Digital Data Management Practices for Effective Embodied Carbon Estimation: A Systematic Evaluation of Barriers for Adoption in the Building Sector

Abstract

The pervasive impact of industrialisation on our daily existence has precipitated carbon emissions that demand critical attention. Although international conventions and scholarly research have scrutinised carbon emission sources and reduction strategies, the integration of digital tools and databases for estimating embodied carbon emissions remains in an incipient phase. Consequently, this review study aims to seek to optimise opportunities for digital transformation and sustainable practices while addressing the digital carbon footprint in the building sector. Employing the PRISMA guidelines, we systematically analysed 59 publications amassed from Scopus and Web of Science databases. The study’s search parameters encompassed the analytical dimensions of “embodied carbon”, “emission data”, and “barriers to digital transformation”. Through this rigorous process, 32 salient challenges and barriers were synthesised, encapsulated within four overarching parameters: traceability, accuracy, auditability, and efficiency. At its core, this study’s primary objective resides in the evaluation of existing barriers and challenges within the realm of carbon emission estimation. By doing so, it aspires to proffer a cogent knowledge model capable of catalysing the development of digital methodologies and models that can, with a high degree of accuracy, assess the burgeoning digital carbon footprint within the expansive domain of the building sector.

1. Introduction

The surge in environmental consciousness has precipitated a profound scrutiny of life cycle greenhouse gas (GHG) emissions, particularly carbon dioxide (CO₂), over the recent decades [1]. It is estimated that, without major improvements in the energy efficiency of buildings, the current surge in urbanisation may lead to a doubling of GHG emissions associated with the building and construction industry in the next 20 years [2]. Häkkinen et al. [3] suggest that measures to reduce GHG emissions from buildings include (1) reducing embodied energy in buildings, (2) reducing energy consumption of buildings, and (3) switching to low carbon. Within this paradigm, life cycle carbon emissions (LCO₂), encompassing operational carbon (OC) and embodied carbon (EC), have evolved into pivotal metrics for gauging a building’s environmental performance and energy efficiency [4]. The World Green Building Council reports that buildings account for 39% of global LCO₂ emissions. Within this, OC constitutes 28%, while EC emissions contribute to the remaining 11%. EC encompasses emissions related to materials and construction processes throughout the entire life cycle of a building or infrastructure, encompassing global energy consumption from equipment usage, manufacturing of building materials, transportation, and including embodied energy (EE) [5,6].

As delineated by the EN 15978:2011 [7,8] system boundaries, EC emissions can manifest across various life cycle stages, barring operational energy or water use (B6–B7) stages, and can even extend beyond a building’s life cycle [5]. Drawing upon the EN 15978:2011 life cycle stages, The Institution of Structural Engineers [5] has benchmarked various EC-emitting activities and data inputs, as elaborated in

Table 1. EC-/EE-emitting activities.

Stage	EC-/EE-Emitting Activities
Product stage— (‘cradle to gate’) Modules A1–A3	kgCO2e released during extraction, processing, manufacture (including prefabrication of components or elements,) and transportation of materials between these processes, until the product leaves the factory.
Construction process stage— Modules A4 and A5	kgCO2e released during transport of materials/products to site, energy usage due to activities on site (e.g., machinery use), and the kgCO2e associated with the production, transportation, and end of life processing of materials wasted on site.
Use stage— Modules B1–B7	kgCO2e released due to use, maintenance, repair, replacement, refurbishment, and operational energy and water use while the building is in use. Module B4 (replacement) is often the focus of the use stage when embodied carbon is being considered.
End of life stage— Modules C1–C4	kgCO2e released during decommissioning, stripping out, demolition, deconstruction, transportation of materials away from the site, waste processing, and disposal of materials.
Benefits and loads beyond the system boundary—Module D	This estimates any net kgCO2e benefits or loads beyond the project’s life cycle associated with recycling of materials, energy recovered from materials, and full reuse of materials/products.

and

Table 2. Key emission data required for EC assessments from each life cycle stage.

Data Input Required	Life Cycle Module
Material or product quantities	All stages
A1–A3 carbon factors	A1–A3
Distance, mode emissions intensity of transportation of materials to site	A4
On site material wastage rates	A5
Site activities emissions	A5
Building element replacement cycle	B4
Asset lifespan (reference study period)	B4
Demolition and deconstruction emissions	C1
Distance and mode emissions intensity of transportation of materials away from site	C2
End of life scenarios	C3 and C4
End of life scenarios	D
Difference between A1–A3 carbon factors of the secondary product and the substituted product	D

.

The requirement of different EC emission datasets at various life-cycle stages can be specified as illustrated in Table 2. The data input required is derived from the emission EC activities specified in Table 1 that are essential to understand the various data to consider when ingesting EC data in different stages of the EC data management.

Conversely, OC delineates emissions arising from energy consumption throughout a structure’s entire service life, encompassing elements like building services [9]. Historically, the industry’s gaze was primarily fixated on OC reduction via energy-efficient measures due to its substantial contribution to LCO₂ emissions. However, with the revelation of EC’s enduring impact on climate change, the imperative to reduce EC gained ascendancy [10]. In response to these embodied impacts, the industry has transitioned towards collaborative efforts with research communities, seeking methodologies to assess these upfront embodied impacts.

Regarding the existing literature on the subject, Azari and Abbasabadi [11] have undertaken investigative efforts to scrutinise current issues and identify challenges related to EC data management. Their discussion has predominantly centred on methodological, performance, guidelines, and material database-related challenges. Further, Mohebbi et al. [12] have underscored the absence of a robust strategy for optimising EC emission data management, highlighting the potential for confusion within the Life Cycle Assessment (LCA) process.

Considering the above studies and other related literature on the subject, this study aims to investigate the prevailing barriers in the effective management of EC data. Thereby, the study seeks to introduce a foundational framework for enhancing the effectiveness of EC data management, utilising existing knowledge bases. The goal is to establish a robust theoretical foundation for addressing EC data management challenges in the building sector. Although there are previous studies focusing on EC and related data capturing, this study is unique in articulating the most significant challenges for the development of a robust ‘digital EC data management strategy’ in the building sector that addresses all stages across data management (i.e., data capturing, processing, verification to usage) while creating a linkage to the parameters that have an impact on the overall effectiveness of EC data management and its adoption in the real world. A study of this nature is essential for the building sector, which could contribute to the theory by proposing a solid framework to develop an EC data management strategy, while it could also contribute to the practice by supporting the potential formalisation of the data management approach associated with backend digital databases for EC data, coupled with digital EC estimation tools currently used in the building sector.

