灰色量子粒子群优化通用向量机的中国行业间碳排放转移网络预测研究

doi:10.16381/j.cnki.issn1003-207x.2020.08.018

中国管理科学 ›› 2020, Vol. 28 ›› Issue (8): 196-208.doi: 10.16381/j.cnki.issn1003-207x.2020.08.018

灰色量子粒子群优化通用向量机的中国行业间碳排放转移网络预测研究

吕康娟^1,2, 胡颖²

1. 上海大学悉尼工商学院, 上海 201800;
2. 上海大学经济学院, 上海 200444

收稿日期:2019-12-08 修回日期:2020-03-04 出版日期:2020-08-20 发布日期:2020-08-25
通讯作者: 胡颖(1982-),女(汉族),河南洛阳人,上海大学经济学院,博士研究生,研究方向:资源与环境经济学应用,研究,E-mail:huying1217@126.com. E-mail:huying1217@126.com
基金资助:
国家自然科学基金资助项目（71774108）

Prediction of Inter-industry Carbon Emissions Transfer Network in China Based on Grey Quantum Particle Swarm Optimizing General Vector Machine

LV Kang-juan^1,2, HU Ying²

1. SHU-UTS SILC Business School, Shanghai University, Shanghai 201800, China;
2. School of Economics, Shanghai University, Shanghai 200444, China

Received:2019-12-08 Revised:2020-03-04 Online:2020-08-20 Published:2020-08-25

摘要/Abstract

摘要： 针对行业间碳排放转移量预测问题，以中国1997-2017年间9年度28个行业间碳排转移量数据为样本，本文提出了基于小样本随机振荡序列的灰色量子粒子群优化通用向量机混合预测模型ROGM-QPSO-GVM。该模型首先使用ROGM（1，1）模型得到各行业对其他行业碳排放转移量的预测序列和残差序列，然后提出了一种新的量子粒子群优化（QPSO）算法优化GVM模型网络参数，构建了QPSO-GVM模型对残差序列进行修正，再将两部分的预测值相加得到行业间碳排放转移量预测值，最后根据所有预测值构建出行业间碳排放转移网络。结果表明ROGM-QPSO-GVM模型与其他模型相比具有更好的预测效果，并利用该模型对2020年、2025年、2030年中国行业间碳排放转移网络进行了预测及变化趋势分析。

关键词: 行业碳减排, 碳排放转移网络, GVM模型, QPSO算法, 混合预测

Abstract: The state attaches great importance to the carbon emissions reduction of industries. It is shown that the key industries of carbon emissions reduction can be identified by analyzing the carbon emissions transfer network formed by the exchange of intermediate products in industries. Therefore, it is of great significance to establish the forecasting model of carbon emissions transfer between industries and forecast the carbon emissions transfer network.Previous studies have been mainly focused on the prediction oftotal carbon emissions time series, which has a signicantincreasing trend year by year. However, the time series ofinterindustry carbon emissions transfer in China has the characteristicsof small sample, nonlinear, nonmonotoney, volatility and randomness,According to the data characteristics,a hybrid forecasting model of grey quantum particle swarm optimization general vector machine forsmall sample random oscillation sequence(ROGM-QPSO-GVM)is proposed.Firstly, the ROGM (1,1) model is used to obtain the prediction sequence and residual sequence of carbon emissions transfer between different industries. Then a new quantum particle swarm optimization (QPSO) algorithm is proposed to optimize the network parameters of GVM model, and the QPSO-GVM model is constructed to modify the residual sequence, then the prediction values of the two parts are added together to obtain the prediction values of inter-industry carbon emissions transfer network. Finally, an inter-industry carbon emissions transfer network is constructed based on all the predicted values.Empirical analysis is made on the data of carbon emission transfer between 28 industries in China from 1997 to 2017. The results show that the ROGM-QPSO-GVM model has better prediction effect than other models, and China's inter-industry carbon emissions transfer network in 2020, 2025 and 2030 is predicted by this model and the trend is analyzed. It provides a reference for the national policy intervention on industry carbon emissions reduction, and lays a foundation for further clarifying the responsibility of each industry.

Key words: carbon emissions reduction in industry, carbon emissions transfer network, GVM model, QPSO algorithms, hybrid forecasting

中图分类号:

F201

吕康娟, 胡颖. 灰色量子粒子群优化通用向量机的中国行业间碳排放转移网络预测研究[J]. 中国管理科学, 2020, 28(8): 196-208.

LV Kang-juan, HU Ying. Prediction of Inter-industry Carbon Emissions Transfer Network in China Based on Grey Quantum Particle Swarm Optimizing General Vector Machine[J]. Chinese Journal of Management Science, 2020, 28(8): 196-208.

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灰色量子粒子群优化通用向量机的中国行业间碳排放转移网络预测研究

Prediction of Inter-industry Carbon Emissions Transfer Network in China Based on Grey Quantum Particle Swarm Optimizing General Vector Machine

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