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Carbon trading price prediction based on a two-stage heterogeneous ensemble method

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Abstract

Several countries have formulated carbon–neutral plans in dealing with global warming, which have also derived various carbon trading markets. All parties involved in carbon trading aim to obtain the maximum benefit from it, and this requires participants to accurately judge the carbon trading price. This study then proposes a two-stage heterogeneous ensemble method for predicting carbon trading prices. To accurately capture the characteristics of the time series data, we extracted four feature sets based on the lag length, moving average, variational mode decomposition, and empirical mode decomposition methods. Subsequently, four algorithms, linear regression, neural network, random forest, and XGBoost, constructed the first-layer model. We used a neural network algorithm to build the second-layer model to enhance the predictive model fit. Moreover, we used the particle swarm optimization algorithm to optimize the crucial parameters involved in the model. Extensive numerical experiments were conducted on carbon trading data from the Beijing carbon trading market in the past five years (2016–2021), and showed that our proposed method is superior to other popular methods such as LightGBM, support vector machine, and k-nearest neighbor.

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Notes

  1. https://www.c2es.org/content/extreme-weather-and-climate-change/. Access date: 2021/10/31.

  2. https://www.un.org/en/chronicle/article/small-islands-rising-seas. Access date: 2021/10/31.

  3. https://en.wikipedia.org/wiki/United_Nations_Framework_Convention_on_Climate_Change. Access date: 2021/10/31.

  4. https://en.wikipedia.org/wiki/Kyoto_Protocol. Access date: 2021/10/31.

  5. https://en.wikipedia.org/wiki/2015_United_Nations_Climate_Change_Conference. Access date: 2021/10/31.

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Acknowledgements

This paper was supported in part by the National Natural Science Foundation of China under Grant Numbers 72171161, 71971041, 71871148, and 71533001; by the China Scholarship Council under Grant Number 202006060162, by the Outstanding Young Scientific and Technological Talents Foundation of Sichuan Province under Grant number 2020JDJQ0035; and by the Major Program of National Social Science Foundation of China under Grant 20&ZD084; and by Sichuan University to Building a World-class University under Grant No. SKSYL2021-08.

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Authors and Affiliations

  1. School of Economics and Management, Dalian University of Technology, Dalian, China

    Shaoze Cui

  2. Business School, Sichuan University, Chengdu, China

    Dujuan Wang & Xin Fan

  3. School of Economics and Management, University of Electronic Science and Technology of China, Chengdu, China

    Yunqiang Yin

  4. Centre for Simulation, Analytics and Modelling (CSAM), University of Exeter Business School, Exeter, EX4 4PU, UK

    Lalitha Dhamotharan

  5. EMLYON Business School, Ecully, France

    Ajay Kumar

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  1. Shaoze Cui
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  4. Xin Fan
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  6. Ajay Kumar
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Correspondence to Yunqiang Yin.

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Cui, S., Wang, D., Yin, Y. et al. Carbon trading price prediction based on a two-stage heterogeneous ensemble method. Ann Oper Res 345, 953–977 (2025). https://doi.org/10.1007/s10479-022-04821-1

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