Investigating carbon tax pilot in YRD urban agglomerations—Analysis of a novel ESER system with carbon tax constraints and its application
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This paper attempts to explore carbon tax pilot in Yangtze River Delta (YRD) urban agglomerations based on a novel energy-saving and emission-reduction (ESER) system with carbon tax constraints, which has not yet been discussed in present literature. A novel carbon tax attractor is achieved through the discussion of the dynamic behavior of the new system. Based on the genetic algorithm-back propagation neural network, the quantitative coefficients of the actual system are identified. The scenario analysis results show that, under the same tax rate and constraint conditions, the ESER system in YRD urban agglomerations is superior to the average case in China, in which the impacts on economic growth are almost the same. The former's energy intensity is lower and the shock resistance is stronger. It is found that economic property of YRD urban agglomerations is the main cause for the ESER system of YRD urban agglomerations being superior. In the current YRD urban agglomerations' ESER system, energy intensity cannot be adjusted to an ideal level by commercialization management and government control; however, it is under effective control of carbon tax incentives. Therefore, strengthening the economic property of YRD urban agglomerations and effective utilization of carbon tax incentives could perfectly control energy intensity, without obvious potential negative impact on economic growth.
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Studies have considered effects of carbon pricing on economic growth and energy intensity, or on electricity prices.
“Studies have considered effects of carbon pricing on economic growth and energy intensity [233,234], or on electricity prices [569].”
From Tackling climate change with machine learning (2023) · cites this paper as [233]
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- Authors
- Fang, G., Tian, L., Fu, M., Sun, M., Du, R., & Liu, M.
- Affiliations
- Jiangsu University (CN)
- Nanjing University of Finance and Economics (CN)
- Corresponding author
- Fang, Guochang
- Venue
- Applied Energy
- Publisher
- Elsevier BV
- Published
- 2017-05
- Volume
- 194
- Pages
- 635-647
- ISSN
- 0306-2619 (ISSN-L)
- PMC
- PMC7117014
- OpenAlex
- W2283704599
- Cited by
- 29
- References
- 53
- Citations by year
- 2025: 1 · 2024: 3 · 2023: 4 · 2022: 5 · 2021: 1 · 2020: 3 · 2018: 9 · 2017: 2 · 2016: 1
- FWCI
- 7.78
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Applied Energy (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- National Natural Science Foundation of China (71303205, 71340024, 71303095, 71403105, 61403171, 91546118)
- China Postdoctoral Science Foundation (2015T80519, 2014M551524)
- Jiangsu Province Postdoctoral Science Foundation (1401049C)
- Qing Lan Project (JS201423)
- Topics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Climate Change Policy and Economics
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Energy, Environment, Economic Growth
- Physical Sciences › Energy › Renewable Energy, Sustainability and the Environment › Energy, Environment, and Transportation Policies
- Keywords
- carbon tax, Yangtze River Delta urban agglomeration, energy intensity, economic growth, economic property, scenario analysis, genetic algorithm-back propagation neural network
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