1 Introduction

In recent decades, more sustainable production and consumption processes have been formulated (Lukman et al. 2021) in front of several environmental issues such as the depletion of natural resources, climate change, chemical pollution, and biodiversity loss (Zhang et al. 2020; Lin et al. 2021). The Green Deal directs toward a modern economy characterized by a prosperous and competitive society by preserving natural and human capital against environmental and social risks for achieving human well-being. This becomes a priority objective for the transition to a sustainable and resilient economic system where the organizations must revisit their supply chains. Supply chain is understood as the set of material and energy flows, from the extraction phase to the consumption, and the related information and economic flows. Therefore, products and services are designed and manufactured considering environmental, social, and economic impacts affecting to involved stakeholders by temps to close the chain. At the same time, the transition to a circular economy requires an active role from consumers, who increasingly demand transparent, reliable, and verifiable information in order to make informed and responsible choices. Moreover, it requires a systemic approach that monitors and evaluates the degree of circularity and sustainability of products and systems by involving all actors in the supply chain (including both upstream and downstream processes).

In this transition, the 4.0 technologies (i.e., Internet of Things, artificial intelligence, and distributed ledger technologies like blockchain) play a crucial role in advancing policy and industrial strategies aimed at expanding innovative solutions and partnerships (Economic and Social Council 2021). The main goal to achieve a circular, climate-neutral, and inclusive industry by 2030, by the United Nations Sustainable Development Goals (SDGs), requires management and technological efforts to promote and building responsible consumption and production (Economic and Social Council 2021). In this context, Blockchain Technology, being decentralized and based on a consensus mechanism between the involved actors, by ensuring transparency, traceability, and integrity of information along the supply chain (Nakamoto 2008; Nofer et al. 2017; Karaszewski et al. 2021; Rolinck et al. 2021). Indeed, it has been adopted in both the public and private sectors (Lin et al. 2021). In particular, it is obtaining great attention from different industries (i.e., retail, automotive, healthcare, food, and entertainment) (Cole et al. 2019; Huang et al. 2022), whereas Blockchain Technology has been integrated within the management of supply chains for a technologically and simultaneously interconnected system. Indeed, it allows to management of the information and material flows along the supply chain from upstream to downstream by ensuring transparency and affordability. Therefore, the excessive segmentation of the market and the countries of origin of products, semi-finished products, and raw materials, as well as attention to Industry 4.0 (Esmaelian et al. 2020) and the customer expectations, has spread the Blockchain Technology in different areas with respect to its native field (i.e., cryptocurrencies).

The adoption of strategic management systems of the boundary of the organization has limited them to one’s own reality, but it is necessary to assess and manage sustainability with an integrated and holistic approach at the supply chain level (D'Eusanio et al. 2019).

Life cycle thinking (LCT) tools manage these issues by assessing the environmental, social, and economic aspects of the entire life cycle of a product/service and/or organization. The product-oriented LCT tools (i.e., life cycle assessment (LCA), Social Life Cycle Assessment (S-LCA), and Life Cycle Costing (LCC)) (ISO 14040:2006 and ISO 14044:2006) are different from those that, instead, extend the evaluated system by considering the organization or a part of it (i.e., a brand, a geographical area, a facility) (ISO 14072:2014; UNEP/SETAC, 2015) as the Organizational Life Cycle Assessment (O-LCA) and the Social Organizational Life Cycle Assessment (SO-LCA) (Martinez-Blanco et al. 2015; D’Eusanio et al. 2022a). The latter two methodologies, unlike the LCA and the S-LCA, focus on the organization by analyzing its entire value chain (from upstream to downstream) and considering, at the same time, the life cycle of the analyzed product portfolio.

