes?
• Which databases and environmental data sources are most frequently employed for conducting LCA within BIM models?
• What automation systems and tools are currently utilized to facilitate the generation and management of LCA evaluations in BIM workflows, and what emerging technologies (e.g., NLP and LLMs) have been explored?
• What strategies have been proposed in the literature to improve interoperability between BIM and LCA software, the standardization of environmental data, and the overall efficiency of integrated processes?
• What are the main technical and informational barriers hindering full integration between BIM and LCA, and to what extent do these challenges affect practical implementation in design environments?
• What techniques and tools are currently employed to visualize environmental data in digital models, and how do these contribute to supporting decision-making processes during the early design stages?

The review addresses the following thematic areas:

• BIM–LCA Workflows: The main workflows identified in the literature are examined, with attention to emerging approaches.
• Automation and Optimization: The potential offered by the integration of computational models, algorithms, and BIM–LCA tools is investigated.
• Data Standardization: The role of EPDs and international initiatives in harmonizing environmental information is examined.
• Data Visualization: Techniques and tools for transforming quantitative data into accessible visual representations are investigated.
• Limitations and Barriers: Analysis of issues related to the quality, accessibility, and interoperability of environmental data are conducted.
• Future Perspectives: Development directions to enhance the quality of EPDs, the automation of processes, and the communication of environmental outcomes are suggested.

Research Methodology

This work is a scoping review conducted according to recognized methodological frameworks. The aim was to map key concepts, types of evidence, and gaps in the research landscape related to BIM–LCA integration.

The literature search was conducted in May 2025 using five major scientific databases: Scopus, Web of Science, ScienceDirect, ACM Digital Library, and Google Scholar. Boolean operators were employed to combine keywords relevant to the research topic, including the following: ((“AEC” OR “Architecture, Engineering and Construction” OR “Architecture” OR “Engineering” OR “Construction”) AND (“BIM” OR “Sustainability” or “Green Bim” OR “Simulation” OR “Life Cycle Assessment”) AND (“Data Analysis” OR “Level of Detail (LOD)” OR “Level of Information Need (LOIN)” OR “Environmental Product Declaration (EPD)” OR “Global warming potential (GWP)” OR “Energy Consumption” OR “Open BIM” OR “Bim Interoperability Tool” OR “Early Design Stages” OR “Decision Making” OR “Facility Management” OR “Collaborative design” OR “Visualization Technologies” OR “Data Visualization” OR “Color Coding” OR “Immersive Technologies” OR “Virtual Reality” OR “Augmented Reality”)).

The literature search was limited to publications in English or Italian, published between January 2010 and May 2025, including peer-reviewed articles and relevant scientific contributions within the construction sector. Studies prior to 2010 were not considered in the case studies, as earlier papers were primarily theoretical and of limited relevance from an applicative perspective in relation to the analyzed case studies.

A total of 301 articles were initially identified, and at the conclusion of the selection process, 43 articles were included in the final review (Figure 1)

The inclusion criteria were as follows:

• Peer-reviewed studies published between January 2010 and May 2025;
• Articles written in English;
• Studies focusing on the integration of BIM and LCA;
• Presence of descriptions of workflows, software tools, LCA phases, and/or visualization strategies;
• Applications at the building scale.

The exclusion criteria were as follows:

• Absence of actual BIM–LCA integration;
• Generic or theoretical studies lacking practical applications;
• Non-construction sectors (e.g., manufacturing or infrastructure);
• Non-accessible or non-scientific documents (technical reports or popular articles).

The selection process was conducted by three members of the research group, each independently reviewing the articles and discarding those that were not approved by all three. The evaluation focused not only on the integration of BIM and LCA but also on the accuracy and completeness of information related to the modules, databases used, and data visualization systems.

3. Results

A preliminary bibliometric analysis was conducted using VOSviewer 1.6.20 to systematically identify and visualize the main emerging research themes within the selected literature. Subsequently, a more in-depth qualitative analysis was performed to categorize the most relevant topics and enable a rigorous comparison and detailed synthesis of the included studies.

3.1. Bibliometric Analysis and Visualization of Research Topics

The bibliographic data of the selected articles were imported into VOSviewer, where the analysis was conducted based on keywords and titles. The visual representations and conceptual maps generated by VOSviewer provide critical support in identifying emerging research trends and mapping the evolution of the scientific landscape. This approach facilitates a clearer understanding of the interconnections among various themes and highlights key areas that require further investigation.

Three distinct thematic clusters emerged:

• Green Cluster (Environmental Topics and LCA): This cluster includes terms such as Life Cycle Assessment (LCA), global warming potential, environmental impact, energy efficiency, and greenhouse gases. It centers on environmental analysis and the application of LCA to evaluate the sustainability performance of construction materials and building projects. Despite the extensive coverage of these topics, the literature frequently overlooks issues related to the availability and quality of Environmental Product Declaration (EPD) data, particularly in developing countries. This gap represents a significant barrier to the global scalability and applicability of BIM–LCA methodologies.
• Blue Cluster (BIM and Design): This cluster comprises terms such as BIM, building design, intelligent buildings, interoperability, and machine learning. It explores how the literature addresses the integration of BIM with decision-support and data management tools. However, the adoption of interoperable standards remains fragmented, and the investigation into the effective automation of LCA workflows through such technologies appears limited. The marginal presence of concepts like interoperability and data quality suggests the need for more systematic studies on the standardization of environmental data.
• Grey Cluster (Digital Technologies and Visualization): This cluster is defined by terms such as data visualization, virtual reality, big data, and simulation. It reflects a growing interest in the interactive representation of data and the use of immersive technologies to enhance stakeholder understanding of environmental outcomes. Nonetheless, many of these applications remain at an experimental stage, and the integration between visualization tools and decision-making processes is not yet fully developed in the existing body of research.

