Predicting EU Emissions Allowance Prices Using Macroeconomic Indicators and Hybrid AI Models
マクロ経済指標とハイブリッドAIモデルを用いたEU排出権価格の予測 (AI 翻訳)
Saptarshi Ganguly, Pooja Sengupta, D. Aggarwal
🤖 gxceed AI 要約
日本語
本研究は、LSTM、ランダムフォレスト、XGBoost、LightGBMなどのハイブリッドAIモデルを用いてEU ETSにおける排出権価格を予測する。マクロ経済指標を取り入れた結果、XGBoostが最も高い予測精度を示し、LSTMのような時系列特化モデルよりも優れていることがわかった。この手法は炭素市場の予測改善に貢献し、政策立案や投資戦略に有用な情報を提供する。
English
This study predicts EU Allowance (EUA) prices using hybrid AI models (LSTM, Random Forest, XGBoost, LightGBM) combined with macroeconomic indicators. XGBoost outperforms other models, demonstrating that ensemble methods can achieve superior accuracy in carbon price forecasting. The findings provide actionable insights for policymakers and investors in emissions trading markets.
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-ETSの本格稼働やカーボンプライシングの導入が進んでおり、 EU ETSを対象とした価格予測モデルの知見は、日本市場設計や参加企業のリスク管理に示唆を与える。特に、AIを用いた予測手法は日本の排出権取引にも応用可能であり、実務上の参考になる。
In the global GX context
As carbon pricing mechanisms expand globally (EU ETS, UK ETS, China ETS, etc.), accurate price forecasting becomes critical for market participants and regulators. This paper's hybrid AI approach demonstrates that ensemble learning can outperform deep learning for structured data, offering a practical tool for trading and policy evaluation in carbon markets.
👥 読者別の含意
🔬研究者:Provides a comparative evaluation of ML models for carbon price forecasting, highlighting XGBoost's superiority over LSTM for structured data.
🏢実務担当者:Offers a proven forecasting framework that can be used for hedging, trading strategies, or compliance cost estimation.
🏛政策担当者:Demonstrates how AI-based forecasting can inform carbon market design, stability assessment, and policy impact analysis.
📄 Abstract（原文）
Predicting carbon allowance prices has grown more crucial in relation to carbon market regulation, financial strategy, and environmental policy development. This study examines a hybrid forecasting system that combines deep learning with ensemble machine learning models to forecast the price fluctuations of EU Emissions Allowance (EUAs) within the European Union Emissions Trading System (EU ETS). By leveraging a dataset that includes past EUA prices alongside macroeconomic factors like exchange rates, stock indices, natural gas, and crude oil prices, we evaluate the forecasting capabilities of long short‐term memory (LSTM) neural networks, random forest (RF), and extreme gradient boosting (XGBoost) models. These models are assessed using commonly recognized metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). The findings suggest that LightGBM and XGBoost outshine Random Forest and LSTM in performance, with XGBoost emerging as the best predictor model. XGBoost specializes in capturing complex connections within structured data by managing nonlinear connections and interactions. Considering that carbon markets operate in phases, with each phase bringing its own set of reforms, this study offers novel results. In an integrated market prediction of evolving asset class series, LSTM models that are considered more adept at handling sequential data like time series might not yield superior forecasting performance. Our results emphasize the capability of ensemble learning approach forecasting systems to improve prediction accuracy in emissions trading markets, providing important information for policymakers, financial analysts, and sustainability strategists. The hybrid method discussed here demonstrates how AI‐based analytics can enhance more resilient and data‐informed environmental decision‐making.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1002/for.70173first seen 2026-05-31 04:49:06 · last seen 2026-06-03 05:14:58
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