Digital Product Passports: A Systematic Literature Review on Framework Design and Validation

Abstract

Digital Product Passports (DPPs) are being introduced in the European Union to support circular economy strategies, improve product transparency, and enable lifecycle-based compliance and decision-making. Despite growing interest, research on DPPs remains fragmented, and there is limited consensus on how to design and validate DPP frameworks in real-world contexts. This paper presents a systematic literature review of peer-reviewed studies that explicitly define, structure, or assess DPP-related frameworks. Using a transparent search strategy based on Scopus and IEEE Xplore, combined with structured screening, the review assesses framework design elaboration and validation maturity across included studies and interprets recurring framework archetypes across application sectors. The results show that most studies emphasise conceptual or architectural designs. These commonly adopt data-centric, layered, technology-anchored, or ecosystem-oriented structures and frequently refer to enabling technologies such as digital twins, blockchain, data spaces, and knowledge graphs. However, explicit validation remains limited and is primarily restricted to illustrative case studies, stakeholder-informed assessments, or prototypes, with few studies evaluating scalability, interoperability, or lifecycle-spanning operation in real-world contexts. By consolidating design principles and validation practices across sectors in this targeted corpus, the review clarifies the current state of the art and highlights critical research gaps. The findings indicate that DPP research is characterised by a strong emphasis on framework design, with comparatively limited empirical validation. Furthermore, critical research gaps include the lack of rigorous empirical validation, cross-organisational testing, lifecycle-spanning evaluation, clearly defined data governance responsibilities, convergence towards shared reference architectures, and sector-specific adaptation.

1. Introduction

Digital Product Passports (DPPs) are emerging as important instruments for supporting circular economy (CE) objectives, traceability, and lifecycle-oriented product information exchange. In the European Union (EU), this development is closely linked to the Ecodesign for Sustainable Products Regulation (ESPR), which establishes a legislative foundation for product-related sustainability and lifecycle-oriented product data requirements [1,2]. CE can be understood as an economic system that replaces the end-of-life logic with strategies such as reducing, reusing, recycling, and reclaiming materials across manufacturing, distribution, and consumption processes [3]. In this context, product-related data requirements are intended to support more durable products that can be repaired, upgraded, reused, refurbished, and recycled [1,2]. As part of the broader Circular Economy Action Plan (CEAP), the European Commission is working to ensure that physical goods on the EU market become more sustainable, resource-efficient, and aligned with CE principles throughout their lifecycle. To support these objectives, regulated products will be accompanied by DPPs [1]. A DPP can be understood as a digital record that stores and distributes lifecycle-relevant product information. This information can include technical performance, material composition and origin, repair activities, environmental impacts, and end-of-life or circularity-related information [2,4,5]. In this way, the DPP functions as a digital identity mechanism for products, materials, and components, supporting sustainability, circularity, and compliance-related information exchange across lifecycle stages [2].

1.1. Cross-Sector DPP Research

Although DPPs are being explored within multiple sectors, the existing body of research remains fragmented across domains and application contexts, with substantial variation in data requirements, technological approaches, and implementation challenges [6]. As a result, there is still limited clarity regarding how DPP frameworks are actually structured and how far such frameworks have been validated in practice, beyond merely conceptual or demonstrative proposals. From a broader cross-sector perspective, DPPs are increasingly being framed as more than sector-specific product information tools. Existing research shows that DPP development is being shaped by both mandatory and voluntary product-information initiatives across several sectors. The information provided through a passport should therefore be tailored to the needs of different stakeholders and contexts [7]. For DPPs to function as multi-stakeholder arrangements, semantic, technical, organisational, and legal alignment is required to enable information exchange between various organisations, supply chains, and product lifecycles [8].

Recent review studies show that DPP research and implementation activity now spans multiple sectors, including manufacturing, batteries, textiles, construction, and other application domains. Maturity and empirical development, however, remain uneven across the field [4,6,9]. DPPs have been shown to create value across a wide range of supply-chain functions, but current implementation remains constrained by limited data availability, challenges in coordinating stakeholders, and the need for close collaboration across the value chain [9]. To enable wider cross-sector scaling, interoperable data standards, modular semantic frameworks, and secure decentralised infrastructures are required to link diverse data sources across sectors [6].

Recent research also indicates that DPP systems should be designed in relation to the specific CE strategy being pursued, such as reuse, remanufacturing, or recycling, as well as the operational context and objectives of the organisation implementing them [6,8,10]. The construction sector provides a particularly demanding context for digital traceability frameworks. It involves complex, multi-actor value chains and requires traceability of materials and components across the lifecycle of buildings [11]. More broadly, the building sector accounts for 30–40% of final energy consumption worldwide, while around 40% of carbon dioxide emissions arise through direct and indirect pathways within construction [12]. Taken together, these characteristics make the built environment a relevant and demanding application context for DPP implementation, even though DPP development extends across multiple sectors beyond construction [6,11].

1.2. Scope, Conceptual Definitions, and Research Questions

The term framework in the context of this review refers specifically to a structured conceptual, technical, or architectural model that defines the relationships, components, and information flows needed to implement a DPP. Frameworks may range from detailed system designs to high-level reference architectures, but here the term denotes models for structuring lifecycle-related product data. The term validation is used to describe any form of practical or empirical evaluation of a proposed DPP framework. This includes case studies, simulations, performance evaluations, prototype implementations, and structured stakeholder-based evaluations. Conceptual proposals lacking any form of validation are therefore differentiated from validated frameworks.

The scope of the review is limited to peer-reviewed academic studies that propose or assess DPP-related frameworks, models, architectures, or similarly structured implementation approaches. The review, therefore, focuses on explicit framework-oriented contributions rather than the wider body of adjacent literature on traceability, digital product information, or enabling technologies. Vision papers, opinion papers, policy documents, and studies focusing only on supporting technologies without a framework perspective were excluded. This scope establishes a consistent basis for comparing how DPP frameworks are designed and how they are validated.

Because DPPs are relevant across various sectors and involve diverse product lifecycles, regulatory contexts, and data infrastructures, the reviewed literature spans a broad range of application domains. To improve comparability, the review organises the included studies by industrial sector and, within each sector, analyses (i) the design characteristics of the proposed DPP frameworks and (ii) the degree to which these frameworks are supported by validation evidence, as defined in Section 2. The aim of this review, therefore, is to provide a structured comparative synthesis of explicit DPP framework studies. Accordingly, this paper addresses the following research questions:

Main RQ: What is the current state of the art in designing and validating DPP frameworks?

RQ1: How are DPP frameworks designed? RQ2: How are DPP frameworks validated?

2. Research Methodology

2.1. Research Design

This paper uses a systematic literature review (SLR) methodology with a deliberately focused search strategy. It maps, analyses, and synthesises peer-reviewed research on the design and validation of explicit DPP frameworks. The review follows established SLR principles of transparency and reproducibility, ensuring a systematic selection, analysis, and reporting of previously published research within a clearly defined topic area [13,14]. The review design prioritises analytical precision in identifying explicit framework-oriented DPP studies. An explicit search strategy, a multi-stage classification and screening process, and clearly defined eligibility criteria were used to maintain methodological rigour.

