Eco-efficiency in China’s Loess Plateau Region and its influencing factors: a data envelopment analysis from both static and dynamic perspectives

Reasonably assessing the efficiency of green regional development is a key to improving environmental management and implementing sustainable development strategies. From the perspectives of environmental pollutant emissions, energy consumption, and production factor cost, the non-radial data envelopment analysis model based on the Malmquist index was applied to measure the green development efficiency and regional differences of 11 cities in Zhejiang from 2007 to 2016 from both static and dynamic aspects. This paper further analyzes the inherent influencing factors through the panel data model. The result shows: (1) The average static efficiency of green development in Zhejiang Province is 0.844. There is still 15.6% of improvement space from the frontier of production. Pollution emission management has the greatest improvement potential. Pure technical efficiency is the main factor restricting the static efficiency. (2) The dynamic efficiency of Zhejiang’s green development achieves an average annual rate of 0.26%, with a cumulative growth of 2.33%. The improvement of green development efficiency mainly depends on scale efficiency change. (3) The inherent factors affecting the efficiency of green development in the 11 cities mainly include three factors: the industrial structure, environmental regulation, and the urbanization level. The industrial structure has a positive effect, while environmental regulation and the urbanization level have negative effects. (4) The 11 cities are relatively evenly distributed in the four “static–dynamic efficiency” classification quadrants, and there is no "Matthew effect" of high–high, low–low polarization.

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Haze Influencing Factors: A Data Envelopment Analysis Approach

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16060914 ◽

2019 ◽

Vol 16 (6) ◽

pp. 914 ◽

Cited By ~ 1

Author(s):

Yi Zhou ◽

Lianshui Li ◽

Ruiling Sun ◽

Zaiwu Gong ◽

Mingguo Bai ◽

...

Keyword(s):

Data Envelopment Analysis ◽

Influencing Factors ◽

Human Activities ◽

Social Progress ◽

Observation Data ◽

Data Envelopment ◽

Coal Consumption ◽

Stochastic Dea ◽

The Government ◽

Meteorological Elements

This paper investigates the meteorological factors and human activities that influence PM2.5 pollution by employing the data envelopment analysis (DEA) approach to a chance constrained stochastic optimization problem. This approach has the two advantages of admitting random input and output, and allowing the evaluation unit to exceed the front edge under the given probability constraint. Furthermore, by utilizing the meteorological observation data incorporated with the economic and social data for Jiangsu Province, the chance constrained stochastic DEA model was solved to explore the relationship between the meteorological elements and human activities and PM2.5 pollution. The results are summarized by the following: (1) Among all five primary indexes, social progress, energy use and transportation are the most significant for PM2.5 pollution. (2) Among our selected 14 secondary indexes, coal consumption, population density and civil car ownership account for a major portion of PM2.5 pollution. (3) Human activities are the main factor producing PM2.5 pollution. While some meteorological elements generate PM2.5 pollution, some act as influencing factors on the migration of PM2.5 pollution. These findings can provide a reference for the government to formulate appropriate policies to reduce PM2.5 emissions and for the communities to develop effective strategies to eliminate PM2.5 pollution.

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Research on Present Situation and Influencing Factors of University R&D Input-Output Performance Based on the Data Envelopment Analysis Method

2021 2nd International Conference on Big Data and Informatization Education (ICBDIE) ◽

10.1109/icbdie52740.2021.00027 ◽

2021 ◽

Author(s):

Haoran Gao ◽

Haoxuan Li

Keyword(s):

Data Envelopment Analysis ◽

Influencing Factors ◽

Present Situation ◽

Data Envelopment ◽

Analysis Method ◽

Input Output ◽

Output Performance

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Eco-efficiency Assessment of Chinese Petrochemical Enterprises: A Data Envelopment Analysis Approach

Energy & Environment ◽

10.1177/0958305x211065424 ◽

2021 ◽

pp. 0958305X2110654

Author(s):

Yanqiu Wang ◽

Lixia Yao ◽

Shengnan Cui ◽

Zhiwei Zhu

Keyword(s):

Data Envelopment Analysis ◽

Development Strategy ◽

Influencing Factors ◽

Evaluation Model ◽

Operating Efficiency ◽

Scale Efficiency ◽

Data Envelopment ◽

Application Development ◽

Application Procedure ◽

The Difference

This study uses the Charnes, Cooper and Rhodes (CCR) and Banker, Charnes and Cooper (BBC) models of Data Envelopment Analysis (DEA) to evaluate the relative eco-efficiency and operating efficiency of the ten petrochemical enterprises. A random sample of ten petrochemical enterprises were selected from the Northeastern area of China. The data collected from the ten petrochemical enterprises were run on the DEA models and the evaluated results were input for the difference analysis for the scale efficiency and technical efficiency. Then the estimates the petrochemical enterprise's operating efficiency and ecological efficiency along with the influencing factors were run by regression analysis in order to verify the evaluation model and the rationality of influencing factors. After the projection analysis of the DEA, the firms that did not reach the values of effectiveness were identified and provided the suggestions for the targeted improvement values of eco-efficiency. Thus, this application procedure can be viewed as an example of the application development of the new DEA model and provides the reference for related industry making sustainable development strategy.

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