Despite the widespread use of these methodologies and the applicative maturity achieved, especially in the field of environmental assessments, several critical issues are still present, especially regarding the social evaluation and life cycle inventory (LCI) phase (D’Eusanio et al. 2023). Indeed, several studies (e.g., D'Eusanio et al. 2022b; Mondello et al. 2022; Notarnicola et al. 2022) have highlighted the difficulty of acquiring primary data (i.e., data that are directly collected in the field) (UNEP 2020), as well as the difficulty of collecting secondary data (i.e., data from generic sources that have been collected for a purpose of different respect to those considered in the study) (UNEP 2020). In particular, it sometimes is difficult to collect secondary data that are reliable, truthful, and representative respecting to geographical, technological, and temporal criteria (Weidema and Wesnaes 1996). The use of international databases in S-LCA evaluations can compensate for the unavailability (within a reasonable time and cost) of primary data, but they are not always representative of the product evaluated. To improve data quality (i.e., data characteristics related to their ability to meet certain requirements) (ISO 14044:2006) is crucial as it significantly affects the results of an LCA (Guinée 2002) and the overall quality of the study (D'Eusanio et al. 2022b).

By considering the transition to a more digital and sustainable economy, this study aims to explore the context and characteristics of Blockchain Technology to understand the potential synergies and implications that arise from integrating it with S-LCA methodology.

In the following section (Section 2), the theoretical background concerning the S-LCA methodology and its main challenges, as well as the structure of Blockchain Technology, were presented. Section 3 describes the applied method for addressing the research questions pursued by this study, and Section 4 shows and discusses the main findings highlighting the synergies and implications derived from integrating Blockchain Technology in S-LCA. The “Conclusion,” in Section 5, summarizes the main implications derived from this study.

2 Theoretical background

2.1 Blockchain Technology

Blockchain Technology was introduced in 2008 and implemented in 2009 (Zheng et al. 2018) by Satoshi Nakamoto (Kawaguchi 2019; Karaszewski et al. 2021; Bai and Sarkis 2020; Rolinck et al. 2021) in cryptocurrencies (Nakamoto 2008). It is defined as a distributed ledger technology (e.g., Nofer et al. 2017; Rusch et al. 2022; Accordini and Neri 2021; Zheng et al. 2023) or a peer-to-peer network (Teh et al. 2020) of information technology (IT) nodes containing shared and decentralized information and connected in chronological order (Zhang and Shi 2021). Blockchain Technology is based on a consensus mechanism (Nakamoto 2008; Nofer et al. 2017; Karaszewski et al. 2021; Rolinck et al. 2021) between the parties (or actors) involved, whose information and data are defined and verified by a series of nodes (Karaszewski et al. 2021). The nodes are independent (i.e., there is no central authority), and, once synchronized and validated, they are inserted into a data structure called “block” (Teh et al. 2020) or “data packages” (Nofer et al. 2017). Therefore, whether the consensus is achieved for most of the nodes present in the network, the block is added to the chain (Nofer et al. 2017).

In this way, a sequence of blocks is created (i.e., Blockchain), and each block is connected to the previous blocks called “parent block” (Zheng et al. 2018) by creating an irreversible unilateral chain (Lin et al. 2021). Indeed, the block is added to the first one called “genesis block” (Nofer et al. 2017; Zheng et al. 2018), and an asymmetric cryptographic code (i.e., hash value) is attributed to each block for the authentication of the transaction (Zheng et al. 2018). After the validation, the block cannot be modified or deleted (Nofer et al. 2017), but it is possible only to add new ones (Iansiti and Lakhani 2017; Teh et al. 2020). Figure 1 shows the structure of the Blockchain, which is composed of a continuous series of blocks (Zheng et al. 2018). As shown in Fig. 1, the block includes a header and a body (Liang 2020). The block header is composed of the hash value of the previous block, the timestamp, the nonce, and the Merkle root (Liang 2020; Zheng et al. 2018). The hash value is “calculated by passing the header of the previous block to a hash function” (Liang 2020, p. 122). Including the hash value of the previous block within the current one allows growing the Blockchain, and in this way, it ensures avoiding any alterations on the previous block. The timestamp marks the creation time of the block, while the nonce is used for creating and verifying a block. Otherwise, the Merkle root is a binary tree hash that considers the value of all the transactions in the block. The Merkle root summarizes, via code, all the hashes below it in the Merkle tree (i.e., a binary tree including all data regarding the transaction and its counter regarding the block) (Zheng et al. 2018; Liang 2020).