2.2. Data Source

The Scopus database was initially used for the literature search. Scopus was selected for its broad interdisciplinary coverage of peer-reviewed conference proceedings and journal articles in manufacturing, engineering, digitalisation, information systems, and sustainability research. This wide scope makes Scopus particularly well-suited for identifying DPP-related framework research across various application sectors [15]. IEEE Xplore was added as an additional database following the Scopus search. This was to ensure comprehensive coverage of research relating to technology and engineering, especially in areas related to digitalisation, data architectures, framework development, and information systems, which are core to DPP research.

2.3. Search Strategy

To identify studies that explicitly address product passport concepts at the framework level, the literature search used a deliberately focused query design. The aim was to identify studies that explicitly combine product passport concepts with a framework-oriented perspective. This perspective had to be suitable for comparative analysis of design elaboration and validation maturity. The search, therefore, targeted publications that included terms relating to product passport and the term framework in the title, abstract, or keywords. The following search strings were used:

Scopus:

(TITLE-ABS-KEY("product passport*") AND TITLE-ABS-KEY(framework))

AND (LIMIT-TO (LANGUAGE, "English"))

IEEE Xplore:

("All Metadata":"product passport*") AND ("Document Title":framework) AND ("All Metadata":digital)

To capture variations of the term passport, including singular and plural forms, the wildcard operator (*) was used in the search string. To ensure consistency in analysis and interpretation, the search was limited to English-language publications and included all years indexed by Scopus and IEEE Xplore up to 22 August 2025. No validation-related search terms were included. Validation was instead assessed during the full-text classification and review stages using predefined analytical criteria. This made it possible to identify both non-validated and validated DPP framework papers within the same targeted corpus. All retrieved records were exported from Scopus and IEEE Xplore with bibliographic metadata including title, abstract, keywords, publication year, and source. Duplicate verification was performed manually by comparing titles, authors, publication years, and, where available, DOI information. No duplicate records were identified.

2.4. Eligibility Criteria

The retrieved records were assessed against predefined eligibility criteria to ensure consistency and transparency throughout the review process. Initial screening was conducted based on titles, abstracts, and keywords, followed by full-text assessment of the remaining studies. To be included in the review, a study had to meet three criteria. First, it had to directly address product passport concepts consistent with DPPs. Alternatively, it had to present a conceptually aligned passport logic that explicitly structured lifecycle-related product or material information in a way relevant to DPP implementation. Second, it had to propose a framework, either as a stand-alone contribution or alongside a model, architecture, or another structured approach, for implementing or operationalising a product passport-related concept. Third, it had to provide sufficient information to identify the framework’s structure, components, and intended role across the product lifecycle.

This also enabled the inclusion of a limited number of material passport studies in which the passport concept was sufficiently aligned with DPP logic, particularly in relation to lifecycle information structuring, traceability, circularity, and framework-based implementation. Such studies were retained only when they contributed analytically to the review’s focus on framework design and validation rather than merely discussing material documentation in general. Studies were excluded if they fell into any of the following four categories. First, policy, opinion, or vision papers were excluded if they did not propose a framework, either as a stand-alone contribution or alongside a model, architecture, or another structured approach, for implementing product passports. Second, non-peer-reviewed sources such as white papers, reports, or websites were excluded. Third, studies that focused solely on enabling technologies such as blockchain, Internet of Things (IoT), or digital twins were excluded if they did not present a product-passport-oriented framework. Fourth, studies that did not address lifecycle information management, lifecycle traceability, or product-oriented circularity were excluded.

2.5. Screening Process

After defining the eligibility criteria, the screening procedure was conducted in a stepwise manner to identify studies for inclusion in the final review. The process was carried out in four consecutive stages, as illustrated in Figure 1 (Table S1). The first stage involved record identification, in which all records returned by the Scopus and IEEE Xplore searches were exported for initial screening. This was followed by title and abstract screening, where titles and abstracts were assessed against the eligibility criteria to remove clearly irrelevant records. The third stage consisted of full-text availability screening, which was conducted because the analytical assessment used in this review required full-text examination to code framework design elaboration, validation maturity, and study-level evidence consistently. Records for which full text could not be accessed were therefore not taken forward to the coding stage. Finally, the full-text eligibility assessment was performed, in which the remaining publications were read in full to support structured data extraction and to confirm inclusion.

At the end of the screening procedure, each study was classified as either included or excluded based on its compliance with the eligibility criteria. The Scopus and IEEE Xplore searches returned 103 records in total. Following title and abstract screening, 30 records were excluded as clearly irrelevant and 73 were retained. Full-text availability screening excluded a further 10 records because full-text access could not be secured for consistent coding, resulting in 63 records to be evaluated in full. After full-text assessment against the eligibility criteria, 32 studies were included in the final synthesis, as illustrated in Figure 1. Of the 31 records excluded after full-text eligibility assessment, n = 7 were excluded because they did not present a product passport-related framework, n = 9 because they focused only on enabling technologies without a product passport-oriented framework, n = 12 because they did not provide sufficient lifecycle-oriented framework detail, and n = 3 because they fell outside the review scope for other stated eligibility reasons.

2.6. Data Extraction

For studies meeting the inclusion criteria, data were systematically extracted and documented in a structured review table. This step enabled consistent comparison across studies and supported the subsequent qualitative synthesis. The extracted information comprised bibliographic information (title, authors, and publication year), application sector or domain, research approach, a concise summary of key contributions related to DPP framework design or validation, and brief comments on reported limitations or research gaps. In addition, the analytical assessment developed for this review included framework design elaboration level (FDEL) and framework validation maturity level (FVML). Recurring framework archetypes were also identified through cross-study comparison.

2.7. Analytical Coding Framework

To strengthen the comparative analysis, the included studies were analytically assessed in relation to framework design elaboration level (FDEL) and framework validation maturity level (FVML), while recurring framework archetypes were identified through cross-study comparison. This approach makes it possible to distinguish between how DPP frameworks are primarily organised, how far their design logic is developed, and how far they have been validated in practice. Four recurring framework archetypes were identified through cross-study comparison, reflecting the primary organising principles observed across the reviewed literature. The first archetype, data-centric frameworks, is primarily concerned with structuring, modelling, and organising lifecycle-related product data, including ontologies, semantic models, data schemas, and information templates. A second group, layered system architectures, organises the DPP into functional layers, such as data acquisition, processing, storage, interoperability, and application or user-interface layers. The third archetype, technology-anchored frameworks, centres on a specific enabling technology or infrastructure, such as blockchain, digital twins, knowledge graphs, or data spaces. Finally, ecosystem-oriented frameworks emphasise stakeholder coordination, governance structures, collaboration mechanisms, and data sharing across organisational or value-chain boundaries.

