It can provide theoretical guidance for the development of low-carbon policies to research and forecast the growth rate of carbon emissions since carbon emissions have become an important reason for global warming. The growth rate of China's carbon emissions was decomposed into short-term volatility and trend term these two sequences by the use of empirical mode decomposition method, and the national policy, the domestic macroeconomic changes and the financial crisis influence to the short-term volatility and trend term were analyzed respectively. On this basis, using dynamic neural network to forecast the short-term volatility and trend term, and sum the two predicted values as the final growth rate of carbon emissions. Finally, from the error sequence of absolute value maximum, minimum, mean and standard deviation of the four angles to compare this prediction method with neural network model that separate input variables of carbon emissions or the growth rate of carbon emission, and found that the model presented in this paper can predict the growth rate effectively.
ZHANG Guo-xing, ZHANG Zhen-hua, LIU Peng, LIU Ming-xing
. The Running Mechanism and Prediction of the Growth Rate of China's Carbon Emissions[J]. Chinese Journal of Management Science, 2015
, 23(12)
: 86
-93
.
DOI: 10.16381/j.cnki.issn1003-207x.2015.12.011
[1] Paul S, Bhattacharya R N. CO2 emission from energy use in India:A decomposition analysis[J]. Energy Policy, 2004, 32(5):585-593.
[2] Tunç G I, Türüt-A??k S, Akbostanc? E. A decomposition analysis of CO2 emissions from energy use:Turkish case[J]. Energy Policy, 2009, 37(11):4689-4699.
[3] Fan Ying, Liu Lancui, Wu Gang, et al. Analyzing impact factors of CO2 emissions using the STIRPAT model[J]. Environmental Impact Assessment Review, 2006, 26(4):377-395.
[4] Hatzigeorgiou E, Polatidis H, Haralambopoulos D. CO2 emissions, GDP and energy intensity:A multivariate cointegration and causality analysis for Greece, 1977-2007[J]. Applied Energy, 2011, 88(4):1377-1385.
[5] Ang J B. CO2 emissions, energy consumption, and output in France[J]. Energy Policy, 2007,35(10):4772-4778.
[6] 郑长德,刘帅. 基于空间计量经济学的碳排放与经济增长分析[J]. 中国人口资源与环境, 2011,21(5):80-86.
[7] 曹广喜,杨灵娟. 基于间接碳排放的中国经济增长、能源消耗与碳排放的关系研究——1995~2007年细分行业面板数据[J]. 软科学, 2012, 26(9):1-6.
[8] Lean H H, Smyth R. CO2 emissions, electricity consumption and output in ASEAN[J]. Applied Energy, 2010, 87(6):1858-1864.
[9] 孙辉煌. 我国城市化、经济发展水平与二氧化碳排放——基于中国省级面板数据的实证检验[J].华东经济管理,2012,(10):69-74.
[10] Dalton M, O'Neill B, Prskawetz A, et al. Population aging and future carbon emissions in the United States[J]. Energy Economics, 2008, 30(2):642-675.
[11] Schipper L, Bartlett S, Hawk D, et al. Linking life-style and energy use:A matter of time?[J]. Annual Review of Energy, 1989,14(1):271-320.
[12] Lenzen M. Primary energy and greenhouse gases embodied in Australian final consumption:An input-output analysis[J]. Energy Policy, 1998,26(6):495-506.
[13] Weber C, Perrels A. Modelling lifestyle effects on energy demand consumption and related emission[J]. Energy Policy, 2000,28(8):549-566.
[14] Kim J H. Changes in consumption patterns and environmental degradation in Korea[J]. Structural Change and Economic Dynamics, 2002,13(1):1-48.
[15] 李楠,邵凯,王前进. 中国人口结构对碳排放量影响研究[J]. 中国人口资源与环境, 2011, 21(6):19-23.
[16] 王芳,周兴. 人口结构、城镇化与碳排放——基于跨国面板数据的实证研究[J]. 中国人口科学, 2012(2):47-56.
[17] 朱勤,彭希哲,陆志明,等. 人口与消费对碳排放影响的分析模型与实证[J]. 中国人口资源与环境, 2010,20(2):98-102.
[18] 晏文隽,郭菊娥,龚利. 次贷危机影响下我国能源生产态势预测[J]. 中国人口资源与环境, 2009,19(2):7-12.
[19] 秦宇. 应用经验模态分解的上海股票市场价格趋势分解及周期性分析[J]. 中国管理科学, 2008,16(10):219-225.
[20] 阮连法,包洪洁. 基于经验模态分解的房价周期波动实证分析[J].中国管理科学, 2012,20(3):41-46.
[21] Zhang Xun, Lai K K, Wang Shouyang. A new approach for crude oil price analysis based on empirical mode decomposition[J]. Energy Economics, 2008,30(3):905-918.
[22] Zhang Xun, Yu L, Wang Shouyang, et al. Estimating the impact of extreme events on crude oil price:An EMD-based event analysis method[J]. Energy Economics, 2009, 31(5):768-778.
[23] 吴登生,李建平,汤铃,等. 生猪价格波动特征及影响事件的混合分析模型与实证[J]. 系统工程理论与实践, 2011,31(11):2033-2042.
[24] 孟磊,郭菊娥,郭广涛. 基于延期交割费的我国燃料油期现货价格关系辨析[J]. 管理评论, 2011, 23(6):9-15,22.
[25] 张松,金亮. 中国能源消费的EMD分析与预测[J]. 数学的实践与认识, 2011, 41(12):114-119.
[26] 王书平,胡爱梅,吴振信. 基于多尺度组合模型的铜价预测研究[J]. 中国管理科学, 2014, 22(8):21-28.
[27] 王文波,费普生,羿旭明. 基于EMD与神经网络的中国股票市场预测[J]. 系统工程理论与实践, 2010,30(6):1027-1033.
[28] Yu L, Wang Shouyang, Lai K K. Forecasting crude oil price with an EMD-based neural network ensemble learning paradigm[J]. Energy Economics, 2008,30(5):2623-2635.
[29] 杨云飞,鲍玉昆,胡忠义,等. 基于EMD和SVMs的原油价格预测方法[J]. 管理学报, 2010, 7(12):1884-1889.