These clusters exhibit strong interconnections (Figure 2). Notably, the “data” cluster functions as a bridge between the BIM domain, renowned for its efficient information management, and the domain of LCA evaluation, which demands highly precise environmental calculations. The focus on dynamic and simplified data visualization, often supported by immersive technologies such as virtual reality (VR), underscores its vital role in improving communication and decision-making processes. While the green cluster emphasizes LCA and global warming potential, regional disparities in EPD data availability remain insufficiently explored. This limitation hinders the global scalability of BIM–LCA workflows, particularly in contexts where access to standardized environmental data is lacking. The presence of terms such as interoperability also highlights the necessity of developing more structured and in-depth research on data standardization and harmonization.

The temporal analysis (Figure 3) reveals that the most recent research topics focus on LCA evaluation and energy consumption, followed by the implementation of BIM methodologies, which play a pivotal role in streamlining environmental assessments. The emergence of data visualization as a theme further confirms a continuing trend toward enhancing the accessibility and usability of environmental information during the early design stages. This bibliometric analysis confirms that the integration of BIM and LCA represents a well-established research direction. At the same time, it underscores the importance of addressing critical issues such as EPD access, the adaptability of methodologies to regional contexts, and the development of interactive visualization tools. Tackling these challenges is essential to fostering a broader and more effective adoption of digital tools for sustainability assessment at the global scale. Furthermore, the integration of machine learning within this technological ecosystem appears particularly timely. Its potential to significantly enhance the speed and efficiency of LCA processes positions it as a promising solution to overcome one of the main barriers to large-scale implementation: the complexity and time-intensive nature of environmental analytical workflows.

3.2. Scientific Literature Classification on BIM–LCA Integration

The selected studies were analyzed and classified using a structured data extraction framework, and the comparative results are summarized in (

Table A1. Comparison of case studies from the literature review.

