UK Carbon Price Dynamics: Long-Memory Effects and AI-Based Forecasting
英国炭素価格の動態：長期記憶効果とAIベースの予測 (AI 翻訳)
Zeno Dinca, Camelia Oprean-Stan, Daniel Balsalobre-Lorente
🤖 gxceed AI 要約
日本語
本研究は、英国排出量取引制度（UK ETS）の炭素価格動態を、深層学習や統計モデルを用いて分析。長期記憶効果と価格変動性を検証し、効率的市場仮説に反する持続的な長期記憶効果を確認。規制介入が価格に下方圧力を与え、政策不確実性が均衡を乱すことを示した。TGANなどの深層学習モデルは価格依存性の捕捉に優れるが過学習のリスクもあり、炭素市場予測におけるAI活用のトレードオフを明らかにした。
English
This study analyzes UK ETS carbon price dynamics using deep learning and statistical models. It finds persistent long-memory effects contradicting the Efficient Market Hypothesis, and shows that regulatory interventions exert downward price pressure, disrupting equilibrium. Deep learning models like TGANs outperform traditional methods but risk overfitting, highlighting trade-offs in AI-based carbon market forecasting.
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
英国ETSの価格分析は、日本のGX-ETSやカーボンプライシング制度設計に示唆を与える。長期記憶効果や規制介入の影響は、日本の排出量取引制度の安定性評価に応用可能。AI予測の限界も参考になる。
In the global GX context
This paper provides empirical evidence on carbon price dynamics and AI forecasting limitations, relevant for global ETS design and market efficiency debates. The findings on regulatory uncertainty and long-memory effects inform policymakers and researchers working on carbon pricing mechanisms worldwide.
👥 読者別の含意
🔬研究者:Provides empirical evidence on long-memory effects in carbon prices and compares AI forecasting models, contributing to carbon finance literature.
🏢実務担当者:Offers insights on carbon price behavior and forecasting challenges, useful for risk management and trading strategies in carbon markets.
🏛政策担当者:Highlights the impact of regulatory interventions on price stability, emphasizing the need for predictable frameworks in ETS design.
📄 Abstract（原文）
This study examines the price dynamics of the UK Emission Trading Scheme (UK ETS) by integrating advanced computational methods, including deep learning and statistical modelling, to analyze and simulate carbon market behaviour. By analyzing long-memory effects and price volatility, it assesses whether UK carbon prices align with theoretical expectations from carbon pricing mechanisms and market efficiency theories. Findings indicate that UK carbon prices exhibit persistent long-memory effects, contradicting the Efficient Market Hypothesis, which assumes price movements are random and fully reflect available information. Furthermore, regulatory interventions exert significant downward pressure on prices, suggesting that policy uncertainty disrupts price equilibrium in cap-and-trade markets. Deep learning models, such as Time-series Generative Adversarial Networks (TGANs) and adjusted fractional Brownian motion, outperform traditional approaches in capturing price dependencies but are prone to overfitting, highlighting trade-offs in AI-based forecasting for carbon markets. These results underscore the need for predictable regulatory frameworks, hybrid pricing mechanisms, and data-driven approaches to enhance market efficiency. By integrating empirical findings with economic theory, this study contributes to the carbon finance literature and provides insights for policymakers on improving the stability and effectiveness of emissions trading systems.
🔗 Provenance — このレコードを発見したソース
- openaire https://doi.org/10.3390/fractalfract9060350first seen 2026-05-05 19:06:46
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