The Potential Gains from Carbon Emissions Trading in China’s Industrial Sectors

Yanni Yu (), Weijie Zhang and Ning Zhang
Additional contact information
Yanni Yu: Jinan University
Weijie Zhang: Jinan University

Computational Economics, 2018, vol. 52, issue 4, No 8, 1175-1194

Abstract: Abstract The command and control mechanism and the market trading mechanism have been adopted by Chinese government to reduce the industrial carbon emissions. Concerns have arisen over which policy is more effective and what are the potential gains from carbon emissions trading for industrial sectors. A Data Envelopment Analysis based linear programming technology is used to compare the industrial potential gains including both the economic potential gains and the environmental potential gains from the command and control and carbon emissions trading mechanisms. An empirical study containing the data set of 38 sub-industries in China from 2006–2014 is conducted. The empirical results show that the carbon emissions trading mechanism can produce more potential gains compared with the command and control mechanism, with an average of 69.6 and 92.0% economic potential gains and 49.1 and 21.0% environmental potential gains in terms of the overall level and industrial level, respectively. Additionally, the environmental potential gains of each sub-industry can provide theoretical support for the emission quotas allocation. Finally, several policy implications based on the empirical results are proposed.

Keywords: Chinese industries; Carbon emissions trading mechanism; Command and control mechanism; Economic potential gains; Environmental potential gains (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
http://link.springer.com/10.1007/s10614-017-9724-2 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:52:y:2018:i:4:d:10.1007_s10614-017-9724-2

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-017-9724-2

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().