Drivers, barriers and interventions for green hydrogen supply chains: A multi-actor framework

ABSTRACT The number of research studies investigating the importance of circular economy is growing in the supply chain management literature. Most of these studies analyzed the environmental performance improvement or green innovation enhancement without exploring the underlying mechanisms. This study utilizes a system‐focused perspective on how organizations achieve their societal goals through a mechanism in which the concepts of learning and green supply chain are embedded. This mechanism includes sustainable internal and external organizational elements and is activated in such a way that organizations implement two types of green supply chain practices (exploration and exploitation) using feedback collected from both the elements. The implementation of these two categories of learning practices requires various stakeholders to exert pressure and various human resource configurations to build a strong workforce within an organization, which are influenced by strategic leadership behaviors leading firms to move toward socially responsible firms. Theoretical and practical implications are offered in this study.

Purpose This study examines the impacts of green subsidy policies on the decision-making and performance of supply chain members, including a manufacturer that determines green R&D intensity and wholesale price, and a retailer that selects green marketing efforts and sales volume. Design/methodology/approach By constructing a game-theoretic model, this study investigates the impacts of three subsidy policies (R&D subsidies, output subsidies, and hybrid subsidies) on the green activities of supply chain members, and compares the efficiency improvements achieved by different subsidy policies. Findings Comparative analysis of these subsidy mechanisms yields some key findings. First, the output subsidy exhibits superior (inferior) efficiency to the R&D subsidy in incentivizing green R&D and green marketing and in improving social welfare, when the environmental damage coefficient is relatively low (high). Second, while the hybrid subsidy consistently outperforms the R&D subsidy in incentivizing green R&D and marketing efforts, and in enhancing the profits of supply chain members and social welfare, this subsidy fails to motivate green marketing efforts or boost retailer profits more effectively than output subsidy. Finally, extending the analysis to centralized structures, we counterintuitively find that centralized structure does not necessarily generate higher channel profits and social welfare than decentralized structure under these subsidy policies. Originality/value Despite the abundant literature on green subsidy policies, little attention has been paid to examining the comparative efficiency of such policies—particularly hybrid subsidy policies—in green supply chains where manufacturers conduct green investments and retailers undertake green marketing efforts; equally scarce is attention to their impacts on centralized channels. These findings provide theoretical foundations and managerial implications for governments to make optimal green subsidy policy choices.

Financial subsidies provided by the government have been validated as an effective means to stimulate carbon emission reductions among manufacturers. This paper examines the impact of two types of financial subsidies-namely, one-off subsidies and quantity-based price subsidies-on the bank’s optimal interest rate, the manufacturer’s optimal emission reduction level and wholesale price, and the retailer’s optimal retail price. The benefits of these two subsidy policies are compared from both economic and environmental perspectives. Our findings reveal the following insights: Both subsidy policies effectively enhance market demand and improve the economic performance of the green supply chain. However, when subsidies are below certain thresholds, total carbon emissions increase, leading to reduced environmental benefits. Under a fixed subsidy amount, one-off subsidies result in less environmental harm compared to quantity-based price subsidies. Conversely, at a given level of carbon reduction, quantity-based price subsidies yield higher economic benefits due to greater government expenditure, albeit with lower environmental benefits. These results provide valuable guidance for policymakers: quantity-based price subsidies are preferable when prioritizing economic benefits, whereas one-off subsidies are more suitable for enhancing environmental outcomes.

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The strain on the global food sector to reconcile environmental sustainability with operational efficiency has been intensifying. In a growing economy, this study investigates the revolutionary potential of integrated digital ecosystems that include blockchain, big data analytics, and IoT-enabled real-time monitoring on the performance of Green Supply Chain Management (GSCM). The research, that relies on the Technology–Organization–Environment (TOE) framework, utilizes a rigorous mixed-methods approach which utilizes Fuzzy-Set Qualitative Comparative Analysis (fsQCA) and Structural Equation Modeling (SEM) on data from food-processing firms in Pakistan. Green innovation is an important moderating catalyst, and SEM results confirm that digital integration significantly enhances waste reduction and energy efficiency, explaining 62% of performance variance. A further configurational analysis indicates causal equifinality and reveals 3 distinct paths to superior sustainability, from “Innovation-Driven Institutionalization” to “Government-Supported Scaling.” It demonstrates that various combinations of external support and internal readiness may ultimately contribute to success. The findings are supported by post-implementation evaluations, which show a 29% decrease in energy consumption and a 55% reduction in cold-chain losses. These findings offer novel insights for practitioners and policymakers, validating that environmental stewardship and commercial profitability are mutually reinforcing objectives in the digital age.

