Artificial Intelligence, Climate Change, and Human Systems Behaviour: A Behavioural Systems Approach

Climate change is a complex socio-technical challenge whose effective management depends not only on technological capacity but also on how individuals, organisations, and institutions interpret information and translate it into action. This conceptual paper develops a behaviouralsystems framework for explaining the relationship among artificial intelligence, climate change, and human systems behavior and it is anchored primarily in Socio-Technical Systems Theory, the Theory of Planned Behaviour, and Organisational Learning Theory. It conceptualises artificial intelligence as a technological driver that improves climate prediction, monitoring, feedback, and decision support, while recognising that its effectiveness depends on behavioural adaptability, psychological safety, collaborative learning, trust, and ethical governance. The proposed framework explains how AI-generated information moves through individual, team, organisational, and institutional levels to influence climate-related decisions and resilience outcomes. It further identifies risks associated with automation bias, cognitive overload, unequal access, weak accountability, and culturally inappropriate data systems. The paper argues that climate resilience emerges through the continuous co-adaptation of human and algorithmic intelligence rather than through technological deployment alone. It offers theoretical propositions for future empirical testing and provides practical guidance for policymakers, organisations, educational institutions, and communities seeking to implement inclusive and responsible AI-enabled climate strategies