Green Technology Transmission (GTT) offers a powerful framework for addressing global environmental challenges and advancing the Sustainable Development Goals (SDGs). While GTT has the potential to contribute to multiple SDGs, this study specifically examines its role in supporting SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). Conducted within the Australian industrial context, this research employs a qualitative-dominant mixed-methods approach to explore the environmental, economic, and social impacts of GTT. By examining organisational barriers and proposing practical, industry-specific strategies, the research offers actionable solutions to align practises with sustainability goals. This study explores GTT’s role in reducing emissions, improving resource efficiency, and fostering social equity, with leadership and stakeholder collaboration emerging as key enablers. The findings demonstrate how GTT serves as a bridge between policy and practice, empowering high-emission industries to adopt sustainable practises and contribute meaningfully to global sustainability efforts.

This paper presents an Industrial–Ecological Symbiosis Framework that enables industrial operations to achieve quantifiable ecological gains without compromising operational efficiency. The model integrates Mixed-Integer Linear Programming (MILP) with AI-optimised forecasting to allow real-time adjustments to production and resource use. It was tested across the apparel manufacturing, metalworking, and mining sectors using publicly available benchmark datasets. The framework delivered consistent improvements: fabric waste was reduced by 10.8%, energy efficiency increased by 15%, and carbon emissions decreased by 14%. These gains were statistically validated and quantified using ecological equivalence metrics, including forest carbon sequestration rates and wetland restoration values. Outputs align with national carbon accounting systems, SDG reporting, and policy frameworks—specifically contributing to SDGs 6, 9, and 11–13. By linking industrial decisions directly to verified environmental outcomes, this study demonstrates how adaptive optimisation can support climate goals while maintaining productivity. The framework offers a reproducible, cross-sectoral solution for sustainable industrial development.