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Optimizing carbon tax rates and revenue recycling schemes: Model development, and a case study for the Bohai Bay area, China
- Source :
- Journal of Cleaner Production. 296:126519
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Carbon taxation has long been proposed to mitigate carbon emissions. Carbon tax rate determination and tax revenue recycling are two key steps to achieve a double dividend under carbon taxation. However, few research has sought to optimize the allocation proportion of tax revenue to various categories of taxpayers (households, enterprises, sectors, etc.), or to optimize the two steps synchronously based on nonlinear optimization methods. In this study, a computable general equilibrium model was established to explore the influence of carbon tax rate and tax revenue recycling shares on the economy and on carbon emissions. Meanwhile, a nonlinear optimization model was proposed, for optimizing both steps of carbon taxation synchronously, as well as for promoting GDP and CO2 emission reduction. The Bohai Bay area, a typical area with enormous carbon emissions in China, was adopted as the study case for this research. The results showed that the optimized taxation scheme could lead to lower carbon emissions and greater economic growth, i.e., a strong double dividend was obtained. The optimized taxation scheme could lead to both cleaner air and cleaner energy and industrial structures while still promoting economic growth.
- Subjects :
- Computable general equilibrium
Carbon tax
Renewable Energy, Sustainability and the Environment
Natural resource economics
020209 energy
Strategy and Management
05 social sciences
chemistry.chemical_element
02 engineering and technology
Building and Construction
Industrial and Manufacturing Engineering
Nonlinear programming
Tax revenue
chemistry
Greenhouse gas
050501 criminology
0202 electrical engineering, electronic engineering, information engineering
Economics
Dividend
Revenue
Carbon
0505 law
General Environmental Science
Subjects
Details
- ISSN :
- 09596526
- Volume :
- 296
- Database :
- OpenAIRE
- Journal :
- Journal of Cleaner Production
- Accession number :
- edsair.doi...........42cfb918ba0eac73cc2c786a04d16246