2. Literature Review

In recent years, numerous research initiatives have endeavoured to introduce various methodologies for estimating the embodied impact of LCO₂ emissions of buildings. Initially, mathematical equations were devised to quantify carbon dioxide equivalent (kgCO₂e). However, the use of manual models has progressively yielded to IT-based tools, facilitated by rapid advancements in digital technologies [4]. The Life Cycle Assessment (LCA) of buildings has become an essential tool for minimising the environmental impacts of construction and enabling the construction sector to move towards sustainability [13]. Accordingly, contemporary LCA tools, such as the Inventory of Carbon and Energy (ICE) database, EC3, and OneClick LCA, are now commonplace in the field. These tools offer more precise estimations of EC emissions and are available in both licensed and open-source versions [9,14].

Arguably, decision makers rely heavily on modern EC estimation tools predominantly based on published (secondary) data sources of Carbon Emission (CE) data, such as Life cycle Carbon Inventories (LCI), reports, journals, books, and literature surveys. Hence, such a dependence on published data stems from the absence of comprehensive real-time data-capturing mechanisms within the building sector [15]. For instance, the ICE database is essentially a meta-database developed through an extensive literature review, gathering data on the EC of construction materials [16]. Given this heightened reliance on published data, there has been a growing emphasis on standardising the overall embodied carbon data management process to ensure data availability, quality, and consistency. In broad terms, ‘data management’ encompasses the practice of ingesting, processing, securing, and storing a dataset used for strategic decision-making to enhance business outcomes [17].

While well-recognised LCA tools like OneClick LCA were designed with comprehensive EC data management in mind (including reporting, processing, verification, and security), these tools still heavily rely on secondary sources of public LCA and public Environmental Product Declaration (EPD) data from various EPD programs, including the ICE and EPiC databases [18]. Evidently, experts have made several efforts to enhance the overall management of EC data throughout the LCA process. Various programs and schemes have been introduced for this purpose. International EPD Systems and the OneClick LCA EPD generator are prominent examples of such key developments, aiming to integrate renowned compliance tools like BREEAM, LEED, HQE, and C+E- certifications while adhering to ISO 14040/ISO 14044/ISO 14025 [19,20,21] standards to advocate and maintain data consistency [22]. Although the number of program operators publishing EPDs has increased significantly, as has the pressure on the design community to use these documents as decision-making tools, there is no formal oversight of these Program Operators, and the known lack of harmonisation creates questions on the appropriateness for material selection decision-making [23].

Additionally, numerous barriers confront the accuracy of current LCA tools, stemming from issues associated with scope, data representativeness, and reliance on generic data. Comparative investigations have revealed discrepancies in estimating EC between LCA tools like GaBi and SimaPro, even when applied to identical materials, sharing similar origins and employing similar technologies [24]. These divergences underscore the complexities inherent in the EC estimation process. Furthermore, the existence of varied system boundaries, geographical constraints, limited data availability for new products, data incompleteness, coefficient factors, and the opaqueness of Carbon Emission (CE) data can introduce further inconsistencies [25]. These factors compound the challenges associated with achieving reliable EC estimates.

Embodied Carbon Factors (ECFs) represent another crucial dataset that can significantly influence the overall efficacy of LCA [13]. ECFs offer an estimation of the Global Warming Potential (GWP) impact associated with individual products or processes. However, the acquisition of precise and reliable ECF values and data constitutes a fundamental prerequisite for accurate LCA outcomes [9,12]. In addition to EPDs, ECFs can be sourced from various secondary outlets, encompassing (1) industry data, (2) government records, (3) commercial LCA databases, (4) compliance with PAS 2050 carbon footprint standards, and (5) aggregation from pertinent literature [12]. The application of these ECFs, particularly when reporting emission factors for a specific country (or equivalent sources for diverse nations), assumes paramount importance. This practice ensures the precise calculation of kgCO₂e impacts related to transportation, fuel consumption, and supply chains, thereby upholding the accuracy of EC estimations, especially when country-specific data are limited [26].

3. Methodology

3.1. Scope of the Review and Literature Search Strategy Selection

Utilising a Systematic Literature Review (SLR) approach, this study systematically gathered and synthesised existing knowledge concerning challenges and barriers in the realm of EC data management. A well-rounded SLR serves as an essential tool to push the boundaries of knowledge, providing a comprehensive review of pertinent literature. This process aids in grasping the breadth and depth of the existing body of work while pinpointing areas of research exploration, as emphasised by Xiao and Watson [2]. The initial phase of the literature search employed specific keywords, including “embodied carbon”, “data management”, “challenges”, and “buildings”. Notably, Scopus and Web of Science were chosen as the primary indexed databases for this initial search due to their accessibility and rich repository of highly ranked, quality, and indexed works, encompassing scholarly articles, journals, and conference proceedings.

3.2. Literature Search

The initial search yielded 1537 results, which were instrumental in identifying alternative terms and synonyms to expand the search scope. This expansion was achieved by scanning the abstracts and titles of papers. These alternative terms replaced the initial keywords to encompass a broader range of related publications, thereby forming the keywords for the final search. In the second round of the literature search, these key terms and synonyms were combined using Boolean operators, primarily ‘OR’ and ‘AND’, as demonstrated by the Boolean statement: ((“X” OR “X2”) AND (“Y” OR “Y2”)).

To meet the predefined inclusion criteria, the search results were further refined using search filters. The search was constrained to relevant journals, scholarly articles, conference proceedings, indexed publications, technical reports, manuscripts, and theses published in English between 2012 and 2023. Additionally, supplementary resources were sourced from a standard Google Scholar search, encompassing subject-related journals, reports, conference proceedings, and non-journal resources like periodic reports not indexed in the aforementioned databases. After the final search was concluded, the database records were transferred to Endnote reference manager for subsequent analysis and screening. In total, 634 records were retrieved from indexed databases, comprising 402 journal articles and 232 conference proceedings. The Endnote referencing manager was populated with a total of 644 records, including resources obtained from the Google Scholar search to ensure comprehensive coverage of subject-related articles not present in indexed databases. Figure 1 summarises the overall results of the literature screening process.

