Paper Title: A study on AI-enhanced green policy support system for training centers in Liujiang Vernacular area: a dual perspective of eco-infrastructure and sustainable talent development
Authors: Yanan Ma, Chutima Maneewattana (Ajarn Chuti)
Corresponding Author: Chutima Maneewattana (Ajarn Chuti)(chutima.ma@ssru.ac.th)/Thailand
Abstract
This research analyzes policy integration for Zhuang folk song training centers in the Liujiang area through a mixed-methods examination of 60 centers over a 28-month period. Centers implementing green infrastructure achieved 45% greater resource utilization efficiency, 52% energy reduction, and 30% lower operational costs while maintaining cultural authenticity. Teachers receiving ecological-cultural training demonstrated 38% higher teaching effectiveness, with students showing 40% improved comprehension of natural imagery in folk songs. Environmental performance revealed 45-ton average annual carbon reductions per center and 37-42% lifecycle carbon savings through adaptive reuse strategies. Focus groups (n=12) achieved strong stakeholder consensus for multifunctional teaching classrooms (92% approval), ecological-cultural mentorship programs (92% approval), and digital technology integration (85% approval). Statistical analysis revealed significant positive correlations between environmental sustainability and cultural transmission outcomes (r=0.68-0.71, p<0.01). Machine learning algorithms with digital twin technology demonstrated 12% additional energy efficiency improvements while maintaining cultural preservation quality. The study proposes establishing a “Cultural-Ecological Integration Fund” and an “ECO-Cultural Mentor” certification system. This framework addresses the gap between technological advancement and cultural preservation, providing a comprehensive approach for indigenous cultural transmission in sustainable contexts.
Keywords
Green infrastructure, Cultural-ecological integration, Zhuang Folk Song conservation, Sustainable talent development, Artificial Intelligence
