Carbon emission control is an urgent environmental issue that governments are paying increasing attention to. Improving carbon market transaction efficiency in the context of China’s power industry is important for green growth, low carbon transmission, and the realization of sustainable development goals. We used the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method in this empirical study to analyze the carbon market transaction efficiency of China’s power industry. The results showed that the Beijing carbon market has the highest transaction efficiency, followed by those of Guangdong Province and Shenzhen City. Hubei Province also has a relatively high carbon market transaction volume and turnover; its transaction efficiency ranks fourth. Shanghai, Tianjin, and Chongqing are the lowest-ranked regions, having carbon markets with relatively low trading volume and turnover. We, therefore, recommend that to develop a unified national carbon market, governmental agencies at all levels should equitably allocate carbon; strict regulations and penalties are also needed.

In this Letter, three types of bearingless switched reluctance machines (BSRMs) with different hybrid stator structures are designed and analysed. First of all, topologies of three BSRMs, the conventional 8/10 pole, 12/14 pole BSRMs and a novel 12/12 pole BSRM with axial split phase inner stator permanent magnet structure, are given. Then, the electromagnetic performances, including the distribution of magnetic fields, suspension force and electromagnetic torque are comprehensively compared by means of 2D and 3D finite-element method (FEM). The results based on theoretical analysis and FEM reveal that the proposed 12/12 pole BSRM would exhibit complete decoupling, shorter magnetic circuit, higher torque (power) density and suspension force than the conventional ones. Further, it is found that four degree-of-freedom suspension of the rotor can be realised by two-phase suspension windings in the proposed 12/12 pole BSRM. Finally, a prototype machine is designed and manufactured to verify the validity of the topology design and electromagnetic performance.

A nonlinear dynamic equivalent magnetic network model for an axial permanent magnet bearingless flywheel machine (APM-BFM) is proposed in this paper. The model focuses on analyzing the magnetic field changes at the air gap of the machine. According to the relative position of the stator and rotor, the magnetic circuit between the rotor and the suspension pole (the torque pole) is divided into 7 stages (8 stages), and dynamic equivalent magnetic network models are established. The local saturation coefficient is introduced to characterize the local magnetic saturation phenomenon of the rotor yoke during the rotation of the rotor. Using the proposed model and based on the obtained winding flux, the opposing electromotive forces and inductances of the suspension and torque windings of the APM-BFM are all analyzed and calculated. Finally, the validity of the proposed model is verified by finite element analysis (FEA).INDEX TERMS Bearingless flywheel machine, equivalent magnetic network, local saturation coefficient, finite element analysis.

The analytical model (AM) of suspension force in a bearingless flywheel machine has model mismatch problems due to magnetic saturation and rotor eccentricity. A numerical modeling method based on the differential evolution (DE) extreme learning machine (ELM) is proposed in this paper. The representative input and output sample set are obtained by finite-element analysis (FEA) and principal component analysis (PCA), and the numerical model of suspension force is obtained by training ELM. Additionally, the DE algorithm is employed to optimize the ELM parameters to improve the model accuracy. Finally, absolute error (AE) and root mean squared error (RMSE) are introduced as evaluation indexes to conduct comparative analyses with other commonly-used machine learning algorithms, such as k-Nearest Neighbor (KNN), the back propagation (BP) algorithm, and support vector machines (SVMs). The results show that, compared with the above algorithm, the proposed method has smaller fitting and prediction errors; the RMSE value is just 22.88% of KNN, 39.90% of BP, and 58.37% of SVM, which verifies the effectiveness and validity of the proposed numerical modeling method.

In China, the power industry contributes significantly to carbon emissions, reducing carbon emissions in this industry is conducive to China's adaptation and mitigation of climate change. Researches on green and low-carbon power have attracted increasing attention. In this paper, we analyze and compare the carbon emissions from thermal power sector in 30 Chinese provinces, divided into three main regions. Based on the panel data over the period 2002-2016, we use a slacks-based measurement (SBM) model to measure the carbon emission efficiency of China's power sector. The results show that the carbon emission efficiency of the system is relatively low, with marked differences among regions. Based on the Moran's I, we further found spatial heterogeneity in carbon emission efficiency of provincial power sector. Policies for adaptation and mitigation of climate change should have regional differences. Interregional collaboration also plays a key role in adapting to and mitigating climate change. For China, it is an important issue to develop clean coal-fired power generation and vigorously develop renewable energy. From a global perspective, energy transformation needs to be continuously promoted. Promoting low-carbon transformation of global energy system requires deep technical cooperation and synergy. Global mitigation strategy should focus on the orientation of structural reform and constantly optimize the energy structure.