These archetypes were used as an interpretive lens for comparing dominant design logics across the reviewed studies, although several studies also exhibit secondary characteristics associated with other categories. Beyond the archetype classification, two analytical scales were applied. The degree of framework specification was assessed using a five-level Framework Design Elaboration Level (FDEL) scale. Because all included studies had to present a framework-related contribution to meet the eligibility criteria, the scale starts at Level 1 rather than Level 0. In addition, the extent of empirical or practical evaluation was assessed using a six-level Framework Validation Maturity Level (FVML) scale ranging from 0 to 5. The definitions of the FDEL and FVML scales used in this review are summarised in

Table 1. Analytical scales used to assess framework design elaboration level (FDEL) and framework validation maturity level (FVML).

Level	Framework Design Elaboration Level (FDEL)	Framework Validation Maturity Level (FVML)
0	Not applicable in the reviewed sample. All included studies had to present a framework-related contribution; therefore, FDEL starts at Level 1 in this review.	Conceptual only. No validation reported beyond conceptual, architectural, or theoretical description.
1	High-level conceptual framework. A broad conceptual proposal with limited structural detail. The framework idea is visible, but components, relationships, or implementation logic remain only loosely defined.	Illustrative validation. Validation is limited to illustrative scenarios, workflows, reference use cases, or theoretical demonstrations without empirical or technical testing.
2	Structured conceptual framework. The framework includes clearly identified components, categories, or thematic areas, but still provides limited operational or architectural detail.	Technical proof-of-concept. Validation is carried out through limited technical demonstrations, prototype testing, component-level implementation, or controlled-environment experiments.
3	Explicit architectural or model structure. The framework presents clearly defined layers, modules, relationships, data categories, or process elements, giving the design a recognisable internal structure.	Context-bound pilot or case validation. Validation is conducted through a specific case study, pilot, or stakeholder-informed assessment within a single product, organisation, or narrowly defined setting.
4	Implementation-oriented design. The framework specifies technical mechanisms, data models, interfaces, actor roles, or architecture elements in a way that supports implementation planning and partial operationalisation.	Operational pilot validation. Validation takes place in a realistic operational setting involving actual data, users, or multiple actors, but remains limited in scale, duration, or lifecycle scope.
5	Comprehensive implementation-oriented framework. A highly elaborated framework integrating architectural structure, data logic, governance, interoperability, lifecycle considerations, and implementation-relevant detail.	Cross-organisational and lifecycle-spanning validation. Validation extends across organisational boundaries and lifecycle stages, including evidence of interoperability, governance, and sustained operation under realistic or industrial conditions.

.

The coding of FDEL and FVML was based on a second manual review of all full-text studies included in the systematic literature review. The structured review table summarising the reviewed literature served as a supporting basis for this process, including the extracted information reported under Method, Key finding(s), and Comment(s). All included studies were re-examined in full text to assign one FDEL score and one FVML score, while recurring framework archetypes were identified through cross-study comparison.

2.8. Data Synthesis

Following data extraction, a qualitative synthesis was conducted using thematic categorisation and cross-study analysis. The reviewed studies were organised around common conceptual orientations and technical features, enabling a comparative examination of DPP framework design approaches and validation practices. The synthesis focused on identifying commonly observed framework design patterns (e.g., data-focused models and layered architectures), the role and contribution of enabling technologies within the proposed frameworks, and the types and extent of validation approaches reported in the reviewed studies.

2.9. Sector Distribution of Included Studies

Table 2. Distribution of reviewed studies across application sectors.

Sector	n	Distribution
Cross-sector	8
Manufacturing	7
Construction	4
Energy	2
Recycling	1
Textile	1
Healthcare	1
Mechatronics	1
Footwear	1
Livestock	1
Battery	1
Electric vehicles	1
Furniture	1
Reverse supply chains	1
Social housing	1

presents the distribution of the included studies across application sectors. The table shows a clear dominance of cross-sectoral and manufacturing-oriented contributions, with construction-related studies being the next-largest group. The remaining sectors are represented by only one or two studies each, indicating a heterogeneous and fragmented set of application domains within DPP research.

3. Results

This section presents the findings of the systematic literature review derived from the screening, synthesis, and classification process described in Section 2. These findings focus on identifying patterns in DPP framework design and validation across the reviewed studies. No interpretation or implications are addressed in this section; these are discussed in Section 4.

3.1. Overview of Included Studies

The systematic literature review and screening process yielded a final sample of 32 peer-reviewed journal articles and conference papers that explicitly examine product passport concepts at the framework level. The included studies cover a broad range of research domains and publication outlets, illustrating the interdisciplinary and evolving nature of DPP research. As presented in Table 2, the reviewed studies span multiple sectors. Cross-sector contributions represent the largest group (n = 8), with manufacturing-oriented studies (n = 7) as the next-largest group, followed by construction-related studies (n = 4). The remaining studies are spread across a number of smaller sectors, including energy (n = 2) and several sectors represented by single studies, such as recycling, textiles, healthcare, mechatronics, footwear, livestock, batteries, electric vehicles, furniture, reverse supply chains, and social housing. Across these different sectors, the studies exhibit a shared focus on organising lifecycle-related product information through architectural, conceptual, or technical frameworks developed to enable traceability, sustainability assessment, CE strategies, or regulatory compliance.

3.2. Framework Design Elaboration and Validation Coverage

Each included study was assessed using the analytical framework presented in Section 2. This involved a study-level assessment of framework design elaboration level (FDEL) and framework validation maturity level (FVML). This made it possible to move beyond a binary distinction between framework design and framework validation, comparing instead the extent to which DPP frameworks are specified and how far they have been evaluated in practice. Because all included studies had to present a framework-related contribution, variation in the reviewed sample is not primarily located in whether a framework is present, but rather in how elaborate that framework is. Across the reviewed literature, many studies move beyond high-level conceptual framing and provide a structured representation of framework components, data categories, architectural layers, or lifecycle elements. However, the degree of elaboration varies considerably, ranging from broad conceptual proposals to implementation-oriented designs with explicit technical or organisational detail. In contrast, the reviewed studies show a substantially lower level of maturity in validation than in framework design. While some studies report illustrative examples, prototype testing, case-based assessments, or limited stakeholder-informed evaluations, most remain concentrated at the lower end of the validation scale. Explicit evidence of cross-organisational, lifecycle-spanning, or industrial-scale validation is absent from the reviewed sample. Taken together, the results indicate a consistent gap between framework design elaboration and framework validation maturity. In other words, the literature is more advanced in proposing DPP frameworks than in demonstrating that such frameworks work robustly under realistic implementation conditions.

3.3. Framework Design Elaboration Levels

The FDEL-based assessment shows that DPP framework research in the reviewed studies is concentrated above the lowest level of design elaboration. No reviewed study is classified at FDEL 1. Instead, six studies are positioned at FDEL 2, where frameworks are structured conceptually but provide limited operational detail. Thirteen studies are classified at FDEL 3, indicating explicit architectural or model structures with clearly identifiable layers, modules, data categories, or process elements. A further twelve studies reach FDEL 4, where the framework design becomes more implementation-oriented through the specification of technical mechanisms, interfaces, actor roles, or architecture elements. Only one study reaches FDEL 5, representing a comprehensive implementation-oriented framework. This distribution suggests that the literature has moved beyond purely conceptual framing in many cases and is comparatively more mature in framework design than in framework validation. At the same time, higher levels of design elaboration do not in themselves demonstrate implementation readiness, since even technically detailed frameworks often remain weakly validated in practice. The distribution of reviewed studies across the FDEL scale is visualised in Figure 2.