We identified 57 duplicate records and subsequently removed them from the search results. Following this, a title and abstract screening was conducted, leading to the exclusion of 312 records that were deemed irrelevant to embodied carbon or its associated challenges in practice. After completing this preliminary exclusion, a final screening was carried out based on predefined criteria. These criteria included: (1) full-text articles related to embodied carbon estimation in the building sector but lacking meaningful discussions on data management challenges or barriers; (2) contributions discussing embodied carbon data management in review areas beyond building construction practice, such as infrastructure and other civil engineering structures within the built environment discipline, as well as a few other review disciplines like mining, waste management, and appliance manufacturing. As a result, a total of 216 records were removed, leaving the remaining records for in-depth literature analysis and discussion. Additionally, 10 non-indexed records were imported from a Google Scholar search to contribute to the in-depth analysis of subject-related discussions.

4. Results

The next step of the SLR was to analyse and synthesise to identify and extract existing knowledge on prevailing challenges and barriers on the effectiveness of EC data management that would further lead to LCA inaccuracies in the building sector. The state-of-the art study thereby uncovered 32 EC data-related challenges/barriers that lead to LCA inaccuracies.

Table 3. Identified challenges/barriers for EC data management under different processes.

Process (Area)	Theme	Identified Challenge/Barrier/Issue	Focus of the Study/Studies (Enhancement Parameters) *	Source Reference
Data capturing and ingestion	Data fragmentation	1. Lack of mature and accepted database to record EC/EE emissions during building material production at the country/regional level	Traceability, Efficiency	[27]
		2. Raw material supply-chain can be extended to geographical extents where there are no technologies/facilities to record emission data	Accuracy, Traceability	[25,28,29]
		3. EC data reporting procedures/processes are developed specific to organisations, with limited geographies/specifications and procedures in mind	Auditability, Traceability	[30,31]
	Industry commitment on reporting EC data	4. ‘Operational energy prioritisation’ limits the emergence of robust EC/EE benchmarking studies that do not cover diverse building types and systems	Traceability, Efficiency	[11,28,32]
		5. Primary EPD implementation goals were not oriented towards data ingestion	Auditability, Traceability	[23,33,34]
		6. Difficulty in defining the requirement of implementing automation	Traceability, Efficiency	[35]
		7. Simultaneous activities for the same activity make emission data recording even more difficult	Traceability, Efficiency	[36]
	Coefficient factors	8. Some building types are not considered when developing coefficient databases	Traceability, Accuracy	[11,37]
		9. Sources of carbon emission factors are not uniform, including the basic databases, literature, and standards	Auditability, Efficiency	[38,39,40]
Data processing	Methodological differences of LCA process	1. Unclear guidance on unifying different LCA frameworks to enhance the LCA results	Accuracy, Efficiency	[7,41,42]
		2. Poorly defined functional units, impact categories, and cut-off rules when using EPDs	Accuracy, Traceability	[34,43,44]
		3. Use of different temporal and spatial scales for LCA tools	Accuracy, Efficiency	[45]
	Standardisation and Governance	4. LCA tool/framework-related legislation is vaguely defined and thus lacks control over the compliance and authenticity	Auditability, Accuracy	[35]
		5. There is no industrially accepted robust tool/process to map global EC/EE emission data	Accuracy, Efficiency, Traceability	[13,46]
		6. Lower temporal, geographical and technological correlation in databases	Accuracy, Efficiency	[23,34,47,48]
	Adaptation to change and knowledge management	7. Little or no experimental data and lack of design guidelines on the relationship between EC and EE performance	Accuracy	[11,49]
		8. Insufficient dedicated workforce to record emission data and limited research and development	Accuracy, Efficiency, Traceability	[16,50]]
		9. Commitment of site labourers to track emissions on site/daily activities	Efficiency, Accuracy	[51]
Security and data verification	Integrity of LCA data	1. Historical databases can contain numerous unverified data from private LCA or non-LCA data	Auditability, Accuracy	[52]
		2. Differences in functional product categories and product classifications	Accuracy, Auditability	[3,53]
		3. The degree of reliance on generic vs. specific data can cause significant differences in results when developing EPDs	Auditability, Efficiency	[43,54,55]
		4. Many published LCAs lack the necessary transparency to fully understand the processes and specifications considered while modelling data	Auditability	[56]
	Confidentiality and intellectuality	5. Many companies prefer to disclose only the carbon emission over which they possess complete control	Auditability	[46]
		6. Data gaps may occur due to sensitive information such as trade secrets	Auditability, Accuracy, Efficiency	[11]
		7. Unprecedented control over data monitoring, storage, and use by rigorous data-protection laws	Auditability	[57]
		8. Companies pretend to show lower emission data than that of competitors to win competitive advantage	Accuracy, Auditability	[56]
Data storage, Publication, and use	Agility and interoperability	1. Disintegration of sub-tasks allotted under major activities makes it difficult to ensure availability of up-to-date emission data at regular intervals	Efficiency, Accuracy	[11,58]
		2. Construction specifications, guidelines, and standards do not reflect modern production/innovative approaches thus undermine the LCA process/EPD creation	Traceability, Efficiency	[11]
		3. Lack of a robust platform to maintain interoperability across different LCA tools, thus allowing mutual flow of emission data	Accuracy, Efficiency	[59]
	Global awareness and industry readiness	4. Construction organisations are cost-driven and ‘slow-to-innovate’ and thus invest less in personal development to educate on effective usage of LCA	Efficiency	[60]
		5. Most business users know very little about EPDs and find it difficult to interpret the contents	Efficiency, Auditability	[61]
		6. The input from academia and research groups was limited until recently due to misconceptions	Auditability, Efficiency	[62,63]

* Primary focus listed first; the rest of the focus areas are secondary in a given study.

summarises the key EC data management challenges/barriers causing inaccuracies in LCA in buildings. Based on the nature and similarities of the each identified challenge/barrier, the analysis was structured under four processes to cover the various data management stages: (1) EC data capturing and ingestion, (2) processing, (3) security and data verification, and (4) storage, publication, and use.

4.1. Data Capturing and Ingestion

As listed in Table 3, nine key challenges/barriers were identified as specific EC data capturing and ingestion issues that are applicable to any EC emission from stages A1–D. These constraints were further classified under 3 themes: (1) industry nature, (2) commitment, and (3) coefficient factors. Among the key themes, data capturing and ingestion have the highest number of records as presented in Table 3. Challenges include varied country-specific EC/EE strategies, data accessibility issues, lack of scientifically accepted procedures, maturity of EC benchmarking, unclear aims and objectives, resistance to adopting automation, complexity of site data recording, absence of verified emission factors, and immaturity of source data.

