scholarly journals Study of Guangxi's Green Innovation Efficiency of High-tech Industries Based on Data Envelopment Analysis

Author(s):  
Feng Wei ◽  
Pei-pei Wu ◽  
Bo-ming Feng ◽  
Yong-yi Qing
2015 ◽  
Vol 65 (s2) ◽  
pp. 101-113 ◽  
Author(s):  
Ling Jiang ◽  
Yunyu Jiang ◽  
Zhijun Wu ◽  
Dongsheng Liao ◽  
Runfa Xu

In the era of knowledge economy, a country’s economic competitiveness depends largely on the development level of high-tech industry. This paper evaluates the efficiency of China’s high-tech industry in 31 provinces in 2012 with data envelopment analysis. The empirical results are summarized as following. Firstly, when the effects of exogenous environmental variables are not controlled, the comprehensive technical efficiency of 31 provinces will be overestimated, the pure technical efficiency will be underestimated, and the scale efficiency value will be overestimated. Secondly, after eliminating the environmental impact, the comprehensive technical efficiency of 31 provinces with the average of 0.395 is rather low, due to the low scale efficiency.


2019 ◽  
Vol 64 ◽  
pp. 130-139
Author(s):  
Li WEI ◽  
Thomas WARD

High-tech industry is facing the globally competitive situation that the development strategy for technology industry to integrate national power, combine international resources, and conform to the market trend is required for developing the internationally competitive high-tech industry in China. Following high customization and the development of product diversification to conform to various customer needs as well as short product life cycle, it becomes more important to understand the relative business performance of high-tech companies to the industry in China and evaluate the strengths and weaknesses in order to rapidly respond to customer needs and maintain high-quality products. Modified Delphi Method is utilized in this study for selecting inputs and outputs. The variable data used in this study are acquired from open statistical data of enterprises. Data Envelopment Analysis (DEA) is further used for evaluating the efficiency. The research results conclude that 1 DMU shows strong efficiency, with better operation efficiency, 4 DMUs present the operation efficiency between 0.9 and 1 that the operation efficiency can be more easily enhanced, and 5 DMUs appear the operation efficiency lower than 0.9, with obvious inefficiency. Furthermore, inputs and outputs are gradually removed in DEA for understanding the sensitivity to efficiency. Finally, suggestions are proposed according to the results, expecting to assist high-tech industry in China in the business development.


2019 ◽  
Vol 11 (18) ◽  
pp. 5023 ◽  
Author(s):  
Cao ◽  
You ◽  
Shi ◽  
Hu

The purpose of this paper is to provide a contribution to the development of R&D and transformation functional platforms by identifying key performance influencing factors in the use of data envelopment analysis (DEA) to analyze platform operation performance status and reasons. The DEA method is undertaken to calculate the comprehensive efficiency, pure technical efficiency and scale efficiency of R&D and transformation functional platforms in China’s 30 provinces within the period 2016–2018. Based on the 2018 pure technical efficiency and scale efficiency calculations, the K-means clustering method was used to classify the R&D and transformation functional platforms of 30 provinces. Finally, according to the clustering results, the corresponding clustering improvement scheme is given. The operational level of R&D and transformation functional platforms in many provinces of China still needs to be improved: the R&D and transformation capabilities are weak, the market share of leading products is low, the ability of new technology value-added is insufficient, and the development of R&D and transformation functional platforms has regional imbalance. This study is based solely on statistical data, these data alone obviously cannot fully describe and evaluate the real state of R&D and transformation functional platform due to the complexity and diversity of platforms. Further research is needed to generalize beyond the performance indicators constructed in this paper. For the problems of low overall operation efficiency, unbalanced regional development, redundancy of input resources and lack of professional management personnel in the operation of R&D and transformation functional platforms, policy suggestions can be put forward according to clustering results and input and output adjustment values calculated based on relaxation variables. The study presenting a methodology for analyzing R&D and transformation functional platforms’ operation performance, and the conclusions will provide reference for the development of platforms and high-tech industries.


2013 ◽  
Vol 16 (5) ◽  
pp. 67-73 ◽  
Author(s):  
Ridong Hu ◽  
Chich-Jen Shieh

With the rapid change of the social environment, Mainland China has become a new economic market due to the great domestic demand caused by its enormous population and the increasing economic growth rate. Taiwanese businesses have gradually turned to develop in China under the pressure of increasing domestic wages and land costs for expanding factories as well as the enhancement of environmental protection. Mainland China presents the advantages of ample land, low labor costs, monoethnicity, and easy language communication making it an attractive major investment location for Taiwanese high-tech industries. Data Envelopment Analysis (DEA) is applied to measure overseas investment efficiency evaluation of Taiwanese high-tech businesses in China, where the Delphi Method is used for selecting the inputs of the number of employees, R&D expenses, and gross sales in total assets. Sensitivity Analysis is further utilized for acquiring the most efficient unit and individual units with operating efficiency. The research results show that 1.Three high-tech businesses that present constant returns to scale perform optimally with overseas investment efficiency 2.Two high-tech companies with decreasing returns to scale appear that they could improve the overseas investment efficiency by decreasing the scale to enhancing the marginal returns, and 3.Sixteen high-tech enterprises reveal increasing returns to scale, showing that they could expand the scale to enhance the marginal returns and further promote efficiency.


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