A hybrid model for predicting the carbon price in Beijing: a pilot low-carbon city in China

Abstract

Beijing is one of the earliest pilot low-carbon cities in China. It was one of the first cities in China to establish a pilot carbon market to achieve this goal. As an emerging market, China’s carbon pricing mechanism is not yet complete. In this context, it is crucial for market managers and companies to predict carbon prices. This study uses a Prophet-EEMD-LSTM model to predict the carbon price in the Beijing carbon market, which significantly improves prediction performance. The advantage of this hybrid model is that it considers the particularities of carbon prices including trends, cyclical changes, and volatility. Considering that the carbon market has multiple complex characteristics, the carbon price is decomposed into multiple simple sequences using the Prophet and EEMD models. These simple sequences were predicted using an LSTM model. The hybrid model outperformed both econometric and single-machine learning models in terms of carbon price prediction. Based on the findings of this study, market managers and companies can take appropriate measures to prevent carbon price risks. These findings are conducive to the smooth operation of the carbon market, thereby providing sustainable support and guidance for the development of low-carbon cities.