ECMAS-v1
炭素排出枠オークションの段階的導入が電力部門の持続可能な排出削減を促進する (AI 翻訳)
Li Z, Lu‐Tao Zhao, Zhi Qu, Zhe-Yi Chen, Rui-Xiang Qiu, Xing-Yu An, Dai-Song Wang
🤖 gxceed AI 要約
日本語
本研究は、炭素排出枠オークションの導入率と排出枠削減経路が電力市場に与える影響を分析するマルチエージェントシミュレーションモデル（ECMAS）を開発。2241の異質な発電企業をエージェントとして、電力市場と炭素市場の相互作用を再現。段階的なオークション導入と排出枠削減の組み合わせが、炭素価格の安定化と持続可能な排出削減に有効であることを示した。
English
This study develops a multi-agent simulation model (ECMAS) integrating electricity and carbon markets to analyze the impact of carbon allowance auction ratios and quota reduction pathways. With over 2,000 heterogeneous power enterprises, it finds that gradual introduction of auctions combined with progressive quota tightening stabilizes carbon prices, supports low-carbon investment, and achieves sustainable emission reduction in the power sector.
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
中国の炭素市場シミュレーションモデルは、日本のカーボンプライシング設計（特に排出枠オークションの段階的導入）に示唆を提供。ただし、中国の電力市場構造を前提としており、日本への直接適用には調整が必要。
In the global GX context
This paper provides a novel simulation framework for designing carbon allowance auctions in electricity markets. It demonstrates the trade-off between aggressive and gradual auction introduction, offering evidence for policymakers in China and beyond. The findings are particularly relevant for countries implementing or expanding carbon pricing mechanisms.
👥 読者別の含意
🔬研究者:Provides a detailed agent-based modeling approach for analyzing interaction between carbon and electricity markets, useful for researchers in carbon pricing and market design.
🏢実務担当者:Power companies can use the insights on auction dynamics to anticipate carbon cost impacts under different policy scenarios.
🏛政策担当者:Offers evidence that gradual auction introduction combined with progressive quota tightening stabilizes carbon prices and supports emission reduction, informing carbon market policy design.
📄 Abstract（原文）
📌 Introduction This repository contains the official implementation of the paper: “Gradual Introduction of Carbon Allowance Auctions Facilitate the Sustainable Emission Reduction in the Power Sector” This study develops a bottom-up multi-agent simulation model (ECMAS) to analyze the interaction between the electricity market and carbon market under different policy designs. 🔍 Key Features ⚡ Integrated electricity market + carbon market (primary & secondary) 🧠 Multi-agent simulation with 2,000+ heterogeneous power enterprises 🔄 Dynamic policy design: Quota reduction pathways Auction ratio evolution 📊 Evaluation of: Carbon prices Emission reduction Energy structure Market stability 🧠 Model Overview The ECMAS model simulates interactions among three core agents: Government Power grid Power enterprises (2241 agents) And three coupled markets: Electricity market Primary carbon market Secondary carbon market ⚙️ Simulation Modules The model consists of six modules: Electricity production Electricity trading Low-carbon technology transformation Carbon allowance allocation Carbon allowance trading Unit investment and retirement 📊 Main Findings 🚫 Aggressive auction introduction + rapid quota reduction Causes carbon price collapse Leads to supply risk ✅ Gradual auction introduction (Smooth scenario) Stabilizes carbon prices Supports low-carbon investment Achieves sustainable emission reduction ⚖️ Optimal policy path: Gradual tightening + phased auction increase 🚀 Requirements Python 3.9+ Mesa (agent-based modeling framework) 📄 License This project is licensed under the CC BY-NC 4.0 License. 🚫 Non-commercial use only 📚 Research purposes only 📄 Citation required 📑 Citation If you use this code, please cite: @article{li2026ecmas, title={Gradual Introduction of Carbon Allowance Auctions Facilitate the Sustainable Emission Reduction in the Power Sector}, author={Li, Zhao-Yuan and Zhao, Lu-Tao and Qu, Zhi and Chen, Zhe-Yi and Qiu, Rui-Xiang and An, Xing-Yu and Wang, Dai-Song}, journal={iScience}, year={2026} } ⚠️ Disclaimer This code is provided "as is" without warranty. The authors are not responsible for any damages arising from its use.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.5281/zenodo.20092191first seen 2026-05-17 07:17:51
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