基于幅度随机前沿的工业水资源利用效率测度

摘要

文章将随机前沿分析与幅度调整测定方法相结合，构建RAM-SFA-RAM三阶段工业水资源利用组合效率测度模型，选取资本、劳动和水资源投入指标，工业增长与废水污染物排放分别为期望与非期望产出指标，引入经济层次、人口素质和高技术导向水平作为环境变量，并利用中国30个省市2002-2014年历史数据进行实证分析。结果表明，效率测定过程中统计噪音与环境规制因素不可被忽略，环境变量对投入与产出松弛均体现出不同程度的影响，除高技术导向对投入松弛过剩与产出松弛不足呈良好控制作用外，经济层次与人口素质对工业资本和水资源投入、劳动投入松弛过剩呈正向影响；通过对工业水资源利用效率调整可使多数省市的效率排名发生改变，而调整后的效率排名表示工业水资源利用效率已呈现四大梯度分布特征，多数地区效率处于“一般”水平，“领先”水平的区域最少，进一步印证现阶段国内工业水资源利用效率值仍具有较大的提升空间。%The enhanced hybrid three-stage model is constructed in this paper by range-adjusted measure and stochastic frontier analy⁃sis. Capital, labor and water resources are used as the input indexes, and output indexes are divided into two classes, which mainly include the expected and the unexpected output by industrial growth and emissions of waste water and pollutants. In addition, the economic level, population quality and the high level of technical guidance as environment variables are introduced into the model, and then the historical data of 30 provincial-level administrative areas in China from 2002 to 2014 is empirically analyzed. The results show that the statistical noise and environmental regulation factors cannot be ignored in the process of efficiency evaluation, and environment variables have differ⁃ent degrees of influence on the relaxations of input and output. Among them, the high technology oriented has a good control effect on the relaxations of excess input and insufficient output. The economic level and population quality have positive effects on the input of industri⁃al capital and water resources, and the relaxations of excess labor input;Moreover, the majority of provinces ’ranking would be changed through the adjustment of industrial water utilization efficiency, and industrial water utilization efficiency has presented the distribution features of the“four gradient”according to the adjusted efficiency ranking. Among them, the efficiency of most provinces is the“general”level and the“leading”level is the least. The paper also further confirms that the industrial water utilization efficiency still has a great room to be improved in China.