3.4. Framework Validation Maturity Levels

Using the FVML scale, the reviewed studies reveal a clear concentration of validation evidence at low maturity levels. Nine studies are classified at FVML 0, meaning that the framework is presented conceptually without any reported validation. In addition, eleven studies are positioned at FVML 1, where validation is limited to illustrative workflows, reference use cases, or theoretical demonstrations. Eight studies reach FVML 2, typically through technical proof-of-concept implementations, prototype demonstrations, smart contract testing, or controlled-environment experiments. Four studies reach FVML 3 by applying the framework within a specific case, product, or organisational setting, or through a stakeholder-informed assessment. However, such validation remains context-bound and does not demonstrate broader deployment readiness. No reviewed study provides explicit evidence corresponding to FVML 4 or FVML 5. This confirms that the empirical basis of current DPP framework research remains relatively immature, with validation efforts concentrated on feasibility rather than cross-organisational interoperability, lifecycle-spanning operation, or long-term governance. The distribution of reviewed studies across the FVML scale is visualised in Figure 3.

A sectoral comparison further shows that validation maturity remains unevenly distributed across the reviewed sectors. Manufacturing and cross-sector studies contain the largest number of technically oriented proof-of-concept contributions, while construction-related studies include some context-bound validation through case-based applications. By contrast, several smaller sectors, such as textiles, healthcare, footwear, electric vehicles, furniture, and energy, remain concentrated at conceptual or illustrative levels of validation. Even in sectors where FVML 2 or FVML 3 is observed, the evidence remains limited in scale and scope, with no sector demonstrating validation maturity beyond context-bound settings.

3.5. Framework Design Characteristics

The reviewed studies reveal distinct archetypes of DPP framework design. Based on a cross-study comparison, the included studies can be grouped into four main archetypes, each characterised by a dominant design logic: data-centric frameworks, layered system architectures, technology-anchored frameworks, and ecosystem-oriented frameworks. Data-centric frameworks focus mainly on structuring lifecycle-related product information through ontologies, semantic models, data schemas, templates, or information categories. These studies address what types of data a DPP should contain and how such information should be represented and managed. Layered system architectures organise the DPP into functional layers, such as data acquisition, processing, storage, interoperability, and application interfaces. In these studies the framework logic is shaped by architectural decomposition and the separation of technical functions. Technology-anchored frameworks are centred on one or more enabling technologies, such as blockchain, digital twins, knowledge graphs, Asset Administration Shell (AAS) concepts, or data spaces. Here, the framework design is strongly shaped by the capabilities and logic of a particular technological infrastructure. Ecosystem-oriented frameworks predominantly emphasise stakeholder coordination, governance, orchestration, collaboration mechanisms, and information sharing across organisational boundaries or value chains. These studies often focus less on technical architecture and more on how DPPs may function within wider socio-technical or inter-organisational settings.

Although these archetypes overlap in some cases, the classification highlights meaningful differences in design priorities across the reviewed literature. It also shows that the field has not yet converged towards a common reference structure for DPP frameworks. The reviewed studies differ not only in sectoral application but also in whether they prioritise data modelling, system layering, technology integration, or ecosystem coordination as the primary basis for DPP design. Across the reviewed sample, data-centric frameworks, layered system architectures, and technology-anchored frameworks appear to be particularly prominent. This suggests that current DPP research places stronger emphasis on information structure, modelling, and technical architecture than on inter-organisational governance, coordination, and long-term operational arrangements.

3.6. Sectoral Distribution of Framework Approaches

While there are proposals across multiple application areas, sector-specific variation in framework design remains limited. Many studies present transferable or generic architectures intended to be applicable across several industries. Sectoral requirements, constraints, and lifecycle characteristics are frequently discussed descriptively but are not systematically embedded within framework design. Although fewer in number, studies focusing on the construction sector primarily emphasise tracking of materials, lifecycle assessment, and implementation within existing digital building information systems. Manufacturing-focused and cross-sector studies tend to place greater emphasis on data interoperability, decentralised data management, and enabling technologies such as knowledge graphs and blockchain. Taken together, the results indicate a fragmented design landscape, marked by a proliferation of conceptual frameworks and limited convergence towards standardised design principles or shared reference models.

3.7. Summary of Empirical Evidence

Detailed study-level characteristics, including authors, methods, key findings, and reported limitations, are presented in

Table 3. Summary of reviewed literature.

