Abstract:In order to realize the national carbon intensity reduction target, China has decided to establish a unified national carbon emissions trading market in 2017. At the initial stage, eight industrial sectors will be covered in the carbon market and the other industrial sectors will be included gradually. The aim of this paper is to study the issue of how to allocate the carbon emissions quotas among different industrial sectors fairly and effectively. We try to provide theoretical support for how to determine the coverage scope and access order of the carbon market. In this paper, we construct a comprehensive reduction index based on indicators of equity and efficiency principle. We adopt entropy method to get the objective weights of the three indicators. Then, an allocation model is developed to determine each sector's reduction target for the year of 2020. The result shows that our allocation scheme based on entropy method is more reasonable, and our allocation method will promote the equity of carbon quotas allocation and the efficiency of carbon emissions. With consideration of China's current economic situation and industrial background, we discuss some policy implications regarding the construction of carbon market.

Natural convection in an enclosure is a classical problem in heat transfer field. In this study, natural convection induced by the heat source in the enclosure is studied with two analysis methods, i. e. CFD and artificial neural networks (ANN). The heat transfer in the enclosure is an unsteady process. During this process, the temperature fields in the enclosure are changing with time. The vertical temperature field of y = 0 at one moment is picked up for investigation. Firstly, FLUENT software which is a simulation program of CFD is adopted to simulate the temperature fields under different computation conditions. Then part of the simulation condition's temperature data is picked for training an ANN model and the rest of data is used for validating the ANN model. It has been found from the comparison between the CFD simulation and ANN prediction that the two results have a good agreement with each other. In the comparison, the max relative errors are around 12%, mean relative errors are around 0.3%, mean square errors are around 0.6%, values of absolute fraction of variance are all not less than 0.99. The results demonstrated that the ANN prediction have enough accuracy.