Driving effect analysis of energy-consumption carbon emissions in the Yangtze River Delta region

Song, Malin; Guo, Xu; Kai-ya, WU; Wang, Guixin

doi:10.1016/j.jclepro.2014.05.095

Cited by 128 publications

(52 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Song et al applied the model of LMDI to decompose carbon emissions of Shandong Province into five factors, including energy structure, energy intensity, industry structure, economic output intensity, and energy consumption structure and found that per capita GDP was the largest driver in increasing carbon emissions [31,32]. To calculate the energy carbon emissions in the Yangtze River Delta region between 1995 and 2010, Song et al used the LMDI method to analyze the effect of economic scale, population size, energy intensity, and energy structure on carbon emissions [33]. Gao et al analyzed the influence factors of carbon emissions in Inner Mongolia from 2001 to 2010 by using the LMDI model and revealed that the coaldominated energy structure and the fast-growing economy were the main reasons that influenced the carbon emissions [34].…”

Section: Introductionmentioning

confidence: 99%

Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Wang

Yufang

Zhao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO 2 /energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

show abstract

Section: Introductionmentioning

confidence: 99%

Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Wang

Yufang

Zhao

et al. 2017

Mathematical Problems in Engineering

View full text Add to dashboard Cite

show abstract

“…At present, the undesirable SBM model was widely used in the study about the performance evaluation of carbon emission [18][19][20][21][22]. There were also many research took attention to China's provincial carbon emission efficiency, the basic conclusion was that China's provincial carbon performance were differences, and showed gradually rising space trend from the west to the east areas at present [23,24].…”

Section: Literature Reviewmentioning

confidence: 99%

Efficiency Allocation of Provincial Carbon Reduction Target in China’s “13·5” Period: Based on Zero-Sum-Gains SBM Model

2017

View full text Add to dashboard Cite

Firstly, we introduce the "Zero Sum Gains" game theory into the SBM (Slacks-based Measure) model, and establish the ZSG-SBM model. Then, set up 4 development scenarios for the China's economic system in "13·5" (The Chinese government formulates a Five-Year Planning for national economic and social development every five years, "13·5" means 2016 to 2020.) period through two dimensions as economic growth and energy consumption structure, and make the efficient allocation in provincial level of carbon reduction target by using the above ZSG-SBM model based on the China's overall carbon reduction constraint (18%) which is set in "13·5" planning. Finally, we analyze the provincial development path of low-carbon economy by comparing the economic development status with the allocated result of carbon reduction target. Results show that: After the ZSG-SBM model being applied to the efficiency allocation of carbon emission, the input and output indicators of the 30 provinces realize the effective allocation, and the carbon emission efficiency reaches the efficiency frontier. The equity-oriented administrative allocation scheme of government will bring about efficiency loss in a certain degree, and the efficiency allocation scheme, based on the ZSG-SBM model, fits better with the long-term development requirement of low-carbon economy. On the basis of carbon intensity constraint, the re-constraint of energy intensity will force the provinces to optimize their energy consumption structure, thereby enhancing the overall carbon emission efficiency of China. Sixteen provinces' allocation results of carbon reduction target are above China's average (18%) in "13·5" period, all the provinces should select appropriate development path of low-carbon economy according to the status of their resource endowment, economic level, industrial structure and energy consumption structure.

show abstract

“…The logarithmic mean Divisia index (LMDI) method has the desirable properties of perfect decomposition and is consistent in aggregation. The method is a widely accepted analytic tool to identify the relative impacts of different factors [36][37][38]. There are the logarithmic terms in the LMDI formula, complications arise when the data set contains zero values.…”

Section: Factor-separating Of Carbon Emission Decouplingmentioning

confidence: 99%

Measurement Research on the Decoupling Effect of Industries’ Carbon Emissions—Based on the Equipment Manufacturing Industry in China

Wang

2016

Energies

View full text Add to dashboard Cite

Economic development usually leads to increased energy consumption, which in turn will result in an increase in carbon emissions. To break the relationship between economic development and carbon emissions, scholars have turned their attention to the phenomenon of decoupling. In this paper, we studied the decoupling relationship between carbon emissions and economic growth of the equipment manufacturing industry in China from 2000 to 2014. We adapted the LMDI decomposition method, and we used the Tapio decoupling evaluation model to analyze our data. We found that the decoupling relationship between carbon emissions and economic growth of China's equipment manufacturing industry is weak, which indicates the industry is experiencing faster economic growth than carbon emission growth. We found the economic output is the factor that has the strongest influence on the industry's carbon emission, and energy consumption intensity has the strongest relationship with the decoupling of economic growth and carbon emission. The indicators of the industry's decoupling-effort are all less than 1.0, which indicates that the industry is in the state of weak decoupling, and we also observed an annual decreasing trend in the industry's indicators. Toward the end of this paper, we used the Grey forecasting model to predict the decoupling relationship between carbon emission and economic growth for 2015-2024, and we discussed the implications of our research.

show abstract

Driving effect analysis of energy-consumption carbon emissions in the Yangtze River Delta region

Cited by 128 publications

References 30 publications

Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Factors Affecting Energy‐Related Carbon Emissions in Beijing‐Tianjin‐Hebei Region

Efficiency Allocation of Provincial Carbon Reduction Target in China’s “13·5” Period: Based on Zero-Sum-Gains SBM Model

Measurement Research on the Decoupling Effect of Industries’ Carbon Emissions—Based on the Equipment Manufacturing Industry in China

Contact Info

Product

Resources

About