The Dynamic Regulation Mechanism and Optimization of the Carbon Emission Rights Market in the Era of Low-Carbon Economy
低炭素経済時代における炭素排出権市場の動的規制メカニズムと最適化 (AI 翻訳)
Qinghao Zheng
🤖 gxceed AI 要約
日本語
この研究は、低炭素経済下の炭素排出権市場において、深層強化学習（DRL）を用いた動的調整システムを提案。データ層、モデル層、意思決定層、相互作用層からなる知的計算支援システムを構築し、排出監視・予測、価格変動予測、最適化モデルを統合。シミュレーションでは、DRL戦略がベンチマーク戦略やMILP戦略より、純収益、変動性抑制、応答効率で優れることを示した。
English
This study proposes an intelligent computing system for dynamic regulation of carbon emission rights markets using deep reinforcement learning (DRL). It integrates monitoring, prediction, and optimization models, creating a closed-loop 'monitor-predict-decide-feedback' system. Simulations show DRL outperforms benchmarks in net revenue, volatility control, and response efficiency, with cumulative net revenue reaching 7.52 million yuan over 30 days and volatility dropping to 8.7%.
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GXリーグや東証カーボンクレジット市場が始動しつつあるが、取引メカニズムの最適化は未着手の課題。本論文のAIによる動的調整手法は、日本の排出量取引の効率化・安定化に示唆を与える。
In the global GX context
As carbon markets expand globally (EU ETS, China ETS, etc.), optimizing regulation with AI becomes increasingly relevant. This paper demonstrates how deep reinforcement learning can improve market stability and efficiency, offering a novel approach for regulators and exchange operators worldwide.
👥 読者別の含意
🔬研究者:A practical application of DRL to carbon market regulation, providing a benchmark for future AI-driven market design studies.
🏢実務担当者:Carbon market operators can explore the proposed intelligent system to enhance price stability and compliance monitoring.
🏛政策担当者:Relevant for designing adaptive carbon pricing mechanisms that respond dynamically to market conditions.
📄 Abstract（原文）
Under the background of low-carbon economic development, carbon emission trading, as an important institutional arrangement, can effectively coordinate the role of emission reduction constraints, price signals and resource allocation. However, there are still some problems in the current market operation, such as wide and large amount of emission data, wide range of price transmission and long chain, and lagging regulation. In this paper, how to realize the dynamic regulation of carbon emission trading in carbon emission market is studied. With the help of computer science, an intelligent computing support system based on data layer, modeling layer, decision layer and interaction layer is constructed. Based on the system, the carbon emission monitoring and prediction model, the carbon quota market price change prediction model, and the dynamic adjustment optimization model of carbon emission trading using deep reinforcement learning method were established. Finally, the closed-loop system of "monitoring-prediction-decision-feedback" is obtained. Finally, the system is simulated and the effectiveness of different control strategies is compared. The results show that the proposed DRL strategy outperforms the benchmark strategy and MILP strategy in terms of net income, volatility control, and response efficiency. In the typical 30-day simulation window, the cumulative net income reaches 7.52 million yuan, the carbon price volatility rate drops to 8.7%, the compliance deviation rate drops to 3.2%, and the average adjustment response duration is shortened to 6 minutes. The study suggests that embedding intelligent computing in the adjustment process of the carbon emission rights market helps improve the accuracy, forward-looking nature, and coordination of market operation. Povzetek: Študija obravnava uporabo tehnologije navidezne resničnosti pri športnem pouku. Združuje računalniški vid, 3D rekonstrukcijo skeleta, semantično prepoznavanje gibov in virtualno interakcijo. Rezultati kažejo boljšo vizualizacijo gibov, hitrejšo povratno informacijo in večjo interaktivnost. Kljub temu ostajajo izzivi pri večuporabniških scenarijih, udobju opreme, stabilnosti sistema in stroških uvajanja v šolski učni praksi.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.65102/is2026288first seen 2026-05-15 17:17:53
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