ABSTRACT This study examines how supplier and customer ESG performance drives firms’ green innovation through two asymmetric mechanisms: supplier-driven downward collaborative spillover rooted in Knowledge-Based View (KBV) and customer-driven upward coercive pressure stemming from Resource Dependence Theory (RDT). This reveals a strategic trade-off for firms between pursuing substantive green innovation (quality pursuit) and strategic green innovation (compliance response). External factors – green credit policy and supply chain relationship strength – amplify the effect, while internal factor – management myopia – attenuates it. Using Chinese A-share listed firms (2010-2023), we validate these propositions, advancing understanding of supply chain sustainability governance and heterogeneous green innovation drivers.

This study rounds into both the historical context and future projections of sustainable supply chain research practices. It emphasizes the necessity for the advanced analyses of research articles by combining traditional analysis with modern topic modeling and forecasting techniques. This study is organized around four primary research questions. A dataset of n = 8955 indexed article keywords and abstracts for the period of 2000–2025 was analyzed in the Python (version 3.12.) environment using n-grams, top keywords by year, k-means clustering combined with dimensionality reduction, and co-occurrence networks. Time-series forecasting models were also used to project the short-term development of clusters. The dataset retrieval was performed with search string and subject-area filters to focus the analysis on managerial and economic perspectives of sustainable supply chains. The analysis identified four keyword clusters: (1) CSR and Stakeholder Engagement, (2) Circular Economy and Sustainable Production, (3) Decision-making, Resilience and Emerging Technologies, and (4) Green Supply Chain Management. These clusters were then examined to assess current research practices from a managerial and economics perspective and their near-term evolution, with results validated through the additional clustering of abstract-level topics. This study confirms a paradigm change toward the integration of circularity, digitalization, and resilience, with technology-enabled growth. Social sustainability remains underrepresented, revealing a critical gap in current research. This study contributes methodologically by updating and extending current research practices and theoretically by revealing sustainability problems trends in supply chains.

In the stage of deep integration of digital economy and real economy, the supply chain operation of enterprises is facing some challenges such as information asymmetry, accelerated risk transmission and insufficient capabilities of sustainable development. As one of the most important links in the system of deals when handling returns of a product, recycling and remanufacturing processes, reverse logistics has become a bottleneck that restricts the improvement of the overall supply chain efficiency due to many problems such as high management difficulty and disorganized data systems. Taking digital intelligence transformation as the research background, this paper adopts literature research method, case analysis method and comparative analysis method to comprehensively analyze the existing problems of supply chain reverse logistics from four dimensions: on the surface, theory, technology, process and organization, construct an integrated optimization framework of technology-process-organization collaboration mechanism and explore the optimization path of reverse logistics in the era of digital intelligence. The research reveals that the current digital transformation of reverse logistics in terms of intelligence has come across four dilemmas; theoretical lag, the technological mismatch, process rigidity and organizational marginalization. It is necessary to take systematic measures, from the aspect of reconstructing the theoretical system, optimizing the allocation of technical resources, establishing the intelligent processes and the promotion of organizational value transformation. The research conclusions offer theoretical reference and practical guidance for enterprises to enhance the operational efficiency of reverse logistics as well as build a green supply chain system.