Authors	Ref	Software	Building Typology	Data Sources	LCA Phases (A1–A3, A4–A5, B1–B7, C1–C4, D)	BIM–LCA Integration Methods	Visualization
Ajtayné Károlyfi et al., 2023	[42]	Rhinoceros, Grasshopper, ConSteel (Pangolin), Archicad	Steel Frame Structure	ÖKOBAUDAT	A1–A3	5	Bar charts
Alotaibi et al., 2022	[38]	Revit, One Click LCA	Residential Building	One Click LCA Database	A1–A3, A4–A5, B1, B6, B7, C1–C4	4	Bar charts
Alwan et al.	[57]	Grasshopper, EnergyPlus, Bombyx	Residential Building	UK (ICE), OE Database (EnergyPlus+)	A1–A3, B6	1	Bar charts, 3D visualization
Ansah et al., 2021	[44]	Revit, Dynamo, Excel	Prefabricated/Modular Multi-Story Buildings	Functional Database (Hong Kong), Ecoinvent, Supplementary Data, Local Emission Factors	A1–A3, A4–A5, B1–B7, C1–C4, D	5	Bar charts
Arvizu-Piña et al., 2023	[26]	Revit, Excel, EVAMED	Residential Building	MEXICANIUH, Ecoinvent	A1–A3, A4–A5, B4, B6, C1	1	3D visualization
Bertin et al., 2020	[50]	Revit, Dynamo, Grasshopper	Multi-Story Buildings	Reused Structural Elements Material Database	A1–A3	5	Table
Boje et al., 2023	[35]	Brightway, Ecoinvent EF v3.0	Office Building	Ecoinvent, Luxembourg/Germany-Specific Data	Scenario 1. (A1–A3) Scenario 2. Monthly utility operational usage (B6) Scenario 3. LCSA for modules (A1–A3, B6, B7)	2	Bar charts, Charts
Cavalliere et al., 2018	[18]	SimaPro	New Multi-Dwelling Building	Ecoinvent	A1–A3, B4, C3 C4	5	Bar charts
Cavalliere et al., 2019	[9]	Rhinoceros	Multi-Family House	Generic Database, KBOB	A1–A3, B4, C3, C4	1	3Dvisualization
Chen et al., 2024	[29]	LLM, COMPAS, GPT-4, Revit, Dynamo	Office Building	KBOB	A1–A4	1	Table
Cheng et al., 2020	[19]	Revit, DesignBuilder	Museum	Chinese Life Cycle Database (CLCD)	A1–A3, B1–B7, C4, D	1	Bar charts, Charts
Cheng et al., 2022	[20]	Revit, Excel, Dynamo	Cultural and Sports Center	IEA EBC	A1–A5, B4, C1–C4	1	Bar charts, Charts
Di Santo et al., 2023	[27]	Revit, AH-LCA	Residential Building	Database Materials, EPDs	A1–A3, B1–B6, C1–C4	1	Bar charts
Fernández Rodríguez et al., 2025	[21]	Revit, SimaPro, Athena Impact Estimator	Industrial Warehouse	Ecoinvent 3 (SimaPro), Athena Database	A1–A3, A4–A5, B1, B6, B7	1	Bar charts
Felicioni et al., 2023	[23]	Revit, One Click LCA, DesignStudio	Residential Building	EPD	A1–A5, B4–B6, C2–C3	1	Bar charts, Charts
Forth et al., 2023	[30]	Revit, IFC, BERT	Office Building	LKdb ÖKOBAUDAT	A1–A3 B3, B4, B5, C1, C2, C3, C4	2	3D visualization, Bar charts
Forth et al., 2023	[31]	IFC, NLP	5 Case Studies	LCA Knowledge Database (LKdb), ÖKOBAUDAT, BNB Life Cycle, EPD	A1–A3, B4, C3, C4, D	2	Bar charts
Forth et al., 2023	[32]	IFC, Web Server (HTML, JavaScript, CSS)	Office Building	ÖKOBAUDAT, LKdb	A1–A3, B4, C3, C4, D	2	3D visualization, Bar charts
Gu et al., 2025	[28]	Revit, Excel, Claude 3.5, GPT-4o, Word2Vec, Glodon GTJ2024	Mixed-Use Commercial-Residential Building	Chinese Life Cycle Database (CLCD)	A1–A5	1	Bar charts, Charts
Kiamili et al., 2020	[48]	Revit, Dynamo	Office Building	KBOB, Ecoinvent	A1–A3, B4, B6, C1–C3	5	Bar charts
Kyaw et al., 2025	[58]	Revit	Single-Family House	Norwegian EPD (EPD Norge), Ecoinvent	A1–A3	1	Bar charts
Lee et al., 2021	[46]	Revit	Residential Building	EPD (BTEI Library including Impact and Cost Data)	A1–A3, B1–B5	5	Bar charts
Li et al., 2023	[47]	Revit, IBLAT	Elementary School	CLCD, Ecoinvent, ÖKOBAUDAT	A1–A3, A4, A5, B4, B6, C4	5	Bar charts
Lu et al., 2019	[36]	Revit, GTJ2018, Green Building Studio	Hospital	Chinese Life Cycle Database (CLCD), CEC Database	A1–A5, B1–B7, C1–C4	3	Bar charts, Charts
Ma et al., 2024	[51]	Revit, Tally	Office Building	Tally Database	A1–A3, A4, C2–C4, D	4	Bar charts
Martínez-Rocamora et al., 2021	[52]	Revit, Tally, Excel, Python (Scikit-learn)	Residential Building	GaBi	A1, B2, B4, C4, D	4	Bar charts
Mohammed et al., 2025	[53]	Revit, eToolLCD, Tally, Excel	Factory	EPDs, eToolLCD, GaBi	A1–A3, A4, C2–C4, D	4	Barcharts
Mowafy et al., 2023	[43]	Revit, Rhino.Inside, Grasshopper, Bombyx, Wallacei X, OSDEA, EMS	Residential Building	KBOB	A1–A3, B4, C3–C4	5	3D visualization, Bar charts, Charts
Najjar et al., 2017	[34]	Revit	Office Building	GaBi	A1–A3, A4, C2–C4, D	4	Bar charts
Naneva et al., 2020	[25]	Revit, Dynamo	Office Building	Bauteilkatalog, KBOB, Ecoinvent	A1–A3, B4, C3–C4	1	3D visualization, Bar charts
Nehasilová et al., 2022	[37]	Archicad, Revit, SimaPro, DEK Building Library	Residential Building	Ecoinvent	A1–A3	3	Table
Noorzai et al., 2023	[40]	Revit, Rhino, Grasshopper, EnergyPlus, Athena Impact Estimator, Dynamo	Residential Building	Athena	A1–A5, B6	4	Bar charts, Charts
Palumbo et al., 2020	[10]	Generic BIM Software	Office Building	Ecoinvent, GaBi, EPD	A1–A3	1	Bar charts
Płoszaj-Mazurek et al., 2024	[33]	Rhino, Grasshopper, Keras, ChatGPT, SLAD.AI	Residential Building	ÖKOBAUDAT	A1–A3, B6, C3–C4, D	2	3D visualization, Bar charts, Charts
Röck et al., 2018	[49]	Revit, Excel, Dynamo	Residential Building	Generic Database	A1–A3	5	3D visualization, Bar charts
Růžička et al., 2022	[34]	IFC, SBToolCZ	Residential Building	Generic Database	A1–A3	2	Table
Sandberg et al., 2019	[55]	Revit, IFC, Grasshopper GeometryGymIFC, Ladybug Tools, Slingshot, MySQL, EnergyPlus, Octopus	Residential Building	ICE, EPD, Swedish District Heating Association	A1–A3, B6	4	Charts
Santos et al., 2019	[11]	Revit, IFC	Residential Building	EPD, Ecoinvent	A1–A5, B2–B4, C2–C4, D	5	Table
Santos et al., 2020	[59]	Revit, BIMEELCA, Tally, Athena	Office Building	IBU, ÖKOBAUDAT, MRPI, Ecoinvent, GaBi, Athena	A1–A4, B2–B5, B6, C2–C4, D	4	Bar charts
Schneider-Marin et al., 2020	[41]	IFC, SQL Database	Office Building	ÖKOBAUDAT	A1–A3, B4, C3–C4, D	2	Bar charts
Soust-Verdaguer et al., 2018	[22]	Archicad, Excel, EnergyPlus	Residential Building	Ecoinvent	A1–A5, B2–B4, B6, C1–C2, C4, D	1	Bar charts
Van Eldik et al., 2020	[24]	Revit, Excel, Dynamo	Infrastructure Design Project	EIA Database	A1–A5, B2–B4, C1–C4	1	3D visualization, Bar charts
Xu et al., 2022	[45]	Revit, SimaPro, Python	Residential Building	SimaPro Databases	A1–A5	2	Bar charts, Charts

). The table includes the following information: authors and year of publication, LCA methodology, software employed, building typology, data sources (databases), LCA phases considered (A1–A3, A4–A5, B1–B7, C1–C4, and D), and workflow typology.

