Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model

Zhao, Pengjun; Zeng, Liangen; Lu, Haiyan; Zhou, Yang; Hu, Haoyu; Wei, Xinyuan

doi:10.1016/j.scitotenv.2020.140026

Cited by 189 publications

(98 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The DEA method was established by Charnes et al [39] and is a model for calculating the efficiency score of a DMU; it does not require any prior assumption about the production frontier and can deal with complex systems that have multiple inputs and outputs [23]. Hence, DEA is widely utilized in studying EE ( Table 1).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The SBM model requires input or output variables to change proportionally, and cannot cope with other cases properly [8,[14][15][16][17][18][19][20][21][22]. The epsilon-based measure (EBM) model with undesirable outputs is a recent approach to evaluate the EE, which can compensate for the weakness of traditional DEA methods as well as deal with undesirable outputs [15][16][17][18][19][20][21][22][23][24][25][26]. Additionally, the common methods for analyzing the factors driving EE, the Tobit model, Spatial Lag Model (SLM), and Spatial Error Model, also have their own strengths and weaknesses.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, the common methods for analyzing the factors driving EE, the Tobit model, Spatial Lag Model (SLM), and Spatial Error Model, also have their own strengths and weaknesses. The traditional Tobit model fails to consider spatial factors [23]. Although spatial effects are involved in the Spatial Lag Model (SLM) and Spatial Error Model, the lags in both spatial independent variables and the dependent spatial variable are not considered at the same time in these models [27][28][29].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach

Zeng

2021

Sustainability

View full text Add to dashboard Cite

From the Kyoto Protocol to the Copenhagen Conference and the Paris Agreement, eco-environmental problems have gradually become a matter of common concern worldwide. Eco-efficiency (EE) is an essential indicator for measuring levels of sustainable development. This study uses an epsilon-based measure (EBM) model with undesirable outputs to evaluate the EEs of 30 Chinese provinces during the research period 2008 to 2017, and a spatial Durbin model (SDM) to search for the impact factors of EE. The results indicate that most provinces in China have a low EE level. The EE value of the eastern area is higher than are those for the central, western, or northeastern areas. The EE in China as a whole demonstrates an inverted V-shaped trend with a high point in 2011. The SDM shows that economic development level, foreign trade dependence, and technological progress exert significant positive effects on EE, while population density exerts significant negative influences on EE. This paper provides scientific bases for the formulation of policies resulting in sustainable development.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach

Zeng

2021

Sustainability

View full text Add to dashboard Cite

show abstract

“…According to the principle of Super-SBM model, when the GUECL value is greater than or equal to 1, the DMU is relatively effective; when the GUECL value is less than 1, the DMU is relatively invalid [51]. As can be seen from Table 2, there were 20 evaluation units with GUECL value greater than or equal to 1 in the Yellow River Basin in 2000, accounting for about 31% of the 65 evaluation units, of which 10 were relatively effective in the upper reaches, 4 in the middle reaches and 6 in the lower reaches, which indicates that about 69% of the evaluation units were in relatively ineffective green utilization state in 2000.…”

Section: Spatial-temporal Variation Of Guecl In the Yellow River Basinmentioning

confidence: 99%

Green Transition of Cultivated Land Use in the Yellow River Basin: A Perspective of Green Utilization Efficiency Evaluation

Sun

et al. 2020

Land

View full text Add to dashboard Cite

Exploring the green transition of cultivated land use from the perspective of green utilization efficiency evaluation has become an important content of deepening the study of cultivated land use transition, which is of great significance to promote food security and ecological civilization construction. At present, there are few studies on the green utilization efficiency of cultivated land (GUECL), which covers the comprehensive benefits of economy, ecology and society, combined with the requirements of ecological civilization and green development. Taking 65 cities (regions and autonomous prefectures) of the Yellow River Basin as the basic evaluation unit, the GUECL of the Yellow River Basin is evaluated with a Super-SBM model. In general, the GUECL of the Yellow River Basin was not high at four time points of 2000, 2006, 2012 and 2018, which presents a trend of “rising first and then falling”. Analyzing its temporal and spatial evolution pattern, the GUECL in the upper, middle and lower reaches presented an order of the upper reaches area > the lower reaches area > the middle reaches area; and the spatial variation trend showed a decrease from west to east, and a U-shaped change in the south-north direction. Using spatial correlation analysis, except for the year 2000, the GUECL in the Yellow River Basin presents a general distribution characteristic of spatial agglomeration, which is positively correlated in 2006, 2012 and 2018. The change of spatio-temporal pattern is the result of internal and external factors. The former mainly displays in the main characteristics of farmers, family characteristics and farmers’ cognition, while the latter is reflected in natural, social and policy factors.

show abstract

“…Shen et al [22] used the threshold model to investigate the nonlinear dynamic influence of different types of environmental regulations on the environmental total factor productivity of industrial sectors. Zhao et al [23] adopted a super-slacks-based measure model with undesirable outputs to calculate the green economic efficiency in 30 provinces of China, and found that foreign trade dependence and direct investment had significant positive effects.…”

Section: Literature Reviewmentioning

confidence: 99%

Does Environmental Innovation Improve Environmental Productivity?—An Empirical Study Based on the Spatial Panel Data Model of Chinese Urban Agglomerations

Wang

Szmedra

2020

IJERPH

View full text Add to dashboard Cite

Environmental productivity comprehensively measures economic growth and environmental quality. Environmental innovation is considered to be the key to solving economic and environmental problems. Therefore, discussing the impact of environmental innovation on environmental productivity will reveal its economic and environmental effects. This paper measures environmental productivity by value added per unit of pollution emissions (four types of emissions are used) using panel data of 10 Chinese urban agglomerations from 2003 to 2016 to analyze the spatial correlation of environmental productivity, and constructs a spatial panel data model to empirically test the impact of environmental innovation on environmental productivity. It was found that environmental productivity measured by value added per unit of carbon dioxide emissions (gross domestic product (GDP)/CO2) had a significant positive spatial spillover effect, and measured by value added per unit of sulfur dioxide emissions (GDP/SO2), smoke (dust) emissions (GDP/SDE), and industrial sewage emissions (GDP/IS) had a significant negative spatial spillover effect. Environmental innovation has a significant negative inhibitory effect on environmental productivity measured by GDP/SDE and GDP/IS, but no obvious effect measured by GDP/CO2 and GDP/SO2. Control variables such as economic development level, industrial agglomeration, foreign direct investment, and endowment structure factor also show significant differences in environmental productivity measured by value added per unit of pollution emissions. In addition, there are significant differences in direct effects of explanatory variables on environmental productivity of local regions and indirect effects on neighboring regions. These differences are also related to the types of pollution emissions. Therefore, policymakers should set different policies for different types of pollution and encourage different types of environmental innovation, so as to achieve reduced pollution emissions and improved environmental productivity.

show abstract

Green economic efficiency and its influencing factors in China from 2008 to 2017: Based on the super-SBM model with undesirable outputs and spatial Dubin model

Cited by 189 publications

References 52 publications

China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach

China’s Eco-Efficiency: Regional Differences and Influencing Factors Based on a Spatial Panel Data Approach

Green Transition of Cultivated Land Use in the Yellow River Basin: A Perspective of Green Utilization Efficiency Evaluation

Does Environmental Innovation Improve Environmental Productivity?—An Empirical Study Based on the Spatial Panel Data Model of Chinese Urban Agglomerations

Contact Info

Product

Resources

About