Shahreza Shauqi Ismail, Nura Abubakar Allumi, Yousif Munadhil Ibrahim
Purpose: The aim of this study is to analyze the evolution of research trends in green supply chain management (GSCM) in agriculture, constructing an intellectual framework to improve the efficiency and environmental sustainability of the supply chain with smart agricultural technologies. Design/methodology/approach: The review used the bibliometric approach with two science mapping approaches (i.e., co-citation and co-word analysis) were perfomed to analyze 381 articles published in the Web of Science (WoS) database to investigate past and future research direction in GSCM using the VOSviewer software. Findings: Previous studies mainly focused on CE integration, sustainable agri-food supply chain design, and blockchain in supply chain management, whereas future research is expected to emphasize green logistics, low-carbon supply chains, smart circular systems, and digital innovation in green agri-SCM. Limitations and Research implications: This study is limited to the WoS database and two bibliometric techniques. Future research should validate the identified themes through additional bibliometric and empirical studies. Practical Implications: This study provides practical insights for managers, policymakers, and researchers to support the development of sustainable agricultural supply chains through circular economy, green logistics, and digital technologies. Originality/value: This study provides a comprehensive knowledge map of GSCM in agriculture by identifying past research themes and future research directions through bibliometric analysis
This chapter examines the ways in which blockchain smart contracts and responsible artificial intelligence (AI) are transforming many sectors. At the moment, typical contracts in industries like manufacturing or supply chains face several inefficiencies, delays, and the possibility of errors or even fraud. The issue is that those smart contracts lack the intelligence required for real-world scenarios where things are constantly changing, even though blockchain has helped by making things more automated and transparent. The idea here is to make blockchain contracts less rigid by incorporating AI and real-time data analysis. Contracts would adjust in response to events rather than simply adhering to predetermined guidelines. Additionally, the chapter explores how smart contracts are established by fusing AI tools, data feeds from services like Chainlink, and platforms like Ethereum. In general, it involves creating systems that are responsible and intelligent, which seems to be the only viable option at the moment. This chapter examines the evolution of AI in industrial contexts, analyzing its role before and after the integration of smart contracts. It presents relevant industrial case studies, applies responsible AI principles to the development of blockchain-based smart contracts, and underscores the adoption of international frameworks and standards to promote ethical, transparent, and accountable implementation across industries.
The accelerating adoption of Electric Vehicles (EVs) has intensified the need for efficient and sustainable life-cycle management of Lithium-Ion Batteries (LIBs). However, the complexity of battery supply chains, data fragmentation, and varying technological characteristics among distributed ledger platforms create significant uncertainty in selecting an appropriate infrastructure for implementing Digital Battery Passports (DBP). This study proposes a structured decision-support model to evaluate and differentiate among Distributed Ledger Technologies (DLT) under an uncertain decision environment. The proposed framework integrates the plithogenic set theory to capture expert uncertainty and inconsistency, the Best–Worst Method (BWM) to determine the relative importance of evaluation criteria, and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank alternative platforms. The model is applied to assess eight leading DLT platforms for DBP implementation in the automotive context. The results indicate that Hedera is the most suitable platform, achieving the highest TOPSIS closeness coefficient (0.8223), followed by IOTA and EOS. The findings confirm that incorporating contradiction-aware uncertainty modeling into a hybrid MCDM framework enhances the robustness and transparency of DLT platform selection for DBP-oriented applications.
Rouwaida Abdallah, Guillermo Toyos Marfurt, Sara Tucci-Piergiovanni
This work has been accepted for publication in the proceedings of 3SCEA 2026 conference. The deposited manuscript corresponds to the author-accepted version presented at the conference. The final published version will appear in the official conference proceedings. Abstract: Traceability remains a critical challenge in modern supply chains, particularly as industries transition towards sustainability and circular economy models. The Digital Product Passport (DPP) emerges as a vital tool to consolidate and share comprehensive product information across its lifecycle. In this paper, we propose a decentralized, customizable, and self-deployable DPP system, leveraging blockchain technology and an extension of the Fractional Non-Fungible Token (F-NFT) model. This approach enables fine-grained traceability of individual product components and events across the supply chain, ensuring transparency and verifiability. A key strength of our system lies in its flexibility, enabling businesses to deploy tailored solutions without reliance on centralized service providers. The proposed system empowers stakeholders with greater control over product data while supporting selective information sharing. We present a functional implementation of the system and discuss the crucial design decisions that support its real-world applicability.