Fig. 1
figure 1

Structure of the Blockchain (adopted from Zheng et al. 2018 and Liang 2020)

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2.2 Social life cycle assessment

S-LCA is a methodology that assesses social and socio-economic aspects, both positive and negative, of the products or the organizations (via SO-LCA) along its life cycle or supply chain (UNEP 2020). S-LCA could be used as a decision-making support tool (Petti et al. 2018) that allows providing an evaluation of performance, risk, and potential impact levels. The revised guidelines for social life cycle assessment for product and organization (UNEP 2020) defines social performance as “the principles, practices, and outcomes of businesses’ relationships with people, organizations, institutions, communities, and societies in terms of the deliberate actions of businesses toward these stakeholders as well as the unintended externalities of business activity measured against a known standard” (UNEP 2020; p.26), while the social risk is the probability that an event occurs. In the end, the potential social impact foresees “consequences of positive or negative pressures on social endpoints of the area of protection (i.e., well-being of stakeholders)” (UNEP 2020; p.134). S-LCA evaluates different involved stakeholders (i.e., workers, local community, society, value chain actors, children, and consumers) (UNEP 2021), and it can apply at micro, meso, and macro levels (SCORELCA 2017; Huertas-Valdivia et al. 2020; Tragnone et al. 2022). The revised guidelines for S-LCA, published by UNEP in 2020, aims to update the previous version (i.e., UNEP/SETAC 2009). This update incorporated various advancements related to the impact assessment method and methodological refinement (Chabrawi et al. 2023). However, a full consensus on its application has not been achieved, and a standardization process (i.e., ISO/AWI 14075) is currently underway. Indeed, in the S-LCA literature, several methodological challenges of S-LCA are still debated. For example, it is widely known as the difficulty in connecting and using the functional unit (FU) (Zamagni et al. 2011; SCORELCA 2017; Macombe et al. 2018; Tragnone et al. 2022), the definition of epistemological foundation (Iofrida et al. 2018a, b; Huertas-Valdivia et al. 2020; Tragnone et al. 2022), the definition of cut-off criteria, and the identification of relevant stakeholders and subcategories (SCORELCA 2017; Tragnone et al. 2022). Moreover, assessing the entire life cycle of the product (including all “upstream” and “downstream” processes) is often complex (Tragnone et al. 2023), but the use of the social database for S-LCA (i.e., the Social Hotspots Database (SHDB) (Benoit Norris et al. 2012; Benoit Norris and Norris 2015) and the product social impact life cycle assessment (PSILCA) database (Maister et al. 2020; Di Noi et al. 2020) can help to address this problem.

2.2.1 Methodological challenges on data quality in S-LCA

One of the main methodological gaps in the S-LCA is related to the availability of the primary data and their quality (in terms of geographical, technological, and temporal coverage). Indeed, the reliability of the results of a S-LCA study reflects the quality of the data in the evaluation of the product/organization system (Guinée 2002). Despite this, in S-LCA studies, the product system is not always completely modeled on primary data relating to the life cycle of the product and its supply chain. In fact, whereas the supply chain is identified as an organizational network of actors in “upstream” and “downstream” processes (Christopher 2011; Seuring and Müller 2008), an extensive geographical distribution (Genovese et al. 2017) is there. This dispersion complicated the tracking of each phase within the supply chain (D'Eusanio et al. 2019) and the identification of the relative flows. In fact, approximately 70–80% of the time and related costs of an LCT study can be attributed to the LCI phase (Miah et al. 2018; Teh et al. 2020).

