This paper measured the economic and unified efficiency of 24 electric power supply companies in China. With the development of a low carbon economy, further requirements for energy-saving and emission-reducing have been put forward for electric power supply companies. In this case, we considered the desirable (good) outputs (e.g., electricity sale amount) and undesirable (bad) outputs (e.g., line loss) in evaluating the performance of power supply companies. We combined the unified efficiency data envelopment analysis (DEA) model and the super-efficiency DEA model to create tge USDEA model, calculating companies’ unified efficiency. The unified efficiency DEA model can easily incorporate both desirable and undesirable outputs in a unified analytical structure. The super-efficiency data envelopment analysis model can make a comparison among various efficient decision making units (DMUs). Our results showed that the electric power supply companies of Hebei, Qinghai, Ningxia, Beijing and Shanghai achieved the highest levels of unified efficiency during the observed period (2003–2010), which differed from the economic efficiency results. The results meant that using unified efficiency to evaluate power supply companies will motivate them to care more about social and environmental benefit besides economic benefit.

Planning for electricity generation systems is a very important task and should take environmental and economic factors into account. This paper reviews the existing metrics and methods in evaluating energy sustainability, and we propose a sustainability assessment index system. The input indexes include generation capacity, generation cost, and land use. The output indexes include desirable and undesirable parts. The desirable outputs are total electricity generation and job creation. The undesirable outputs are external supply risk and external costs associated with the environment and health. The super-efficiency data envelopment analysis method is used to calculate the sustainability of electricity generation systems of 23 countries from 2005 to 2014. The three input indexes and three undesirable output indexes are used as the input variables. The two desirable outputs are used as the output variables. The results show that most countries’ electricity generation sustainability values have decreasing trends. In addition, nuclear and hydro generation have positive effects. Solar, wind, and fossil fuel generation have negative effects on sustainability.

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 integrate the evaluation and optimization of transnational power grid projects and to quantify the adjustment of energy interconnection schemes, this paper improved the Min DS (minimum distance to strong efficiency frontier) DEA (data envelopment analysis) model and combined it with gray correlation analysis, which can measure the input-output efficiency and quantify and screen the optimization paths. Taking 10 transnational power grid projects and the Asian Energy Internet Program for case analysis, the study found that the input-output efficiency of the third schemes of China's Xinjiang-Pakistan Project is the highest (2.792). For the Southeastern Tibet-Bang Mumbai Project, there is 1047.368-million-yuan investment cost redundancy. The input-output efficiency of Northeast Asia, Southeast Asia and China in 2050 is lower than that of 2030. On the basis of the Asia Energy Interconnection Program in 2030, North-Central Asia, West Asia and South Asia should increase CO2 emissions reduction by 0.750,0. 396 and 0. 236 million tons respectively. North-Central Asia, West Asia and South Asia should build more hydro-power, wind energy and solar energy power generation bases respectively in order to achieve this adjustment. This work can provide a tool for the evaluation and optimization of transnational power grid project decision makers and energy interconnection scheme planners.

The prospect of China’s wind power industry is bright, but from the angle of investment, investment amount, operation and maintenance costs of wind power project are relatively high, at the same time, it is facing high policy risk and environment risk. Therefore, we should consider all kinds of risk factors to make appropriate risk management and control plan. Based on this, the paper analyzes the risk factors including risk of planning and construction, risk of finance and market, risk of management and operation, risk of environment and resources. And an assessment index system is obtained which can be applied to evaluate the risk level. Furthermore, the model of Matter-Element Extension is applied to quantify the risk of the project. Integrating the methods of the qualitative and quantification analysis, then it proposes a reasonable assessment about the risk factors of the project of wind power. Finally, it proves the methods and the model through the case of the project of wind Power. The results could help us improve the level of the management of the project of wind power, and bring down the risk of the project.