4.1.1. Data Fragmentation

This section mainly focuses on the challenges of EC data management identified due to the fragmented nature of the industry. The major reason for this fragmentation is the extent of construction supply chains that have been extended beyond regions, territories, and countries [25]. Moreover, these extended construction supply chains mainly appear with the raw material supply for construction work, especially during A1–A3 stages of a building life cycle. However, the carbon emission during the production of such raw materials belongs to the life-cycle EC emissions accounted for by a specific building, and therefore assessing these emissions for a low-carbon design cannot be neglected [25,27,28]. From the carbon emission data point of view, it is crucial to record and transact every single activity that has an embodied impact on the building, including kgCO₂e released during extraction, processing, manufacture (including prefabrication of components or elements), and transportation of materials between these processes, until the product leaves the factory gates [55,57,64].

In terms of raw material supply, a product can contain a mixture of raw supplies from multiple origins, thus leading to complex data ingestion at multiple levels of construction supply chains creating many discrepancies in final EC emission numbers. For example, Nawarathna et al. [28] accentuates that accessibility to such specific EC emission data in developing economies (countries like Sri Lanka, one of the major raw material suppliers in the Asia–Pacific region for numerous production companies) can be challenging and is a common barrier for effective LCA outcomes.

This can mainly occur due to detachment from the main technology used by producers to record/transact emission data (e.g., inability to transact paper-based manual log records) and lack of a national mature and accepted database for building materials [27,28]. In addition, Berggren et al. [27] further pointed out that even today, few countries have defined the real requirements regarding a national level EC/EE database requirement for buildings, and the unavailability of such country/regional specific coefficient material databases could push LCA results towards further uncertainty due to higher dependence on assumed figures. On a separate note, Anand and Amor [30] emphasised that a lack of scientifically verified/accepted methodologies can lead to LCA variations, especially during EPD creation. Although the current ISO standards guidance for data collection are industrially accepted and capture data to a greater extent in construction supply chains, they seem insufficient and need sophisticated, technology-driven methodology to further evaluate the source of the data, the age, and point estimates from construction supply chains. Further, Lasvaux et al. together with Anand and Amor [30,31] noted the number of issues arising while complying solely with ISO reporting standards (especially with EE reporting). These data variations have been reported to be significant enough to impact decision making based on LCA results, also causing difficulty in the comparison of building LCA [65].

4.1.2. Industry Commitment to Report EC Data

This section attempts to uncover the level of commitment-related challenges by the industry and interest groups in streamlining data reporting and ingestion in the building sector. Although there are procedures in place to capture EC/EE emissions in the sector, the effort made by the industry to define the level and commitment to record and transact emission data is highly questionable [11,23,28,32,33,34]. A major barrier to this slow uptake of embodied impact is the ‘Operational Energy (OE) prioritisation’ until recently [32]. Studies suggested that the priority had been given to OC reduction while EC was considered to be proportionally insignificant. As a result, buildings became more efficient in terms of OE, but enabling EC to gain a significant proportion of whole-building life-cycle carbon emission [28]. As a result of this, EC/EE benchmarking seems to be premature especially when comparing different LCA results with other cases in the industry. In their study, Azari and Abbasabadi [11] indicated that performance benchmarking challenges in the OE area of research have been strengthened, while clear performance metrics and targets have been defined and adopted to help lower OE use and communicate goals; however, they have also resulted in limited opportunities to develop such performance benchmarks for EE use of buildings. In addition, as one of the least digitalised industries, the construction industry has not yet reached the expected height of technology adoption and automation [66]. On one hand, this is primarily due to higher resistance for technology adaptation and on the other hand, difficulty in defining the requirement of such acceptance, especially for carbon reporting. Automation seems to be a solid alternative to minimise the human effort on recording/transacting EC emission data especially during A1–A3 stages. However, Alwan and Ilhan Jones [35] pointed out that it is challenging to implement complete automation utilising emerging technologies due to unclear roles and responsibilities and skills development of key personnel to use the full digital technologies needed to capture full upfront carbon. In addition, although EPDs were originally created with the goal of comparison in mind, their primary implementation was limited to obtaining scores in different certification systems, such as the Leadership in Energy and Environmental Design (LEED) ratings; thus, the EPD inclusion required a different approach to the writing of specifications [23,33,34]. There is a strong need for industry input to harmonise between certification and data declaration, especially when using different Product Category Rules (PCRs) for data recording and comparison [23]. For example, AzariJafari et al. [34] suggest that more consensus regarding PCR allocation rules is required, as each PCR committee generally mandates a rule that maximises the benefits for the main product for that specific industry. Moreover, the complexity of site data recording discourages the recording of data such as transportation of materials to the building site. In their investigative study, Davies et al. [36] found that some operators used multiple sign-in sheets to record material supply and transportation records to the site; however, evidently, there was lack of awareness demonstrated among the contractor operatives regarding the importance of the onsite energy data uptake. The study also indicated that this may pose a barrier for effective assessment of EE related to transportation impacts, as these impacts were not monitored throughout the entire construction phase.

4.1.3. Coefficient Factors

This section of the analysis discovers the relevance of ECF when converting EC/EE emission data in data reporting. Numerous tools embedded with reliable emission factors have been developed by practitioners to maintain the consistency of data ingestion. Some software can embed one or more databases for ECF of materials and energy. For example, Eco-bat software 2.0 provides 140+ emission factors of materials collected from the Ecoinvent database, which is supposed to provide accurate conversions when creating EPDs [37]. However, the current databases of emission factors need to be improved in order to be applicable to more building types [39]. A case analysis conducted based on 22 types (55 cases from Ecoinvent and 53 cases from ICE) of EC databases revealed that multiple sources of ECF might result in unreliable results if such cases were located in different places with different construction material-processing procedures [37]. Moreover, the same study suggested an adjustment of emissions factors when using global emission factors with different energy mix compositions; however, normalising the databases used in different cases is impractical when creating EPDs. Another review of EE studies in tall buildings reveals similar limitations, as EE studies on most EC inventory databases used in tall building EE estimations do not represent tall building construction practices and therefore, there is additional uncertainty in carrying out EE reporting of tall buildings for LCA assessments, as compared with low-rise construction [11]. On the other hand, popular databases such as ICE provide ECF of building materials based on the references collected from a range of public sources such as journal papers, reports, books, and conference papers. Nevertheless, Xi and Cao [39] emphasised that the primary sources of ECF are not uniform, including the basic databases, literature, and standards, which can lead to further inaccuracies when converting emission data in the absence of specific information.