Therefore, the use of secondary data, collected from statistical sources, literature, or international databases, can support the finalization of the study by providing data regarding the “background” processes (i.e., processes located upstream and downstream of the product under study (Notarnicola et al. 2017; Tragnone et al. 2023). Nevertheless, the databases do not always provide an effective representation of the product evaluated, even though they allow including background processes in the evaluation.

The quality of the data can be assessed using indicators by considering, for example, the credibility of the sources used; the access to data and information of the evaluated sector, as well as consistency, validity, and representativeness regarding to goal and scope; the transparency of documentation; the specificity of the country and region; and the completeness of the data (Baitz 2016). In this framework, the transparency of the collected data in an S-LCA study is essential for obtaining reliable and trusted evaluation (Gonzales 2018) and useful results at the decision-making level (Teh et al. 2020).

3 Method

A qualitative research approach based on three steps was performed, as shown in Fig. 2. The first phase identified the main research questions (RQ) starting from the need to investigate the digital innovation for supporting the ecological transition as well as the main gaps related to S-LCA. The defined RQ aims to understand and analyze how Blockchain Technology is used in the supply chain management (SCM) context and whether it is already applied to support the S-LCA study and more generally if it supports the LCT tools.

Fig. 2
figure 2

Qualitative research approach performed (elaborated by the authors)

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In detail, the RQs defined are as follows:

  • RQ1: How does Blockchain Technology facilitate the interconnection and integration of the actors within the supply chain? What are the specific characteristics of Blockchain Technology?

  • RQ2: Could Blockchain Technology effectively support S-LCA studies? Which synergies and implications arise from this integration for data quality?

A literature review was conducted to answer the RQs in May 2023. This literature review used both the Web of Science and Scopus databases, focusing specifically on English-language articles, without any time limit, and using keyword combinations of the words “life cycle assessment,” “LCA,” and “blockchain technology.” The Boolean operator “AND” was used (as shown in Fig. 2). The search was conducted in “article title, abstract, and keywords” for Scopus while in “topic” for Web of Science that includes the previously mentioned concepts (Clarivate 2024). After the elimination of duplicates, a total of 41 results emerged, all analyzed by reading the full text.

The factors evaluated are the general characteristics of Blockchain and which of them reward the application of Blockchain in the supply chain context by providing benefits along the supply chain.

Consequently, these characteristics were analyzed to understand whether they could be useful for S-LCA applications, in particular for improving data quality.

In the third phase, a cross-check was carried out on the characteristics that emerged from the Blockchain and the main peculiarities of the S-LCA methodology in order to highlight potential synergies deriving from this integration. Ultimately, the potential implications of using Blockchain for data collection of S-LCA were explored.

4 Results and discussion

4.1 The characteristics of the Blockchain Technology within the context of SCM

Blockchain Technology was used in different sectors (Crosby et al. 2016; Nofer et al. 2017): from the financial one (i.e., cryptocurrencies, such as bitcoin, Litecoin; insurance) to manufacturing (Jabbar et al. 2021) and more widely for smart contracts (Lim et al. 2021; Zheng et al. 2018, 2023). It is considered a revolutionary tool for supporting SCM (Saberi et al. 2019; Bai and Sarkis 2020), which addresses the network of relations (Christopher 2011) integrated and unitary connected, along the entire supply chain, through a systematic approach (D’Eusanio et al. 2019). The involved actors within the supply chain collaborate among themselves to integrate procurement, production, delivery, and customer. In this framework, the availability of an adequate flow of information is the necessary condition for integrating and coordinating the actions along the supply chain. Therefore, Blockchain Technology provides a distributed data recording and tracking assets platform, allows supporting transparency, and provides real-time information in SCM (Chen et al. 2022; Sunny et al. 2020; Huang et al. 2022). Figure 3 shows how Blockchain Technology characteristics are suitable for bolstering SCM. Thus, the interactions within a supply chain can be more affordable, integrated, and digitally regulated through Blockchain Technology (Asif and Gill 2022).

