摘要
本文以非径向、投入松弛导向的方向性距离函数为基础,在序列数据包络分析(DEA)框架下,应用具有差分结构的改进型卢恩伯格生产率指标(MLPI),将能源与环境因素引入到中国地区全要生产率测度之中,对2000～2009年中国各地区、三大区域的绿色全要素生产率增长现状、来源、区域差异进行了实证分析,并揭示要素利用、节能减排对地区绿色全要素生产率增长的影响机制。研究表明,2000～2009年,全国绿色全要素生产率实现了年均0.68%、累计6.27%的增长,主要由技术进步驱动,技术效率起到阻碍作用;资本利用、劳动利用、能源利用、环境保护分别在绿色全要素生产率增长中贡献了9.82%、66.43%、2.42%、21.33%,且均来自于各自领域的技术进步,而非技术效率改善;中国总体绿色全要素生产率、各要素利用生产率均表现出明显的区域异质性特征,东部地区的表现远优于中西部地区。为了进一步提升绿色全要生产率在地区经济增长中的贡献,必须同时重视技术进步水平及技术效率的提高。
China has achieved rapid economic growth with an average annual rate of over 9% since the imple- mentation of economic reform and the opening up to the outside policy in 1978, the highest in the world in that pe- riod. However, this achievement has also led to inefficient natural resource utilization and given rise to serious envi- ronmental problems. At present, China is facing rigorous pressure from resource shortage and environment pollution. Theretbre, how to improve quality and sustainability of China＇s economic growth has been a hot issue. In recent years, a growing number of studies have utilized the index of ＂total factor productivity＂ to analyze China＇s economic growth, and provide quantitative information for economic policy analysis and decision making. Nevertheless, when measuring China＇s total factor productivity, most studies only considered two input fac- tors of capital and labor, and neglected energy and environment-related factors, which is unreasonable. With the growing emphasis on energy conservation and emission reduction, measuring and improving China＇s total factor pro- ductivity with the consideration of energy and environmental constraints is very important for China to reduce energy consumption, mitigate environment pollution and achieve sustainable economic growth. Data Envelopment Analysis （DEA） has been widely used to assess the performance of organizations, such as banks, schools, factories and economies. As a nonparametric approach, DEA can be utilized to measure relative ef- ficiency and gauge productivity without requiring the production to take a specific mathematical function. Hence, we use DEA method as the tool of measuring China＇s regional green total factor productivity in this paper. The specific research methods, research contents and empirical results of this paper are as tbllows. Based on non-radial, input slack-based directional distance function, applying the Modified Luenberger Pro- ductivity Indicator （MLPI） with difference-based structure under sequential Data Envelopment Analysis framework, this paper introduces the energy and environmental factor into the measurement of China＇s regional green total-factor productivity and investigates the growth situation, sources and regional differences of China＇s all areas and three ma- jor regions over the period 2000 -2009, on the basis of which we reveal the influence mechanism among factor uses, energy-saving and emission reduction, and green total-factor productivity growth. Findings show that China accomplishes average annual 0. 68% and cumulative 6.27% green total-factor productivity growth from 2000 to 2009, behind which technical progress is the dominant force rather than technical efficiency improvement. Capital use, labor use, energy use and environmental protection contributes 9. 82% , 66. 43% , 2.42% , 21.33% for the China＇s overall green total-factor productivity growth, respectively, and which are all driven by technical progress in their own domains. There exist obvious regional differences on China＇s overall green total-factor productivity and all factors＇ use productivity, and the Eastern area enjoys much faster productivity growth than The Central and Western area. In order to increase the contributions of green total-factor productivity for China＇s economic growth, we must place the same emphasis on the technical progress and the technical efficiency improvement. The contributions of this research are as follows. Firstly, this paper utilizes a new method--MLPI to measure China＇s regional green total factor productivity over 2000 ~ 2009, and decomposes it into technical efficiency change and technical progress and acquires the sources of productivity change. And then, this paper decomposes China＇s regional green total factor productivity from the viewpoint of factor inputs and reveals the relationship between factor use, energy-saving and emission reduction, and green total factor productivity. Subsequently, this paper analyzes the spatial and temporal heterogeneity of green total factor productivity of China＇s each region and three major areas. Finally, much more valuable implication for improving China＇s regional green total factor productivity is pro- vided.
出处
《经济管理》
CSSCI
北大核心
2012年第11期30-43,共14页
Business and Management Journal ( BMJ )
基金
国家自然科学基金项目"基于小样本数据的煤矿安全评价方及其应用研究"(71071003)
安徽省哲学社会科学规划项目"安徽城市低碳转型发展机理与政策支持研究"(AHSK11-12D107)
安徽省高校省级人文社会科学研究项目"安徽省工业企业二氧化碳排放
影响因素及低碳经济政策研究"(SK2012B146)
关键词
绿色全要素生产率
要素利用
节能减排
改进型卢恩伯格生产率指标(MLPI)
green total-factor productivity
factor use
energy-saving and emission reduction
Modified Luen- berzer Productivity Indicator （MLPI）
