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• The new power supply structure resulting from the new policy has been considered. • The new models project the inefficient points onto a more realistic efficient frontier. • The new models have the power to allocate the shared resources. • Results show that the power production stage is allocated less shared resources. • The power sale stage's efficiency is lower than the one of power production stage. To relieve the problems of energy shortage and environmental pollution, many countries have implemented a variety of policies to improve the energy efficiency of the power industry. To understand the impacts of such policies on energy efficiency, this paper constructs novel network data envelopment analysis models to recognize the policy-induced structural changes in the power system. Compared with previous studies, the new models project the inefficient points onto a more realistic efficient production frontier, considering how the distribution proportion of shared resources will change when the power system, modeled as a power generation stage and a power sale stage, is led by the latter stage. Our empirical study on China's power system shows that the province of Guangdong and the eastern region of China perform best, and all power production stages perform better than the corresponding power sale stages. The development of the two power subsystems in the four regions is unbalanced. More than half of the power supply systems choose to allocate more proportion of the shared resources to the sale stage rather than use a more balanced distribution between the two stages. [ABSTRACT FROM AUTHOR]

Abstract: To find the key impact factors of CO2 emissions to realize the carbon intensity target, this paper examined the impact factors of population, economic level, technology level, urbanization level, industrialization level, service level, energy consumption structure and foreign trade degree on the energy-related CO2 emissions in Guangdong Province, China from 1980 to 2010 using an extended STIRPAT model. We employed ridge regression to fit the extended STIRPAT model. Empirical results indicate that factors such as population, urbanization level, GDP per capita, industrialization level and service level, can cause an increase in CO2 emissions. However, technology level, energy consumption structure and foreign trade degree can lead to a decrease in CO2 emissions. The estimated elastic coefficients suggest that population is the most important impact factor of CO2 emissions. Industrialization level, urbanization level, energy consumption structure, service level and GDP per capita are also significant impact factors, but the other factors such as technology level and foreign trade degree are less important impact factors. Some policy recommendations are also given on how to mitigate the growth of CO2 emissions. [Copyright &y& Elsevier]