主管：中国科学院
主办：中国优选法统筹法与经济数学研究会
　　　中国科学院科技战略咨询研究院
中国管理科学

中国管理科学 >

2018 , Vol. 26 >Issue 11: 31 - 41

DOI: https://doi.org/10.16381/j.cnki.issn1003-207x.2018.11.004

论文

能源价格波动、诱导性技术进步与中国环境全要素生产率

展开
  • 1. 华中农业大学经济管理学院, 湖北 武汉 430070;
    2. 武汉大学经济与管理学院, 湖北 武汉 430072;
    3. 武汉大学人口·资源与环境经济研究中心, 湖北 武汉 430072;
    4. 甘肃银行股份有限公司, 甘肃 兰州 730000

收稿日期: 2017-08-14

  修回日期: 2018-01-27

  网络出版日期: 2019-01-23

基金资助

国家自然科学基金资助项目（71503094，71774122，71303177）；教育部哲学社会科学研究重大课题攻关项目（15JZD014）

收起

Energy Price, Induced Technological Change and China's Environmental Total Factor Productivity

Expand
  • 1. College of Economics & Management, Huazhong Agricultural University, Wuhan 430070, China;
    2. Economics and Management School, Wuhan University, Wuhan 430072, China;
    3. Center for Population, Resource & Environmental Economics Research, Wuhan University, Wuhan 430072, China;
    4. Bank of Gansu Limited Company, Lanzhou 730000, China

Received date: 2017-08-14

  Revised date: 2018-01-27

  Online published: 2019-01-23

Fold

摘要

随着能源的"金融属性"日益凸显，科学评估能源价格波动对经济增长和环境改善的综合影响具有重要的理论和现实意义。本文基于参数化的双曲线距离函数，将能源价格因素纳入环境全要素生产率（ETFP）的分析框架，剖析能源价格波动影响ETFP变动的内在机理；在此基础上，运用1995~2015年省际面板数据，考察能源价格诱导性技术进步对我国ETFP提升的促进作用及其时空分异规律，并识别该技术进步的具体偏向类型。研究结果表明：近20年来我国能源价格诱导性技术进步的平均速率为0.22%，其对ETFP变动的提升作用相对较弱，并表现出较强的时空分异特征。此外，从要素投入来看，能源价格诱导性技术进步总体上呈现资本和能源节约—劳动力使用型特征；从产出侧来看，其主要偏向于SO2的减排，但对CO2减排和GDP扩张的诱导作用不甚明显。

关键词： 能源价格; 诱导性技术进步; 环境全要素生产率; 双曲线距离函数

本文引用格式

杨福霞, 徐江川, 杨冕, 史岩 . 能源价格波动、诱导性技术进步与中国环境全要素生产率[J]. 中国管理科学, 2018 , 26(11) : 31 -41 . DOI: 10.16381/j.cnki.issn1003-207x.2018.11.004

Abstract

In the past few years, the financial character of energy becomes increasingly obvious, and the volatility of energy price presents more complicated feature.An increasing number of researchers focus on the impact of energy price volatility on economic growth, social welfare and environmental quality, respectively. In practice, the increase of energy price will stimulate producers developing energy-saving technologies, which improve productive efficiency and reduce emissions simultaneously. Given that the environmental total factor productivity index (ETFP index) can capture the comprehensive performance of economic growth and environmental protection, energy price-induced technological change (PITC) and its impact on ETFP change in China are investigated. To this end, the hyperbolic distance function is parameterize in a tanslog functional form, where energy price is incorporated. Based on this distance function, ETFP change is decomposed into the three components including technical efficiency change, energy price-induced and exogenous technological change. The effects of energy price-induced to ETFP change is assessed using the dataset for 30 administrative provinces in China over the period 1995-2015. On average, the rate of energy price-induced technological progress at the national level is 0.22% in the past 20 years, and its contribution to ETFP growth is 4.16%. The spatial-temproal variations for PITC are also observed. In addition, the direction of energy price-induced technological change is capital and energy saving and labor using. For the output bias, it is SO2 emissions reduction during the study period.Finally, some policy recommendations are also put forward on how to enhance China's energy price-induced technological change.

Key words： energy price; induced technological change; environmental total factor productivity; hyperbolic distance function

参考文献

[1] Valadkhani A, Babacan A, Dabir-Alai P. The impacts of rising energy prices on non-energy sectors in Australia[J]. Economic Analysis & Policy, 2014, 44(4):386-395.

[2] Arshad A, Zakaria M, Xi Junyang. Energy prices and economic growth in Pakistan:A macro-econometric analysis[J]. Renewable & Sustainable Energy Reviews, 2016, 55:25-33.

[3] 揭昌亮, 石峰, 庞一楠. 我国能源价格波动对宏观经济影响的动态研究[J]. 经济问题探索, 2015,(10):157-164.

[4] 从荣刚. 能源价格波动与中国宏观经济关系的实证研究[J]. 新金融, 2012,(7):51-54.

[5] 吴振信, 薛冰, 王书平. 基于VAR模型的油价波动对我国经济影响分析[J]. 中国管理科学, 2011, 19(1):21-28.

[6] 曹飞. 石油价格冲击与中国实际经济波动研究——基于开放RBC模型的分析[J]. 中国管理科学, 2015, 23(7):45-52.

[7] 任泽平. 能源价格波动对中国物价水平的潜在与实际影响[J]. 经济研究, 2012,(8):59-69.