Fig. 3
figure 3

Characteristics of Blockchain Technology (elaborated by the authors)

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The characteristics of Blockchain Technology allows providing transparent data and real-time information tracked along the entire supply chain, by guaranteeing its immutability and integrity (Jabbar et al. 2021). Indeed, decentralization protects transactions against failures (Bischoff and Seuring 2021) since all the nodes involved in the network have a copy of the ledger (Liang 2020). Consequently, the information is not stored in a single server (i.e., centralization) but distributed throughout multiple servers or nodes (see Fig. 3), and data are spread among different actors involved in the SC (Esmaelian et al. 2020; Zheng et al. 2018). All actors in the supply chain have full access to consistent data and information pertaining to a specific transaction (Bischoff and Seuring 2021) without a centralized trusted authority (Galvez et al. 2018). This system is designed to operate on a register that is replicated, shared, and synchronized across multiple parties, regardless of their geographical locations.

Figure 4 illustrates the progression of network evolution from a centralized to a distributed model which strengthens the coordination and collaboration among the supply chain actors. Blockchain Technology allows the immutability of the information along the supply chain because it cannot be modified over time. Indeed, once the nodes are validated, data and information are recorded (Bischoff and Seuring 2021), and cannot yet modified. For each process, the monitored information is related to the origin of the raw materials, the production, the transport system, and distribution strategy along the network supply chain.

All nodes of the network are blocked through a consensus of the user involved in the chain, by ensuring traceability among the nodes (Breidbach and Tana 2021).

Fig. 4
figure 4

The stages of network evolution useful for SC actors (adapted from Esmaelian et al. 2020)

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The transparency and traceability of material and energy flows, as well as the sharing of information, are necessary for the supply chain to produce value for all the involved actors. Consequently, it is possible to track the identity, the location, and the status of supply chain transiting flows by capturing this information in timely event messages, along with the scheduled and actual dates/times for these events. Thus, verifiability, safety, and transparency of the data and information along the involved chain are guaranteed.

4.2 Integration of Blockchain Technology within LCT tools

Among the analyzed studies, some of that has explored the synergies emerging from the integration between Blockchain Technology and SCM (e.g., Esmaelian et al. 2020; Bischoff and Seuring 2021; Jabbar et al. 2021; Rusch et al. 2022; Huang et al. 2022). However, the connection with LCT tools is still poor (e.g., Mieras et al. 2019; Farooque et al. 2020; Pigné et al. 2019; Teh et al. 2020; Zhang et al. 2020; Agrawal et al. 2021; Rolinck et al. 2021; Carriers et al. 2022). Among studies addressing Blockchain in LCT tools, it was found that this technology improves information transparency along the supply chain and reduces the possibility of data manipulation and falsification (Zhang et al. 2020). Indeed, traceability allows finding information and identification of product registrations along supply chains in a bidirectional way through a registration system (Zheng et al. 2023).

The challenges of LCT tools concerning data availability and quality are addressed by the literature which describes different potential benefits to integrate Blockchain Technology into LCA (e.g., Mieras et al. 2019; Farooque et al. 2020; Pigné et al. 2019; Teh et al. 2020; Zhang et al. 2020; Agrawal et al. 2021; Rolinck et al. 2021; Carriers et al. 2022). Thus far, the existing literature has predominantly focused on the environmental dimension, while the social dimension has not received the same attention. Indeed, some LCA studies attempt to show the benefits and limitations of integrating Blockchain through initial case studies, although not systematically applied. For example, Rolinck et al. (2021) created a test network with open-source software to understand data management needed for an LCA study but limited to the maintenance, repair, and overhaul processes of aircraft (MRO). Lin et al. (2021) devised a framework that incorporates Blockchain Technology to safeguard and convey inventory data throughout the supply chain. They emphasize that this approach enables the automatic computation of environmental impact, as well as the secure transfer and backup of data. Asif et al. (2022) discuss how Walmart is examining its processes related to the agri-food supply chain to prevent food waste and monitor energy performance and related emissions. Carrières et al. (2022) evaluate how Blockchain traceability data could be a significant resource for conducting LCA at the production level, providing differentiated data on its composition and origin. Shau and Domenech (2022) try to provide further key elements (i.e., mapping of supply chain actors, tracking methods, data collection, and validation protocols) concerning the framework proposed by Zhang et al. (2020) by assessing the circularity of a leather handbag. Moreover, through semi-structured interviews with Blockchain experts, they highlight the main obstacles to this integration (i.e., the presence of errors and manipulations during data entry, lack of digitalization within the industry, lack of hardware equipment and IT skills, supplier unwillingness to share data).

