Artificial Intelligence for Sustainable Engineering: Integrating Intelligent Systems, Energy Efficiency and Environmental Performance
DOI:
https://doi.org/10.63345/Keywords:
artificial intelligence, sustainable engineering, energy efficiency, environmental performance, intelligent control, lifecycle assessment, green computing.Abstract
Artificial intelligence (AI) offers opportunities to improve engineering sustainability through predictive control, resource optimization, renewable-energy integration and environmental monitoring. However, improvements in operational efficiency must be evaluated alongside the environmental burden of developing and operating intelligent systems. This paper presents a focused literature review integrating engineering applications, energy-performance measurement and environmental assessment. Thirteen verified publications and institutional sources were examined through structured thematic synthesis and descriptive analysis of selected published findings. The evidence includes an operator-reported reduction of up to 40% in data-centre cooling energy and an expert assessment identifying potential positive AI contributions to 134 of 169 Sustainable Development Goal targets. These findings represent different evidence types and cannot be combined into a single effectiveness estimate. The review develops an assessment framework connecting sensing, prediction, constrained optimization, operational verification and lifecycle accounting. Results indicate that AI can support sustainable engineering when environmental objectives are explicit, baselines are credible and computational burdens are included. Prediction accuracy alone does not establish energy savings, while reduced electricity consumption does not automatically demonstrate lower overall environmental impact. The paper concludes that sustainable AI deployment requires measurable net benefits, reliable operation and transparent reporting.
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