[8] Doroodian K, Boyd R. The linkage between oil price shocks and economic growth with inflation in the presence of technological advances:A CGE model[J]. Energy Policy, 2003, 31(10):989-1006.

[9] 林伯强, 牟敦国. 能源价格对宏观经济的影响——基于可计算一般均衡(CGE)的分析[J]. 经济研究, 2008,(11):88-101.

[10] 原鹏飞, 吴吉林. 能源价格上涨情景下能源消费与经济波动的综合特征[J]. 统计研究, 2011, 28(9):57-65.

[11] 胡宗义, 刘亦文. 能源要素价格改革对宏观经济影响的CGE分析[J]. 经济评论, 2010,(2):5-15.

[12] 石敏俊, 王妍, 朱杏珍. 能源价格波动与粮食价格波动对城乡经济关系的影响——基于城乡投入产出模型[J]. 中国农村经济, 2009,(5):4-13.

[13] 吴海霞, 葛岩, 霍学喜,等. 国际能源价格对我国玉米价格波动的影响研究[J]. 中国农业大学学报, 2016, 21(6):164-172.

[14] 何凌云, 杨雪杰, 尹芳,等. 综合性能源价格指数对中国省域碳强度的调节作用及其比较-来自30个省份面板数据的实证分析[J]. 长江流域资源与环境, 2016, 25(6):877-888.

[15] 龚瑶, 严婷. 技术冲击、碳排放与气候环境——基于DICE模型框架的模拟[J]. 中国管理科学, 2014,22(S1):801-809.

[16] Ley M C, Stucki T, Woerter M. The impact of energy prices on green innovation[J]. The Energy Journal, 2016,37(1):41-75.

[17] 何凌云, 程怡, 范若滢,等. 国内能源相对价格对碳排放的价格杠杆作用[J]. 中国人口·资源与环境, 2015, 25(11):1-11.

[18] Kloess M, Müller A. Simulating the impact of policy, energy prices and technological progress on the passenger car fleet in Austria-A model based analysis 2010-2050[J]. Energy Policy, 2011, 39(9):5045-5062.

[19] Buus T. Energy efficiency and energy prices:A general mathematical framework[J]. Energy, 2017,139:743-754

[20] Sun Qi, Xu Lin, Yin Hua. Energy pricing reform and energy efficiency in China:Evidence from the automobile market[J]. Resource & Energy Economics, 2016, 44:39-51.

[21] 周五七. 能源价格、效率增进及技术进步对工业行业能源强度的异质性影响[J]. 数量经济技术经济研究, 2016,(2):130-143.

[22] Popp D. Induced innovation and energyprices[J]. American Economic Review, 2002, 92(1):160-180.

[23] 潘慧峰, 张金水. 基于ARCH类模型的国内油价波动分析[J]. 统计研究, 2005, 22(4):16-20.

[24] Bollerslev T.Generalizedautoregressive conditional het-eroskedasticity[J]. Journal of Econometrics,1986, 31(3):307-327.

[25] Emrouznejad A, Yang Guliang. A survey and analysis of the first 40 years of scholarly literature in DEA:1978-2016[J]. Socio-Economic Planning Sciences, 2018, 61:4-8.

[26] Zhou Peng, Ang B W, Poh K L. A survey of data envelopment analysis in energy and environmental studies[J]. European Journal of Operational Research, 2008, 189(1):1-18.

[27] Long Xingle, Chen Bin, Park B. Effect of 2008's Beijing Olympic Games on environmental efficiency of 268 China's cities[J]. Journal of Cleaner Production, 2017, 172:1423-1432.

[28] Long Xingle, Sun Mei, Cheng Faxin, et al. Convergence analysis of eco-efficiency of China's cement manufacturers through unit root test of panel data[J]. Energy, 2017, 134:709-717.

[29] 王兵, 吴延瑞, 颜鹏飞. 中国区域环境效率与环境全要素生产率增长[J]. 经济研究, 2010,(5):95-109.

[30] 匡远凤, 彭代彦. 中国环境生产效率与环境全要素生产率分析[J]. 经济研究, 2012,(7):62-74.

[31] Cuesta R A, Lovell C A, Zofío J L.Environmental efficiency measurement with translog distance functions:A parametric approach[J]. Ecological Economics, 2009, 68(8):2232-2242.

[32] Farrell M J.The measurement of productive efficiency[J]. Journal of the Royal Statistical Society, 1957, 120(3):253-290.

[33] Lovell C A K, Travers P, Richardson S, et al. Resources and functionings:A new view of inequality in australia[M]//Models and Measurement of Welfare and Inequality. Berlin:Springer Berlin Heidelberg, 1994:787-807.

[34] Diewert W E. Exact and superlative index numbers[J]. Journal of Econometrics, 1976, 4(2):115-145.

[35] Antle J M. The structure of U.S. agricultural technology, 1910-78[J]. American Journal of Agricultural Economics,1984, 66(4):414-421.

[36] Battese G E, Coelli T J. Frontier production functions, technical efficiency and panel data:With application to paddy farmers in India[J]. Journal of Productivity Analysis, 1992, 3(1-2):153-169.

[37] Zhang Zibin, Ye Jianliang. Decomposition of environmental total factor productivity growth using hyperbolic distance functions:A panel data analysis for China[J]. Energy Economics, 2015, 47:87-97.
Options
文章导航

/

〈 〉