标题：Determining the optimal carbon tax rate based on data envelopment analysis
作者：Jin, Minyue; Shi, Xiao; Emrouznejad, Ali; Yang, Feng
作者机构：[Jin, Minyue; Yang, Feng] Univ Sci & Technol China, Sch Management, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China.; [Shi, Xiao] Shandong Univ 更多
通讯作者地址：[Shi, X]Shandong Univ Finance & Econ, Sch Finance, Jinan 250000, Shandong, Peoples R China;[Emrouznejad, A]Aston Univ, Aston Business Sch, Birmingham, 更多
来源：JOURNAL OF CLEANER PRODUCTION
关键词：Carbon tax policy; Government revenue; Government expenditure; Carbon; emissions; Centralized DEA approach
摘要：Carbon tax policy is widely used to control greenhouse gases and how to determine a suitable carbon tax rate is very important for policy makers considering the trade-off between environmental protection and economic development. In an industry regulated by carbon tax policy, we consider two competing firms who sell ordinary products and green products respectively. In order to promote the firm who sells ordinary product to reduce carbon emissions, the government of China imposes carbon tax on the or- dinary products. For the government, three objectives are considered when it makes carbon tax policy. They are increasing the government revenue, reducing the government expenditure and decreasing the carbon emissions. For the firms, it is important to explore their pricing strategies taken into account of the government tax policy. To find an optimal carbon tax rate and to achieve the three objectives simultaneously, we consider this as a multiple criteria decision-making problem. Hence, we propose to use a centralized data envelopment analysis (DEA) approach to solve it. We find that when one firm produces ordinary products and the other produces green products; the government may set a high tax rate. While when both firms sell ordinary products, the optimal tax policy for each firm is different and the government may impose a higher tax rate for one firm and a lower tax rate for the other firm. (C) 2017 Elsevier Ltd. All rights reserved.