The process of efficiently meeting customer needs through the seamless transfer of goods, services, and information is known as supply chain management. Because of centralized control, trustless networks, and occasionally manual processes, modern supply chain management solutions lack traceability, transparency, security, and decentralization. As blockchain technology is decentralized and immutable, it provides a solution that ensures transactions are transparent and viewable in real time. Due to traditional proof-based algorithms like Proof of Work (PoW) and Proof of Stake (PoS) or other voting-based algorithms, the Blockchain utilized for traditional supply chain management (SCM) has issues with high energy consumption, scalability, and centralization dangers. In this study, we will offer an optimal Blockchain-based model that evaluates nodes based on multiple parameters, including processing power, network latency, uptime, and reputation, using a consensus mechanism based on a weighted leader selection technique. By dynamically assigning weights to these factors, this study can overcome the limitations of single factor leader selection and guarantee validator selection that is efficient, adaptable, fair and sustainable. This study also examines the concept to demonstrate how it might support decentralization in modern supply chain management (SCM) systems while enhancing traceability, security, and transparency through more efficient use of resources.
Tomi Setiawan, Muhammad Farras Samith, Muhammad Hammam Mughits, Aulia Fitrah Uswatun Hasanah · 5 authors
This study synthesizes Web3-based governance strategies in closed supply chains for electric vehicle batteries in Indonesia, focusing on reducing environmental impacts and improving resource efficiency. It includes a theoretical framework and case studies to address the overall issues in empirical validation and contextual adaptation. This paper aims to expand the governance framework, compare digital product passport implementations, identify incentive mechanisms including CSR integration, quantify regulatory models, and analyze practical case studies. An integrated analysis of multidisciplinary literature, using theoretical modeling, game theory, and empirical case studies, reveals that blockchain-based digital product passports improve transparency and lifecycle traceability but face challenges in large-scale implementation and data standardization. Incentive mechanisms based on game-theoretic frameworks and CSR promote resource efficiency, although empirical evidence in Indonesia remains limited and its social dimensions remain underexplored. A comparative regulatory analysis highlights the relevance of EU policies as a benchmark while also highlighting regulatory gaps and enforcement challenges in Indonesia. The case studies demonstrate the feasibility of Web3 technologies in improving environmental outcomes and operational efficiency, but scalability and stakeholder coordination require further investigation. These findings highlight the need for an integrated, multi-stakeholder governance model that bridges theoretical innovation with practical implementation in Indonesia's unique socio-regulatory context. This study informs future research and policy development to optimize sustainable battery supply chains through digital governance and circular-economy principles.
The increasing complexity of global supply chains has intensified the need for stronger environmental, social, and governance (ESG) oversight and transparency. Traditional governance mechanisms often rely on fragmented information systems and periodic reporting processes that limit the credibility of sustainability monitoring across distributed supply networks. Emerging technologies such as blockchain and tokenization provide new opportunities to enhance transparency, traceability, and accountability in ESG governance. Drawing on the Natural Resource-Based View, Agency Theory, and Stakeholder Theory, this chapter explores how decentralized technologies can improve data verifiability, reduce information asymmetries, and support more reliable ESG governance in sustainable supply chains.