3.2.1. Framework for BIM–LCA Integration Workflows

Several studies have explored methods for integrating Building Information Modeling (BIM) and Life Cycle Assessment (LCA), proposing different strategies to connect environmental data with digital models. In particular, Anton and Diaz [15] identified two main approaches: The first consists of extracting data directly from the BIM model using standardized formats such as IFC, which allows for an automated environmental performance analysis. The second approach involves embedding environmental properties within BIM objects themselves, enabling the environmental information to evolve alongside the model throughout the design process.

A more detailed classification was proposed by Wastiels and Decuypere [17], who categorized BIM–LCA integration methods into five main types:

• Export of Bill of Quantities (BoQ):

Material quantities are extracted from the BIM environment and imported into external LCA tools.

Cavalliere et al. [9,18] implement a continuous LCA approach based on LOD-specific quantity take-offs, exported progressively as the BIM model evolves. Similarly, Cheng et al. [19] utilize the BIM model to extract material quantities for life cycle GHG estimation, relying on BoQ export to support early-stage environmental design decisions. In their follow-up work, Cheng et al. [20] expand this methodology into a more comprehensive embodied impact analysis, still rooted in quantity export from BIM. Fernández Rodríguez et al. [21] directly assess the reliability of BoQ data transfers, highlighting issues in manual extraction processes and underlining the importance of semantic consistency during export. Palumbo et al. [10] also stress the role of quantity-based BoQ exports in early-phase assessments using EPDs, noting that the precision of results depends heavily on the quality and granularity of exported BIM data. Other studies, such as those by Soust-Verdaguer et al. [22] and Felicioni et al. [23], employ BoQ-based exports to compare environmental impacts of alternative design scenarios—specifically building envelopes and timber structures—demonstrating the value of this method for comparative assessments. Van Eldik et al. [24] while proposing an integrated EIA framework, still rely on BoQ exports during the data collection phase, maintaining compatibility with existing LCA tools. Naneva et al. [25] also utilize exported quantities to enable fast LCA evaluations or cost-linked assessments, despite integrating additional national databases. Even in more advanced approaches, such as that by Arvizu-Piña et al. [26], which compares parametric and BIM–LCA methods, the workflow begins with a traditional quantity export. Di Santo et al. [27], while advocating a holistic evaluation of comfort and environmental impact, base their LCA input on quantities derived from the BIM model. Lastly, although Gu et al. [28] and Chen et al. [29] propose AI-supported systems (CECA and COMPAS, respectively), their validation and workflow development still begin with conventional BoQ data exported from BIM environments, thus demonstrating that the BoQ export method remains a foundational component even within next-generation systems.

2.

Import of surfaces via IFC:

Geometric information is used to associate building elements with predefined environmental profiles.

Forth, Hollberg, and Borrmann [30] present an interactive LCA framework that leverages Open BIM and IFC geometry to manage uncertainty in early-stage carbon assessments, supported by NLP-based classification tools and the LCA knowledge database (LKdb). Expanding this approach, Forth, Abualdenien, and Borrmann [31] develop a decision-support interface that integrates IFC-derived data with semantic enrichment from the LKdb to visualize GHG uncertainties dynamically. The combination of IFC geometry and semantic data extraction is further explored by Forth, Borrmann, and Hollberg [32], who apply Natural Language Processing (NLP) to interpret design intent from BIM and match it with environmental profiles. Płoszaj-Mazurek and Ryńska [33] also capitalize on the IFC format in their 3D AI-based application for carbon footprint estimation. Their tool processes IFC-based models to extract surface-level and typological features, integrating them with artificial intelligence algorithms to support low-carbon design choices in early planning stages. The role of IFC in automated and scalable LCA workflows is further reinforced by Růžička et al. [34], who develop a semi-automated evaluation framework (CBQA) that uses IFC data to assess complex buildings in terms of material and technical performance. Their method showcases the value of IFC in streamlining geometric data extraction for optimization purposes.

Finally, Boje et al. [35] present a comprehensive framework for integrating BIM and digital twins into Life Cycle Sustainability Assessment (LCSA). Their work distinguishes between static, conventional, and dynamic approaches to BIM–LCA coupling, with IFC playing a pivotal role in facilitating interoperability across digital environments. Collectively, these contributions demonstrate that IFC-based workflows enable structured, interoperable data exchange, which is particularly valuable in early design stages, where geometric typologies, rather than detailed BoQ, drive environmental evaluations. Moreover, the integration of IFC with AI by Płoszaj-Mazurek and Ryńska [33] and semantic enrichment tools by Forth [31] signals a shift toward intelligent, data-rich pipelines that combine geometry, semantics, and uncertainty analysis.

3.

Transfer of BIM data to dedicated LCA software:

Data is processed in visualization tools and then imported into specialized LCA analysis platforms.

