持続可能なウェブデザインに向けて：ウェブサイトのAIベース炭素フットプリント最適化のためのGreenscan-AICO2R
Toward sustainable web design: Greenscan-AICO2R for AI-based carbon footprint optimization of websites (原題)
Manoj D. S, Sankari M, Aruneswar Meera S, Jai Vinita L
🤖 gxceed AI 要約
日本語
ウェブサイトの炭素排出を評価・最適化するフレームワークGreenscan-AICO2Rを提案。マルチページクロール、画像分類、NLPによる個別最適化提案を行い、画像圧縮や遅延読み込みで排出量を30〜40%削減可能とシミュレーションで示した。ケーススタディでは月間CO2排出量を32%削減。将来はCNNやリアルタイム炭素強度データの統合を計画。
English
Greenscan-AICO2R is a framework for evaluating and optimizing website carbon emissions. It performs multi-page crawling, rule-based image classification, and NLP-driven tailored recommendations. Simulations show 30-40% emission reduction via image compression and lazy loading; a case study achieved 32% monthly CO2 reduction. Future work includes CNN classification and real-time carbon intensity data.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本のデジタル企業にとって、ウェブサイトの炭素排出削減はScope 3や情報開示の観点で注目される。SSBJ開示や投資家対応でデジタルフットプリントの可視化が求められる中、本手法は実践的な最適化手段を提供する。
In the global GX context
As digital infrastructure's carbon footprint gains attention under CSRD and other disclosure frameworks, this framework offers a practical tool for companies to measure and reduce website emissions, aligning with global sustainability reporting trends.
👥 読者別の含意
🔬研究者:AIと炭素会計の交差領域で、ウェブサイトの炭素排出を自動評価する手法の参考になる。
🏢実務担当者:自社ウェブサイトの炭素排出を削減し、環境報告に活用できる具体的な最適化手法を提供する。
📄 Abstract（原文）
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 2 R (CO 2 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 2 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 2 R empowers web developers to create more energy-efficient and environmentally responsible digital applications.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3389/frsus.2026.1913543first seen 2026-09-05 05:15:59
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