4.3 Synergies and implications arising from using Blockchain in S-LCA

The information flows managed by the Blockchain are often focused on financial and economic aspects by leaving out environmental and social ones. Instead, Blockchain could also monitor and collect social and environmental data directly connected to the supply chain (and therefore the life cycle of the product involved in the supply chain). Consequently, Blockchain Technology can be useful for supporting the implementation of S-LCA, since it would allow the collection of social and socio-economic data.

At the methodological level, the different synergies are identified considering each phase of S-LCA, following the technical framework of ISO 14040/44:2006 and the UNEP Guidelines (UNEP 2020). In detail, Fig. 5 presents these synergies for each phase of S-LCA. In the goal and scope phase, the definition of the product system can be supported by information collected on a specific supply chain. Blockchain tracks all processes involved in the supply chain from a technological perspective because it can effectively provide information on set conditions of a process that influence the final product (e.g., specific technology or mix technology, quality of the materials). Moreover, Blockchain Technology maps material and informative flows directly linked to a specific product and its supply chain, enabling the identification of all input involved and collecting information regarding the geographical (e.g., country of origin), temporal (date of data added and blocked within blockchain technology), and technological aspects of inputs.

Fig. 5
figure 5

The synergies emerged from the integration between S-LCA and Blockchain at the methodological level (elaborated by the authors)

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Concerning the life cycle inventory phase, the integration enables the collection of larger amounts of primary and specific data on the evaluated product system. This facilitates conducting a risk assessment and/or a social hotspot assessment, as it provides information on certain areas where social risks could occur. Furthermore, these data are valuable for assessing social performance, as they can be directly linked to the social indicators considered in a S-LCA study. Consequently, integrating social impact assessment improves the quality of results and, thus, the overall evaluation. Due to its characteristics, Blockchain can enable S-LCA to better comply with the consistency, completeness, and quality checks of the life cycle interpretation phase.

At the operation level, it is necessary to define a set of social indicators directly related to the social data collected. Table 1 suggests some examples of indicators that can be used to collect social data to insert into the supply chain of the organization managed with blockchain technology. This list of indicators at the organizational and product level can eventually be used for assessing the social performance of the supply chain.

Table 1 Suggested social information and indicators for S-LCA inventory through Blockchain
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Nevertheless, a standardized set of indicators cannot be defined since it is well known that social assessment is context-related (Di Cesare et al. 2018; D’Eusanio et al. 2018; UNEP 2020), and therefore, for each supply chain, a set of social indicators should be defined by considering the sector, the country, and/or the organization under study. The indicators suggested are compliant with the Methodological Sheets for Social LCA (UNEP 2021).

One of the primary challenges with integrating Blockchain Technology with S-LCA is the substantial implementation costs. Moreover, even if an organization has already adopted it to manage its supply chain, additional efforts are necessary to effectively monitor social and socio-economic aspects. Indeed, when an organization decides to evaluate a product system from a social perspective, it must consider its supply chain, including mapping the involved value chain actors and other affected stakeholders (e.g., workers, consumers). Consequently, the selection of social topics to be monitored by Blockchain should be done, and it often is a challenge. To address this, a participatory approach involving consumers and/or customers can facilitate the identification of relevant social aspects (e.g., conducting a materiality assessment). Therefore, according to the relevant social topics, a set of social indicators should be defined in order to identify the specific data to be collected and monitored along the supply chain using blockchain technology. To achieve this aim, a cultural change is necessary within organizations involved in Blockchain, as they should acknowledge the critical role of monitoring and measuring social sustainability along their supply chain.

