AN EMPIRICAL STUDY ON CARBON PRICE PREDICTION USING STACKING ENSEMBLE MACHINE LEARNING
學年 112
學期 2
出版（發表）日期 2024-05-01
作品名稱 AN EMPIRICAL STUDY ON CARBON PRICE PREDICTION USING STACKING ENSEMBLE MACHINE LEARNING
作品名稱（其他語言）
著者 Chih-Feng Liao; Wang Zhang
單位
出版者
著錄名稱、卷期、頁數 Environmental Engineering and Management Journal 23(5), p.1047-1056
摘要 Carbon pricing is an essential instrument for reducing climate change and has substantial environmental protection as a co-benefit. This paper proposes a technique for predicting the price of carbon emission futures based on a stacking ensemble machine learning approach. This method incorporates the historical prices of carbon, fossil energy, and renewable energies, all of which influence carbon price fluctuations. Notably, there are no existing studies that employ renewable energy data and stacking ensemble models to forecast the futures price of European Union Allowances (EUA). By integrating diverse data sources and leveraging the power of ensemble learning, this research aims to fill that gap. The results of the experiments demonstrate that the stacking support vector regression model outperforms traditional machine learning models and earlier single-factor approaches in predicting future carbon prices. This superior performance is attributed to the model's ability to capture complex interactions among various influencing factors, thus providing more accurate and robust predictions. Overall, using the stacking learning model for pricing carbon has significant implications. It can lead to more informed policy decisions, better management of carbon markets, and more effective strategies for mitigating the impact of fossil fuels on the environment. By accurately forecasting carbon prices, stakeholders can enhance their planning and investment decisions, ultimately contributing to the reduction of pollution emissions and the advancement of sustainable energy practices. This approach represents a significant step forward in the utilization of advanced machine learning techniques for environmental and economic sustainability.
關鍵字 carbon price prediction; ensemble-learning algorithm
語言 en_US
ISSN 1582-9596; 1843-3707
期刊性質 國外
收錄於 SCI
產學合作
通訊作者
審稿制度 是
國別 ROU
公開徵稿
出版型式 ,電子版
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/127896 )