摘要
在SBM-Malmquist全要素生产率指数模型的基础上,通过构建VAR模型,对群组前沿下中国东、中、西部水资源绿色效率TFP及其分解指数进行脉冲响应函数分析,旨在对中国水资源绿色效率未来发展趋势进行预测,为各地区有针对性地提高水资源绿色效率提供依据。研究表明:中国水资源绿色效率TFP及其分解指数在1%的显著水平下呈一阶单整序列,并具有长期且稳定的均衡性,东、中、西部VAR模型均通过了平稳性检验,满足后续分析的运行条件;通过脉冲响应和方差分解分析可知,未来中国水资源绿色效率TFP受其自身影响明显,但有下降趋势;东部地区TFP的增长有赖于技术进步的提高(其贡献度高达20%),中部地区主要依赖于管理水平的提高和生产规模的扩大,而西部地区得益于技术进步、纯技术效率和规模效率的协同发展。模型较好地模拟了TFP本身及其分解指数对水资源绿色效率TFP的影响,证明了VAR模型在水资源绿色效率研究中应用的合理性。
By constructing the VAR model,the impulse response function of TFP and its decomposition index of water resources in the east,middle and west of China under the group front was analyzed on the basis of SBM-Malmquist total factor productivity index Model. The purpose was to predict the future development trend of green efficiency of water resources in China and provide the basis for improving green efficiency of water resources in various regions. The results show that the green efficiency of water resources presents a first-order monolithic sequence under the significant level of 1%,within a long-term and stable equilibrium in China; the VAR model in east,middle and west has passed the stability test and met the operating conditions of the follow-up analysis; the future TFP of water resources green efficiency in China is obviously affected by its own and has a downward trend; the growth of TFP in the eastern region depends on the improvement of technological progress( its contribution is up to 20%),and the central region mainly depends on the improvement of management level and the expansion of production scale,while the western region benefits from the coordinated development of technological progress,pure technical efficiency and scale efficiency. The model has well simulated the effect of TFP itself and its decomposition index on the green efficiency of water resources,which has proved the rationality of VAR model in application.
出处
《人民黄河》
CAS
北大核心
2018年第2期42-48,共7页
Yellow River
基金
国家社会科学重点基金资助项目(16AJY009)
关键词
水资源绿色效率
SBM模型
VAR模型
脉冲响应函数
方差分解
water resources green efficiency
SBM model
VAR model
impulse response function
variance decomposition
