中国能源与经济发展关系实证研究
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摘要
摘要：能源是一个国家经济发展的物质基础,对社会的进步和发展具有重要的作用,任何一个国家的经济发展离不开能源。中国自改革开放以来,经济飞速发展,引起世界人们的关注。在国内伴随着经济的迅猛发展,其国内的能源消费也出现快速地增加。因此经济发展与能源之间的关系受到越来越多人的关注。在此背景基础上对我国能源与经济发展关系进行研究,目的在于为我国能源政策和国民经济发展计划的合理制定提供价值参考。其主要研究成果包括：
(1)为客观、正确地研究能源消费与经济发展关系,文中首先分析了能源消费的影响因素。建立了能源消费影响因素的联立方程模型,并对模型进行识别和分析,最后运用二阶段最小二乘法(2SLS)、三阶段最小二乘法(3SLS)以及广义矩估计法(GMM)对模型分别进行参数估计。考虑到各影响因素之间也会相互影响,即某一影响因素通过另一影响因素的作用再影响能源消费,即通径分析问题。所以文章进一步讨论了能源消费影响因素的通径分析。
(2)经济的发展对于整个社会的进步与发展具有重要的意义,因此合理的预测经济发展以便于制定国民经济计划和指导经济活动,为国家及时调整产业结构以及建立相应的经济政策等提供重要的依据。文中运用改进的量子粒子群算法和BP神经网络对中国的国内生产总值进行合理的预测。
(3)运用ADF单位根检验、协整检验、格兰杰因果检验、误差修正模型以及相关分析等对我国能源消费总量与GDP总量的关系、能源消费增长率与GDP增长率的关系、能源消费总量与人均GDP总量的关系以及能源消费与经济发展的解耦关系进行分析。结果表明：自改革开放到2011年为止,中国能源消费总量与中国GDP总量之间其线性相关系数为0.9939,即具有显著的正线性相关性；能源消费增长率与GDP增长率的变化情况基本保持一致,在GDP增长率变大的同时往往伴随着能源消费增长率的变大；中国能源消费总量以及人均GDP之间存在一种长期的稳定的关系,能源消费总量以及人均GDP互为因果关系；中国能源消费与GDP处于相对解耦状态,且煤炭、石油以及总的能源消费与经济发展的解耦指数的趋势变化基本保持一致,且总的解耦指数呈现下降趋势。
(4)对能源经济效率进行了诠释,并利用广义C-D函数和DEA模型对中国自二十世纪九十年代以来各年度能源经济效率和各省市的能源经济效率进行测算并提出相应的政策建议。
(5)利用中国的能源需求及其影响因素的数据建立VAR模型,并进行脉冲分析和方差分析。利用改进灰靶决策和微分verhulst模型与差分verhulst模型建立能源需求预测模型。
(6)能源价格是能源与经济发展之间联系的桥梁,因此本文利用小波分析、粒子群算法(PSO)、BP申经网络和灰色贝努利模型(NGBM)对能源价格进行预测。
(7)经济的发展需要消费能源,而能源的消费往往对环境产生负面影响。本文利用灰色关联对能源消费与环境污染进行实证分析；利用层次分析法、变异系数法和灰色关联法分析我国各类能源消费对CO2排放量的影响。并且为促进社会经济的发展与环境友好提出一些建议。
(8)为促进能源和经济的可持续发展,本文利用主成分分析、一般耦合协调模型、灰色耦合协调模型以及熵变方程法探索了国内这两个系统之间的协调发展状况,并提出相应的政策建议。
Abstract:Energy plays an important role in the social progress and economic development. Any country cannot do without energy. Since the reform and opening up, the rapid economic growth has attracted the world people's attention. With the high-speed growth of the domestic economy, China's energy consumption also continues to grow rapidly. Therefore, more and more people care for the relationship between energy and economic development. Under this background, in order to provide reasonable references for China's energy policy and developing plans of national economy, we will study relationship between energy and economic development in China. The main research contents and results are as follows.
(1) To study the relationship between energy consumption and economic development, the affecting factors of energy consumption are firstly analyzed. A simultaneous equation model for affecting factors of energy consumption was established. And then this model was identified and analyzed. Finally the model parameters were respectively estimated by the use of two-stage least squares (2SLS), three-stage least squares (3SLS) and Generalized Method of Moments (GMM) methods. Considering various factors will also influence each other, we introduced the path analysis, which means a certain factor affects energy consumption by the action of the other factors. So in this paper the affecting factors of energy consumption are further studied by path analysis.