4.2. Data Processing

Another nine major challenges/barriers were identified specific to EC data processing, which are applicable for any EC emission from life-cycle stages A1–D. Data processing is a key activity in the data management process which converts ingested raw information into a useable set of data for LCA assessments [65]. The identified challenges were further classified under 3 specific impact categories: (1) methodological differences in the LCA process, (2) standardisation and governance of LCA data, and (3) adaptation to change and knowledge management. It is evident that more authors have attempted to cover data-processing-related challenges, which is equally as significant as data ingestion as per Table 3. Challenges including lack of transparency and coherence of LCA frameworks, inconsistencies in LCA methodology, major differences in mathematical foundations, lack of legislative framework, unavailability of a globally accepted framework, limited guidance on standard PCR development, epistemic uncertainty, resource scarcity, and prioritising primary tasks are further analysed below.

4.2.1. Methodological Differences in the LCA Process

This section analyses the challenges/barriers due to different methodologies adapted in various LCA tools and databases currently in use. One key barrier that effects the EC estimation is the underlying mathematical foundation of commonly used LCA tools. A review by Bi and Little [45] suggested that the software framework models themselves operate naturally at different temporal and spatial scales, and individual LCA models have different mathematical foundations. Although these systems are coupled through information exchange, their models may have different inputs and outputs, which must be further logically connected and scaled for better results. Moreover, a survey analysis uncovered that inconsistencies in the LCA methodology, such as poorly defined functional units, impact categories, use of specification, and cut-off rule issues, are of primary concern when using EPD results for EC/EE estimations [34,43]. For example, PCRs written for construction products following EN 15804 [67] but written for a particular product category usually provide a specific functional unit more relevant to its category [43]. This is a problem in product categories like insulation, where EPDs use functional units that cannot be converted when feeding into a LCA database designed to work on ISO 14025’s requirements. Another state-of-the-art study by [41] showcased that the LCA tools/methods have not been well unified due to the variations in equations in LCA standards regarding how the LCA is quantified and how the multiple life cycles should be inserted into the equations. For example, the End-of-Life (EoL) allocation factors for burdens and benefits are different in European Commission Product Environmental Footprint (EC PEF) Guidance v6.3 and European Committee for Standardization (EC) EN15804/EN15978 standards [7,41].

4.2.2. Standardisation and Governance of LCA Data

This section mainly focuses on challenges/barriers due to limited legislative frameworks, guidelines, and standards which could severely impact the effectiveness of EC data processing when carrying out LCA. It is evident that a strong legislative framework to govern overall LCA processes sets the grounds for statutory compliance pressures on carbon reduction, especially to govern data processing for LCA databases. However, ref. [35] found that the lack of a legislative framework governing the integration of carbon data, particularly EC, retards the adoption of technology-based data exchange solutions such as utilising a standardised and integrated library of Building Information Modelling (BIM) that contains whole life cycle energy information for each material for LCA data processing. Moreover, refs. [13,46,68] reported the absence of an internationally recognised rating system that is approved by all countries, which can be adjusted to suit various regional conditions and will incorporate the measurement and quantification of EC emissions from construction activities. Furthermore, limited standard guidance on PCR development can be seen as another barrier for effective EC data processing in LCA. It is argued that the PCR development process generally lacks clear guidance and background reporting on how the robustness of conclusions influences the selected rules that are finally agreed upon [34]. While there are certain specifications for foreground processes in the PCR, the reason behind these choices may not be clear, as there are multiple LCI datasets available that have their benefits and flaws (e.g., incompleteness of the environmental flows in a database as opposed to a lower temporal, geographical, and technological correlation in a more complete database) [23].

4.2.3. Adaptation to Change and Knowledge Management

This is another key impact category for processing data for LCA assessment. As discussed in a previous section, the construction industry is cost-driven, and therefore, while technology adaptation is at a low level, investment in knowledge management for adaptation to changes and knowledge sharing is at a considerably lower level. In their review, ref. [51] highlighted that the commitment to tracing ‘anthropogenic emissions’ at the site level is often a trade-off among other primary activities. To date, very little information has been collected from actual construction sites, limiting how much is truly understood regarding the contribution that the construction stage can have on a building’s total EC footprint [11,51]. In addition, gathering construction-activity-related emission data is largely dependent on the knowledge possessed by the labourers working on the site. Moreover, different skill categories might require separate levels of standard knowledge-sharing sessions to understand the recording of emission data to a level they can be processed at a later stage for LCA [11,36]. It is evident that companies pay less attention to conducting necessary training, workshops, and on-site training for targeted skill categories, making it difficult to collect actual site data for LCAs. Another study showed that that most construction companies have insufficient workforces and research and development (R&D) capabilities to fully evaluate and adopt new technologies in innovative technological solutions to process and transact data complying with standards and specific guidelines [16]. Another common barrier for effective data management at the data-processing level is ‘epistemic uncertainty.’ This type of uncertainty arises from incomplete knowledge about a specific system/standard or procedure that represents a lack of knowledge about fundamental phenomena. A study focused on predicting uncertainty of EC data uncovered that most LCA tools driven by ECF do not account for epistemic uncertainty, while assessment results are often reported as a single deterministic value without acknowledging the range of uncertainty which leads towards ‘measurement uncertainty’ [11,49].

4.3. Security and Data Verification

Secure transaction of data and verification can be classified as major pillars in the EC data management process. These actions entrust the safety of data while assuring the reliability of data throughout the data management process. Our analysis exposed eight key challenges/barriers that could compromise secure data transactions and data authentication. The identified challenges were primarily classified under two specific impact categories: (1) integrity of LCA data and (2) confidentiality and intellectuality. Evidently, only a few studies (11 out of 58 studies screened for the review; Table 3) have attempted to cover data security- and verification-related barriers/challenges. These challenges, including higher reliability on historical datasets, data compatibility issues, uncertainty of data selection, lack of transparency, complexity of data governance, dealing with sensitive information, effects of data protection regulations, and skewness of data, are further analysed below.

