Abstract
Introduction
The rapid growth of internet usage has significantly increased the energy demand of digital infrastructure, contributing to rising global carbon emissions. Modern websites, often characterized by large media assets and complex front-end structures, are major contributors to this environmental burden.
Methods
To address this challenge, proposed Greenscan-AICO
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R (CO
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Emission Reduction by AI), a practical framework designed to evaluate and enhance the sustainability of websites. Unlike widely used tools such as Website Carbon Calculator or Page Speed Insights, which primarily report performance metrics or provide generic suggestions, proposed framework performs multi-page crawling, applies rule-based image classification to distinguish essential from decorative content, estimates monthly carbon emissions based on page weight and assumed visitor tra_c, and leverages natural language processing (NLP) to generate tailored, content-specific optimization recommendations.
Results
Simulation shows that proposed optimization strategies such as image compression, lazy loading, and optimized media format can help to reduce the web page size as well as carbon emissions by approximately 30%–40%.
Discussion
A representative case study has revealed that the proposed framework helped in achieving a 32% reduction in monthly CO
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emissions in comparison with the baseline scenario.
Future Direction
Future enhancements include replacing rule-based classification with convolutional neural networks (CNNs), deepening NLP for more context-aware suggestions, supporting JavaScript-rendered content, integrating real-time carbon intensity data, and developing automated resource optimization. By integrating analysis, simulation, and actionable recommendations, Greenscan-AICO
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R empowers web developers to create more energy-efficient and environmentally responsible digital applications.