Lu et al. [36] proposed the BLCCE framework, which automates the transfer of BIM data for lifecycle carbon emission analysis. Their method significantly reduces time and effort by eliminating manual input, using a structured data pipeline that reformats BIM outputs into compatible inputs for LCA tools. This structured pre-processing step improves the reliability and repeatability of carbon assessments, particularly for standardized building typologies. Similarly, Nehasilová et al. [37] developed a workflow that enables rapid environmental evaluation by linking BIM data with environmental and cost databases. While not fully automated, their approach employs visualization and data processing layers to make BIM-derived quantities readily usable within LCA environments, supporting fast, early-stage assessments.

4.

Use of BIM-specific plug-ins:

BIM-specific plug-ins were developed to enable direct execution of LCA analysis within the BIM software environment.

The fourth approach involves the use of plug-ins specifically developed for the BIM environment, with the objective of performing LCA analysis directly within the modeling software. This strategy reduces workflow fragmentation and ensures consistency between geometric data, material information, and environmental results. Alotaibi et al. [38] propose a six-stage strategy for integrating LCA into the BIM process, including the automated quantification of materials and the assignment of environmental profiles. Their method aims to establish a linear and internal workflow within the BIM platform, enhancing traceability in design decision-making. Najjar et al. [39] directly integrate the assessment of material environmental impacts into the early design phases of BIM. The use of plug-ins enables immediate feedback on material choices, thereby facilitating sustainable selection from the conceptual design stage.

Noorzai et al. [40] employ advanced BIM tools to optimize the selection of external cladding materials, using plug-ins that connect environmental databases with energy simulation software. Their approach enables simultaneous evaluation of energy costs, embodied environmental impacts, and heat loss reduction, effectively integrating LCA and thermal performance within a single platform. Finally, Schneider-Marin et al. [41] analyze the uncertainty of environmental data, including embodied energy and GHG emissions, during the early stages of BIM-based design. Their study highlights how plug-ins can provide sensitivity analysis tools already at the conceptual stage, supporting more informed decisions even under conditions of incomplete information.

5.

Direct integration of LCA information into BIM objects:

Data are assigned to materials during the design phase, eliminating the need for separate environmental analysis tools.

Ajtayné Károlyfi et al. [42] adopt a parametric approach that integrates geometry-based LCA directly into BIM to evaluate alternative structural design solutions, enabling the generation and comparison of sustainable options within the same environment. Similarly, Mowafy et al. [43] implement a parametric BIM–LCA model that quantifies embodied, operational, and end-of-life emissions directly in the model, optimizing material selection and reuse strategies.

A strong focus on prefabricated systems is evident in the works of Ansah et al. [44] and Xu et al. [45]. Ansah et al. [44] propose a complete BIM-integrated method for automated, multi-level LCA of prefabricated buildings, while Xu et al. [45] automate embodied carbon assessments in prefabrication using BIM-linked databases to reduce manual effort and address interoperability challenges. Several contributions emphasize data structuring and systematization. Cavalliere et al. [18] focus on how LCA data can be organized for BIM compatibility, laying the foundation for integrated assessment. Lee et al. [46] propose the creation of a BIM template and BTEI (Building Type Environmental Impact) library, enabling consistent environmental evaluation across projects. Similarly, Li et al. [47] present a scalable BIM–LCA framework for green building design in China (IBLAT), where environmental data is natively embedded in BIM components for holistic design-phase evaluation. In the domain of building systems, Kiamili et al. [48] assess the embodied carbon of HVAC systems by directly linking LCA data with BIM elements, providing granular impact evaluations of technical systems. Röck et al. [49] focus on visualization, integrating environmental impacts into BIM by assigning LCA results to surfaces and elements, enabling designers to perceive and interpret embodied impacts during early design stages. Similarly, Santos et al. [11] describe a six-phase method that integrates both LCA and Life Cycle Costing (LCC) directly into BIM, comparing it with commercial tools like Tally and ATHENA, and highlighting the advantages of native data integration.

Design for reuse (DfReu) is explored by Bertin et al. [50], who present a BIM framework for embedding traceability and reuse-related metadata into BIM components. This structure allows for tracking reused materials and evaluating their environmental impacts directly within the BIM model. Ma et al. [51], along with Martínez-Rocamora et al. [52] and Mohammed et al. [53], all apply BIM–LCA workflows where material and environmental data are embedded in the model for benchmarking, comparison, or steel structure assessment. These studies emphasize the efficiency and consistency gained by eliminating the need for repeated data export/import cycles.

The classification of the different case studies analyzed by this research, according to the categories proposed by Wastiels and Decuypere [17], illustrates that BoQ extraction and direct integration of information into the BIM model turn out to be statistically the most used methodologies. The classification of the case studies analyzed in this research (Figure 4), based on the categories proposed by Wastiels and Decuypere [17], shows that the export of Bills of Quantities (BoQs) emerges as the most adopted approach (34.9%), followed by the direct integration of LCA data into BIM objects (25.6%) and the use of BIM-specific plug-ins (18.6%). Less frequently applied strategies include the import of surfaces via IFC (16.3%) and the transfer of BIM data to dedicated LCA software (4.7%). These distributions highlight a prevailing preference for workflows that either externalize quantitative data for analysis or embed environmental information directly within the model, reflecting the ongoing balance between interoperability and data continuity in BIM–LCA integration processes.

3.2.2. Automation and Optimization

Based on the literature reviewed, one of the main goals of BIM–LCA integration is to automate environmental assessment processes to make analysis faster and more efficient, enabling effective decision support from the earliest stages of design, where the potential for impact optimization is greatest [34,35,51,54]. The use of parametric models combined with BIM methodologies and LCA databases enables rapid assessment of the impact of different structural configurations or material choices [33,42].

