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Research on the whole chain low-carbon transformation Path of Yunnan fresh cut Rose under the guidance of AI-driven ESG -- From the perspective of LCA and intelligent collaborative governance

AI駆動型ESGに基づく雲南省切りバラ産業の全チェーン低炭素化経路——LCAとインテリジェント協調ガバナンスの視点から (AI 翻訳)

Ni Li

Ingegneria Sismica📚 査読済 / ジャーナル2026-06-10#AI×ESGOrigin: CN経営インパクト: コスト削減対象セクター: agriculture
DOI: 10.65102/is20261061
原典: https://doi.org/10.65102/is20261061

🤖 gxceed AI 要約

日本語

本研究は、LCAを用いて雲南省切りバラ産業のサプライチェーン全体のカーボンフットプリントを定量化し、AIによる精密農業、ブロックチェーンによるトレーサビリティ、IoTによる協調ガバナンスを組み合わせた低炭素化経路を提案する。マルチステークホルダーの協力とESGフレームワークの活用により、排出削減と市場競争力向上を同時に実現する。

English

This study quantifies the carbon footprint of Yunnan's cut rose supply chain using LCA, and proposes a low-carbon transformation path combining AI precision agriculture, blockchain traceability, and IoT-enabled collaborative governance. It highlights the synergy of AI and ESG frameworks to achieve emission reductions while enhancing market competitiveness through multi-stakeholder cooperation.

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

中国雲南省の花卉産業は日本の花き輸入とも関連する可能性がある。AI×ESGによるサプライチェーン全体の可視化と削減策は、日本の農業分野やScope3対応にも示唆を与える。

In the global GX context

This paper provides a practical AI-ESG case study in agricultural supply chains, relevant to global Scope 3 accounting and the application of digital tools for decarbonization. It offers insights for regions like Southeast Asia and Japan that import floriculture products.

👥 読者別の含意

🔬研究者:Demonstrates AI-ESG integration with LCA for a perishable supply chain; methodology can be adapted to other agricultural sectors.

🏢実務担当者:Provides actionable low-carbon pathways (precision ag, renewables, certification) for flower producers and exporters facing carbon regulations.

🏛政策担当者:Illustrates how AI-driven governance frameworks can support carbon reduction targets in regional agricultural industries.

📄 Abstract（原文）

Yunnan, as a major global production base for cut roses, faces the challenge of high carbon emissions in its industry, including greenhouse gas emissions and resource consumption in planting, harvesting, processing, transportation, and sales. This study quantifies the carbon footprint across the entire supply chain using the LCA method, investigating carbon emissions and their main sources during the planting and transportation stages. AI technology is introduced to optimize planting forecasts using machine learning algorithms, blockchain enables transparent supply chain tracking, and an IoT-supported intelligent collaborative governance framework promotes collaboration among multiple stakeholders (farmers, businesses, and government) to achieve carbon reduction targets. The transformation path includes: (1) AI-assisted precision agriculture to reduce fertilizer input; (2) promoting renewable energy substitution under the ESG framework to enhance social responsibility and governance efficiency; and (3) establishing a low-carbon certification system to enhance market competitiveness. This study emphasizes the synergistic effect of AI and intelligent governance, providing theoretical and practical guidance for the sustainable development of Yunnan's cut rose industry.

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