Authors	Method	Key Finding(s)	Comment(s)
Manufacturing sector (n = 7)
Tolcha et al. [16].	System-architecture design and proof-of-concept implementation.	GS1 has developed both Core Business Vocabulary (CBV) and Electronic Product Code Information Services (EPCIS) standards. The EPCIS 2.0 standard is implemented to facilitate interoperable data capture and sharing. The study proposes a knowledge graph schema that connects events and product IDs, representing recursive relationships for transformations and aggregations through transitional and hierarchical edges. It develops an efficient traceability framework that combines data ingestion, querying capabilities, and graph processing.	Addresses the challenges of product traceability in complex supply chains under the EU DPP regulation. The system needs to be large-scale and tested in industrial environments to validate its scalability, robustness, and performance in practical supply chain environments.
Wicaksono et al. [17].	Systematic literature review.	Proposes a conceptual model for DPP architecture in six layers: Data collection, Data processing, Distributed ledger, Digital passport, Data management and interoperability, and Application. Furthermore, a DPP adoption framework is proposed.	The DPP adoption framework is entirely based on findings from other papers in the systematic literature review. The framework lacks industrial validation and implementation.
Psarommatis and May [18].	Literature review.	Develops a holistic unified DPP framework designed for industries and researchers. Also provides a DPP template to support the DPP standardisation phase. The DPP model is based on six main areas: how it exchanges data, connectivity of the DPP, the relation of the DPP to various product lifecycle steps, the actors that will be using the DPP, the update frequency of data, the accessibility options of the information of the DPP, and the level of detail of the DPP.	Lacks empirical validation through experiments and hands-on case studies. Furthermore, sector-specific technical performance and requirements evaluation are missing.
Jensen et al. [19].	Multiple case study and conceptual framework.	The study creates an ecosystem orchestration framework for the design of DPPs to support implementation in the CE. The framework identifies orchestration practices across four stages of ecosystem maturity: ecosystem initiation, ecosystem momentum, ecosystem control, and ecosystem self-renewal. It improves data sharing for DPP implementation and provides actionable guidance for aligning industrial stakeholders.	Empirical foundation using multiple industrial cases. Conceptual design for DPPs, but the technical requirements involve uncertainties and represent an important direction for further research. The framework focuses on data-sharing and orchestration mechanisms, and has not been tested or validated.
Rodionova and Eeva [20].	Framework proposal and case study.	Presents a digital framework based on Finnish national guidance, integrating Digital Building Logbooks (DBLs), Architectural Decision Records (ADRs), and DPPs to support reuse and repair of multi-storey light timber frame buildings. Identifies how missing documentation, regulatory updates, and data changes affect safety and reuse. Emphasises Architectural Decision Records as a means of documenting decision rationale and ensuring alignment with the ESPR.	The framework is not a focused DPP framework, and the Value of Information (VoI) methodology was not directly applied in the study. The prototype analytic toolset and data structure require further analysis using a wider building portfolio. It includes a DPP design outlining key information categories and lifecycle relationships. However, the study includes context-bound, case-based evaluation through four subcases based on a representative Finnish light timber frame (LTF) building. The framework is not solely a DPP design; DPPs are simply one part of the integrated structure.
Voulgaridis et al. [5].	Systematic literature review.	Proposes a CE framework integrating DPPs structured around the following technologies as framework components: data collection, data curation, data leverage, and data sharing.	The framework is based on digital CE technologies that integrate DPP characteristics, but it is not presented as a complete stand-alone DPP framework.
Miron and Hulea [21].	Architectural design.	Proposes a DPP framework using Hyperledger Fabric blockchain technology to enable inter-system communication in the DPP context. The DPP framework combines two distinct blockchain technologies: DPP Data Blockchain (DPP-DB) and Decentralised Identifiers Management Blockchain (DID-MB). Presents a Unified Modeling Language (UML) diagram of the DPP data model, including supplier, product, manufacturer, material, mappings, end-of-life, and lifecycle options. Develops a smart contract in JavaScript with a focus on the CRUD operations: create, read, update, and delete. The results show that blockchain can be used to manage product data transparently and securely in the context of DPPs.	Validation is a blockchain-only performance test restricted to a fabric test network. The validation lacks a CE use case, user study, or industrial deployment scenario. Lacks sector-specific data requirements, regulatory compliance, and interoperability testing across industries.
Construction sector (n = 4)
Çetin et al. [22].	Mixed-method research design.	Proposes a material passport (MP) framework to address data gaps in creating MPs, including material composition, condition assessment, presence of hazardous or toxic contents, and recycling and reuse potential.	Centres on material passports, not DPPs directly, but DPPs are conceptually aligned. Technical aspects of DPP standardisation and interoperability are limited.
Morganti et al. [23].	Literature review.	Proposes a Semantic Data-Driven Framework (SDF) and LCA-based framework that can work as a fundamental part to support and develop digital eco-design tools. The framework integrates environmental data and project data from IT systems and suppliers. Through this integration, the framework collects and structures essential data about components, materials, and lifecycle aspects. Afterwards, the collected data can be organised automatically into a DPP.	The framework is currently difficult to apply at building scale because large-scale projects are complex and require detailed data from multiple supply-chain actors. The framework relies on reliable, accessible data from multiple sources, and gathering comprehensive, up-to-date data can be challenging.
Pracucci and Giovanardi [24].	Case study.	Outlines a conceptual sensor-based DPP architecture designed for low-tech bio-block manufacturing in the construction sector. The architecture is presented as a theoretical framework with a five-layer structure. These layers are business intelligence, user interface, integration, data processing, and data storage.	Eight sensor types were evaluated, and the optimal number of sensors depends on budget constraints, granularity requirements, and product complexity. The framework lacks the ability to determine the minimum number of sensors required per type of product. The case study is based on a single company and is limited to the production stage (A1–A3) and the construction stage (A4–A5).
Kebede et al. [25].	Literature review and conceptual framework design.	Presents a conceptual framework for implementing DPPs using Knowledge Graphs (KGs) to promote the CE in the built environment. The key components of the framework are case identification, modelling, data collection, maintenance and updating, governance, integration, access, and querying.	Owing to scope limitations, the solutions to DPP implementation challenges across different organisations have not been addressed. The framework is preliminary and should be expanded to incorporate stakeholder feedback. It also lacks practical testing and validation.
Cross-sector (n = 8)
Nowacki et al. [26].	Use-case framework.	Presents a use-case DPP framework across sectors, collecting core lifecycle stages including raw material, material, product, final product, consumption, and new life. The framework is also presented visually.	The framework lacks real-world validation, is limited to a code-based prototype in a non-real-world setting, and has no multi-stakeholder evaluation or industry testing.
Wan and Jiang [27].	Systematic literature review.	Reviews 25 DPP-related framework papers. Proposes a conceptual DPP framework to enable dynamic information updating during the product use phase. The framework emphasises repair, maintenance, and data contribution, supported by smart contracts and blockchain technology.	The framework is conceptual and derived from literature synthesis. No prototype, real-world pilot, or technical validation is presented. Validation is limited to illustrative scenarios and workflows.
Kannappan et al. [28].	Architectural design.	Presents a blockchain-based framework to facilitate DPP implementation, enabling the generation and exchange of data using a digital twin-based approach.	The framework is limited to two digital twins: a product digital twin and a component digital twin. Validation is limited to a prototype deployed on a local parity Ethereum blockchain.
Maló et al. [29].	Mixed-method research design.	Proposes Digital Product–Service System Passports (DPSSPs) as a service-aware extension of the traditional DPP concept. The framework is based on the Asset Administration Shell (AAS) and provides semantic data rooms, decentralised microservices, fine-grained access control, dynamic lifecycle updates, and certification layers.	Advanced capabilities have been identified, including end-user engagement mechanisms, cross-border interoperability, AI-based decision support, and integration with design and simulation tools. However, these are not yet fully implemented or validated.
