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Purpose: Sustainable supply chain finance (SSCF) is a fascinated consideration for both academics and practitioners because the indicators are still underdeveloped in achieving SSCF. This study proposes a bibliometric data-driven analysis from the literature to illustrate a clear overall concept of SSCF that reveals hidden indicators for further improvement. Design/methodology/approach: A hybrid quantitative and qualitative approach combining data-driven analysis, fuzzy Delphi method (FDM), entropy weight method (EWM) and fuzzy decision-making trial and evaluation laboratory (FDEMATEL) is employed to address the uncertainty in the context. Findings: The results show that blockchain, cash flow shortage, reverse factoring, risk assessment and triple bottom line (TBL) play significant roles in SSCF. A comparison of the challenges and gaps among different geographic regions is provided in both advanced local perspective and a global state-of-the-art assessment. There are 35 countries/territories being categorized into five geographic regions. Of the five regions, two, Latin America and the Caribbean and Africa, show the needs for more improvement, exclusively in collaboration strategies and financial crisis. Exogenous impacts of wars, natural disasters and disease epidemics are implied as inevitable attributes for enhancing the sustainability. Originality/value: This study contributes to (1) boundary SSCF foundations by data driven, (2) identifying the critical SSCF indicators and providing the knowledge gaps and directions as references for further examination and (3) addressing the gaps and challenges in different geographic regions to provide advanced assessment from local viewpoint and to diagnose the comprehensive global state of the art of SSCF. [ABSTRACT FROM AUTHOR]

• This study aims to imply a data-driven analysis for sustainable supply chain management. • A hybrid methods are adopted due to the uncertainty and complexity. • Big-data, closed-loop supply chain, industry 4.0, policy, remanufacturing, supply chain network design are the indicators for trends and challenges. • Latin America and Caribbean, and Africa shows for improvement by distinct apprehensions on eco-efficiency and risk management. This study proposes a data-driven analysis that describes the overall situation and reveals the factors hindering improvement in the sustainable supply chain management field. The literature has presented a summary of the evolution of sustainable supply chain management across attributes. Prior studies have evaluated different parts of the supply chain as independent entities. An integrated systematic assessment is absent in the extant literature and makes it necessary to identify potential opportunities for research direction. A hybrid of data-driven analysis, the fuzzy Delphi method, the entropy weight method and fuzzy decision-making trial and evaluation laboratory is adopted to address uncertainty and complexity. This study contributes to locating the boundary of fundamental knowledge to advance future research and support practical execution. Valuable direction is provided by reviewing the existing literature to identify the critical indicators that need further examination. The results show that big data, closed-loop supply chains, industry 4.0, policy, remanufacturing, and supply chain network design are the most important indicators of future trends and disputes. The challenges and gaps among different geographical regions is offered that provides both a local viewpoint and a state-of-the-art advanced sustainable supply chain management assessment. Image, graphical abstract [ABSTRACT FROM AUTHOR]

This study aims to present a systematic data-driven bibliometric analysis on municipal solid waste management as a foundation in a circular economy. The current literature has yet to be fully developed given the complexity of the corresponding concept and knowledge. Traditional bibliometric analysis lacks the ability to screen out important keywords for future directions, and the keyword frequencies are described numerically. This study applies the entropy weight method to convert the frequencies to weights and performs regional comparisons based on a database; hence, this study contributes to the literature by providing potential future directions. The database includes 413 published articles, and 41 indicators are listed. The results are used to identify valid indicators for improvement and provide a regional state-of-the-art comparison. The top 5 indicators for future study are incineration, life cycle assessment, plastic waste, sorting solid waste, and sustainability. A bibliographic coupling analysis provides a comparison of 5 regions and reveals that Africa and North America have less studies than other regions. Image 1 • Bibliometricof municipal solid waste management in circular economy is scaned f • Fuzzy Delphi andentropy weight method are to weigh the indicators and compare the regions. • Top five indicators includes incineration, life cycle assessment, plastic wastes, sorting solid wastes, and sustainability. • Africa and North America show fewer publications than other regions and need to be improved. [ABSTRACT FROM AUTHOR]