4.3.1. Integrity of LCA Data

Integrity of data is a crucial element of overall quality and consistency of data, and verification is an essential element of managing overall quality and consistency of EC data. A major barrier to maintaining the consistency of data is the higher dependency on historical data (published literature) for LCA. A review by [52] pointed out that a detailed estimation of the EC of a material is very complex, and sometimes the values found in literature are very dispersed because of the presence of a wide range of different approaches that set a different system boundary, such as including only carbon dioxide or all GHGs, and including/excluding transport or EoL scenarios or service life or maintenance. Moreover, the study uncovered that greater quality issues and verification issues might be seen if carbon sequestration or recycling are considered [69]. In addition, ref. [56] argued that many published assessments lack the necessary transparency to fully understand system boundaries as well as the modeller’s assumptions. Future events and decisions are characterised by high uncertainty, which decreases the reliability of predictions and estimates, and there is great underestimation of how much influence methods have on the final numbers. Moreover, several studies [43,54,55] suggested that the quality of EC data can be compromised due to overuse of generic datasets, lack of common data sources, limited data availability, and poor reliability of results due to uncertainty and use of point estimates without confidence intervals or margins of error. Differences between EPDs in their degree of reliance on specific data versus generic data can cause significant differences in results. Furthermore, mapping PCR and EPD activity is difficult due to the lack of compatibility between functional product categories and the material and production-based product classification system [3,53]. For example, although more than a third of registered EPD documents belong to construction products under the UN CPC, they do not fit easily into the UN CPC material-based system [53].

4.3.2. Confidentiality and Intellectuality

Data verification is the process of assessing the authenticity of data for managing the quality and consistency before using them for LCA, mainly with third-party support. However, the user’s rights to distinguish between what data to disclose and what not could significantly affect the entire verification process. For example, new laws such as the European Union’s General Data Protection Regulation (GDPR) grant users unprecedented control over personal data stored and processed by businesses [57]. Moreover, in this scenario, two important aspects contradict each other: the need for transparency and the protection of intellectual property. Indeed, if a comparative database divulges all projects, the risk exists that companies will try showing “lower” EC emissions compared to competitors, which could result in skewed data points [56]. In another way, taking ownership of emission data into account, an option would be to allow anonymous data input, which then undermines the transparency. A review suggested that many construction companies prefer to disclose just the EC emissions over which they possess full control and information, which means the chances of verifying the authenticity of the data can be compromised [46]. Another study suggests that safeguarding the privacy of sensitive information, such as bespoke production techniques/construction methods/patented product recipes of individual firms, creates data gaps by not disclosing these data on EPDs [11].

4.4. Data Storage, Publication, and Usage

Our analysis identified six challenges/barriers that could affect the storage, use, and publication of EC data for comparison or EC estimation purposes. This section attempts to uncover various challenges/barriers that could hinder the effectiveness of the data management process if there are complications in storage, publishing, and use of processed/verified data. The identified challenges were further classified under two specific impact categories: (1) agility and interoperability, and (2) global awareness and industry readiness. It is evident that only a few studies (8 out of 58, Table 3) have attempted to cover the data phase of the process. Challenges including extended update intervals, rapid innovation in design, materials and production, consistency of tools, industry specific cultural barriers, intellectual compatibility, and misconceptions are further analysed below.

4.4.1. Agility and Interoperability

This section mainly focuses on barriers/challenges to the use of EC data for comparisons and effective and timely decision making. Data agility is how quickly the data can be processed to reflect frequent changes in source data. Regular updating of product methodology changes is critical when making effective EC estimations for comparison purposes and use of ECFs when there are gaps in data for adjustments [11,58]. However, most popular ECF databases such as the ICE database take extended time to update such ECFs, which can cause inaccuracies when making decisions [58]. The study further exemplified that the ICE database is currently the most reliable in the UK, most recently updated in 2019; however, the seven year period between this update and the previous update in 2012 arguably created difficulties for users, as they had to source ECFs separately from most recent and appropriate EPDs when conducting LCA. Ref. [11] emphasised that the materials (factory-fabricated products) and processes are subject to rapid change following technological advancements and innovation, but many will not be published under the material or process specifications until much later (until they are commercially available/accepted for practice). Such unprecedented changes could bring considerable variations for LCA results, perhaps leading to costly erroneous design decisions. Moreover, another review by [59] highlighted that most industrial and academic research groups focus on developing new tools to overcome barriers/challenges with EC data management; however, very limited studies have focused on improving the data exchangeability between tools and enhancing the interoperability. It is equally important to address data weaknesses and inconsistencies in different tools, to focus on more holistic approaches to integrate existing LCA tools and datasets rather than developing new tools.

4.4.2. Global Awareness and Industry Readiness

This part of the analysis highlights some seldom considered points that could severely affect the growth of EC data management practices globally. As a slow-to-innovate industry, construction and building sector organisations pose various socio-cultural barriers that could indirectly affect decision-making, including the culture of zero-carbon buildings (ZCBs) [70]. A review of New Zealand’s building sector by [60] revealed that immaturity of organisation-specific supply chains, inconsistent language issues, and understanding about ZCBs can discourage the full utilisation of tools for decision making. Moreover, lack of ‘intellectual compatibility’ can create a negative culture for the growth of positive global EC data management practices. A review by [61] accentuates that most business users know very little about EPDs and would find it difficult to interpret the contents. Some earlier reviews highlighted that various industry-specific misapprehensions in the past still affect the slow growth of positive EC data processes and industry readiness to adapt the ZCB concept worldwide. A few studies [62,63] showed that for typical buildings, the ratio of embodied to operational impacts was approximately 1:10; therefore, the embodied contribution to life cycle energy was within the uncertainty range of the energy demand forecast for building use and thus not considered relevant.

5. Limitations and Future Research

The study’s sample size was confined to 59 papers, constrained by the utilisation of two databases (Web of Science and Scopus) and the application of extensive filters related to topics, time horizons, and specific keywords. Notably, the abundance of search results for keywords such as ‘embodied carbon’ underscores the relative scarcity of meaningful discussions on ‘data’ or ‘data management’ concerning embodied carbon. To enhance the comprehensiveness of future investigations, it is recommended to explore additional databases and employ a broader array of keywords, integrating scientometrics and bibliometrics for a more comprehensive analysis of the scientific landscape pertaining to EC data management in the building sector.

Furthermore, certain findings carry limitations stemming from methodological constraints and potential inaccuracies in responses from study participants, often attributed to a limited understanding of the impact of ‘data’ on effective decision-making for reducing embodied impact in the building sector. While acknowledging these limitations, it is crucial to emphasise that the study’s scope is confined to the building sector and its relevance to EC data and its management. Subsequent review endeavours could extend into a systematic evaluation of barriers for adoption across diverse sectors in construction, encompassing digital data management practices for effective embodied carbon estimation.

