CarbonLens AI- powered footprint tracker
CarbonLens: AI搭載のカーボンフットプリントトラッカー (AI 翻訳)
Sejal Jain, Pranav Singh, Sachin kushwaha, Sanskar Rathore
🤖 gxceed AI 要約
日本語
本論文では、個人の二酸化炭素排出量を監視・削減するためのAI駆動プラットフォームCarbonLensを提案する。React.js、Node.js、Express.js、MongoDB、Ollamaベースの大規模言語モデル（LLM）を統合し、ユーザーの日常活動（交通、エネルギー消費、食習慣など）から排出量を自動推定する。インタラクティブなダッシュボードでリアルタイム可視化、カーボンスコア、週間トレンド、カテゴリ別分析を提供し、持続可能な行動変容を促進する。評価実験により、AIによる排出推定の有効性と分析の有用性を示した。
English
This paper presents CarbonLens, an AI-powered carbon footprint tracking and sustainability analytics platform for personal emissions. It uses React.js, Node.js, Express.js, MongoDB, and Ollama-based LLMs to automatically estimate emissions from daily activities (transport, energy, diet). Interactive dashboards provide real-time visualization, carbon score, trends, and personalized recommendations. Experimental evaluation confirms effectiveness in promoting sustainable behavior.
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
日本では、個人のカーボンフットプリントに対する意識が高まっており、企業のサプライチェーンScope3対応や家庭部門の脱炭素化にも関連する。CarbonLensのようなツールは、個人の排出量可視化を通じて、企業の製品カーボンフットプリントへの理解促進や、環境教育に貢献し得る。
In the global GX context
As personal carbon footprint tracking gains traction globally, this tool can complement corporate disclosure (GHG Protocol Scope 3) and support citizen engagement. The paper demonstrates a practical AI-integrated approach that could inform consumer-facing climate action platforms and align with growing demand for accessible sustainability analytics tools.
👥 読者別の含意
🔬研究者:This paper offers a reference architecture for AI-based personal carbon footprint estimation, though the evaluation is limited in scale.
🏢実務担当者:Companies developing consumer sustainability tools can adopt the CarbonLens integration of LLMs for emission estimation.
🏛政策担当者:Could inform policies promoting digital tools for individual climate action, but no specific regulatory recommendation.
📄 Abstract（原文）
Climate change and environmental sustainability have become major global concerns, highlighting the need for accessible tools that help individuals understand and reduce their carbon emissions. This paper presents CarbonLens, an AI-powered carbon footprint tracking and sustainability analytics platform designed to monitor personal carbon emissions and provide actionable insights for environmentally responsible decision-making. The platform integrates a modern web-based architecture consisting of React.js, Node.js, Express.js, MongoDB, and Ollama-based Large Language Models (LLMs) to deliver intelligent emission analysis and personalized sustainability recommendations. CarbonLens enables users to record daily activities related to transportation, energy consumption, dietary habits, and other carbon-generating behaviors. The system automatically estimates carbon emissions using AI-assisted activity analysis and categorizes them into meaningful sustainability metrics. Interactive dashboards provide real-time emission visualization, carbon score tracking, weekly trend analysis, category-wise breakdowns, and personalized recommendations aimed at reducing environmental impact. The platform also includes secure authentication, activity history management, report generation, and customizable themes to enhance usability and accessibility. Experimental evaluation demonstrates the effectiveness of AI-assisted emission estimation and the usefulness of interactive analytics in promoting sustainable behavioral changes. CarbonLens contributes toward environmental awareness by combining artificial intelligence, data analytics, and user-centric design to create an accessible and scalable sustainability monitoring solution.
🔗 Provenance — このレコードを発見したソース
- openalex https://github.com/Sejaljain208/carbon_footprint_trackerfirst seen 2026-07-09 05:06:54
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。