Panza et al. [30].	Conceptual framework.	Proposes a conceptual DPP framework that incorporates absolute environmental sustainability thresholds (Planetary Boundaries) and social indicators, and describes how lifecycle data can be gathered using cyber–physical systems.	Lacks technical implementation and validation, and the contribution remains conceptual, with no stakeholder testing or case study.
Mateo-Casali et al. [31].	Architectural design.	Presents a reference architecture that explicitly incorporates the DPP as a fundamental lifecycle element within the AIDEAS (AI-Driven Industrial Equipment Product Lifecycle Boosting Agility, Sustainability, and Resilience) framework. The architecture integrates DPP with IoT, AI, and standards. This is complemented by a Machine Passport to support sustainability, traceability, and circular-economy practices.	Lacks pilot deployment, performance evaluation, and empirical validation.
Fares et al. [32].	Bibliometric analysis based on a structured and systematic literature review process.	Develops a conceptual framework for DPP adoption and implementation. The framework includes several aspects related to requirements, impact, enablers, and barriers.	The DPP framework lacks validation and is not implementation-oriented.
Deich et al. [33].	Conceptual framework + literature review.	Proposes a Core Digital Product Passport Ontology (C-DPPO) integration framework that bridges baseline DPP ontologies with domain-specific standards and ontologies.	The framework validation is illustrative and conceptual only.
Energy sector (n = 2)
Siska et al. [34].	Architectural design.	Outlines the system design and concept for a digital battery passport that supports a traceable, sustainable supply chain. A prototype architecture based on International Data Spaces and Gaia-X frameworks for secure, decentralised data exchange is presented.	The framework lacks operational validation and is limited to a demonstrator- level system architecture. Data gaps exist for both the usage phase and the batteries’ second life.
Voulgaridis et al. [35].	Conceptual framework, systematic literature review and a reference use case scenario.	Presents a DPP framework working as a reference for smart grids. Defines DPP requirements and integrates CE principles, crowdsourcing, enabling technologies, a layered architecture, and User/Business Passport concepts, supported by a reference use case.	The use case is illustrative and has no real-world or empirical validation.
Recycling sector (n = 1)
Kim et al. [36].	System architecture + prototype implementation.	Proposes a blockchain-based DPP system combining distributed storage and federated learning to enhance data security and automation in recycling. The framework enables collaboration between recycling centres and manufacturers while enhancing model performance through federated averaging.	Validation was performed only through prototype testing, and large-scale industrial validation has not yet been addressed.
Textile sector (n = 1)
Telfort and Valilai [37].	Framework proposal.	Proposes a framework for sustainable textile waste management combining DPPs and AI. The model illustrates the exchange of DPP data across multiple stages of the textile value chain. These stages include production, distribution, use, reverse logistics, and recycling to facilitate circularity and traceability, as well as to support informed decision-making.	The study focuses on the design of a DPP framework for the textile sector. However, the framework has not yet been deployed at scale, and its transferability beyond textiles remains untested; the authors identify both as priorities for future research.
Healthcare sector (n = 1)
Stodt et al. [38].	System-architecture design and use-case demonstration.	Proposes a blockchain-enabled DPP framework to improve traceability, management, and security for medical devices through all phases of the lifecycle. The architecture uses five principal functions to automate the DPP lifecycle: creation, updating, transferring ownership, enabling view access, and closing a smart contract. Demonstrates a use case on healthcare devices lifecycle management.	The framework faces implementation and interoperability challenges in existing healthcare systems owing to legacy incompatibilities and heterogeneity. Validation is limited to illustrative and conceptual use cases. Integration with hospital systems remains challenging and depends on further assessment of scalability and interoperability.
Mechatronics sector (n = 1)
Hammadi et al. [39].	Framework development and case-study validation.	Introduces a CE framework integrated with a DPP (CEF-DPP). The framework is created to advance sustainability within mechatronic design practices and is validated through an electric motor case study.	The CEF-DPP framework remains limited to a single product type, an electric motor. Its scalability across sectors and other product types has not yet been demonstrated. Although the framework includes conceptual and implementation-oriented DPP elements, broader empirical validation is still needed.
Footwear industry (n = 1)
Sousa et al. [40].	Architectural design.	Presents a DPP architecture based on data spaces and W3C Decentralised Identifiers (DIDs). The architecture is proposed as an open framework to support circularity and is illustrated using the footwear industry supply chain.	The DPP architecture is a conceptual approach, supported by an illustrative scenario, with no technical implementation, pilot testing, or empirical validation. The scenario is also limited to the footwear industry, while broader multi-industry applicability is proposed but remains to be validated.
Livestock sector (n = 1)
Ghafoor et al. [41].	Design Science Research.	Presents a Framework for Livestock Empowerment and Decentralised Secure Data eXchange (FLEX). FLEX is a DPP-enabling infrastructure and a hybrid edge-global data space framework for the secure sharing of multi-stakeholder livestock data.	This is a DPP-enabling traceability and trust infrastructure rather than a full DPP framework. The study includes implementation and technical evaluation, so validation is not purely conceptual.
Battery industry (n = 1)
Kühn et al. [42].	Architectural design and case study of battery passports.	Proposes a DPP model based on the AAS framework. It implements existing Industrial Digital Twin Association (IDTA) templates and introduces three new submodels: “Circularity Assessment”, “Return Options”, and “Usage Data”. CE data requirements are mapped to AAS structures, and the applicability of the model is demonstrated through a case study of a battery passport.	Framework validation remains limited to a single demonstrative battery passport case study with no technical implementation, multi-industry pilot, or interoperability testing. Usage data modelling is proposed but not empirically validated.
Electric vehicles sector (n = 1)
Boßung and Severengiz [43].	Semi-systematic literature review + stakeholder questionnaires.	Presents a conceptual DPP information framework focused on defining required information for Light Electric Vehicles (LEVs). Identifies DPP information requirements including product development, use, repair, and recycling. The requirements are then supplemented by data collected through a survey of various stakeholders.	The DPP framework is conceptual and lacks detailed technical specifications, including data exchange mechanisms, system integration, and data architecture requirements. Furthermore, validation is limited to stakeholder-informed confirmation of information needs, opportunities, and challenges in the LEV context. Technical validation was not included.
Furniture sector (n = 1)
Krüger et al. [44].	Design Science Research.	Develops a DPP prototype for the sustainable furniture sector and derives five socio-technical design principles for DPP development. The prototype integrates centralised lifecycle data, condition monitoring, spare parts and repair guidance, environmental context integration, and stakeholder-specific views. These support transparency, lifecycle management, and regulatory compliance.	The contribution is a structured socio-technical DPP design and high-fidelity prototype for the furniture sector, rather than a technically implemented DPP system. Validation remains preliminary, as the artefact has not yet been formally evaluated with stakeholders; the paper reports research in progress and planned future evaluation rather than technical or operational validation.
Reverse supply chains sector (n = 1)
Xia et al. [45].	Conceptual framework.	Presents a conceptual DPP framework integrated with blockchain to support Reverse Supply Chain (RSC) information. DPP data across the lifecycle are structured within the framework, integrating DPP with blockchain layers including the user interface, application services, blockchain layer, and data layer.	The presented framework lacks validation and requires future qualitative and quantitative simulation-based studies. A technical implementation has not yet been developed.
Social housing sector (n = 1)
Çetin et al. [46].	Mixed-method research design.	Presents a Material Passport (MP) framework for social housing stock to support the implementation of CE strategies. A data template for an MP has been created and covers the renovation, demolition, and maintenance stages. The data template is tested in a case study to identify data gaps in MP production, including material composition, the presence of hazardous or toxic constituents, condition assessment, and the potential for recycling and reuse. To address these data gaps, an MP framework is proposed by leveraging enabling digital technologies.	The validation is limited to European social housing organisations, which limits transferability to other building types, countries, and stages, such as the design stage.