Shan Chang, Khuram Shahzad, Ali Nawaz Khan, Faisal Shahzad · 5 authors
Purpose This study aims to investigate the factors that drive and enhance sustainable supply chain management (SSCM) practices and performance, leveraging smart-contract-enabled green blockchain technology (BT). Specifically, it builds a research framework based on the dynamic capability view (DCV) theory to explore the interrelationships among green knowledge acquisition (GKA), generative AI (GAI), green BT, green organizational memory, and organizational environmental and operational performance. Design/methodology/approach A quantitative research approach was adopted. Cross-sectional survey data were collected via an online survey, yielding 318 valid responses from supply chain professionals in China. The hypothesized relationships within the proposed framework, grounded in DCV theory, were tested using Structural Equation Modeling via AMOS 24 and SPSS 23. Confirmatory factor analysis was conducted to validate the measurement model, and moderation effects were examined using mean-centered interaction terms within the SEM framework. Findings The analysis confirms the proposed framework’s validity and reveals several key findings: GKA positively impacts green BT. GAI significantly moderates the effect of GKA on green BT, strengthening the transformation of green knowledge into smart-contract-enabled blockchain capability. Green BT positively influences both environmental performance and operational performance. Green organizational memory acts as a crucial moderator, strengthening the positive relationships between green BT and both environmental and operational performance, amplifying the performance effects of green BT on both environmental and operational outcomes. Environmental performance ultimately contributes positively to operational performance. Practical implications Organizations in the supply chain sector can leverage these insights by prioritizing and maximizing GKA to effectively embed sustainability. Managers should promote the effective use of GAI in their processes, as it enhances the conversion of knowledge into tangible green BT implementations. Furthermore, maintaining a robust green organizational memory is recommended to maximize the performance returns from investments in green BT innovations. Originality/value This paper offers significant theoretical novelty by integrating GAI and green organizational memory as key dynamic capabilities within the DCV framework to examine green BT adoption in SSCM, a perspective previously underexplored. It is among the first to empirically confirm the moderating roles of both GAI and green organizational memory in the context of sustainable technology implementation, providing a unique model for achieving superior environmental and operational outcomes.
This study aimed to examine the role of blockchain technology in transforming digital marketing within the emerging Web3 environment, with a specific focus on its impact on consumer trust and marketing efficiency. The research primarily investigated the relationship between blockchain technology as the independent variable and digital marketing transformation as the dependent variable, while also considering consumer trust and marketing efficiency as key outcome variables. A quantitative research design was adopted, and data were collected from 250 respondents, including digital platform users and marketing professionals, using a structured questionnaire based on a 5-point Likert scale. Statistical analyses, including reliability, correlation, and regression, were conducted to test the proposed hypotheses and evaluate the relationships among variables. The findings revealed that blockchain technology had a significant positive impact on digital marketing transformation (β = 0.68, p < 0.001) and consumer trust (β = 0.72, p < 0.001). Digital marketing transformation significantly influenced marketing efficiency (β = 0.70, p < 0.001). The results also indicated that consumer trust played a mediating role in enhancing marketing efficiency, highlighting the importance of transparency and data security in digital environments. The study provides practical implications for organizations by emphasizing the adoption of blockchain-based marketing systems to improve transparency, reduce operational inefficiencies, and strengthen consumer trust. It also highlights the strategic importance of decentralized platforms in shaping the future of digital marketing. References Basal, M., & Şarkbay, Ö. F. (2026). Web3-driven digital marketing and consumer protection. 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Effective demand forecasting is crucial for reducing food waste. However, data privacy concerns often hinder collaboration among retailers, limiting the potential for improved predictive accuracy. In this study, we explore the application of Federated Learning (FL) in Sustainable Supply Chain Management (SSCM), with a focus on the grocery retail sector dealing with perishable goods. We develop a baseline predictive model for demand forecasting and waste assessment in an isolated retailer scenario. Subsequently, we introduce a Blockchain-based FL model, trained collaboratively across multiple retailers without direct data sharing. Our preliminary results show that FL models have performance almost equivalent to the ideal setting in which parties share data with each other, and are notably superior to models built by individual parties without sharing data, cutting waste and boosting efficiency.
The construction industry is a major global consumer of energy and a leading source of greenhouse gas emissions, underscoring the need for transparent, data-driven, and energy-efficient supply chain strategies. This study develops an integrated mixed-integer linear programming (MILP) model for a multi-echelon, multi-product construction supply chain that explicitly incorporates differentiated building energy efficiency levels ( A +, A ++, A +++) as exogenous determinants of material requirements, production processes, and logistics flows. By embedding blockchain-enabled smart contracts, the model automates supplier governance and ensures compliance with delivery reliability, quality standards, and CO 2 performance through predefined incentives and penalties, thereby enhancing transparency and accountability. The framework jointly optimizes facility location, material and product flows, supplier selection, and reverse logistics operations under a CO₂ emission cap, while simultaneously capturing the implications of greenfield and brownfield project conditions. A real-scale numerical case study demonstrates the model’s ability to evaluate the economic–environmental trade-offs arising from increasingly stringent sustainability requirements. The results reveal that although higher energy efficiency levels incur greater initial supply chain costs due to advanced materials and more complex logistics, they lead to substantial reductions in long-term operational energy consumption, rendering the A +++ option the most economically favorable from a lifecycle perspective. Furthermore, the integration of blockchain-enabled smart contracts partially offsets cost escalations by penalizing non-compliant suppliers and rewarding high-performing ones. Overall, the proposed model provides a rigorous and transparent decision-support framework that enables contractors to align supply chain design with energy-efficiency targets, CO 2 -reduction policies, and circular-economy objectives while preserving operational feasibility and supply reliability.
