EU-Asia Green Energy Supply Chain (2020-2025)
EU-アジア グリーンエネルギーサプライチェーン（2020-2025） (AI 翻訳)
Mohamed H. Abdelaziz
🤖 gxceed AI 要約
日本語
本データセットは、EUのグリーンエネルギー移行におけるアジア製造への依存をモデル化し、サプライチェーン物流、経済指標、環境政策の交差点を捉える。太陽光パネル輸入量、リチウムイオン電池価格、輸送コスト、PMI、EU炭素価格などの高頻度時系列データを含み、供給ショックや価格変動の予測モデル構築を支援する。
English
This dataset models the EU's reliance on Asian manufacturing for green energy transition, capturing the intersection of supply chain logistics, economic indicators, and environmental policies. It includes high-frequency time-series data on solar imports, lithium battery prices, freight costs, PMI, and EU carbon prices, enabling predictive models for supply shocks and price volatility.
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
日本企業にとって、EUのグリーンエネルギーサプライチェーン依存構造と炭素価格の影響を理解することは、自社の調達戦略やリスク管理に示唆を与える。特に、アジア製造拠点との連携やEU炭素国境調整メカニズム（CBAM）への対応を検討する上で参考になる。
In the global GX context
This dataset provides a valuable resource for global researchers and practitioners analyzing the EU's green energy supply chain dependencies. It enables exploration of how carbon pricing and freight volatility interact with renewable energy deployment, offering insights for supply chain resilience and policy design.
👥 読者別の含意
🔬研究者:Researchers can use this dataset to build predictive models for supply chain disruptions and analyze correlations between carbon prices and clean energy costs.
🏢実務担当者:Corporate sustainability teams can leverage the data to assess supply chain risks and optimize procurement strategies in the context of EU carbon pricing.
🏛政策担当者:Policymakers can use the dataset to simulate the impact of carbon pricing on supply chain dynamics and inform policies for energy security.
📄 Abstract（原文）
High-frequency data simulating dependency, freight volatility and carbon impact. About Dataset The European Union’s transition to green energy heavily relies on Asian manufacturing. This dataset models the critical intersection between supply chain logistics, economic indicators and environmental policies. It is designed to help analysts build predictive models for supply shocks and price volatility. (Note: The macro-indicators such as PMI and Freight Costs have been interpolated to a 6-hour granularity to facilitate advanced high-frequency time-series forecasting and BI dashboard simulations). Data Dictionary Timestamp: 6-hour interval timeframe (2020 to 2025). EU_Solar_Imports_GW: Volume of solar panels imported to the EU (Gigawatts). Lithium_Battery_Index: Price index reflecting the cost of lithium-ion batteries. Shanghai_to_Rotterdam_Freight_USD: Container shipping costs. Asia_Manufacturing_PMI: Purchasing Managers' Index (Economic health indicator). EU_Carbon_Price_EUR: Price of carbon emissions trading in the EU. Lead_Time_Days: Average delay from manufacturing in Asia to delivery in the EU. Research Ideas Supply Chain Forecasting: Can we predict Lead_Time_Days spikes using Freight and PMI? Cost Impact Analysis: How does the EU_Carbon_Price correlate with the Lithium_Battery_Index? BI Dashboarding: Build a strategic dashboard to alert stakeholders of impending supply shocks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.19480014first seen 2026-05-05 19:12:25
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