A cooperative game analysis for the allocation of carbon emissions reduction responsibility in China's power industry

2020 ◽  
Vol 92 ◽  
pp. 104960
Author(s):  
Quande Qin ◽  
Yuan Liu ◽  
Jia-Ping Huang
Energies ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 706 ◽  
Author(s):  
Xianxian Pan ◽  
Hong Liu ◽  
Jiajia Huan ◽  
Yu Sui ◽  
Haifeng Hong

The electric power industry plays a vital role in carbon emissions reduction efforts. The initial allocation of carbon emission permits to the electric power industry is the key to ensuring the effective operation of the carbon trading market. In this study, the multiple correlated factors that affect the carbon emission permit allocation system were extracted. Then, based on the experts’ knowledge and experience, the subjective weight of each index was determined using an improved analytic hierarchy process. Subsequently, the indices were mapped using an improved entropy weight method, and the objective weight of each index was adaptively determined. Finally, the comprehensive weight of each index was determined by optimizing the combination of its subjective and objective weights, and an allocation model of carbon emission permits for the electric power industry was established. A case study of a province by comparative simulation was performed. The simulation results showed that compared with conventional allocation schemes that consider single factors, the theoretical estimates obtained using the proposed model more objectively reflected the actual situation of carbon emissions reduction permits and responsibilities in the region.


Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2398 ◽  
Author(s):  
Lin Zhu ◽  
Lichun He ◽  
Peipei Shang ◽  
Yingchun Zhang ◽  
Xiaojun Ma

The power industry is the industry with the most direct uses of fossil fuels in China and is one of China’s main carbon industries. A comprehensive and accurate analysis of the impacts of carbon emissions by the power industry can reveal the potential for carbon emissions reductions in the power industry to achieve China’s emissions reduction targets. The main contribution of this paper is the use of a Generalized Divisia Index Model for the first time to factorize the change of carbon emissions in China’s power industry from 2000 to 2015, and gives full consideration to the influence of the economy, population, and energy consumption on the carbon emissions. At the same time, the Monte Carlo method is first used to predict the carbon emissions of the power industry from 2017 to 2030 under three different scenarios. The results show that the output scale is the most important factor leading to an increase in carbon emissions in China’s power industry from 2000 to 2015, followed by the energy consumption scale and population size. Energy intensity levels have always promoted carbon emissions reduction in the power industry, where energy intensity and carbon intensity effects of energy consumption have great potential to mitigate carbon levels. By setting the main factors affecting carbon emissions in the future three scenarios, this paper predicts the carbon emissions of China’s power industry from 2017 to 2030. Under the baseline scenario, the maximum probability range of the potential annual growth rate of carbon emissions by the power industry in China from 2017 to 2030 is 1.9–2.2%. Under the low carbon scenario and technological breakthrough scenario, carbon emissions in China’s power industry continue to decline from 2017 to 2030. The maximum probability range of the potential annual drop rate are measured at 1.6–2.1% and 1.9–2.4%, respectively. The results of this study show that China’s power industry still has great potential to reduce carbon emissions. In the future, the development of carbon emissions reduction in the power industry should focus on the innovation and development of energy saving and emissions reduction technology on the premise of further optimizing the energy structure and adhering to the low-carbon road.


2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.


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