Applying a Ruggiero three-stage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China

Dong, Feng; Long, Ruyin; Bian, Zhengfu; Xu, Xihui; Yu, Bolin; Wang, Ying

doi:10.1007/s11069-017-2826-2

Cited by 38 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-parametric models do not need to establish function forms and assumptions about prior conditions, which can effectively avoid the subjectivity of parameter weighting [9,10]. The Data Envelopment Analysis (DEA) method is a typical non-parametric method and has been widely used in the evaluation of total factor CEE [8,[11][12][13][14]. For example, Zaim and Taskin, Zofio and Prieto, and Zhou et al evaluated carbon emission performance of OECD countries and the world's top 18 emitter countries by using different DEA models [8,11,12].…”

Section: Evolution Of Methods To Measure Ceementioning

confidence: 99%

“…The environmental variables were mainly selected to have a significant impact on the CEE; at the same time, the DMU itself is uncontrollable. Based on a comprehensive consideration of data availability, representativeness of variable indicators, and existing research [14], the paper selected six indicators as environmental variables, including economic development, industrial structure, energy structure, government regulation, technological innovation, and openness.…”

Section: Environmental Factor Variablesmentioning

confidence: 99%

See 1 more Smart Citation

Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

Wei

2020

Sustainability

View full text Add to dashboard Cite

To promote economic and social development with reduced carbon dioxide emissions, the key lies in determining how to improve carbon emission efficiency (CEE). We first measured the CEE of each province by using the input-oriented three-stage Data Envelopment Analysis (DEA) and DEA-Malmquist model for the panel data of 30 provinces in China during 2000–2017. Then we explored the CEE differences and characteristics of different regions obtained by using hierarchical clustering of each province’s CEE. Finally, based on the regression model, we conducted an empirical analysis of the impact of each factor of total factor productivity (TFP) on CEE. The main findings of this research are as follows: (1) The industrial structure, energy structure, government regulation, technological innovation, and openness had a significant impact on CEE; (2) The variation trends of CEE and TFP in the eight regions we studied were convergent, while the variations of CEE among regions were diverse and all distributed stably in different ranges; (3) The eight regions’ efficiency basically showed a downward trend of eastern, central and western China; (4) Technological regression was the main reason for the decline in TFP. Technological progress and technological efficiency can contribute to an improvement in CEE. Based on the findings above, we provide decision-making references for comprehensively improving the efficiency of various regions and accelerating China’s energy conservation, emissions reduction, and coordinated development.

show abstract

Section: Evolution Of Methods To Measure Ceementioning

confidence: 99%

Section: Environmental Factor Variablesmentioning

confidence: 99%

Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

Wei

2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…E i is the consumption of the i- th energy source. The carbon emission coefficients for the three energy sources refer to the research of Hu and Huang [ 54 ] and Dong et al [ 55 ], as well as reports from the Intergovernmental Panel on Climate Change [ 56 ].…”

Section: Methodology and Datamentioning

confidence: 99%

Can Environmental Quality Improvement and Emission Reduction Targets Be Realized Simultaneously? Evidence from China and A Geographically and Temporally Weighted Regression Model

Dong

Wang

Zhang

2018

IJERPH

Self Cite

View full text Add to dashboard Cite

The reductions of industrial pollution and greenhouse gas emissions are important actions to create an ecologically stable civilization. However, there are few reports on the interaction and variation between them. In this study, the vertical and horizontal scatter degree method is used to calculate a comprehensive index of industrial pollution emissions. Then based on carbon density, a geographically and temporally weighted regression (GTWR) model is developed to examine the interaction between industrial pollution emissions and carbon emissions. The results specify that there exists spatial autocorrelation for carbon density in China. Overall, the average effect of industrial pollution emissions on carbon density is positive. This indicates that industrial pollution emissions play a driving role in carbon density on the whole, while there are temporal and spatial differences in the interactions at the provincial level. According to the Herfindahl index, neither time nor space can be neglected. Moreover, according to the traditional division of eastern, central and western regions in China, the situation in 30 provinces is examined. Results show that there is little difference in the parameter-estimated results between neighboring provinces. In many provinces, the pull effect of industrial pollution emissions on carbon density is widespread. Thus, carbon emissions could be reduced by controlling industrial pollution emissions in more than 60% of regions. In a few other regions, such as Shanghai and Heilongjiang, the industrial pollution emissions do not have a pull effect on carbon density. But due to spatial and temporal heterogeneity, the effects are different in different regions at different times. It is necessary to consider the reasons for the changes combined with other factors. Finally, the empirical results support pertinent suggestions for controlling future emissions, such as optimizing energy mix and reinforcing government regulation.

show abstract

“…Under pressure to reduce carbon emissions, China must shoulder its share of responsibility as one of the world’s leading economic and political powers and contribute to global energy conservation, carbon emissions reduction, and low-carbon development. China pledged to reduce carbon intensity by 60–65% compared with the level in 2005 [5,6,7], increase non-fossil energy to approximately 20% of primary energy consumption [8], and achieve peak carbon emissions in 2030 [9,10,11,12,13]. However, as industrialization and urbanization gather momentum and the economy develops vigorously, China will inevitably continue to consume a large amount of fossil-based energy, and thus will continue to generate a large amount of carbon emissions.…”

Section: Introductionmentioning

confidence: 99%

Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches

Dong

Gao

et al. 2018

IJERPH

Self Cite

View full text Add to dashboard Cite

As the world’s top carbon-emitting country, China has placed great emphasis on understanding the driving factors of carbon emissions and developing appropriate emissions reduction policies. Due to the obvious variations in carbon emissions among various industries in China, corresponding policies need to be formulated for different industries. Through data envelopment analysis, this study introduced the Shephard distance function into the logarithmic mean Divisia index (LMDI) for decomposition analysis, built a carbon emissions decomposition model of 23 industries in China during 2003–2015, and analyzed the impact of 10 factors driving carbon emissions. The main results are as follows. (1) Potential gross domestic production (GDP) is a crucial factor for increasing carbon emissions, whereas potential energy intensity and technological advances of carbon emissions have a significant inhibitory effect on carbon emissions; (2) the technological progress of energy usage and the technological advances of GDP output are manifested by inhibiting carbon emissions at the early stage of development and increasing emissions at the later stage; (3) the structure of coal-based energy consumption is difficult to change in the long term, resulting in a weak effect of energy mix on carbon emissions and an increase in carbon emissions due to the potential energy carbon intensity factor.

show abstract

Applying a Ruggiero three-stage super-efficiency DEA model to gauge regional carbon emission efficiency: evidence from China

Cited by 38 publications

References 35 publications

Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

Analysis and Evaluation of the Regional Characteristics of Carbon Emission Efficiency for China

Can Environmental Quality Improvement and Emission Reduction Targets Be Realized Simultaneously? Evidence from China and A Geographically and Temporally Weighted Regression Model

Drivers of China’s Industrial Carbon Emissions: Evidence from Joint PDA and LMDI Approaches

Contact Info

Product

Resources

About