6. Discussion of Findings

Evidently, the pursuit of EC reduction has garnered significant attention in recent decades, driven by the global shift towards achieving ZCBs. This focus has revealed numerous obstacles stemming from various data-related challenges and barriers that can hinder the informed design decisions required to minimise irreversible EIs. A comprehensive analysis of review data has unveiled that concerns related to carbon data can manifest at any stage within the EC data management process, spanning from data ingestion to utilisation. It was evident that the authors of the selected studies for this analysis attempted to focus their discussion mainly on four main process-improvement parameters, i.e., traceability, accuracy, auditability, and efficiency (either individually or collectively within the framework of EC data management). The impact of these four parameters on the efficacy of the EC data management process is encapsulated in a ‘Metaphorical Pillar Model’, as depicted in Figure 2. This model conceptualises the essential elements of each data management stage as the bedrock of EC data management, upholding its overall effectiveness. Furthermore, these findings underscore the pivotal role played by four key ‘metaphors’—traceability, accuracy, auditability, and efficiency—within the framework of EC data management. These metaphors can be regarded as the foundational pillars of successful EC data management and underscore the need for unwavering consistency across the EC data management process, which in turn fortifies the four pillars. The metaphorical model further symbolises the collective objective of achieving a high level of effectiveness in EC data management, facilitated by a solid foundation and equal support from all four pillars. It emphasises that the stability of each individual section of the foundation is paramount in the overall EC data management process, as any disparities in these elements could jeopardise the entire undertaking.

6.1. Effects of Traceability on Effective EC Data Management

Enhancing the traceability of EC data during the EC data management process has become a prominent and widely addressed theme in current research. Given the fragmented nature of construction supply chains, an extensive network of data touchpoints is scattered globally, demanding robust integration for the assured traceability of actual EC emission data. The key to addressing this lies in adopting a cohesive data strategy that can mitigate the adverse effects of data fragmentation while avoiding the creation of data silos [71]. The discourse surrounding the need for a decentralised universal EC database has been pervasive, albeit largely at the rhetorical level [12]. Such a database would allow data reporting from any geographical location with minimal doubt, serving as a primary step towards implementing a unified data strategy. It is noteworthy that the industry’s focus on the time–cost trade-off has led organisations to prioritise key activities over data reporting. While various platforms have been introduced to optimise emission data reporting, processing, and verification, the commitment to ingest/report precise data from each touchpoint remains critical to maintain a robust data inflow and ensure the resilience of EC data traceability.

Additionally, the significance of reliable coefficient ECFs cannot be overstated, as they play a pivotal intermediary role in bridging information gaps within LCA. ECFs have gained prominence due to the relative immaturity of traceability mechanisms in current practice. Early-stage design decisions often heavily rely on ECFs for comparing EPDs and making informed choices, especially regarding low or ZCB design decisions. However, it is important to acknowledge that both external factors (pertaining to the industry’s nature) and internal factors (related to the industry’s dedication) have been identified as influential forces that may hinder the development of robust solutions for improving EC data traceability. Although well-researched solutions exist to stabilise the traceability pillar in current practice, there remains a promising avenue for future research in the form of technology-driven ‘decentralised solutions’. These solutions should aim to seamlessly integrate data touchpoints while minimising human intervention in EC data ingestion, and process offering the potential to revolutionise the field.

6.2. Effects of Accuracy on Effective EC Data Management

The data accuracy in processing has gained equivalent prominence to traceability due to its substantial influence on the overall efficacy of LCA and carbon reduction strategies, particularly at the design decision level. Raw EC data, sourced from a diverse array of touchpoints, including cross-border data sources, necessitate a uniformity of functional units, system boundaries, and underlying assumptions. This uniformity is crucial to ensure a higher degree of precision when transforming these data into a usable dataset for LCA applications [72]. Significant improvements have been made in enhancing the accuracy of EC data, primarily through collaborative efforts involving regulatory bodies, environmental organisations, academic institutions, and industry research groups on a global scale. A substantial body of research has been dedicated to harmonising the methodological disparities among various LCA tools, thereby ensuring consistency in units and boundaries. Concurrently, considerable attention has also been directed towards regulating EC data governance and identifying key areas that require legislative reinforcement to fortify the EC data processing mechanism, particularly concerning elements beyond the control of individual parties [73]. Notably, the building construction sector, already renowned for its rigorous regulations, exhibits a certain reluctance to embrace additional directives such as EC data laws, prioritising other compliance measures. Furthermore, the sector’s responsibilities regarding knowledge management and maintaining unwavering commitment have undergone rigorous examination, both at the site-wide and institutional levels. It has been observed that the building sector often grapples with budget constraints stemming from factors like market fluctuations, project variations, and economic recessions. Consequently, investments in EC data governance, knowledge management, and R&D are often limited or non-existent. In response, extensive research has been conducted into data processing mechanisms, incorporating emerging digital technologies such as machine learning, digital twins, and BIM. However, these investigations have predominantly focused on the A1–A5 life cycle stages in most scenarios. Therefore, a prospective avenue for future research lies in extending technology-driven data processing across the entire life cycle, encompassing the cradle-to-grave and beyond (A1–D) stages, offering substantial potential for further exploration and development.

6.3. Effects of Auditability on Effective EC Data Management

Auditability of EC data, aimed at ensuring and verifying data authenticity, has received comparatively less attention from the industry in the past decade compared to the emphasis on data accuracy and traceability. Although there has been substantial discourse on the need to ensure the comprehensive nature of EC data, translating these discussions into practical implementation has been a gradual process. The advent of global initiatives such as the International EPD systems and eco-labelling, underpinned by standardisation efforts from ISO/EN, has sought to bolster organisational confidence in disclosing accurate emission data, even when it involves commercially sensitive information [43]. However, the commitment of major players, including the United States and the United Kingdom, which collectively house over three million construction businesses (including nearly one million exclusively dedicated to building construction and related manufacturing) contributing to GHG emissions, to declare EC emission data through globally recognised schemes requires closer scrutiny [74]. At present, only approximately 400 organisations from 50 countries have registered to publish their EPDs via the International EPD system’s Global EPD programme [22]. This represents less than 1% of the total global construction businesses, highlighting the need for more credible and secure platforms to audit commercially sensitive data. These platforms, involving bespoke techniques and technologies that can confer a competitive advantage in the market, could encourage greater business participation in EC data disclosure. Such participation would not only ensure transparency but also bolster the authenticity of EC emission data. Therefore, further research is warranted to delve into the various factors that discourage builders from embracing EC emission data declaration through recognised EPD schemes. This exploration should also focus on enhancing data security and verification at the exchange level, as well at processing level exploring alternatives to conventional mediums such as email, Excel spreadsheets, or web-based reporting tools developed using basic programming languages. These efforts are pivotal for advancing the auditability of EC data in the industry.

