Carbon emissions are the major cause of the global warming; therefore, the exploration of carbon emissions reduction potential is of great significance to reduce carbon emissions. This paper explores the potential of carbon intensity reduction in Beijing in 2020. Based on factors including economic growth, resident population growth, energy structure adjustment, industrial structure adjustment and technical progress, the paper sets 48 development scenarios during the years 2015–2020. Then, the back propagation (BP) neural network optimized by improved particle swarm optimization algorithm (IPSO) is used to calculate the carbon emissions and carbon intensity reduction potential under various scenarios for 2016 and 2020. Finally, the contribution of different factors to carbon intensity reduction is compared. The results indicate that Beijing could more than fulfill the 40%–45% reduction target for carbon intensity in 2020 in all of the scenarios. Furthermore, energy structure adjustment, industrial structure adjustment and technical progress can drive the decline in carbon intensity. However, the increase in the resident population hinders the decline in carbon intensity, and there is no clear relationship between economy and carbon intensity. On the basis of these findings, this paper puts forward relevant policy recommendations.

In order to evaluate the development efficiency of different types of power projects, combined with the characters of power projects, the evaluation index system of power generation efficiency decided by input indicators and output indicators. The model based on super-efficiency data envelopment analysis (SE-DEA) is proposed, the method of quantitative calculation, the comprehensive efficiency of different types of power projects is obtained by the method of quantitative calculation. All aspects of society considered, on the current stage of our country, natural gas cogeneration is a kind of environment-friendly power generation projects, and it should be supported.

In order to overcome the problem that the support vector machine (SVM) is not timely in the early warning of the electric power system operation status and the prediction precision is not high, the improved quantum particle swarm optimization algorithm is combined with the SVM to establish a model of hybrid quantum particle swarm optimization SVM for the electric power system operation state early warning, and carry on the simulation for evaluation indexes. The simulation results show that the model is feasible and effective.