National Carbon Price Prediction Considering Carbon Emissions from the Power Industry

doi:10.11930/j.issn.1004-9649.202308084

Abstract

Abstract:

In order to better predict the trend of national carbon prices, a national carbon price prediction model is constructed based on the autoregressive integrated moving average with exogenous variable model (ARIMAX), using different exogenous variables during the fulfillment and non-fulfillment period. Firstly, based on research on the institutional rules of the national carbon market and analysis of trading characteristics, it is found that the national carbon price is mainly influenced by the expectations of participants during the non-fulfillment period, and is mainly driven by the fulfillment demand of enterprises during the fulfillment period. Secondly, in terms of model training, an autoregressive differential moving average model is adopted to introduce different exogenous variables at different stages to improve the effectiveness of carbon price prediction. Finally, the real price data of the first compliance period in the national carbon market are used for verification, and the results show that the proposed national carbon price prediction model in this article is superior to the benchmark model in terms of accuracy.

Key words: power industry, carbon emission, carbon allowances, price forecast, national carbon market, ARIMAX model

Yirong WANG, Haolin CHEN, Lishen LIN, Jin TANG. National Carbon Price Prediction Considering Carbon Emissions from the Power Industry[J]. Electric Power, 2024, 57(5): 79-87.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202308084

https://www.electricpower.com.cn/EN/Y2024/V57/I5/79

Figures/Tables 6

Fig.1 The overall operation process of the national carbon market in China

Fig.2 Closing price and trading volume trend of the national carbon market quotas in China during July 7th and December 31st, 2021

Fig.3 Carbon emissions from the power industry and the closing price trend of the national carbon market quotas in China during July 16th and October 31st, 2021

Fig.4 Comparison between fulfillment period model predicted prices and actual prices

Table 1 Errors between model predicted prices and actual prices

	E_MA	E_RMS	E_MS	E_MAP/%	E_SMAP/%
非履约期	0.96	0.98	1.77	1.96	1.95
履约期	0.45	0.67	0.74	0.98	0.98

Fig.5 Comparison between fulfillment period model predicted prices and actual prices

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