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Xianzi Yang
School of Management, Hefei University of Technology, Heifei 230001, China

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Journal article
Published: 01 April 2020 in Sustainability
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To address climate change, the carbon emission trading scheme has become one of the main measures to achieve emission reduction goals. One of the core problems in constructing the carbon emissions trading market is determining carbon emissions trading prices. The scientific nature of carbon emissions pricing determines the effectiveness of market regulation. Research on the influencing factors and heterogeneous tail distribution of carbon prices can increase the accuracy of carbon pricing, which is particularly important for the development of the carbon emissions trading market. The current studies have some limitations and lack heterogeneous tail description. We employ the arbitrage pricing theory-standardized standard asymmetric exponential power distribution model to analyze China’s regional carbon emissions trading price and use a genetic algorithm to solve linear programming. The results confirm the theoretical results and efficiency of the proposed algorithm. First, the new model can capture the skewness, fat-tailed distribution, and asymmetric effects of China’s regional carbon emissions trading price. Second, the macroeconomy, similar products, energy price, and exchange rate influence the carbon price fluctuation; investors’ behavior plays an important role in the heterogeneous tail distribution of carbon price. The findings provide references for the government to take appropriate measures to promote carbon emission reduction and improve the effectiveness of China’s carbon market. Therefore, our findings can help enhance emission reduction and achieve sustainable development of a low-carbon environment.

ACS Style

Xianzi Yang; Chen Zhang; Yu Yang; Yaqi Wu; Po Yun; Zulfiqar Ali Wagan. China’s Carbon Pricing Based on Heterogeneous Tail Distribution. Sustainability 2020, 12, 2754 .

AMA Style

Xianzi Yang, Chen Zhang, Yu Yang, Yaqi Wu, Po Yun, Zulfiqar Ali Wagan. China’s Carbon Pricing Based on Heterogeneous Tail Distribution. Sustainability. 2020; 12 (7):2754.

Chicago/Turabian Style

Xianzi Yang; Chen Zhang; Yu Yang; Yaqi Wu; Po Yun; Zulfiqar Ali Wagan. 2020. "China’s Carbon Pricing Based on Heterogeneous Tail Distribution." Sustainability 12, no. 7: 2754.

Journal article
Published: 02 March 2020 in Sustainability
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Predicting the carbon price accurately can not only promote the sustainability of the carbon market and the price driving mechanism of carbon emissions, but can also help investors avoid market risks and increase returns. However, previous research has only focused on the low-order moment perspective of the returns for predicting the carbon price, while ignoring the shock of extreme events and market asymmetry originating from its pricing factor markets. In this paper, a novel extended higher-order moment multi-factor framework (EHM-APT) was formed to improve the prediction and to capture the driving mechanism of the carbon price. Furthermore, a multi-layer and multi-variable Long Short-Term Memory Network (Multi-LSTM) model was constructed so that the parameters and structure could be determined experimentally for testing the performance of the proposed framework. The results show that the pricing framework considers the shock of extreme events and market asymmetry and can improve the prediction compared with a framework that does not consider the shock of higher-order moment terms. Additionally, the Multi-LSTM model is more competitive for prediction than other benchmark models. This conclusion proves the rationality and accuracy of the proposed framework. The application of the pricing framework encourages investors and financial institutions to pay more attention to the pricing factor of extreme events and market asymmetry for accurate price prediction and investment analysis.

ACS Style

Po Yun; Chen Zhang; Yaqi Wu; Xianzi Yang; Zulfiqar Ali Wagan. A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network. Sustainability 2020, 12, 1869 .

AMA Style

Po Yun, Chen Zhang, Yaqi Wu, Xianzi Yang, Zulfiqar Ali Wagan. A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network. Sustainability. 2020; 12 (5):1869.

Chicago/Turabian Style

Po Yun; Chen Zhang; Yaqi Wu; Xianzi Yang; Zulfiqar Ali Wagan. 2020. "A Novel Extended Higher-Order Moment Multi-Factor Framework for Forecasting the Carbon Price: Testing on the Multilayer Long Short-Term Memory Network." Sustainability 12, no. 5: 1869.