Initial Allocation of SO2 Emission Rights Based on the Combination Weighting Method: Evidence from China’s Thermal Power Plants

Emission trading system is an effective market-oriented means to control pollutant emission and reasonable initial allocation of emission rights is the premise of its smooth implementation. However, at present, the initial allocation of emission rights depends largely on the amount of emissions, which leads to weak positive guidance effect for enterprises. So to explore the optimal initial allocation method of SO2 emission rights, this paper takes 8 thermal power plants in Dalian, China as the research objects to calculate the initial allocation of SO2 emission rights. Because SO2 is the main cause of acid rain, which is one of the most serious air pollution in China, and thermal power plants are among the main SO2-emitters. Firstly, an indicator system is established considering enterprise size, pollutant discharge and social contributions, as well as pollution control capacity. Then, the combination weighting method is developed through integrating the subjective methods G1 and G2 with the objective ones, entropy and maximum deviation. The empirical results show that the enterprises with more desulfurization equipment or large heating supply are supposed to get more emission rights; the actual emission value of SO2 in half of the enterprises exceeds the theoretical ones; SO2 removal rate, desulfurization equipment quantity and heating supply exert the most positive effects on the initial allocation of emission rights. The constructed model can be used as a reference for future research of initial allocation of other pollutants' emission rights. Also, the implications have been proposed for the government, industry, and enterprises.

Credit Evaluation Model of Road Transportation Enterprises Based on the Combination Weighting Method

In the road transportation industry, the high risk of default by enterprises will have a serious impact on consumer interests and market order. To improve the scientific nature of the credit evaluation of road transportation enterprises, this paper establishes a credit evaluation model based on the combination weighting method, considering the information volume, volatility, and difference of the road transportation enterprises data and using normalized constraints of maximum variance to determine the combination weights. The model fully considers the degree of difference between the indicators and makes up for the deviation of the single weighting method. Finally, the paper makes an empirical analysis of 115 road transportation enterprises in a certain province of China and verifies the rationality of the combination weighting model. The results show that the credit level of a road transport enterprise in a certain province is at a medium level. Strengthening the supervision and examination of the management, qualification, and safety production capacity of the enterprise can further improve the credit level of the road transportation enterprise.

Identifying key node in multi-region opportunistic sensor network based on improved TOPSIS

The topology of multi-region opportunistic sensor networks is evolving, and it is difficult to identify the key nodes in the networks by traditional key node identification methods. In this paper, a novel method based on the improved TOPSIS method is proposed to identify the key node from the ferry node. The dynamic topology information is represented by the graph model which is modeled by the temporal reachable graph. Based on the temporal reachable graph, three attributes are constructed to identify the key node, which are average degree, betweenness centrality and message forwarding rate. The game theory with a combination weighting method is employed to combine the subjective weight and objective weight, so as to obtain the combined weight of each attribute. The TOPSIS method is improved by the combined weight. The key node is identified by the improved TOPSIS. The experiments in three simulation situations show that, compared with the TOPSIS method and MADM_TOPSIS method, the proposed method has better accuracy for the key node identification in the network.