DOI: 10.3390/challe17040045 ISSN: 2078-1547

Student-Reported Green Chemistry Implementation Capability and Sustainability-Related Competencies Among Pre-Service Natural Science Teachers: A Bayesian Multilevel Modeling Study

María Clarisa Tovar-Torres, Angelica Norma Taipe-Poma, Luis Alberto Núñez-Lira, Yurfa Carolina Medina-Bedón, Edith María Llerena-Espinoza, Cesar Castañeda-Campos, José Carlos Ayuque-Rojas, Russbelt Yaulilahua-Huacho

Green chemistry education is essential for preparing future science teachers to address environmental sustainability and planetary health challenges. This cross-sectional study examined student-reported Green Chemistry Implementation Capability (GCIC) and its associations with sustainability-related competencies among 640 pre-service natural science teachers from 40 classes across eight departments at a Peruvian university. GCIC was conceptualized as a second-order construct comprising six dimensions: strategic planning, sustainable laboratory practice, systems thinking integration, circular economy education, environmental decision-making integration, and transformative sustainability pedagogy. Bayesian latent-variable modeling and separate hierarchical analyses were employed to examine measurement properties, structural associations, and contextual variation. The measurement model supported the multidimensional GCIC framework. Variance decomposition attributed 63.1% of Environmental Sustainability Perception variation to students, 23.4% to classes, and 13.5% to departments. Positive structural associations linked GCIC with Systems Thinking Competency, Environmental Literacy, Sustainability Self-Efficacy, and Environmental Sustainability Perception. The reconstructed sequential indirect association was 0.150, while the total model-implied association was 0.360. Findings highlight the relevance of multidimensional green chemistry education and contextual heterogeneity for sustainability-oriented teacher preparation. However, the cross-sectional, self-reported, single-university design limits causal interpretation and generalizability, warranting longitudinal and multi-institutional validation.

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