, organised by application sector. This table provides the descriptive basis of the review by showing who studied DPP frameworks, in which sectors, and through which research approaches. By presenting these characteristics in a structured form, the table supports the analytical synthesis that follows and allows readers to trace the underlying evidence for each sector across the reviewed studies.

Table 4. Framework design elaboration level (FDEL) and framework validation maturity level (FVML) in selected DPP studies.

Authors	FDEL	FVML
Manufacturing sector (n = 7)
Tolcha et al. [16].	4	2
Wicaksono et al. [17].	3	0
Psarommatis and May [18].	3	1
Jensen et al. [19].	3	0
Rodionova and Eeva [20].	3	3
Voulgaridis et al. [5].	3	1
Miron and Hulea [21].	4	2
Construction sector (n = 4)
Çetin et al. [22].	2	0
Morganti et al. [23].	4	2
Pracucci and Giovanardi [24].	4	3
Kebede et al. [25].	3	0
Cross-sector (n = 8)
Nowacki et al. [26].	4	2
Wan and Jiang [27].	2	1
Kannappan et al. [28].	4	2
Maló et al. [29].	5	2
Panza et al. [30].	2	0
Mateo-Casali et al. [31].	4	0
Fares et al. [32].	2	0
Deich et al. [33].	3	1
Energy sector (n = 2)
Siska et al. [34].	4	1
Voulgaridis et al. [35].	3	1
Recycling sector (n = 1)
Kim et al. [36].	4	2
Textile sector (n = 1)
Telfort and Valilai [37].	2	0
Healthcare sector (n = 1)
Stodt et al. [38].	4	1
Mechatronics sector (n = 1)
Hammadi et al. [39].	4	3
Footwear industry (n = 1)
Sousa et al. [40].	3	1
Livestock sector (n = 1)
Ghafoor et al. [41].	3	2
Battery industry (n = 1)
Kühn et al. [42].	4	1
Electric vehicles sector (n = 1)
Boßung and Severengiz [43].	2	1
Furniture sector (n = 1)
Krüger et al. [44].	3	1
Reverse supply chains sector (n = 1)
Xia et al. [45].	3	0
Social housing sector (n = 1)
Çetin et al. [46].	3	3

provides the analytical synthesis of the reviewed studies using the FDEL and FVML scales introduced in Section 2. Rather than indicating simply whether or not framework design or validation is present, the table shows the degree of framework design elaboration and the maturity of reported validation evidence for each study. The reviewed literature spans multiple application sectors and primarily focuses on conceptual, architectural, and implementation-oriented designs for managing lifecycle product information. Many studies present explicit, sometimes well-elaborated frameworks. However, validation remains limited, typically restricted to illustrative scenarios, prototypes, proof-of-concept demonstrations, or context-specific case studies. Higher-maturity validation involving cross-organisational deployment or lifecycle-spanning operation is absent across the reviewed sectors. For interpretive clarity, Table 4 reports FDEL on a scale from 1 (high-level conceptual framework) to 5 (comprehensive implementation-oriented framework), and FVML on a scale from 0 (conceptual only) to 5 (cross-organisational and lifecycle-spanning validation). Scores were assigned conservatively based on a more detailed manual full-text review of all included studies, supported by the evidence summarised under Method, Key finding(s), and Comment(s) in Table 3.

4. Discussion

This section interprets and synthesises the results of the systematic literature review in relation to the research questions stated in Section 1. The discussion centres on identifying key patterns in DPP framework design (RQ1), evaluating the scope and character of validation methods (RQ2), and reflecting on the implications of these findings for practical implementation and future research. Findings are interpreted through the FDEL and FVML codings together with the recurring framework archetypes identified in the results, with particular attention to how design elaboration relates to validation maturity across the reviewed sample.

4.1. RQ1: Design Characteristics of DPP Frameworks

The reviewed literature emphasises the design of DPP frameworks, but with substantial variation in the level of elaboration of those designs. Across the sample, the FDEL coding shows that studies range from structured conceptual proposals to implementation-oriented designs with explicit architectural, data, or organisational detail. As illustrated in Figure 2, most reviewed studies cluster around FDEL 3 and FDEL 4, indicating that the literature is generally more developed in specifying framework structures than in validating them under real-world conditions. A recurring pattern across sectors is that many frameworks move beyond general conceptualisation and provide at least a structured representation of components, lifecycle elements, data categories, or functional layers. This pattern suggests that DPP framework research currently prioritises the structuring of data and technical system design more strongly than the governance and coordination challenges that become critical in real-world cross-organisational implementation.

With regard to the recurring archetypes identified in this review, DPP framework design is shaped by four main organising logics: data-centric frameworks, layered system architectures, technology-anchored frameworks, and ecosystem-oriented frameworks. Data-centric studies primarily specify what lifecycle information a DPP should contain and how such information should be structured, represented, and managed. Layered system architectures instead organise the DPP through functional decomposition into layers such as data acquisition, processing, storage, interoperability, and application. Technology-anchored frameworks are often designed around enabling technologies such as digital twins, blockchain, data spaces, IoT systems, knowledge graphs, or AAS concepts. Ecosystem-oriented frameworks, by contrast, place greater emphasis on stakeholder coordination, governance, orchestration, and inter-organisational information sharing. Although these design logics often overlap, the reviewed literature shows no clear convergence towards a single shared reference structure for DPP frameworks. Taken together, the findings indicate that DPP framework design research is characterised by considerable conceptual richness but also structural fragmentation. Frameworks are often presented as transferable or generic across sectors, with limited incorporation of operational realities or sector-specific lifecycle constraints.

4.2. RQ2: Validation Practices and Evidence Maturity

In comparison with framework design elaboration, the maturity of validation evidence is markedly lower. Using the FVML scale defined in Section 2, 9 of the 32 reviewed studies remain at FVML 0, meaning that no validation is reported beyond conceptual or architectural description. A further 11 studies are classified at FVML 1, where validation is limited to illustrative workflows, reference use cases, or theoretical demonstrations. Eight studies reach FVML 2 through technical proof-of-concept demonstrations, prototype testing, or controlled-environment implementation, while only four studies reach FVML 3 through context-bound case-based or stakeholder-informed validation. No study reaches FVML 4 or FVML 5.

The validation strategies identified remain, therefore, partial and narrowly scoped. Where validation is reported, it typically focuses on technical feasibility, limited prototype behaviour, or a specific organisational or product context, rather than on broader implementation readiness. Important aspects such as cross-organisational interoperability, long-term governance, lifecycle-spanning operation, and large-scale industrial deployment remain largely untested. Importantly, no study reports holistic validation that addresses long-term data governance, multi-organisational deployment, large-scale industrial adoption, or full lifecycle operation. Instead, validation is generally limited to demonstrative use cases, technical feasibility, prototype testing, or narrowly bounded case applications. There is also minimal attention to interoperability across heterogeneous systems, sustained lifecycle updates, or organisational responsibilities. These findings suggest that the current DPP framework literature occupies an early-to-intermediate stage of empirical maturity. While the field has moved beyond purely conceptual discussion in many cases, robust evidence of operational readiness remains limited. This gap between design elaboration and validation maturity is one of the clearest patterns emerging from the review.

