Optimizing green training for decarbonization through a grey-based hybrid multi-stage MCDM approach

Green training (GT) has become a strategic practice within green human resource management to enhance employees’ environmental competencies and support sustainability transitions under rising regulatory pressures. However, GT planning involves complex multi-criteria decision-making (MCDM) challenges due to diverse learning objectives, resource constraints, and scheduling limitations. To address this issue, this study proposes a three-stage hybrid grey-based MCDM framework integrating grey-based Decision-Making Trial and Evaluation Laboratory (DEMATEL), GDEMATEL-based Analytic Network Process (DANP), and Grey-based Performance Calculation–Integrated Multiple Multi-Attribute Decision-Making (PCIM-MADM). The framework identifies key GT criteria, evaluates their interdependencies, and ranks alternatives across four dimensions and sixteen criteria. The results indicate that “job rotation” exerts the strongest causal influence, while “training of eco-trainers” holds the highest weight. Overall, “training of eco-trainers” and “training of environmental auditors” are identified as pivotal factors with both strong influence and high importance, underscoring the need for enterprises to strengthen internal expertise in these roles to advance sustainable workforce development. The study provides a replicable, data-driven framework for organizations to design and evaluate GT programs, offering actionable guidance for accelerating workforce transformation toward low-carbon and sustainable development.