China's high‐tech industry efficiency measurement with virtual frontier data envelopment analysis and Malmquist productivity index

2019 ◽  
Author(s):  
Xin Liu ◽  
Jingkai Huang
Keyword(s):  
Data Envelopment Analysis ◽  
Efficiency Measurement ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
High Tech ◽  
Data Envelopment ◽  
High Tech Industry

scholarly journals The Measurement of Innovation Efficiency of Chinese High-tech Industry Using Data Envelopment Analysis

2015 ◽  
Vol 65 (s2) ◽  
pp. 101-113 ◽  
Author(s):  
Ling Jiang ◽  
Yunyu Jiang ◽  
Zhijun Wu ◽  
Dongsheng Liao ◽  
Runfa Xu
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Environmental Variables ◽  
Scale Efficiency ◽  
High Tech ◽  
Data Envelopment ◽  
High Tech Industry ◽  
Economic Competitiveness ◽  
Development Level ◽  
Using Data

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.

scholarly journals Measuring the Productivity of Energy Consumption of Major Industries in China: A DEA-Based Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Xishuang Han ◽  
Xiaolong Xue ◽  
Jiaoju Ge ◽  
Hengqin Wu ◽  
Chang Su
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Envelopment Analysis ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Productivity Change ◽  
Data Envelopment ◽  
Policy Makers ◽  
Time Periods ◽  
Decision Making Units

Data envelopment analysis can be applied to measure the productivity of multiple input and output decision-making units. In addition, the data envelopment analysis-based Malmquist productivity index can be used as a tool for measuring the productivity change during different time periods. In this paper, we use an input-oriented model to measure the energy consumption productivity change from 1999 to 2008 of fourteen industry sectors in China as decision-making units. The results show that there are only four sectors that experienced effective energy consumption throughout the whole reference period. It also shows that these sectors always lie on the efficiency frontier of energy consumption as benchmarks. The other ten sectors experienced inefficiency in some two-year time periods and the productivity changes were not steady. The data envelopment analysis-based Malmquist productivity index provides a good way to measure the energy consumption and can give China's policy makers the information to promote their strategy of sustainable development.

scholarly journals Machine learning Clustering Algorithms Based on the DEA Optimization Approach for Banking System in Developing Countries

2020 ◽  
Vol 5 (6) ◽  
pp. 651-658 ◽  
Author(s):  
Mirpouya Mirmozaffari ◽  
Azam Boskabadi ◽  
Gohar Azeem ◽  
Reza Massah ◽  
Elahe Boskabadi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Developing Countries ◽  
Data Envelopment Analysis ◽  
Clustering Algorithms ◽  
Banking System ◽  
Additive Models ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Optimization Approach ◽  
Data Envelopment

Machine learning grows quickly, which has made numerous academic discoveries and is extensively evaluated in several areas. Optimization, as a vital part of machine learning, has fascinated much consideration of practitioners. The primary purpose of this paper is to combine optimization and machine learning to extract hidden rules, remove unrelated data, introduce the most productive Decision-Making Units (DMUs) in the optimization part, and to introduce the algorithm with the highest accuracy in Machine learning part. In the optimization part, we evaluate the productivity of 30 banks from eight developing countries over the period 2015-2019 by utilizing Data Envelopment Analysis (DEA). An additive Data Envelopment Analysis (DEA) model for measuring the efficiency of decision processes is used. The additive models are often named Slack Based Measure (SBM). This group of models measures efficiency via slack variables. After applying the proposed model, the Malmquist Productivity Index (MPI) is computed to evaluate the productivity of companies. In the machine learning part, we use a specific two-layer data mining filtering pre-processes for clustering algorithms to increase the efficiency and to find the superior algorithm. This study tackles data and methodology-related issues in measuring the productivity of the banks in developing countries and highlights the significance of DMUs productivity and algorithms accuracy in the banking industry by comparing suggested models.

scholarly journals Eco-Efficiency Assessment for Some European Countries Using Slacks-Based Measure Data Envelopment Analysis

2020 ◽  
Vol 10 (5) ◽  
pp. 1760 ◽  
Author(s):  
Chia-Nan Wang ◽  
Hsien-Pin Hsu ◽  
Yen-Hui Wang ◽  
Tri-Tung Nguyen
Keyword(s):  
Data Envelopment Analysis ◽  
Poor Performance ◽  
European Countries ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Measure Data ◽  
Productivity Change ◽  
Data Envelopment ◽  
Efficiency Assessment ◽  
Energy Inputs

One problem raised by the lack of energy efficiency is the generation of more greenhouse gases (GHGs) that can cause air pollution and climate change. Ecological efficiency (eco-efficiency) means the efficiency of resources used. A poor performance from this efficiency can then be detected for further improvement. In this research, we conduct an assessment on the eco-efficiency for some European countries as they consume a large part of global energy annually. A total of 17 European countries were selected as decision making units (DMUs) and assessed by the Slacks-based measure (SBM) Data Envelopment Analysis (DEA) model. Indices including Catch-Up, Frontier-Shift, and Malmquist Productivity Index (MPI) have been used to evaluate eco-efficiency, as well as efficiency change, technological change, and productivity change, over 2013–2017. In the model, energy consumption and share of renewable energy are used as energy inputs, and labor productivity and gross capital formation are used as economy inputs. On the other hand, GDP is used as a desired output, and CO2 emissions is used as one undesired output. The experimental results show that the 17 countries as a whole lacked eco-efficiency in 2013–2017, implying more efforts are required to improve their eco-efficiency.

