Study on the initial allocation of carbon emission permits in the provinces of China based on Shapley method
- DOI
- 10.2991/mse-15.2016.63How to use a DOI?
- Keywords
- Carbon emissions, Initial allocation, Gravity model, Shapley method
- Abstract
In China, the initial allocation of carbon emission permits in different provinces is an important issue. It is not only related to whether China can achieve the carbon emission reduction targets, but also the establishment of a national total control and carbon emissions trading market. The cooperation between different provinces and cities can also be more effective in reducing carbon emissions in China. Therefore a plan should be put forward about how to allocate the quota of carbon emissions in China for supporting and promoting cooperation between provinces. For such a purpose, this article use the Gravity model and Shapley value method to estimate the distribution of 2020 carbon emissions quota reallocation in eight different areas of China. The results show that: for different areas, the economic factors, population effects, geographical positions and collaborative carbon reductions should be considered fairly and reasonably to reallocate carbon emission quotas. The results of this paper can provide important information and method for decision makers to establish fair and reasonable carbon-permit market in China.
- Copyright
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Hailin Mu AU - Yuxuan Huang AU - Nan Li AU - Zhaoquan Xue AU - Longxi Li PY - 2016/03 DA - 2016/03 TI - Study on the initial allocation of carbon emission permits in the provinces of China based on Shapley method BT - Proceedings of the 2015 International Conference on Mechanical Science and Engineering PB - Atlantis Press SP - 390 EP - 396 SN - 2352-5401 UR - https://doi.org/10.2991/mse-15.2016.63 DO - 10.2991/mse-15.2016.63 ID - Mu2016/03 ER -