题目：“Comparing the Cost of a Carbon Tax in China and the United States”

主讲人：Dr. Anthony Liu, Reserach Fellow, Resources for the Future

时间：2014年11月25日（周二）12：05-14：00

地点：北京大学国家发展研究院万众楼一楼小教室

论文摘要：  Conventional wisdom holds that, without considering environmental benefits, a carbon tax would reduce welfare and that these costs will be higher in developing countries. We find strong evidence for the opposite conclusions.  We show how three factors explored in the prior literature can combine to reduce the welfare cost of a carbon tax, and that these effects are particularly strong in developing economies. Incorporating informal production, untaxed Ricardian rents, and tax evasion, we conduct a series of numerical simulations for China and the U.S. We find that the costs of carbon tax policy in China are lower than those in the U.S. for emissions reductions up to 12%. Further, we see that overall efficiency costs are negative in both countries for a significant range of abatement targets: raising government revenue using a carbon tax is in fact cheaper than existing tax policy. We believe our results extend to the tax systems in many developing economies.

主讲人简介：Anthony Liu’s research focuses on two broad areas: climate change policy and the environment in developing countries. Some of his current work addresses carbon taxes and the unique properties that could make them attractive components of modern tax systems. Using a combination of analytical models and general equilibrium simulations, Liu has found that the cost of carbon taxes could be much lower than has been previously believed. Liu is also interested in pollution issues and the interactions between the environment and the economy in China, and has studied large-scale water treatment infrastructure in China.

Anthony Liu obtained Ph.D. in economics from UC-SD (2012), MA. Economics from UC-SB(2005), and BA. Economics from Stanford(2000). He is currently research fellow at RFF and visiting Asistant Professor of Economics at Cheung Kong Graduate School of Business.