Plug-in software such as Tally facilitates this rapid assessment, providing almost instantaneous feedback on the BIM model; however, this instrument often uses generic databases that reduce the precision and accuracy of the assessment. Moreover, many of these plug-ins operate as “black boxes”, making it difficult to understand how data are processed and how environmental indicators are calculated. This lack of transparency hinders both traceability and clarity, critical aspects when environmental performance is used as a decision-making criterion. For this reason, several studies advocate for the use of Environmental Product Declarations (EPDs) and explicitly linked parameters within BIM objects to enhance process transparency and control.

Sandberg et al. [55] developed an automated framework for building performance optimization based on a neutral BIM master model (IFC). This central model serves as a single repository from which multiple multidisciplinary representations are automatically generated and managed through middleware that coordinates data flow and process integration. The framework automates the creation, evaluation, and updating of models, incorporating both energy and life cycle cost (LCE and LCC) simulations. For optimization, a multi-objective genetic algorithm iteratively explores various design configurations by modifying key parameters, such as insulation thickness. Each solution is assessed in terms of energy consumption and cost, and the system progressively selects the most efficient combinations, resulting in a set of Pareto-optimal solutions. The use of neutral BIM formats ensures high interoperability and minimizes the need for conversions between different software platforms, thereby enhancing workflow efficiency.

Research evidences the application of new techniques for data processing using artificial intelligence (AI) and, in particular, Natural Language Processing (NLP) to improve the automation of the matching process between material descriptions in BIM models and environmental data in LCA databases. The use of large language models (LLMs) or techniques based on vectorization (Word2Vec) and machine learning (LR, RF, XGBoost, SVM, and GB) is being investigated to interpret textual information in BIM and automatically match the correct emission factors or LCI data [28,30,32]. However, as highlighted by [55], these techniques are not without their limitations. NLP-based automatic classification of materials can be adversely affected by the terminological variability used in BIM models, the poor semantic quality of the input data, and the lack of transparency in the matching criteria adopted by the algorithms. In addition, the machine learning systems employed require large, labeled, and balanced datasets to ensure reliability in the results, which poses a challenge in pro-design practice, where such data are often not uniformly available. There is thus a real risk of overfitting and false matching, especially when generalized models are applied to projects with very specific typological or lexical characteristics. In conclusion, while AI- and NLP-based automation offers promising prospects for improving the scalability and efficiency of BIM–LCA workflows, current technical limitations need to be critically addressed by ensuring validation, transparency, and human control in decision-making. In the case studies reported by Płoszaj-Mazurek and Ryńska [33], they present three distinct application scenarios: (1) the MLCO₂ tool, which enables early-stage estimation of a building’s carbon footprint and achieved a 25% reduction in a conceptual design case; (2) SLAD.AI, a web-based platform assisted by large language models (LLMs), which provides design suggestions aimed at reducing the carbon footprint, albeit with limitations related to the AI’s contextual understanding; and (3) a fully integrated system combining BIM, LCA, and AI within a collaborative 3D environment, which automates material take-off from IFC models and generates design suggestions directly within the three-dimensional interface.

The study by Martínez-Rocamora et al. [52] proposes an automated methodology for generating environmental benchmarks for building typologies through the integration of BIM modeling, LCA analysis (via the Tally plug-in and GaBi database), and machine learning algorithms. The case study, based on a representative residential building, involved the automatic assessment of 240 construction combinations. Hybrid solutions were employed, combining BIM plug-ins with energy optimization systems and machine-learning-based environmental impact prediction tools, specifically the Random Forest algorithm. The model achieved an R² of 0.9999, a mean absolute error (MAE) of 1937.96 kgCO₂eq (with a relative error of 0.00038%), and a root mean square error (RMSE) of 3235.19 kgCO₂eq. This study demonstrates the potential of automation in environmental assessment for promoting more sustainable building design.

Baehr et al. [56] propose an advanced hybrid model based on the integration of Artificial Neural Networks (ANNs) and Residual Gaussian Process Regression (rGPR), applied to the prediction of environmental impacts of products using digitized Environmental Product Declarations (EPDs). The model is trained on EPD databases to forecast the environmental impacts associated with various construction materials. Although the study does not focus on BIM methodologies or building-level impact assessments, it provides a practical example of the use, normalization, and harmonization of product-level data using public databases. The model achieves a coefficient of determination (R²) of 0.97 and includes uncertainty estimation, thereby addressing a significant gap in automated Life Cycle Assessment (LCA) modeling. This approach underscores the critical importance of LCA and EPD data quality in the effectiveness of AI-based predictive models.

These examples illustrate how different AI paradigms, genetic algorithms, machine learning, deep learning, and large language models can be leveraged not only to enhance analysis speed but also to increase the environmental intelligence of BIM-based design workflows. Regulatory developments, particularly the European Green Deal and the Ecodesign for Sustainable Products Regulation (ESPR), are increasingly mandating LCA reporting. In this context, AI technologies are emerging as essential tools for scaling environmental assessments across product portfolios and complex projects.

3.2.3. Database and Data Standardization

The effectiveness of integrating Building Information Modeling (BIM) with Life Cycle Assessment (LCA) is strongly influenced by the availability and consistency of the environmental data employed. An analysis of 43 case studies (Figure 5)reveals considerable heterogeneity in the selection of LCA databases, with significant implications for the quality, comparability, and automation of environmental assessments.

