Greenscore: Sustainability Scoring and Effective Environmental Management System for Quick-Commerce
Greenscore: クイックコマースのためのサステナビリティスコアリングと効果的な環境マネジメントシステム (AI 翻訳)
Sampada G. Kulkarni, S. Harshitha, Asish Kumar Yeleti, Disha A., D. Patankar, Jyoti Shetty, Vinod A.R.
🤖 gxceed AI 要約
日本語
本論文は、クイックコマース向けにAIを活用したサステナビリティ評価システム「GreenScore」を提案する。ISO14040/44準拠のLCAに基づき、炭素排出、水使用、包装、輸送、季節調達の5指標で製品をスコアリング。消費者向けには代替製品推奨、製造業者向けにはグリーンプレミアム分析と投資最適化を提供する。
English
This paper introduces GreenScore, an AI-based sustainability intelligence system for quick-commerce. It uses an ISO 14040/44 compliant LCA scoring engine with five weighted measures (carbon, water, packaging, transport, seasonal sourcing). The platform offers consumer recommendations for greener alternatives and a manufacturer module for green premium analysis, risk assessment, and investment optimization.
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
日本でもクイックコマースの拡大に伴い、環境負荷評価と開示（SSBJ等）の重要性が増している。本フレームワークは、LCAとAIを組み合わせた実践的な意思決定支援ツールとして、小売・物流業界の脱炭素施策の優先順位付けに貢献し得る。
In the global GX context
Under global frameworks like ISSB and CSRD, companies need data-driven tools linking sustainability to financial performance. GreenScore provides a practical, AI-powered approach for quick-commerce to measure environmental impact and align investments with ROI, addressing the growing demand for integrated ESG reporting.
👥 読者別の含意
🔬研究者:Provides a novel integration of AI, LCA, and portfolio optimization for sustainability scoring in retail.
🏢実務担当者:GreenScore can help sustainability managers identify cost-effective green investments and communicate environmental performance to stakeholders.
🏛政策担当者:Demonstrates how AI can operationalize SDG 13 and support regulatory compliance through measurable environmental metrics.
📄 Abstract（原文）
The fast-moving consumer goods (FMCG) sector is under increasing pressure to align environmental sustainability with profitability, in part because it lacks practical, decision-focused frameworks that use data to guide choices. Many current sustainability metrics do not provide clear guidance for decision-making and do not measure the financial returns linked to sustainability efforts. This paper introduces GreenScore, an AI-based sustainability intelligence system that supports measuring and managing environmental performance across consumer and manufacturer settings for Quick-Commerce Systems. GreenScore presents a scoring engine based on an ISO 14040/14044 compliant life cycle assessment (LCA). It rates products using five weighted environmental measures: Carbon Footprint, Water Use, Packaging Sustainability, Transport Emissions, and Seasonal Sourcing. The platform includes a consumer recommendation system that identifies greener alternatives and substitutes in real time and suggests sustainable products based on user preferences. It also includes a manufacturer intelligence module that estimates green premium opportunities, assesses competitive risk, and ranks sustainability investments by expected return on investment. Analytic methods such as Multi-objective Portfolio Optimization and Predictive demand models allow manufacturers to rank potential actions while accounting for budget and schedule limits. GreenScore suggests that spending on sustainability can be aligned with revenue growth, so that meeting environmental requirements becomes a measurable source of competitive advantage and supports UN Sustainable Development Goal 13, Climate Action.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1109/access.2026.3701784first seen 2026-06-29 09:08:56
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