EconPapers    
Economics at your fingertips  
 

Support for a Carbon Tax in China: A Collective Decision-Making Process Simulation Using the KAPSARC Toolkit for Behavioral Analysis

Imtenan Al-Mubarak, Brian Efird, Leo Lester and Sun Xia
Additional contact information
Sun Xia: King Abdullah Petroleum Studies and Research Center

Discussion Papers from King Abdullah Petroleum Studies and Research Center

Abstract: China, the world’s largest emitter of carbon dioxide, is taking steps to combat the effects of climate change on its environment. The path it takes to mitigate the effects of pollution will have a significant impact on the global carbon reduction agenda. In this study, we focus on the political feasibility of implementing a carbon tax in China within the next five years. We do this using the KAPSARC Toolkit for Behavioral Analysis (KTAB) platform, a model of collective decision-making processes (CDMPs) developed at KAPSARC to assess the expected support in China for, and reactions to, this potential policy choice.

Keywords: Air Pollution; Climate Change; Collective Decision-Making Processes (CDMPs); Energy Companies; Greenhouse Gas Emissions (GHG); KAPSARC Toolkit for Behavioral Analysis (KTAB); Policy Analysis; Policy Development; Political Feasability (search for similar items in EconPapers)
Pages: 28
Date: 2017-05-01
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.kapsarc.org/research/publications/supp ... behavioral-analysis/ First version, 2017 (application/pdf)

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:prc:dpaper:ks-2017--dp09

Access Statistics for this paper

More papers in Discussion Papers from King Abdullah Petroleum Studies and Research Center Contact information at EDIRC.
Bibliographic data for series maintained by Michael Gaffney ().

 
Page updated 2025-04-18
Handle: RePEc:prc:dpaper:ks-2017--dp09