Climate-Regulating Industrial Ecosystems: An AI-Optimised Framework for Green Infrastructure Performance

Shamima Rahman (), Ali Ahsan and Nazrul Islam Pramanik
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Shamima Rahman: Faculty of Business, Torrens University, Melbourne, VIC 3000, Australia
Ali Ahsan: Faculty of Business, Torrens University, Melbourne, VIC 3000, Australia
Nazrul Islam Pramanik: Faculty of Business, Torrens University, Melbourne, VIC 3000, Australia

Sustainability, 2025, vol. 17, issue 15, 1-24

Abstract: 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.

Keywords: optimisation; sustainability; green infrastructure performance; AI-driven forecasting; climate adaptation metrics; industrial ecosystems; multi-criteria decision-making; SDG implementation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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