Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model

Su, Shuai; Fan, Zhang

doi:10.1016/j.jenvman.2020.110227

Cited by 291 publications

(106 citation statements)

References 11 publications

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“…Therefore, following Shuai and Fan ( 2020 ), this research applied the super-efficiency DEA technique to construct a model to evaluate environmental efficiency. The DMU j ( j = 1, 2, 3, ….…”

Section: Methodology and Data Sourcementioning

confidence: 99%

The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-Pacific

Twum

Long

Salman

et al. 2021

Environ Sci Pollut Res

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This study employs super-efficiency DEA model with desirable inputs and an undesirable output in calculating environmental efficiency values in different regions in Asia-Pacific from 1990 to 2018. The study compares environmental efficiency index in South East Asia, South Asia and East Asia. The study also evaluates the determinants of environmental efficiency using truncated regression. The mean environmental efficiency score demonstrates that East Asia region is highly efficient whereas South East Asia is the least efficient. Results from the truncated regression established an inverted U-shape relationship between environmental efficiency and Technological Innovation (TI) in the main panel, and the three regions. Also, economic growth shows an inverted "U" shape link with environmental efficiency in the panels except in South East Asia. Human capital promotes environmental efficiency in the main panel and the rest of the regions. Moreover, while FDI promotes environmental efficiency in the main panel and East Asia, it reduces environmental efficiency in both South East and South Asia regions within the Asia-Pacific. In addition, an interaction effect between technological innovation and renewable energy use, advances environmental efficiency within the entire study countries. Based on the findings the study proposes several policy recommendations.

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Section: Methodology and Data Sourcementioning

confidence: 99%

The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-Pacific

Twum

Long

Salman

et al. 2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

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“…e Tobit regression model is used because the EEII value computed by a new SeUo-SBM-DEA model belongs to the truncated data. For such a situation in which multiple samples become a certain limit value within a specific range, the general regression method can not explain the difference in properties between the limit value and the nonlimiting observation value to ensure the consistency of the estimates [28]. Hence, the Tobit regression model is often used in combination with the DEA model, which is called a DEA-Tobit two-stage method (i.e., the DEA model is used to assess the efficiency of DMUs in the first stage, and the Tobit regression model is used to analyze its influencing factors in the second stage).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Zhu et al [31] proposed an extended DEA model (i.e., the WIN-US-SBM model), as a comprehensive ecoefficiency assessment method, and the main influencing factors of eco-efficiency were analyzed through the Tobit regression model. Shuai and Fan [28] used the superefficient DEA model to measure the efficiency of Chinese green economy, and the Tobit model is applied to examine the impact of environmental regulation on the efficiency of Chinese regional green economy extent and direction.…”

Section: Literature Reviewmentioning

confidence: 99%

Eco-Efficiency of Industrial Investment and Its Influencing Factors in China Based on a New SeUo-SBM-DEA Model and Tobit Regression

Zhu

Shan

2021

Mathematical Problems in Engineering

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Nowadays, eco-efficiency is one of the most widely used comprehensive indicators in many fields and sectors. Understanding eco-efficiency is of great significance to implement sustainable socioeconomic development for decision makers. To assess the comprehensive performance of industrial investment, the eco-efficiency of industrial investment (EEII) is constructed under the comprehensive perspective of economic benefits, energy consumption, and environmental impact in this paper. Then, a new superefficient undesirable-output slack-based measure DEA (SeUo-SBM-DEA) model is proposed and applied to assess the EEII of 30 provinces in China from 2015 to 2017, and its influencing factors are analyzed using the Tobit regression. The empirical results show the following: (1) the eco-efficiency of China’s industrial investment is generally low (0.613), and there exists a significant regional disparity; namely, the average value of EEII was the highest in the eastern regions (0.838), followed by the central regions (0.6) and western regions (0.397). (2) R&D expenditure, economic development level, and foreign direct investment all had a significant positive effect on the eco-efficiency of industrial investment, while investment in treatment of industrial pollution sources and total education funds all had a significant negative effect. Finally, this paper puts forward some suggestions to promote sustainable development of industrial investment based on our findings.

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“…u i is the individual effect in the panel model estimation. In addition, the Tobit panel model can further distinguish between random effects models and fixed effects models, depending on whether it is related to explanatory variables [22,23].…”

Section: Tobit Modelmentioning

confidence: 99%

Study on the Spatial Differentiation of Public Health Service Capabilities of European Union under the Background of the COVID-19 Crisis

et al. 2020

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Access to public health services is a cause that benefits the people and concerns the vital interests of the people. Everyone has access to basic health care services. The continuous improvement in people’s health is an important indicator of the improvement in people’s quality of life. This paper selects data from the European Union (EU) on aspects of public health expenditure, medical care resources, and government emergency coordination capacity from the period 2008 to 2017. Principal component analysis and factor analysis are used to measure their public health service capacity scores and conduct a comparative analysis. On this basis, the TOBIT model is adopted to explore the driving factors that lead to the spatial differentiation of public health service capabilities, and to combine it with the data of the COVID-19 epidemic as of 8 August 2020 from the official announcements of the World Health Organization and governments for further thinking. The results indicate that the public health service capacity of countries in the EU is showing a gradual increase. The capacity in Western Europe is, in turn, higher than that of Northern Europe, Southern Europe and Eastern Europe. In addition, the overall capacity in Western Europe is relatively high, but it is not balanced and stable, while Northern Europe has remained stable and balanced at a high level. Population density, degree of opening up, education level, economic development level, technological innovation level, and degree of aging have a positive effect on public health service capabilities. The level of urbanization has a negative effect on it. However, in countries with strong public health service capabilities, the epidemic of COVID-19 is more severe. The emergence of this paradox may be related to the detection capabilities of countries, the high probability of spreading thCOVID-19 epidemic, the inefficient implementation of government policy, the integrated system of the EU and the adverse selection of youth. This paper aims to improve the ability of the EU to respond to public health emergencies, improve the utilization of medical and health resources, and better protect people’s health from the perspective of public health service capacity.

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Modeling the role of environmental regulations in regional green economy efficiency of China: Empirical evidence from super efficiency DEA-Tobit model

Cited by 291 publications

References 11 publications

The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-Pacific

The influence of technological innovation and human capital on environmental efficiency among different regions in Asia-Pacific

Eco-Efficiency of Industrial Investment and Its Influencing Factors in China Based on a New SeUo-SBM-DEA Model and Tobit Regression

Study on the Spatial Differentiation of Public Health Service Capabilities of European Union under the Background of the COVID-19 Crisis

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