(2) Because the economic development has a great significance for the progress and development of the society, the reasonable prediction of the economic development is conducive to formulate plans of national economy and guide economic activity. And it can also provide important bases for the nation to adjust the industrial structure and to establish corresponding energy policy. So China's GDP is predicted by using quantum particle swarm optimization (QPSO) algorithm and BP neural network.
(3) The relationship between China's total energy consumption and GDP, the relationship between the growth rate of energy consumption and GDP and the relationship between energy consumption and per capita GDP were all analyzed by using ADF unit root test, cointegration test, Grainger causality test, error correction model, correlation analysis and so on. Finally, decoupling relationship between the economic development and energy consumption was studied. The result shows that the linear correlation coefficient between total energy consumption and GDP is0.9939from1978to2011, which means a significantly positive correlation between them. The growth rate of energy consumption and the growth rate of GDP keep the same pace in changes.When the growth rate of GDP is increasing, the number of growth rate of energy consumption usually becomes larger. And there is a long-term stable relationship between China total energy consumption and per capita GDP. There are bidirectional causality relationship between total energy consumption and per capita GDP by Granger causality test. China's energy consumption and GDP are in the relatively decoupling state. The decoupling index between the total energy consumption and economic development is consistent with the decoupling index between coal, oil and economic development. And the total decoupling index shows a downward trend.
(4) The concepts of energy efficiency and energy economic efficiency were explained. The annual energy economic efficiency in the studied years and the energy economic efficiency in the provinces and cities of China were calculated by using generalized C-D function and DEA model. Some policy recommendations were put forward based on the above conclusions.
(5) Using the data of energy demand and its influencing factors to establish the vector auto-regression (VAR) model. This paper applies impulse response function and variance analysis to portray the dynamic correlations between energy demand and its influencing factors. Using the improved grey target decision, differential and difference Verhulst models to build the forecasting model of energy demand.
(6) Energy prices have linked energy with economic development like a bridge. This paper used wavelet analysis, particle swarm optimization (PSO), BP neural network and grey Bernoulli model (NGBM) to forecast energy prices.
(7) Economic development needs to consume energy. And the energy consumption will inevitably have a negative impact on the environment. In this paper, the relationship between energy consumption and environmental pollution were analyzed by using the grey correlation model. Using the analytic hierarchy process (AHP), the variation coefficient and grey relational analysis methods to analyze impacts of different types of energy consumption on CO2emissions in China. In order to promote the social development and protect the environment against pollutions, some suggestions were proposed later in the7th chapter.
(8) In order to promote the sustainable development of energy and economy, the coordinated development between energy and economic systems is studied based on principal component analysis, the general coupling coordination model, grey coupling coordination model and the entropy change equation. According to the coordination coefficient, the corresponding policy recommendations are given.
(1)为客观、正确地研究能源消费与经济发展关系,文中首先分析了能源消费的影响因素。建立了能源消费影响因素的联立方程模型,并对模型进行识别和分析,最后运用二阶段最小二乘法(2SLS)、三阶段最小二乘法(3SLS)以及广义矩估计法(GMM)对模型分别进行参数估计。考虑到各影响因素之间也会相互影响,即某一影响因素通过另一影响因素的作用再影响能源消费,即通径分析问题。所以文章进一步讨论了能源消费影响因素的通径分析。
(2)经济的发展对于整个社会的进步与发展具有重要的意义,因此合理的预测经济发展以便于制定国民经济计划和指导经济活动,为国家及时调整产业结构以及建立相应的经济政策等提供重要的依据。文中运用改进的量子粒子群算法和BP神经网络对中国的国内生产总值进行合理的预测。
(3)运用ADF单位根检验、协整检验、格兰杰因果检验、误差修正模型以及相关分析等对我国能源消费总量与GDP总量的关系、能源消费增长率与GDP增长率的关系、能源消费总量与人均GDP总量的关系以及能源消费与经济发展的解耦关系进行分析。结果表明：自改革开放到2011年为止,中国能源消费总量与中国GDP总量之间其线性相关系数为0.9939,即具有显著的正线性相关性；能源消费增长率与GDP增长率的变化情况基本保持一致,在GDP增长率变大的同时往往伴随着能源消费增长率的变大；中国能源消费总量以及人均GDP之间存在一种长期的稳定的关系,能源消费总量以及人均GDP互为因果关系；中国能源消费与GDP处于相对解耦状态,且煤炭、石油以及总的能源消费与经济发展的解耦指数的趋势变化基本保持一致,且总的解耦指数呈现下降趋势。
(4)对能源经济效率进行了诠释,并利用广义C-D函数和DEA模型对中国自二十世纪九十年代以来各年度能源经济效率和各省市的能源经济效率进行测算并提出相应的政策建议。
(5)利用中国的能源需求及其影响因素的数据建立VAR模型,并进行脉冲分析和方差分析。利用改进灰靶决策和微分verhulst模型与差分verhulst模型建立能源需求预测模型。
(6)能源价格是能源与经济发展之间联系的桥梁,因此本文利用小波分析、粒子群算法(PSO)、BP申经网络和灰色贝努利模型(NGBM)对能源价格进行预测。
(7)经济的发展需要消费能源,而能源的消费往往对环境产生负面影响。本文利用灰色关联对能源消费与环境污染进行实证分析；利用层次分析法、变异系数法和灰色关联法分析我国各类能源消费对CO2排放量的影响。并且为促进社会经济的发展与环境友好提出一些建议。
(8)为促进能源和经济的可持续发展,本文利用主成分分析、一般耦合协调模型、灰色耦合协调模型以及熵变方程法探索了国内这两个系统之间的协调发展状况,并提出相应的政策建议。
Abstract:Energy plays an important role in the social progress and economic development. Any country cannot do without energy. Since the reform and opening up, the rapid economic growth has attracted the world people's attention. With the high-speed growth of the domestic economy, China's energy consumption also continues to grow rapidly. Therefore, more and more people care for the relationship between energy and economic development. Under this background, in order to provide reasonable references for China's energy policy and developing plans of national economy, we will study relationship between energy and economic development in China. The main research contents and results are as follows.
