AI-based green technology implementation simulation for achieving carbon neutrality: exploring the role of subsidies and knowledge management

This study investigates the role of green technology implementation (GTI) based on artificial intelligence (AI) at the household level to achieve carbon neutrality by addressing gaps in the existing research. The research focuses on understanding how education on green consumption preferences, green invention and emission impacts can optimally influence AI-based GTI decisions. Through behavioural analysis at the household level, this study quantifies the effects of education and preferences on emissions and proposes subsidies as accelerators for carbon–neutral transitions. Furthermore, the study employs regression analysis and simulation-based optimisation, which are then validated against prior methodologies, with a focus on Punjab, Pakistan. Utilising a simple random sampling technique, approximately 1000 households were surveyed to represent the province’s diverse demographics comprehensively. Findings reveal that higher education levels correlate with less enthusiasm for AI-based GTI. Simulations measured optimal subsidy levels by striking a balance between encouraging green behaviour and technological adoption. By integrating diverse factors and AI-based GTI optimisation, this study defines important thresholds for education and subsidies, thus highlighting their pivotal role in advancing AI-based green technologies and sustainable household practices. This research significantly enhances the understanding of the complex relationship between AI-based GTI decisions and educational influences, thereby contributing to the advancement of environmental sustainability.