Ecoinvent emerged as the most frequently used database (17.0% of the cases), followed by ÖKOBAUDAT (13.0%) and GaBi (9.0%). While these databases offer broad coverage and are compatible with widely used LCA software, the specific versions adopted are often not disclosed, and inconsistencies in data accuracy and compliance with international standards are common. Sources such as Environmental Product Declarations (EPDs), used in (6.0% of the cases), present further challenges. The lack of standardized formats and the heterogeneity of Product Category Rules (PCRs) hinder the comparability of data across products and databases [13,53].

This lack of standardization is widely identified in the literature as one of the primary barriers to the effective integration of environmental data into BIM–LCA processes [13]. Another critical issue concerns the adequacy of existing BIM data structures, particularly the Industry Foundation Classes (IFCs) schema, in representing environmental information in a structured and comprehensive manner. Although the IFC4 version has introduced specific properties for LCA at the building element level, substantial limitations persist in encoding relevant environmental and economic information at both the material (IfcMaterial) and project (IfcBuilding) levels [11].

Essential parameters for comprehensive LCA analyses, such as durability, density, manufacturing impacts, transportation phases (A4 and C2), and end-of-life scenarios, are often missing or poorly defined in current BIM schemas.

In several case studies, local or ad hoc databases (e.g., those related to Hong Kong, Switzerland, or Mexico) were used, while in 6.0% of the cases no clearly defined data source was reported. This lack of structured and transparent data usage compromises the reproducibility of studies and the ability to perform reliable comparisons across projects.

Finally, the literature highlights the potential of developing BIM object libraries enriched with standardized environmental information, ideally provided directly by manufacturers, as a future strategy to improve the interoperability between BIM and LCA and to ensure the availability of robust and verifiable datasets [11].

In parallel, as demonstrated by case studies such as the Life Cycle Assessment (LCA) of a Norwegian [57] timber building, the influence of both Building Information Modeling (BIM) Levels of Development (LODs) and regional environmental data is critical to achieving reliable results. These studies show that variations in global warming potential (GWP) estimates can exceed 140% when comparing low versus high LOD models, and that regional environmental factors can alter outcomes by up to 95%. This highlights the need to consider both the version (LOD level) and the regional context in sensitivity analyses. Although this study does not include an empirical comparison of LOD ranges (100–500), the relevance of this two-dimensional approach is acknowledged and will be further developed through targeted case-based validation.

Additionally, recent studies [10] underscore that the integration of Environmental Product Declarations (EPDs) into BIM-based LCA processes is generally limited to higher development levels, such as LOD 400 and LOD 500, where product-specific information (e.g., brand and manufacturer) becomes available. When generic data are used at lower LODs, such as LOD 100–300, deviations in LCA results may occur due to the lack of detailed quantities and material specifications. While EPDs are recognized as reliable data sources, in alignment with ILCD guidelines, their full integration into BIM processes remains constrained by the level of detail provided by the model. Moreover, as highlighted in previous research, early-stage decisions significantly influence the environmental performance of buildings. Therefore, ongoing efforts aim to develop methods that will enable more consistent and accurate results even at lower LODs, while acknowledging the current limitations in data availability and model granularity.

3.2.4. Analysis of the Most Investigated Modules in the Case Studies

The analysis of the data (Figure 6) reveals that modules A1, A2, and A3 are the most frequently examined in the case studies, collectively accounting for 33.7% of the total occurrences. These modules typically correspond to the production, extraction, and transportation of materials during the construction phase, commonly referred to as the “Product Stage” in Life Cycle Assessment (LCA). This focus aligns with the significant interest in assessing the environmental impacts associated with the initial construction phase.

Following these are modules B4 (Maintenance) and C4 (End-of-life—Disposal), each representing 8.1%, and module B6 (Waste processing) with 6.9%. These modules pertain to the use phase and final treatment of building elements, which are essential aspects for more comprehensive and circular sustainability evaluations.

In summary, the case studies primarily concentrate on the production and construction stages but also incorporate significant analyses of the use and end-of-life phases, reflecting a holistic view of the building’s life cycle.

3.2.5. Data Visualization

To make the results of LCA analyses accessible and understandable to planners, stakeholders, and decision-makers, particularly in the early stages where choices have the greatest impact, clear effective visualization strategies are essential [52].

The literature explores the use of graphical interfaces (Figure 7) that present environmental impacts directly on the BIM model, often using color-coding techniques to visually indicate the impact associated with specific elements or materials [39]. In addition to 3D visualization, summary diagrams, tables, and charts are used to present results aggregated by life cycle stages, impact categories, or material groups [39,58]. LCA software such as SimaPro and Athena, or BIM-integrated plug-ins, offer report and graph generation capabilities to facilitate interpretation of results [21,35]. It is clear from the literature review that the most established trend still appears to be through histogram graphs and reports; however, there is also a growing tendency to a three-dimensional visualization of information through color-coding so as to make problems clearer and more effective. In some cases, 3D model visualization has been further developed through the use of immersive environments, such as virtual reality, in order to facilitate a more intuitive and in-depth understanding of environmental impacts. This approach has been applied in various case studies, such as Alwan et al. [59], with the aim of supporting decision-making during the design phase and enhancing communication with stakeholders.