The nexus between circular economy practices, TQM, and green supply chain management in firm performance

Long-standing challenges in the identification of risks in green supply chain finance stem from the complexity of data structures and the intertwining of non-linear information, leading to uncertainty. This study aims to construct a two-stage deep learning model that integrates Generative Adversarial Networks with a Residual Autoencoder (GAN-SAE) and Deep Neural Networks with Multi-Kernel Support Vector Machines (DNN-SVM) to achieve the intelligent and precise identification of risks. Based on a sample of 168 firms observed over the period 2015-2024, this study first employs a Conditional Generative Adversarial Network combined with a Residual Autoencoder (GAN-SAE) to generate and reconstruct high-risk samples. This process alleviates the scarcity of high-risk observations and mitigates feature redundancy, while simultaneously extracting discriminative risk representations. Building on this enhanced feature space, a hybrid classification framework integrating Deep Neural Networks and a Multi-Kernel Support Vector Machine (DNN-SVM) is further introduced to strengthen the mapping capability for complex nonlinear risk patterns and to achieve robust adaptive classification. Through this two-stage design, the proposed framework substantially improves the overall accuracy and stability of risk classification. Empirical results indicate that the proposed model demonstrates a significant advantage on the test set, achieving a classification accuracy of 0.923 ± 0.009, a recall rate of 0.893 ± 0.010, and an AUC value of 0.938 ± 0.008, outperforming the other comparative models. Moreover, the distribution of asset returns and liabilities of the generated samples aligns with that of the actual high-risk samples, validating the economic rationality of the model during the data augmentation phase. The risk heatmap results indicate that financial health and credit scores exhibit the highest contribution levels in the model's risk prediction, serving as the most sensitive feature dimensions for risk discrimination. In contrast, ESG performance and supply chain stability function as auxiliary variables that further refine the model's risk differentiation. The study achieves hierarchical expression of risk identification and optimization of feature coordination, offering a scalable intelligent recognition framework and methodological innovation for green financial risk control.

ABSTRACT The pursuit of Sustainable Development Goals (SDGs) 7, 9, 12, and 13, centered on affordable clean energy, industrial innovation, responsible production, and climate action, has gained momentum across Latin American economies. However, the region continues to face the dual challenge of achieving economic growth while mitigating rising carbon emissions. This study examines the moderating role of green finance in the relationship between green supply chain management, eco‐innovation, renewable energy, environmental taxation, and carbon emissions across Latin America from 2000 to 2020. Using advanced econometric methods, including feasible generalized least squares (FGLS), panel‐corrected standard errors (PCSE), fully modified ordinary least squares (FMOLS), and method of moments quantile regression (MMQR), the study provides robust empirical insights into the determinants of carbon mitigation. The results show that green supply chain management, eco‐innovation, and renewable energy significantly reduce carbon emissions, while environmental taxes have a positive effect. Moreover, green finance significantly enhances the effectiveness of these sustainability drivers, increasing their contribution to emission reduction and sustainable industrial transformation. The study highlights that policy coherence, institutional innovation, and financial inclusivity are essential for transitioning Latin America toward a low‐carbon, resilient, and equitable future.

Drivers of organizational performance in the green supply chain: a two-step SEM-NCA approach

This study contributes to the hydrogen supply chain (HSC) literature by addressing, at a national scale, the lack of carbon emissions, system responsiveness, and demand uncertainty that prior studies neglected. This study develops a multi-objective optimization model for Japan's green HSC that integrates imported hydrogen carriers and domestically produced hydrogen from renewable electrolysis. The proposed model incorporates electrolyzer facilities, dehydrogenation ports, ammonia storage terminals, and multiple demand centers, such as the cement and steelmaking industries, power generation, and hydrogen refueling stations, located across East and West Japan. It accommodates multi-modal transportation systems and supports three hydrogen carrier types: liquid hydrogen, ammonia, and compressed hydrogen. The model minimizes total costs, including CAPEX, OPEX, procurement, transportation, storage, and carbon emissions, while maximizing responsiveness under demand uncertainty using triangular fuzzy numbers. A weighted sum method is applied to solve the proposed multi-objective optimization model. The results show a total cost of approximately 44.71 trillion JPY over the 2030–2050 planning horizon, with a total carbon emission of 163.79 Mt-CO 2 across production, transport, and storage activities. The model also achieves an average responsiveness of 99.99%. Sensitivity analysis reveals the impact of varying demand levels on the performance of the supply chain. These findings can help policymakers and energy stakeholders make informed decisions to support Japan's clean hydrogen transition and achieve its net-zero targets by 2050. • Multi-objective model integrates renewable electrolysis and imported hydrogen carriers in Japan. • Results show 44.7 trillion JPY cost, 163.78 Mt-CO 2 , and 99.99% responsiveness. • Pipeline 2.06 Mt-CH 2 ; truck 138 t-LH 2 ; NH3 29.69 t-NH3; train 16.13 Mt-LH 2 in 2050. • CCUS and electric transport cut total HSC carbon emissions by up to 62.5%. • Green technology 0.80-0.95 kg-CO 2 /kg-H 2 ; hybrid 2.00-2.38 kg-CO 2 /kg-H 2 , below UK/EU limits.