6.4. Effects of Efficiency on Effective EC Data Management

The efficiency of EC data management denotes the frequency of data sharing and update intervals. Ensuring efficiency is imperative for the precise utilisation of EC data in estimation processes, distinguishing it from the effectiveness of EC data management, which is the degree to which digital EC data are successful in producing desired EC databases for more accurate estimations. Despite their acknowledged significance, endeavours to improve the efficiency of EC data exchange, reporting frequency, publication, and effective utilisation have received comparatively limited attention in the past decade, notwithstanding extensive discussions on their importance. Within an industry often criticised for its slow adoption of technology, there is a notable surge in innovation in both design and products. Paradoxically, the development of specifications for these novel methods and products has not kept pace, potentially serving as a foundation for standardisation and the development of PCR [34]. These delays in the specification development process can result in extended update intervals for ECFs, leaving designers reliant on outdated datasets when making time-sensitive design decisions. While several advanced stand-alone LCA tools have been introduced, research suggests that the industry should equally focus on improving the efficiency of existing tools and databases by optimising interoperability, enabling seamless data exchange among various tools. Although some tools like One-Click LCA have made strides in integrating key databases for their users, there is a pressing need for more research and development efforts to facilitate cross-functional integrations across widely used master databases. Such efforts hold the potential to save both time and costs. In contrast to OC reduction, the industry’s readiness to embrace EC reduction strategies requires a more concerted effort to dispel misconceptions and enable designers to effectively utilise EC estimation results in their decision-making processes. It is also imperative to recognise that this goal cannot be achieved unless businesses are prepared to make the most precise use of ingested, processed, and verified EC data for decision-making, as this plays a pivotal role in preserving the collective efforts invested in previous stages. Therefore, further closed-loop research studies are warranted to capture global awareness and evaluate the value proposition of LCA in the building sector. These studies are essential for advancing the industry’s capabilities in reducing EC and promoting sustainable practices.

7. Implications for Theory and Practice

This section elucidates the original contribution of this study in providing a novel perspective on innovation adoption through the lens of EC data management, which holds significant theoretical and practical significance.

7.1. Theoretical Implications

The metaphorical-pillar model, as elucidated in this study, offers a conceptual framework delineating the fundamental elements buttressing the comprehensive efficacy of each stage in data management. Notably, the study underscores the indispensability of four key metaphors—traceability, accuracy, auditability, and efficiency—within the context of EC data management. These metaphors serve as the foundational pillars underpinning successful EC data management, highlighting the imperative of unwavering consistency throughout the process, thereby reinforcing these four pillars. The metaphorical model symbolises the collective pursuit of achieving heightened effectiveness in EC data management, grounded in a robust foundation with equitable support from each pillar. It accentuates the critical significance of the stability of individual foundation segments in the overarching EC data management process, recognising that disparities in these elements could imperil the entire EC data endeavour. This framework provides a sturdy groundwork for addressing challenges encountered by research groups embarking on the development of digital EC databases for managing data within the building sector.

7.2. Practical Implications

This study presents noteworthy contributions to the field by proposing the potential formalisation of the data management approach associated with back-end digital databases for EC data, coupled with digital EC estimation tools in the building sector. While existing reviews tend to concentrate on specific stages of the data management process, the study distinguishes itself by addressing and identifying barriers within EC data management practices to enhance overall effectiveness, thus facilitating informed decision-making. Furthermore, the study underscores the significance of sustainability in alignment with sustainable development programs adopted by organisations in the building sector. It actively contributes to the realisation of the United Nations’ 2030 Agenda on Sustainable Development, addressing aspects such as climate change impacts, the influence of globalisation on local, national, and regional sustainability, and stability goals.

8. Conclusions

In this review, the main aim revolved around an investigation into the barriers and challenges related to effective EC data management and the consequential impacts on informed low-carbon design decision-making, with a particular focus on the perspective of ‘EC data management’. It was apparent from the outset that the concept of EC data management remained rather nebulous and underdeveloped within the context of the building sector. Consequently, the study set out to consolidate existing knowledge pertaining to various stages of data management, spanning from EC data ingestion to utilisation, through a systematic analysis of 59 indexed articles published over the past decade.

The primary findings of this investigation underscored the pressing need for a sustainable and comprehensive EC data management framework to govern the overall EC data strategy, thereby facilitating the attainment of ZCB targets on a global scale. Furthermore, most of the selected studies had a focal point, often on the enhancement of specific parameters—accuracy, traceability, auditability, and efficiency—each of which holds a consequential role in shaping the overall effectiveness of the EC data management process. The analysis unearthed a total of 32 key challenges and barriers, which were subsequently categorised within each phase of the EC data management process.

Notably, the literature on data capturing and ingestion featured prominently in this review, reflecting a heightened emphasis on seeking strategies for barriers like data fragmentation, commitment issues, and the management of CEFs within highly fragmented construction supply chains. The discourse surrounding technology-focused digital solutions to mitigate these challenges has gained traction, but it is imperative to conduct more mature and scientifically rigorous studies to fully harness the potential of emerging technologies.

The review also made attempts to propose various approaches to ensure EC data accuracy, albeit mainly in the pre-construction stages (cradle–practical completion). Data verification and the subsequent publication and utilisation stages received somewhat less attention compared to the processes of data capture and processing. From a verification perspective, the emphasis rested on enhancing transparency (for auditability) in EC data by mitigating barriers related to data integrity and ensuring seamless control over data protection, confidentiality, and intellectual property. The discussions, at a rhetorical level, underscored the need for a more comprehensive digital infrastructure as a replacement for conventional methods to facilitate secure data exchange, all while safeguarding intellectual property and privacy. These enhancements aimed to foster organisational willingness to declare EC emission data in a timely manner. A few recent studies delved into challenges and barriers related to the publication, storage, and utilisation of EC data, shedding light on factors influencing efficiency in the overall EC data management process. These factors encompassed data agility, interoperability, and industry readiness, all of which were recognised as equally pivotal for informed design decision-making.

Overall, this review underscores the imperative of adopting a collective approach to fortify the EC data management process, especially considering the absence of a framework singularly dedicated to ensuring the effectiveness of the entire EC data cycle, from reporting to utilisation. The metaphorical diagram, featuring the four pillars, as presented in this study, provides a solid theoretical foundation for the prospective development of a comprehensive EC data management framework tailored to the building sector’s unique requirements.