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Computational Economics

, Volume 53, Issue 1, pp 169–189 | Cite as

The Limit of Global Carbon Tax and its Climatic and Economic Effects

  • Gaoxiang Gu
  • Zheng Wang
Article

Abstract

Global carbon tax has been widely studied for a long time. However, its economic feasibility in specific countries and sectors has not been taken seriously. This study focuses on the limit of carbon tax in carbon reduction and its economic and climatic impacts. To accurately predict the economic impact of carbon tax for assessing its feasibility, a climatic-economic IAM named CIECIA is applied and improved by adding a carbon tax module. In this model, two levy types of carbon tax with an adjustable revenue distribution mode are designed. On the basis of this, the emission reduction limits of carbon tax and its economic and climatic effects are simulated. The results indicate that carbon tax reduces emissions in two ways: directly, by reducing the output of high-emission sectors, and indirectly, by promoting the adoption of low-carbon technologies. Global carbon tax can achieve the \(2\,{^{\circ }}\hbox {C}\) climate mitigation target under a national independent mode, whereas under a global uniform mode, the limit of temperature control is around \(2.46\,{^{\circ }}\hbox {C}\). As the cost of carbon reduction, the economic loss is also significant, especially in developing countries. Investing R&D by using carbon tax revenue is an effective way to both reduce emissions further and ease economic loss. On the basis of this, we propose a Pareto improving scheme that both ensures the economic benefits of all participating countries and achieves climate mitigation targets.

Keywords

Limiting carbon tax Integrated assessment model Tax revenue distribution R&D investment Process technology progress Pareto improvement 

Notes

Acknowledgements

This work was supported by Chinese National Natural Science Foundation (Grant No. 41501130) and the National Basic Research Program of China (Grant No. 2012CB955800).

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Population Research InstituteEast China Normal UniversityShanghaiChina
  2. 2.Institute of Policy and ManagementChinese Academy of SciencesBeijingChina
  3. 3.Key Laboratory of Geographical Information Science, Ministry of State Education of ChinaEast China Normal UniversityShanghaiChina

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