非期望产出下我国能源消耗产出效率差异研究

提高能源消耗产出效率是新常态下经济发展的必然趋势，随机扰动和外部环境是研究能源消耗产出效率不可回避的问题，而传统的DEA模型无法剔除非期望产出对效率值的影响。本文通过构建四阶段Bootstrap-DEA-Malmquist模型，采用1990~2014年的省际面板数据作为样本数据，通过松弛指标调整能源投入，剔除外部环境和随机因素对能源效率和能源全要素生产率的扰动，并借助Bootstrap随机抽样法进一步减小由于样本差异造成的影响，从静态和动态两个维度测算了中国能源消耗产出效率和全要素生产率。研究发现，外部环境和随机扰动对能源效率的估算具有显著影响，剔除外部环境影响因素后得到的能源效率值、技术效率变化以及全要素生产率变动均有不同程度的下降，而调整初始投入后的技术进步值有所上升。此外，考虑环境因素时，能源效率值下降，但是进一步运用Bootstrap随机抽样法消除随机因素的影响后，能源效率值有所上升。本文的主要贡献在于将环境因素和随机因素从能源效率与能源全要素生产率的测算中剥离，获得更为准确的能源效率值和能源全要素生产率值。

Difference of Energy Efficiency in China Based on Non-Expected Output

In the past 10 years, China's economy has developed rapidly. The rapid development of industry and real economy has prompted China's energy consumption to rise rapidly. While promoting energy consumption, extensive development mode makes hidden danger of economic growth and social development. The energy issue is not only a Chinese problem, but also a global issue which is closely watched by all the other countries in the world. Under the new normal, increasing the efficiency of energy consumption is the inevitable trend of economic development. The random disturbance and the external environment are an unavoidable to study the energy efficiency.Traditional DEA model can't eliminate the influence of non-expected outputon efficiency. In this paper, through the construction of four stage Bootstrap-DEA-Malmquist model, the provincial panel data in 1990~2014 is used to adjust energy input index by input slack, eliminate disturbance of external environment and random factors on energy efficiency and total energy productivity, and the Bootstrap random sampling method is used to further reduce the impact caused due to sample differences. From static and dynamic perspective, China's energy efficiency and total factor productivity in 1990~2014 are measured.It is found that the external environment and random disturbance estimation have significant impacts on energy efficiency. By eliminating the influence of external environment, technical efficiency change and the change of total factor productivity decrease in varying degrees. In addition, the energy efficiency drops when the environmental factors are considered. However, the energy efficiency value increases again after using bootstrap method to eliminate the influence of random factors. The main contribution of this paper is to separate environmental factors and stochastic factors from the calculation of energy efficiency and total energy productivity, and obtain more accurate values of energy efficiency and total energy factor productivity. In addition, the input-output data of energy consumption from 1990 to 2013 are systematically combed, especially for pollutant emission indicators, such as sulfur dioxide emissions, nitrogen oxides emissions, smoke and dust emissions. Besides, there were a lot of indicators data missing before 2001, and it needs to be integrated. A large number of calendar statistical yearbooks are referenced to complete the missing data, and the panel data collected in this paper is more comprehensive and complete than the previous references.