【作者】 张炎治；

【导师】 聂锐；

【作者基本信息】 中国矿业大学 ， 管理科学与工程， 2009， 博士

【摘要】 “2010年单位GDP能耗比2005年降低20%”作为一项约束性指标被写入我国的“十一五”发展规划,节约资源作为转变经济增长方式的重要途径也上升到了基本国策的战略地位。在当前我国能源、环境、经济三者之间的矛盾十分突出且将持续较长时期的背景下,研究我国能源强度演变的影响因素、影响效应及其未来变化趋势显的尤为必要。本文遵循现状研究——机理研究——模拟研究——政策研究的研究思路,将我国的能源强度演变划分为三个倒“U”型,分析了其阶段性特征和总体特征,从中归纳总结了我国能源强度演变的主要影响因素,并进行了单因素静态分析和多因素动态冲击响应分析。为进一步挖掘我国能源强度演变的深层动因,利用结构分解(SDA)模型从产业关联和经济增长的根本驱动力视角,研究了我国能源强度演变的技术因素作用和需求结构因素作用。论文第3章、第4章的研究结论表明,技术进步是我国能源强度降低的主要因素,但这两章对技术进步的表征利用了技术经济指标(如投入产出消耗系数)和能源经济指标(如部门能源强度),没有反映出技术进步的实质。为进一步精确分析技术进步对能源强度演变的作用,第5章利用经济学方法将技术进步(以全要素生产率表示)分解为科技进步指数和技术效率指数,面板回归了技术进步与能源强度演变之间的关系。在各因素对能源强度演变的影响机理和影响效应分析清楚之后,利用基于投入产出的能源强度非线性优化模型分情景研究了我国能源强度未来的变化趋势。最后,从技术节能、结构节能和制度节能三方面提出了节能相关政策建议。论文创新主要体现在能源强度演变的多因素动态冲击响应,能源强度演变的SDA分解分析以及能源强度演变的非线性优化模拟三个方面,论文中使用的定量研究方法主要有:指数分解分析、结构分解分析、向量自回归(VAR)模型、脉冲响应函数、遗传算法、面板数据模型、DEA—Malmquist指数法等。论文的创新性研究结论包括以下几个方面:(1)利用向量自回归模型,脉冲响应函数和方差分解方法研究了我国能源强度变化的多因素动态冲击响应。研究结论表明:第二产业比重、第二产业能源强度、FDI、GDI对能源强度的影响周期较长,影响时滞分别为6年、3年、5年、5年;煤炭消费比重和能源价格对能源强度的影响周期较短,影响时滞分别为3年和2年;能源强度的影响因素按重要程度排序为:能源强度自身>外商直接投资>技术进步>产业结构>固定资产投资>能源消费结构>能源价格。(2)利用结构分解分析(SDA)研究了我国生产能耗强度和行业完全能耗强度变化的原因。研究结论表明:在不同的时间区间,技术因素(包括能源技术和生产技术)和最终需求结构因素(包括最终消费、资本形成总额、出口、进口)对生产能耗强度变化的作用方向和大小是不同的,但总体上,促使我国生产能耗强度降低的因素是技术。对行业完全能耗强度变化的原因分析,结论较多,在此不便列举,请参阅本文第4章。(3)利用基于投入产出的能源强度非线性优化模型(遗传算法求解)分情景研究了我国能源强度未来的变化趋势。情景模拟结果表明:在不同方案(3个方案9个情景)下,随着产业结构,投资、消费结构调整力度的加强,能源强度也不断下降。当GDP年均增长率为12.18%,三次产业结构为9.4:42.6:48,投资、消费结构为35:68时,能源强度达到最小值0.9996tce/万元,但离节能降耗20%的目标仍相差2.86个百分点。强化节能情景模拟结果显示,2010年我国的能源强度可以达到0.94391tce/万元,相比2005年下降了21.75%,达到了“十一五”规划节能降耗20%的目标。更多还原

【Abstract】 The restricted indicator that reducing energy consumption per unit GDP by 20% in 2010 compared with 2005 was written into China’s“Eleventh Five-year Plan”. Resource conservation, as an important measure to change economic growth mode, has been raised to strategic position of basic national policy. So in the background that the contradiction among energy, environment and economy becomes outstanding increasingly and this condition will last for a long time, studying the influencing factors, influencing effect and change trend of China’s energy intensity is very necessary.In this paper specific research idea is followed, viz. present condition research, influencing mechanism research, simulation research and policy design. In this paper, China’s energy intensity evolution is divided into three reverse“U”stages, the periodic and overall characteristics are analysed, and some important factors influencing energy intensity are found. By using single-factor static analysis and multi-factors dynamic analysis the influencing mechnasim of the factors to China’s energy intensity evolution was studied. For further exploring its impetus the effect of technical advancement and final demand structrue on energy intensity shift was studied by using structural decomposition analysis method. The research results in chapter 3 and chapter 4 revealed that technical advancement was the dominant factor to decrease China’s energy intensity. But technical advancement was replaced with energy-economy indictor (such as sectoral energy intensity) and technology-economy indicator (such as direct consumption coefficient) in the two chapters, the essence of technical advancement was not reflected. Based on the above considersation, the following chapter, chapter 5, divided technical advancement into technology index and technology efficiency index and analyzed their relation between technical advancement and energy intensity evolution using panal data model. After some necessary analysis finished, the future development trend of China’s energy intensity was simulated making use of non-linear optimization model. In the end, this paper put forward some suggessions of energy conservation.The innovation of this paper is embodied in three aspects, namely, (1) dynamic impulse response of energy intensity evolution to multi-factors, (2) structural decomposition analysis (abbreviated SDA) on China’s energy intensity evolution, (3) non-linear optimization simulation of energy intensity change. There are some quantitative analysis methods such as index decomposition analysis, structural decomposition analysis (abbreviated SDA), vector autoregression model (abbreviated VAR), impulse response function (abbreviated IRF), panal data model and Genetic Algorithm, etc..更多还原