4.4 Discussion

Modeling a Blockchain suitable for including social data can support the identification of social and environmental risks throughout the supply chain and monitor significant issues over time. Therefore, it allows to spread and raise awareness of the responsibility along the entire supply chain and not just attributed to “downstream” actors.

Indeed, the cost of traceability is borne by the actors downstream of the supply chain (i.e., retailers, resellers, distributors) who are normally responsible for the product. Blockchain can push supply chain actors to engage in proactive activities, promoting and raising awareness, for instance, about ethical behavior and assuming more responsibility also in upstream processes.

In this way, organizations can implement stronger supply chain management practices that are better able to trace products and, consequently, provide consumers with transparent, reliable, and verifiable information on the product’s life cycle. Indeed, by ensuring traceability throughout the supply chain, the actors involved can guarantee the origin, quality, and safety of products. The literature-derived findings confirm that the traceability of the data via Blockchain could be a significant resource for conducting LCI studies; for improving differentiated data on its composition and origin; for providing automatic computation of environmental impact, as well as for securing transfer and backup of data; and for mapping of supply chain and tracking methods, technologies, and practices performed along the supply chain.

Moreover, Blockchain Technology can also support the organization in the integration of measuring and governance of sustainability within risk management. Measuring the social, as well as environmental, risks will be key to creating value along the supply chain and the network of organizations over time.

In this way, the responsibility of the supply chain would be distributed, not only in economic but also environmental and social terms and, consequently, along the entire life cycle of the product. The synergies and implications derived from the integration of Blockchain Technology and S-LCA can be summarized in Fig. 6. Transparency and traceability of the Blockchain enable data access for S-LCA by providing primary and specific data for unit process and life cycle stages. The reliability and security of Blockchain Technology allow for improving the data quality and the cost-time efficiency supporting the applicability of S-LCA application by facing the difficulty and time-consuming efforts of collecting data.

Fig. 6
figure 6

Main aspects of S-LCA supported by Blockchain (elaborated by authors)

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5 Conclusions

The characteristics of Blockchain Technology, such as its unidirectional irreversible chain, ensure integrity, traceability, and reliability of information, material, and energy flows within the supply chain, thus interesting the life cycle of analyzed products and organizations. The demand for transparency and traceability of the supply chain has become a priority for policymakers, suppliers, and consumer stakeholders driving the adoption of Blockchain Technology to manage information and material flows efficiently. Blockchain Technology can be useful for collecting social and socio-economic information, offering a means to address data quality challenges in LCT, and therefore S-LCA studies by providing primary data (including both upstream and downstream processes) directly related to the analyzed product system. The data and information flow tracked and collected will be regarding working conditions, natural resource utilization, community involvement, living wages, occupational health and safety violations, anticompetitive practices, and the presence of child and/or forced labor. Tracking every input of product along the supply chain allows obtaining specific information on the country-of-origin data, the date of information (when it is recorded), and information regarding the specific reference technology. By tracking every input along the supply chain, stakeholders can access transparent, reliable, and verifiable information on product life cycles, mitigating phenomena such as greenwashing and social washing. At methodological levels are different advantages, which can be expressed by considering each phase of an S-LCA study. Nevertheless, at the operative level, a set of social indicators should be defined by considering the sector and the country of the product system under study. Thus, each social indicator will be related to a specific social theme, where the related data will be collected and managed by the Blockchain within the supply chain.

While there are apparent constraints, such as the need for substantial investments in technology and capacity-building initiatives, further developments are needed to fully explore the potential of integrating Blockchain Technology and S-LCA. This includes investigating data mapping and validation processes along the supply chain and understanding whether integration encourages more responsible behavior among stakeholders. Ultimately, Blockchain Technology offers a promising avenue for enhancing transparency, accountability, and sustainability in supply chains, but ongoing research and collaboration are essential to realize its full potential.