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- Publisher Website: 10.1007/s10644-022-09431-2
- Scopus: eid_2-s2.0-85135367941
- WOS: WOS:000836123400001
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Article: Spillover effect among independent carbon markets: evidence from China’s carbon markets
Title | Spillover effect among independent carbon markets: evidence from China’s carbon markets |
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Authors | |
Keywords | China’s carbon market Improved directed acyclic graph approach Spillover effect Structural vector error correction model |
Issue Date | 2022 |
Citation | Economic Change and Restructuring, 2022 How to Cite? |
Abstract | Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design. |
Persistent Identifier | http://hdl.handle.net/10722/333703 |
ISSN | 2023 Impact Factor: 4.0 2023 SCImago Journal Rankings: 0.713 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Yan, Yaxue | - |
dc.contributor.author | Liang, Weijuan | - |
dc.contributor.author | Wang, Banban | - |
dc.contributor.author | Zhang, Xiaoling | - |
dc.date.accessioned | 2023-10-06T05:21:43Z | - |
dc.date.available | 2023-10-06T05:21:43Z | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | Economic Change and Restructuring, 2022 | - |
dc.identifier.issn | 1573-9414 | - |
dc.identifier.uri | http://hdl.handle.net/10722/333703 | - |
dc.description.abstract | Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China’s pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of “two small-world networks.” Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design. | - |
dc.language | eng | - |
dc.relation.ispartof | Economic Change and Restructuring | - |
dc.subject | China’s carbon market | - |
dc.subject | Improved directed acyclic graph approach | - |
dc.subject | Spillover effect | - |
dc.subject | Structural vector error correction model | - |
dc.title | Spillover effect among independent carbon markets: evidence from China’s carbon markets | - |
dc.type | Article | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1007/s10644-022-09431-2 | - |
dc.identifier.scopus | eid_2-s2.0-85135367941 | - |
dc.identifier.eissn | 1574-0277 | - |
dc.identifier.isi | WOS:000836123400001 | - |