Year-long demonstration of green hydrogen supply chain with portable and fast-charging metal hydride tanks

Advancing green lithium-ion battery supply chains: A two-stage framework integrating reinforcement learning and mathematical modeling

The purpose of this study is to examine the impact of green innovation products on an organization’s social performance through mediation of green supply chain management. Using stakeholder resource-based view theory as a theoretical framework, the research model was developed with quantitative data from 170 managers in various industries in Israel that operate in international markets. The analyses of this study were done by structural equation modeling method via AMOS28 software. The findings show that both green innovation products and green supply chain management directly affect an organization’s social performance. Moreover, green supply chain management partially mediates the relationship between green innovation products and an organization’s social performance. That is, social performance in this study is driven by technological improvements in developing green innovative products, such as pre-designing products that minimize waste in production, more efficient utilization of raw materials, less hazardous materials, and reduction of risks to the public, as well as by selecting green suppliers throughout the supply chain and logistics. The main contribution of this study is an integrative model that combines two different disciplines – marketing and supply chains – into one integrative model that examines their joint impact on the organization’s social performance. The findings provide five main insights. First, managers who want to promote social performance in their organizations should adopt green innovation products to fulfill their customers’ needs. Second, combining green supply chain management with green innovation products can create new organizational processes that may respond to stakeholders’ requirements and environmental changes, improving the organization’s social performance. Third, these findings help stakeholders such as policymakers and regulators to encourage the integration of green innovative products based on green technology, along with the development and regulatory prioritization of green suppliers, leading to a green supply chain. Fourth, the introduction of green operational indicators and data transparency enables customers and the organization to purchase greener products, services, and raw materials. Finally, researchers can extend the study findings to identify similarities across industries, make cross-country comparisons, and examine the long-term impacts of green innovation products and green supply chains on social performance. • Green innovation and supply chain enhance organizational social performance. • Green supply chain partially mediates green innovation and social performance. • Green supply chain and innovation advance processes that respond to stakeholders.

This study aims to analyze the role of green logistics as a mediating variable in the relationship between sustainable manufacturing and reverse logistics on green supply chain performance in manufacturing companies in East Java. A quantitative approach was used through a questionnaire survey of manufacturing managers, with covariance-based Structural Equation Modeling analysis using AMOS. The measurement model was tested for validity and reliability, while the structural model was used to examine the direct and indirect influences between variables. The results show that sustainable manufacturing has a significant impact on green logistics, and green logistics has a significant positive impact on green supply chain performance. Conversely, reverse logistics has no significant impact on either green logistics or green supply chain performance, so its contribution to green supply chain performance is still limited. These findings indicate that improving green supply chain performance is more effectively achieved through the integration of sustainable manufacturing practices with green logistics rather than relying solely on reverse logistics, which is still partial and reactive. This study positions green logistics as a key mediator explaining how sustainable manufacturing and reverse logistics practices contribute to green supply chain performance in the context of developing countries.

The Green Pulse of Hospitality: Assessing Green Supply Chain Practices on Organisational Performance in the Sri Lankan Hotel Industry

Green supply chain management and enterprise resource allocation: A Supply Chain Trade Credit Perspective