(1) To study the relationship between energy consumption and economic development, the affecting factors of energy consumption are firstly analyzed. A simultaneous equation model for affecting factors of energy consumption was established. And then this model was identified and analyzed. Finally the model parameters were respectively estimated by the use of two-stage least squares (2SLS), three-stage least squares (3SLS) and Generalized Method of Moments (GMM) methods. Considering various factors will also influence each other, we introduced the path analysis, which means a certain factor affects energy consumption by the action of the other factors. So in this paper the affecting factors of energy consumption are further studied by path analysis.
(2) Because the economic development has a great significance for the progress and development of the society, the reasonable prediction of the economic development is conducive to formulate plans of national economy and guide economic activity. And it can also provide important bases for the nation to adjust the industrial structure and to establish corresponding energy policy. So China's GDP is predicted by using quantum particle swarm optimization (QPSO) algorithm and BP neural network.
(3) The relationship between China's total energy consumption and GDP, the relationship between the growth rate of energy consumption and GDP and the relationship between energy consumption and per capita GDP were all analyzed by using ADF unit root test, cointegration test, Grainger causality test, error correction model, correlation analysis and so on. Finally, decoupling relationship between the economic development and energy consumption was studied. The result shows that the linear correlation coefficient between total energy consumption and GDP is0.9939from1978to2011, which means a significantly positive correlation between them. The growth rate of energy consumption and the growth rate of GDP keep the same pace in changes.When the growth rate of GDP is increasing, the number of growth rate of energy consumption usually becomes larger. And there is a long-term stable relationship between China total energy consumption and per capita GDP. There are bidirectional causality relationship between total energy consumption and per capita GDP by Granger causality test. China's energy consumption and GDP are in the relatively decoupling state. The decoupling index between the total energy consumption and economic development is consistent with the decoupling index between coal, oil and economic development. And the total decoupling index shows a downward trend.
(4) The concepts of energy efficiency and energy economic efficiency were explained. The annual energy economic efficiency in the studied years and the energy economic efficiency in the provinces and cities of China were calculated by using generalized C-D function and DEA model. Some policy recommendations were put forward based on the above conclusions.
(5) Using the data of energy demand and its influencing factors to establish the vector auto-regression (VAR) model. This paper applies impulse response function and variance analysis to portray the dynamic correlations between energy demand and its influencing factors. Using the improved grey target decision, differential and difference Verhulst models to build the forecasting model of energy demand.
(6) Energy prices have linked energy with economic development like a bridge. This paper used wavelet analysis, particle swarm optimization (PSO), BP neural network and grey Bernoulli model (NGBM) to forecast energy prices.
(7) Economic development needs to consume energy. And the energy consumption will inevitably have a negative impact on the environment. In this paper, the relationship between energy consumption and environmental pollution were analyzed by using the grey correlation model. Using the analytic hierarchy process (AHP), the variation coefficient and grey relational analysis methods to analyze impacts of different types of energy consumption on CO2emissions in China. In order to promote the social development and protect the environment against pollutions, some suggestions were proposed later in the7th chapter.
(8) In order to promote the sustainable development of energy and economy, the coordinated development between energy and economic systems is studied based on principal component analysis, the general coupling coordination model, grey coupling coordination model and the entropy change equation. According to the coordination coefficient, the corresponding policy recommendations are given.
引文
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