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Carbon emissions and environmental protection issues have brought pressure from the international community during Chinese economic development. Recently, Chinese Government announced that carbon emissions per unit of GDP would fall by 60–65% compared with 2005 and non-fossil fuel energy would account for 20% of primary energy consumption by 2030. The Beijing-Tianjin-Hebei region is an important regional energy consumption center in China, and its energy structure is typically coal-based which is similar to the whole country. Therefore, forecasting energy consumption related carbon emissions is of great significance to emissions reduction and upgrading of energy supply in the Beijing-Tianjin-Hebei region. Thus, this study thoroughly analyzed the main energy sources of carbon emissions including coal, petrol, natural gas, and coal power in this region. Secondly, the kernel function of the support vector machine was applied to the extreme learning machine algorithm to optimize the connection weight matrix between the original hidden layer and the output layer. Thirdly, the grey prediction theory was used to predict major energy consumption in the region from 2017 to 2030. Then, the energy consumption and carbon emissions data for 2000–2016 were used as the training and test sets for the SVM-ELM (Support Vector Machine-Extreme Learning Machine) model. The result of SVM-ELM model was compared with the forecasting results of SVM (Support Vector Machine Algorithm) and ELM (Extreme Learning Machine) algorithm. The accuracy of SVM-ELM was shown to be higher. Finally, we used forecasting output of GM (Grey Prediction Theory) (1, 1) as the input of the SVM-ELM model to predict carbon emissions in the region from 2017 to 2030. The results showed that the proportion of energy consumption seriously affects the amount of carbon emissions. We found that the energy consumption of electricity and natural gas will reach 45% by 2030 and carbon emissions in the region can be controlled below 96.9 million tons. Therefore, accelerating the upgradation of industrial structure will be the key task for the government in controlling the amount of carbon emissions in the next step.

As China's power market becomes more orderly, electricity retailing companies are influenced by multiple factors restricting their healthy, stable, and sustainable development. This paper explores these issues through four dimensions, the external basic environment, operating credit risk, financial credit risk, and transaction credit risk, and determines the credit risk evaluation index system, which includes 22 factors relevant to electricity retailing companies. To achieve this, this paper utilizes the characteristics of the intuitionistic fuzzy set theory and, in an uncertain environment, the proposed credit risk assessment model based on the intuitionistic fuzzy analytic hierarchy process (IFAHP). In order to improve the identification degree of the credit risk evaluation of electricity retailing companies, a penalty factor is introduced into the model, and the variable weight mechanism of dynamic adjustment hesitancy is proposed. Finally, we select five electricity retailing companies and use different evaluation methods to conduct a comprehensive credit risk evaluation, so as to improve the credit risk level of electricity retailing companies.

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With the promulgation and implementation of a large number of renewable energy policies in China, the rapid development of renewable energy is accompanied by the prominent phenomenon of renewable energy abandoning wind and light. Energy storage technology is the key technology to promote the consumption of renewable energy. The government can promote the energy storage technology through the incentive policy of energy storage industry. Firstly, content analysis method is used to analyze China's energy storage policy, and five incentive policies for promoting energy storage technology are obtained. Secondly, built a game model of energy storage technology promotion based on the evolutionary game theory. Finally, use MATLAB software for numerical simulation. Numerical simulation results show that: (a) When the local government chooses to promote less, energy enterprises will eventually adopt the nonconfigure strategy; (b) when the local government chooses to promote more vigorously, energy enterprises will finally adopt the configure strategy; (c) increase in the total electricity sold by energy enterprises with energy storage devices, the sales price of energy stored per unit, the compensation price of energy stored per unit, tax relief standards, and incentive costs of local governments can promote energy enterprises to choose configure strategies; and (d) the reduction of unit energy storage cost, configure cost, and comprehensive tax rate can promote energy enterprises to choose configure strategy.