scholarly journals A comparative evaluation of operational efficiency of wood industry using data envelopment analysis and Malmquist productivity index

2019 ◽  
Vol 70 (3) ◽  
pp. 287-298 ◽  
Author(s):  
Jože Kropivšek ◽  
Matej Jošt ◽  
Petra Grošelj ◽  
Darko Motik ◽  
Andreja Pirc Barčić ◽  
...  
Keyword(s):  
Data Envelopment Analysis ◽  
Comparative Evaluation ◽  
Operational Efficiency ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Data Envelopment ◽  
Low Carbon ◽  
Wood Industry ◽  
International Comparative ◽  
Using Data

The wood industry, as a traditional sector, represents a very important part of the economy in terms of ensuring a sustainable development of society and transition to a low-carbon society in both countries studied, Slovenia and Croatia. For its further development, it is crucial to know the current position of the industry. The best way to achieve this is an analysis of financial data and international comparative evaluation of its operational efficiency. The aim of the research is to compare the relative efficiency of the wood industry using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index (MI), focusing on the Slovenian and Croatian wood industry sectors (C16 and C31) for a recent five-year period (from 2013-2017). With this purpose, the combined measure DEA/MI was applied. The analysis includes only the highest rated companies with more than fi ve employees, divided into 12 clusters regarding the company size. As a result, it was established that clusters CRO-C31- micro, CRO-C16-micro and SI-C16-larger have the highest operational efficiency, due to the effects of different financial indicators, especially activity and liquidity ratios. In general, within the grouped clusters regarding country and subsector, groups SI-C16 and CRO-C31 achieve the highest values for the average of weighted score of efficiency, while CRO-C16 achieves the lowest values.

scholarly journals Performance Assessment Based on the Relative Efficiency of Indian Opencast Coal Mines Using Data Envelopment Analysis and Malmquist Productivity Index

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4902
Author(s):  
Biswaranjita Mahapatra ◽  
Chandan Bhar ◽  
Sandeep Mondal
Keyword(s):  
Data Envelopment Analysis ◽  
Technical Efficiency ◽  
Primary Source ◽  
Efficiency Score ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Scale Efficiency ◽  
Data Envelopment ◽  
Significant Difference ◽  
Using Data

Coal is the primary source of energy in India. Despite being the second-largest coal-producingcountry, there exists a significant difference in demand and production in India. In this study, the relativeefficiency of twenty-eight selected opencast mines from a large public sector undertaking coal companyin India for 2018–2019 was assessed and ranked by using data envelopment analysis (DEA). This studyused input-oriented DEA with efficiency decomposition to pure technical efficiency, technical efficiency,and scale efficiency. The result showed that 25% and 36% of mines were efficient in technical efficiencyand pure technical efficiency, respectively, whereas the eight mines scale efficiency was inefficient witha decreasing return to scale. Further, in this study, theMalmquist Productivity Index (MPI)was employedto measure the efficiency of the selected mines for three consecutive years (2016–2017 to 2018–2019).The result shows that in only three mines the efficiency is continuously improving from 2016–2017 to2018–2019, whereas in more than 20% of mines the efficiency score is decreasing. Comparing theMPIefficiency and productivity assessment throughout the years, changes in innovation and technology areincreasing from 2017–2018 to 2018–2019. Finally, the study concluded with a comprehensive evaluationof each variable with mines performance. The author formulated the strategies, which in turn help coalprofessionals to improve the efficiency of the mine.

scholarly journals ASSESSMENT OF CONTINENT-WISE ENERGY EFFICIENCY BASED ON CO2 EMISSIONS: A SLACK-BASED DEA APPROACH

2021 ◽  
pp. 17-31
Author(s):  
Mohd Afjal ◽  
Kavya C S
Keyword(s):  
Energy Efficiency ◽  
Data Envelopment Analysis ◽  
Co2 Emissions ◽  
Malmquist Index ◽  
Efficiency Score ◽  
Malmquist Productivity Index ◽  
Productivity Index ◽  
Data Envelopment ◽  
Dea Approach

This study uses the Data Envelopment Analysis (DEA) slack-based model (SBM) and Malmquist Productivity Index (MPI) to evaluate energy efficiency based on CO2 emissions in 42 countries belonging to 6 continents. First, the data envelopment analysis was employed to calculate the efficiency scores for the countries individually and continent basis and then Malmquist index was used to examine the improvement. The study period chosen was 2011-2020. The results of this study showed that on the basis of continents there has been fluctuations in energy efficiency except for Australia, with an efficiency score of equal to one throughout the study period. Additionally, from the results of Malmquist Productivity Index it was found that the 42 countries showed no significant energy enhancement during the period of 2011-2020. KEYWORDS: Energy Efficiency, CO2 emissions, Continents, Data Envelopment Analysis, Malmquist Productivity Index

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