4. Discussion

Issues related to data represent one of the main barriers to the widespread and effective adoption of BIM–LCA integration, as extensively documented in the reviewed literature. Specifically, the accuracy of Life Cycle Assessment (LCA) in the construction sector significantly depends on the quality and precision of the data employed, both in terms of Life Cycle Inventory (LCI) and information integrated within the BIM model. During the early design phases, when the level of detail (LoD) of the BIM model is generally insufficient, quantitative estimates (quantity take-offs (QTO)) are often inaccurate or unverified, thereby increasing the margin of error. Furthermore, the variability of emission factors in LCI databases further amplifies these uncertainties, reducing data accuracy particularly when baseline data are inadequately verified or standardized, as is frequently the case with workflows relying on plug-ins that are often difficult for users to validate. Data availability and completeness also emerge as critical issues. LCI databases and Environmental Product Declarations (EPDs) do not always provide comprehensive information, especially regarding end-of-life (EoL) scenarios, which are often simplified or omitted. Additionally, the limited availability of EPDs for innovative or less common products constitutes a further obstacle, restricting the possibility to explore alternative solutions that could potentially have a lower environmental impact. Another challenge concerns the use of generic data, which are easily accessible, versus the need for specific data such as EPDs, highlighting the difficulty of aligning the level of detail and granularity. The lack of standardization in LCA methodologies, EPD formats, and calculation rules (e.g., system boundaries or allocation techniques) represents an additional barrier. The diversity of approaches across various databases and tools makes it difficult to reliably compare results from different LCA studies, hindering the creation of a coherent comparative framework that would enable uniform evaluation of solutions. Moreover, data regionalization is another factor contributing to result distortion. LCI databases are often developed considering specific national or regional contexts, such as ÖKOBAUDAT in Germany or EPD Norge in Norway. The use of data that are not representative of the project’s geographical context may produce misleading results, compromising the reliability of assessments in different local settings. The lack of complete and localized LCI databases for certain territories or material types constitutes a significant barrier to adopting the LCA approach in specific design contexts. Finally, interoperability between the various tools used for LCA analysis and the BIM model remains an unresolved issue that limits the overall effectiveness of the process. Differences in data formats and structures between BIM software, LCA databases, EPDs, and analysis tools often require manual data export and import operations, resulting in information loss and errors. Future research aims to overcome barriers related to data quality and management, with the goal of fully unlocking the potential of BIM–LCA integration. Key directions include the development of advanced solutions capable of addressing current challenges and making the process more seamless and reliable. The development of more robust and standardized BIM–LCA integration frameworks and methodologies is essential. The creation of systems facilitating data exchange among various software tools throughout the project lifecycle is fundamental to improving efficiency and reducing errors. These frameworks should be designed to ensure optimal information management from the initial design stages through construction and post-occupancy monitoring. Harmonization of EPD formats and Product Category Rules (PCRs) is indispensable for ensuring reliable comparability of data from diverse sources. Concurrently, an enhancement of the informational content of BIM models is required, with a focus on the quality and reliability of environmental and economic data included in object libraries. The inclusion of validated data at different levels of detail (LoDs) would allow for more precise analyses and better support informed design decisions. In particular, it is desirable that manufacturers of building materials and components provide BIM objects accompanied by detailed EPDs, structured according to standardized formats, thereby facilitating their use by designers and improving the efficiency of the entire process. Artificial intelligence (AI) offers further opportunities for innovation. Techniques such as Natural Language Processing (NLP) and machine learning could be employed to automate the matching of data between the BIM model and LCA databases, reducing the risk of human error and accelerating the process. Moreover, AI could manage incomplete or ambiguous textual information within the model, enhancing the reliability and quality of data used in the analysis. Another area of development concerns the management of uncertainty in LCA results, a particularly relevant issue during the early design phases when data are less precise. It is necessary to implement methods and tools capable of effectively quantifying and visualizing uncertainty, in order to provide decision-makers with a more realistic and reliable picture of the building’s environmental performance. This would enable decisions based on more concrete and less uncertain scenarios, positively impacting the sustainability of the project. Energy and water consumption analyses, as well as reuse, recycling, and disposal strategies for construction waste, should be integrated into BIM models, fully leveraging the system’s informational capabilities. This would allow for a more comprehensive and accurate view of environmental performance, extending the analyzed lifecycle and improving the precision of the assessment.

5. Conclusions

The quality, availability, and standardization of data remain significant barriers to the effective integration of BIM and LCA. Early design stages are particularly affected by the limited level of detail in BIM models and the considerable uncertainty associated with environmental data, while inconsistencies in LCA methodologies and the fragmentation of regional databases undermine the comparability and reliability of assessments. In addition, persistent interoperability issues between BIM software and LCA tools hinder the development of coherent and automated workflows. Although the current literature highlights the urgency of standardization, it rarely defines clear priorities or implementation timelines. In light of recent regulatory developments, including initiatives by CEN/TC 442 and ISO 22057, it is essential to establish a structured roadmap. In this regard, three progressive objectives are proposed: (1) the publication of a minimum set of IFC-EPD attributes by 2026, (2) the establishment of a standardized mapping service among different environmental data sources by 2028, and (3) the integration of semantic and regulatory consistency checks within major BIM platforms by 2030. To enable more reliable, scalable, and automated sustainability assessments, future research should focus on the development of shared integration frameworks, the harmonization of EPD formats, the enrichment of BIM libraries with validated environmental data, and the application of artificial intelligence to support data alignment and manage uncertainty. In parallel, it will be essential to foster the active involvement of stakeholders, including designers, manufacturers, standardization bodies, and clients, in defining open, transparent, and interoperable information models. Only through coordinated action among research, industry, and policy can the full potential of BIM as a digital infrastructure for the ecological transition of the construction sector be realized.