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| 1 |
ID:
166942
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| Summary/Abstract |
An assessment model is built to investigate the degree of order in the organisational structure of the electricity regulatory institution (ERI) in China. The model is based on Shannon entropy and is constructed from the perspective of timeliness and accuracy of the flow of information. The model is then used to evaluate the degree of order in the organisational structures of the ERI during three stages of reform that occurred during 2002–13. The results indicate that the reforms and improvements made in the organisational structure of China's ERI have resulted in a stepwise increase in their degree of order (corresponding to 0.3156, 0.3277, and 0.3324 in the three stages, respectively). On this basis, a scheme is put forward to optimise the degree of order in the structure of the energy regulatory institution in the current stage. The results show that downsizing the internal and subordinate departments appropriately and creating more governmental agencies to regulate energy are conducive to further improving the degree of order of the energy regulatory institution. Finally, we use principal component analysis to propose a priority scheme for adding more regulatory governmental agencies based on sorted energy production and consumption data.
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| 2 |
ID:
193771
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| Summary/Abstract |
The Gini coefficient has been widely used as a key indicator to measure income inequality. However, differences in the measurement methods and information in the sample are the main reasons for the bias in the Gini coefficient in China. In order to improve the accuracy of the measurement, we revisit income inequality among Chinese families and propose a multi-group Gini coefficient method from the perspective of optimizing the income distribution function. Based on the disposable income of households in the Chinese Household Income Project (CHIP), a generalized logistic distribution function is used to measure national, urban and rural Gini coefficients and their contribution rates. The results indicate that: The multi-group Gini coefficient method based on the particle swarm optimization (PSO) algorithm makes full use of valid microdata-related information, improves the accuracy of traditional methods of fitting urban or rural income distribution and reduces measurement bias based on the realities of China's binary economic structure and the large size of the population. Overall, the income inequality in China has widened over the five-year period from 2013 to 2018. On the one hand, it has been consistently found that the urban-rural income gap is the most important source of income inequality in China (making a contribution exceeding 50%); on the other hand, the contribution of income inequality within urban areas has increased significantly. Education and industry of urban and rural households as well as the difference in their rates of return are the main causes of the income gap between the urban and rural areas in China. Addressing the root causes of income inequality warrants the creation of institutional conditions for equitable access and points of departure in education and industry.
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