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As the scale of mega transportation infrastructure projects (MTIs) continues to expand, the complexity of engineering construction sharply increases and decision-making sustainability faces severe challenges. Decision-making for mega transportation infrastructure projects unveils the knowledge-intensive characteristic, requiring collaborative decisions by cross-domain decision-makers. However, the exploration of heterogeneous knowledge fusion-driven decision-making problems is limited. This study aims to improve the deficiencies of existing decision-making by constructing a knowledge fusion-driven multi-attribute group decision model under fuzzy context to improve the sustainability of MTIs decision-making.

This study utilizes intuitionistic fuzzy information to handle uncertain information; calculates decision-makers and indicators weights by hesitation, fuzziness and intuitionistic fuzzy entropy; applies the intuitionistic fuzzy weighted averaging (IFWA) operator to fuse knowledge and uses consensus to measure the level of knowledge fusion. Finally, a calculation example is given to verify the rationality and effectiveness of the model.

This research finally constructs a two-level decision model driven by knowledge fusion, which alleviates the uncertainty and fuzziness of decision knowledge, promotes knowledge fusion among cross-domain decision-makers and can be effectively applied in practical applications.

This study provides an effective decision-making model for mega transportation infrastructure projects and guides policymakers.

Megaprojects can play a crucial role in achieving Sustainable Development Goals (SDGs) and tackling Global Challenges. However, they are often criticized for their massive resource consumption, poor cost/time performance and significant social-environmental impacts, including irreversible environmental damage. This study aims to chart the evolution of research on megaprojects sustainability and to offer a roadmap for future developments.

This study adopted a systematic literature review (SLR) to identify emerging themes and gaps. This study follows a three-step process of planning, conducting and reporting, based on the Tranfield approach. The overview of megaprojects sustainability research was captured through bibliometric analysis. In addition, content analysis was carried out to reveal the development of this field and get an insight into the future directions towards sustainability transition.

This study provides a longitudinal, in-depth analysis of megaprojects sustainability studies. Drawing on sustainability science and project management theories, we introduce a three-dimensional analytical framework consisting of sustainability, scope and stakeholder. This framework explains the evolution of megaprojects sustainability research from sustainability of megaprojects, to sustainability for megaprojects and then to sustainability by megaprojects. Three future avenues are proposed: (1) SDGs orientation at multi-level; (2) scope enlargement at temporal and spatial scales and (3) inclusive development for stakeholders.

This research contributes to the literature by providing a comprehensive and forward-looking analysis of megaprojects sustainability research. The framework and three research agendas provide a comprehensive picture of megaproject sustainability research; the agenda for future research is intended to inspire more studies and disruptive actions towards sustainability transition.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.