4.3. Validation Maturity Levels in DPP Research

Drawing on the reviewed studies, validation practices in DPP framework research can be seen to span different levels of maturity. At the lowest level, studies rely solely on illustrative examples and conceptual argumentation. Intermediate levels include limited-scope case studies and prototype demonstrations that establish contextual or technical feasibility. Higher maturity levels would involve evaluating governance and accountability mechanisms, conducting interoperability testing across organisational boundaries, deploying under realistic industrial conditions, and conducting lifecycle-spanning validation. Such studies are largely absent from the reviewed literature. As shown in Figure 3, most of the reviewed studies remain concentrated at the lower levels of validation maturity. The absence of validation for FVML 4 and FVML 5 suggests that even the more advanced studies have not yet been subjected to the complexity of real-world product ecosystems at scale. As a result, claims related to interoperability, scalability, long-term usability, and trust often remain only partially substantiated. Addressing this gap represents a critical direction for industrial experimentation and future research.

4.4. Interoperability and Standards in DPP Framework Research

Interoperability is frequently invoked as a key promise in the reviewed DPP literature, yet is less frequently operationalised or explicitly validated. Across the reviewed studies, interoperability is commonly discussed in relation to structured lifecycle information exchange, cross-system communication, and the ability to maintain DPP data across multiple actors, platforms, and lifecycle stages. However, in many cases, interoperability remains simply an intended design objective rather than a demonstrated outcome. Several studies reference standards, standardisation initiatives, or standard-like infrastructures as enabling mechanisms for DPP implementation. These include, for example, EPCIS and CBV for event-based traceability, AAS concepts for digital representation, decentralised identifiers (DIDs) for identity management, ontology-based approaches for semantic alignment, and data-space-related infrastructures such as IDS or Gaia-X for controlled data exchange. Nevertheless, these standards and infrastructures are often incorporated at the conceptual or architectural level rather than being evaluated through cross-organisational implementation or interoperability testing. This suggests that the literature is stronger in identifying interoperability as a requirement than in demonstrating how interoperability can be achieved and sustained in practice. In particular, there remains limited evidence on semantic consistency across domains, compatibility between heterogeneous digital systems, governance of shared data structures, and the long-term maintenance of interoperable DPP information across organisational boundaries. For practitioners, this is a critical limitation, as interoperability is likely to be one of the decisive conditions for scalable DPP deployment.

4.5. Sectoral Implications with Emphasis on Construction and Materials

Although the reviewed studies cover a range of application sectors, sector-specific variation in framework design and validation remains limited. Several frameworks are presented as generic solutions in which sector-specific requirements are discussed descriptively rather than embedded structurally. Construction-focused DPP frameworks tend to emphasise lifecycle assessment, integration with existing digital building information systems, and material traceability. Unlike many other sectors represented in the reviewed sample, the construction-related studies show some evidence of technical or context-bound validation, but this remains limited in scope and maturity. Validation is typically restricted to demonstrative, case-based, or narrowly bounded settings rather than broader lifecycle-spanning or cross-organisational deployment. This remains notable given the sector’s long asset lifetimes, fragmented stakeholder landscape, and complex supply chains.

For building materials, particularly wood-based products, the results indicate a mismatch between empirical evidence and conceptual ambition. DPPs are frequently presented as enablers of circularity, lifecycle transparency, and regulatory compliance. However, the reviewed literature provides limited evidence on their performance under realistic construction-lifecycle conditions. This gap underscores the necessity of sector-tailored validation studies that address ownership transitions, data responsibility, material flows, and long-term information maintenance.

4.6. Limitations

This review has several limitations that should be considered when interpreting the findings. First, the literature search was limited to the Scopus and IEEE Xplore databases and to English-language publications, which may have excluded relevant studies published elsewhere or indexed in other sources. Second, the search strategy deliberately combined product passport terminology with the term framework to identify explicit DPP framework studies suitable for comparative coding. While this improved analytical precision, it may have excluded adjacent studies that use alternative terminology, such as architecture, model, or sector-specific passport terminology. Third, a limited number of conceptually aligned material passport studies were included because they contributed directly to the review’s analytical focus on lifecycle-oriented passport frameworks. Although these studies were retained on explicit conceptual grounds, their inclusion introduces some boundary complexity between DPP and adjacent passport concepts. Fourth, records without an accessible full text were not advanced to the analytical coding stage because the review design required full-text assessment to consistently code framework design elaboration, validation maturity, and study-level evidence. This may have excluded potentially relevant publications that could not be accessed in full. Finally, the empirical evidence base in the reviewed literature remains relatively immature. The limited validation observed in the sample may partly reflect the early stage of the research field’s development rather than the absence of relevant industrial activity.

4.7. Summary of Discussion

In summary, the discussion reveals a research landscape primarily shaped by conceptual and architectural DPP framework proposals, with limited empirical validation. While the diversity of design approaches indicates active exploration, the lack of validation and convergence limits the practical applicability and transferability of the proposed frameworks. Addressing these limitations through sector-specific, empirically grounded validation studies presents both a key challenge and an opportunity to advance DPP implementation and research.

5. Conclusions and Future Work

This paper presents a systematic literature review of DPP frameworks, focusing specifically on explicit framework-oriented studies addressing design and validation practices across sectors. By analysing peer-reviewed conference and journal publications in this targeted corpus, the study offers a structured overview of the current state of the art in explicit DPP framework research and identifies key research gaps, limitations, and recurring design patterns. In response to the main research question, the review shows that existing DPP research is characterised by a strong emphasis on framework design, with comparatively lower levels of validation maturity. Addressing RQ1, the findings indicate that DPP frameworks are most commonly developed as data-centric structures, layered system architectures, technology-anchored frameworks, or ecosystem-oriented frameworks, with levels of design elaboration ranging from structured conceptual proposals to implementation-oriented designs. Addressing RQ2, the review shows that validation maturity remains limited: most studies are positioned at conceptual, illustrative, or proof-of-concept levels, while no study demonstrates cross-organisational and lifecycle-spanning validation under real-world conditions. Overall, the results suggest that research on explicit DPP frameworks remains at an early-to-intermediate stage of development. While many frameworks have been proposed and, in several cases, elaborated beyond high-level conceptualisation, there remains limited evidence on scalability, operational feasibility, interoperability under realistic conditions, and long-term lifecycle effectiveness. The lack of systematic validation across heterogeneous digital systems, lifecycle stages, and organisational boundaries represents a critical gap in the reviewed literature.

Future research should follow a more explicit validation roadmap. A first step is to strengthen technical proof-of-concept studies of core DPP functions, including data modelling, updating, access control, and interoperability across systems. A second step is to test DPP frameworks in context-bound pilots within single products, organisations, or supply-chain settings to examine feasibility under realistic constraints. A third step is to extend validation across organisational boundaries and lifecycle stages, with particular attention to governance, data responsibility, sustained updating, and interoperability across heterogeneous digital ecosystems. A fourth step is to evaluate sector-tailored DPP frameworks under longer-term operational conditions, including realistic industrial use, ownership transitions, and lifecycle-spanning information management. In parallel, future studies should work towards synthesising shared reference architectures and common design principles to reduce fragmentation and support more aligned DPP implementation. By integrating existing knowledge on DPP framework design and validation, this review provides a foundation for advancing both the practical deployment and theoretical development of DPPs in future research.