How will the emissions trading scheme save cost for achieving China’s 2020 carbon intensity reduction target?
By Lianbiao Cui, Ying Fan, Lei Zhu, Qing-Hua Bi. 2014
331 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Environmental Engineering, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/4edd41271583105dba13d7afb4ff86c7600c8d02
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Boqiang Lin
- Zhijie Jia
- Bin Ye
- Gorm Bruun Andresen
- Bo Tranberg
- Neeraj Dhanraj Bokde
- Bo Shen
- Xiaoling Ouyang
In Environmental Engineering
- Boqiang Lin
- Zhijie Jia
- Bin Ye
- Gorm Bruun Andresen
- Bo Tranberg
- Neeraj Dhanraj Bokde
- Bo Shen
- Xiaoling Ouyang
In Renewable Energy, Sustainability and the Environment
Citations in the corpus
- Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling (2020, methodology): “Furthermore, promising mathematical methods [24, 25] were proposed for CO2 intensity predictions and suggested policies for low carbon economy.”
- Potential gains from carbon emissions trading in China: A DEA based estimation on abatement cost savings (2016, neutral): “Omega, 41(2), 164-175. Sun, J., Wu, J., Liang, L., Zhong, R.Y., & Huang, G.Q. (2014). Allocation of emission permits using DEA: Centralised and individual points of view.”