Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets
Since China implemented its carbon trading mechanism, trading risks arising from unstable carbon prices have significantly reduced its emission-reduction efficiency. Unlike traditional research, which focuses on the impact of fossil fuels on carbon prices, this study emphasises risk spillovers from...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25000811 |
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| author | Ruo-Yang Pu Qiao-Mei Liang Yi-Ming Wei Song-Yang Yan Xiang-Yu Wang De-Hua Li Chen Yi Chang-Jing Ji |
| author_facet | Ruo-Yang Pu Qiao-Mei Liang Yi-Ming Wei Song-Yang Yan Xiang-Yu Wang De-Hua Li Chen Yi Chang-Jing Ji |
| author_sort | Ruo-Yang Pu |
| collection | DOAJ |
| description | Since China implemented its carbon trading mechanism, trading risks arising from unstable carbon prices have significantly reduced its emission-reduction efficiency. Unlike traditional research, which focuses on the impact of fossil fuels on carbon prices, this study emphasises risk spillovers from new energy markets amidst large-scale renewable energy deployment. Moreover, to address subjectivity in variable selection and overfitting issues in carbon price determinants, the LASSO algorithm is integrated with the multivariate GARCH model. Using daily carbon quota prices from seven Chinese pilot markets (2014–2022), factors driving carbon price volatility are systematically identified, and the heterogeneous influence of new energy markets on carbon market risks is rigorously analysed. The results indicate that new energy market volatility significantly contributes to carbon price fluctuations. A 1 % increase in the CNI New Energy Index induces co-movement in carbon prices: Hubei (+0.08 %), Beijing (+0.01 %) and Shenzhen (+0.06 %), while Shanghai exhibits inverse sensitivity (−0.19 %). Prices in Guangdong, Tianjin and Chongqing show minimal responsiveness. Additionally, the correlation between new energy markets and carbon markets exhibits temporal heterogeneity. Furthermore, the asymmetric leverage effect suggests that negative news in new energy markets has a more significant impact on carbon markets than positive news. This study advances theoretical understanding of carbon price dynamics and offers practical insights for enhancing risk management frameworks in emissions trading systems. |
| format | Article |
| id | doaj-art-ca4dd922aa2a45829c6c79e5de687ac9 |
| institution | DOAJ |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-ca4dd922aa2a45829c6c79e5de687ac92025-08-20T03:10:25ZengElsevierEnergy Strategy Reviews2211-467X2025-05-015910171810.1016/j.esr.2025.101718Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon marketsRuo-Yang Pu0Qiao-Mei Liang1Yi-Ming Wei2Song-Yang Yan3Xiang-Yu Wang4De-Hua Li5Chen Yi6Chang-Jing Ji7School of Management, Beijing Institute of Technology, Beijing, 100081, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China; Beijing Key Laboratory of Energy Economics and Environmental Management, Beijing, 100081, ChinaSchool of Management, Beijing Institute of Technology, Beijing, 100081, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China; Beijing Key Laboratory of Energy Economics and Environmental Management, Beijing, 100081, China; Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, 100081, China; Corresponding author. School of Management, Beijing Institute of Technology, Beijing, 100081, China.Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China; Beijing Key Laboratory of Energy Economics and Environmental Management, Beijing, 100081, China; Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, 100081, China; Corresponding author. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China.School of Management, Beijing Institute of Technology, Beijing, 100081, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China; Beijing Key Laboratory of Energy Economics and Environmental Management, Beijing, 100081, ChinaEnvironmental Development Center of the Ministry of Ecology and Environment, Beijing, 100081, ChinaAdam Smith Business School, University of Glasgow, Glasgow, G12 8QQ, Scotland, UKSchool of Management, Beijing Institute of Technology, Beijing, 100081, China; Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China; Beijing Key Laboratory of Energy Economics and Environmental Management, Beijing, 100081, ChinaInstitute of Carbon Neutrality, ShanghaiTech University, Shanghai, 201210, ChinaSince China implemented its carbon trading mechanism, trading risks arising from unstable carbon prices have significantly reduced its emission-reduction efficiency. Unlike traditional research, which focuses on the impact of fossil fuels on carbon prices, this study emphasises risk spillovers from new energy markets amidst large-scale renewable energy deployment. Moreover, to address subjectivity in variable selection and overfitting issues in carbon price determinants, the LASSO algorithm is integrated with the multivariate GARCH model. Using daily carbon quota prices from seven Chinese pilot markets (2014–2022), factors driving carbon price volatility are systematically identified, and the heterogeneous influence of new energy markets on carbon market risks is rigorously analysed. The results indicate that new energy market volatility significantly contributes to carbon price fluctuations. A 1 % increase in the CNI New Energy Index induces co-movement in carbon prices: Hubei (+0.08 %), Beijing (+0.01 %) and Shenzhen (+0.06 %), while Shanghai exhibits inverse sensitivity (−0.19 %). Prices in Guangdong, Tianjin and Chongqing show minimal responsiveness. Additionally, the correlation between new energy markets and carbon markets exhibits temporal heterogeneity. Furthermore, the asymmetric leverage effect suggests that negative news in new energy markets has a more significant impact on carbon markets than positive news. This study advances theoretical understanding of carbon price dynamics and offers practical insights for enhancing risk management frameworks in emissions trading systems.http://www.sciencedirect.com/science/article/pii/S2211467X25000811Carbon pricingPrice volatility riskNew energy stock marketDynamic correlationVolatility spillover effect |
| spellingShingle | Ruo-Yang Pu Qiao-Mei Liang Yi-Ming Wei Song-Yang Yan Xiang-Yu Wang De-Hua Li Chen Yi Chang-Jing Ji Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets Energy Strategy Reviews Carbon pricing Price volatility risk New energy stock market Dynamic correlation Volatility spillover effect |
| title | Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets |
| title_full | Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets |
| title_fullStr | Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets |
| title_full_unstemmed | Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets |
| title_short | Impact of the China's new energy market on carbon price fluctuation risk: Evidence from seven pilot carbon markets |
| title_sort | impact of the china s new energy market on carbon price fluctuation risk evidence from seven pilot carbon markets |
| topic | Carbon pricing Price volatility risk New energy stock market Dynamic correlation Volatility spillover effect |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25000811 |
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