Christian Finke, Tamino Marahrens, Matthias Schümann
As supply chains (SCs) face increasing pressure from ecological demands, ethical expectations, and global disruptions, Distributed Ledger Technology (DLT) is gaining attention as a potential enabler of transparent, secure, and automated processes, helping to meet the expectations of customers and regulatory authorities. Nevertheless, the lack of generally valid design recommendations hinders its implementation. Therefore, we adopted grounded theory principles within a design science research approach to address this gap. Subsequently, we derived 11 overarching expert insights for developing a DLT operating model in SCs and 15 for its implementation by conducting 16 expert interviews. These insights were finally used to extract 19 generally valid design recommendations for applying DLT in SC processes that contribute to practical implementations and the framing of realistic adoption expectations by guiding researchers and practitioners.
The Digital Product Passport (DPP) is a cross-sectoral framework for sharing data across value chains to promote circular economy practices such as repair, reuse, and recycling. A key challenge is ensuring trust and integrity in DPP data, which is often fragmented among multiple stakeholders. Distributed ledger technologies (DLTs) ofer a promising solution by enhancing data integrity, transparency, and reliability. However, a comprehensive understanding of how DLTs can support DPPs remains underexplored. This paper reviews academic and industrial initiatives using DLTs to build trustworthy DPP infrastructures. It identifies three core benefits of DLTs for DPPs: (1) tamper-proof data integrity for traceability, (2) peer-to-peer private data sharing without central authority, and (3) decentralized DPP lifecycle management, from creation to deactivation. Based on these findings, the paper proposes design guidelines to inform the development of DLT-based DPP systems that can support long-term trust and collaboration in circular economy ecosystems.
Quang Huy Duong, Carlos F.A. Arranz, Mao Xu, Li Zhou · 5 authors
The rapid transition to electric vehicles has intensified challenges in electric vehicle battery (EVB) closed-loop supply chains (CLSC), particularly regarding material traceability, supply chain transparency, and recycling efficiency. While decentralised technologies, particularly Web3 and Metaverse, offer promising solutions, their integration into EVB CLSC remains fragmented and insufficiently examined. We introduce an Operational Decentralisation Framework enabling a systematic analysis of centralised operations and a critical evaluation of decentralised alternatives as transformational forces. By adopting a holistic perspective, the framework equips firms with strategic guidance for transitioning from centralised structures to decentralised ecosystems. We analyse 588 academic articles and 1,168 industry documents through two advanced text mining techniques – Dynamic Latent Dirichlet Allocation and Burst Detection. Web3 and metaverse can potentially reconfigure the design, manufacturing, end-of-life diagnostics, procurement, waste management, load balancing, capacity planning, inventory management and service operations of two key areas: (1) EVB CLSC operations and (2) EVB circular energy/grid operations. We also found that while blockchain and digital twins show established applications, Web3 and Metaverse applications face significant barriers, including scalability, technology complexity, and expertise gaps, despite their great potentials. Therefore, we propose four visionary models integrating Web3, Metaverse, and AI technologies that have the potential to overcome existing barriers and enable transformative decentralisation. Extending the TOE framework, the study contributes to the theory by developing an integrated framework for evaluating decentralised technology adoption in EVB CLSCs. For practitioners, we provide actionable insights and pathways for technology implementation across different CLSC stages and guidance for addressing key adoption barriers.
