Psychological antecedents of youth sustainability awareness: a multimethod investigation from an emerging economy

Purpose This paper aims to examine the impact of various psychological factors (Intelligence Quotient, Emotional Quotient, Happiness Quotient and Environmental Awareness Quotient) on sustainability awareness among youth in emerging markets like India. Design/methodology/approach Data from 299 college students in Delhi were analyzed through an innovative hybrid approach involving path analysis using observed composite variable for hypothesis testing, ensemble ML models to enhance predictive robustness and to identify key indicators and Necessary Condition Analysis (NCA) to determine necessary conditions for heightened sustainability awareness. Findings The findings provide strong empirical evidence that environmental worldview (EAQ) and emotional intelligence are both statistical and necessary drivers of youth sustainability awareness. Cognitive ability exhibits a weaker but significant role, while subjective well-being or happiness does not demonstrate a significant direct effect. Practical implications The study results inform policymakers, institutions and educators on designing experiential and emotionally resonant learning interventions that foster sustainability awareness through environmental education and emotional intelligence development, thereby advancing SDGs 4.7 (education for sustainable development) and 13.3 (capacity building for climate action). Social implications This study’s integrated theoretical and methodological insights offer a strategic blueprint for fostering emotionally and environmentally conscious youth, pivotal for advancing global Sustainable Development Goals and engendering long-term societal transformation. Originality/value Theoretically, this work clarifies foundational psychological determinants that underpin youth-led sustainability engagement, especially in emerging economies. Methodologically, this study applies an integrated analytical framework combining path analysis, machine learning and NCA in sustainability research.