As global pressure increases for sustainable and transparent supply chains, logistics organisations are exploring ways to strengthen environmental, social and governance (ESG) performance. This article examines how artificial intelligence (AI) and distributed ledger technologies (DLT) contribute to ESG integration in logistics. The study applies a qualitative desk research approach based on secondary data from 2017–2025, including sustainability reports, port authority publications, and the industry press. The comparative case analysis covers four Baltic logistics actors: the Port of Klaipėda (Lithuania), Vlantana (Lithuania), the Freeport of Riga/ Baltic Container Terminal (Latvia), and HHLA TK Estonia (Estonia). The findings show that Klaipėda’s LNG, OPS, and hydrogen projects enhance environmental outcomes; the Vlantana Norge case exposes social and governance compliance risks; and Riga and Tallinn demonstrate governance-oriented digitalisation through 5G networks and blockchain documentation. AI primarily supports efficiency and risk detection, while DLT secures the transparency and auditability of ESG data. Together, they function as complementary enablers of ESG reporting, though broader adoption requires regulatory alignment, interoperability, and investment in the digital infrastructure.
Luqman Hakim Abdul Majid, Yudi Fernando, Ming K. Lim, Ming‐Lang Tseng
Achieving carbon neutrality in supply chains is a complex challenge, given the urgency of climate change mitigation. This paper explores how non-fungible tokens, carbon transparency, and blockchain carbon credits can support reaching carbon neutrality. We surveyed 140 Malaysian semiconductor firms involved in carbon-neutrality initiatives and conducted necessary condition analysis using the bottleneck technique. Our results show that non-fungible tokens enhance carbon transparency by providing traceable, verifiable carbon data. This transparency positively influences the issuance of blockchain carbon credits and carbon neutrality, though its effect is limited. Carbon transparency serves as a mediator between non-fungible tokens and carbon neutrality, underscoring its role in leveraging digital tools for effective carbon management. However, regulatory compliance and scalability challenges hinder both carbon transparency and digital transformation. This paper provides a foundation for integrating non-fungible tokens and blockchain technology into supply chains, offering policymakers pursuing transparent decarbonisation strategies valuable insights.
The competitive hospitality sector faces a growing credibility crisis, where rising consumer skepticism regarding "greenwashing" severely limits the ability of hotels to capture the Sustainable Revenue Premium. This research addresses a critical gap in Sustainable Supply Chain Management (SSCM) literature by empirically modeling the "Credibility Mechanism"—the process by which digital technology resolves information asymmetry to monetize sustainability claims. Focusing on the complex Food and Beverage (F&B) supply chains of emerging archipelagic economies, the study employs a rigorous sequential mixed-methods design. First, Design Science Research was utilized to architect a permissioned cross-chain blockchain framework integrating Zero-Knowledge Proofs (ZKPs) for verifiable, private provenance. Subsequently, Partial Least Squares-Structural Equation Modeling (PLS-SEM) confirmed that blockchain-enabled transparency significantly mitigates perceived greenwashing risk, which in turn fosters Customer Trust. Critically, the study validates financial outcomes using a Stochastic Frontier Bayesian Model (SFBM) applied to longitudinal hotel data. Results demonstrate that adopting this traceable framework yields an 8.4% increase in F&B revenue efficiency and sustains a 5.1% price premium for ethically sourced items. These findings provide profound theoretical advancements by redefining SCM risk mitigation through Information Governance rather than material redundancy. Managerially, the research offers a data-driven justification for high-tech investment, proving that verifiable transparency is a direct revenue driver essential for competitive advantage in opaque markets.
Blockchain technology has revolutionized the way digital transactions and data are secured, verified, and stored. As the basis of blockchain lies the consensus algorithm, it is a critical component that determines how transactions are validated and blocks are added to the chain. However, the environmental implications of these algorithms have become a growing concern, particularly with energy-intensive models like proof of work (PoW). This chapter explores the environmental impact of various consensus mechanisms, including PoW, Proof of Stake (PoS), delegated proof of stake (DPoS), and newer eco-friendly alternatives. It analyzes the energy consumption, carbon footprint, and scalability of each approach, with a focus on how emerging consensus models aim to balance security, decentralization, and sustainability. This work evaluates blockchain’s broader environmental footprint across industries, including supply chain, energy, and carbon credits, highlighting both challenges and opportunities.