TY - JOUR
T1 - Impact of the China's new energy market on carbon price fluctuation risk
T2 - Evidence from seven pilot carbon markets
AU - Pu, Ruo Yang
AU - Liang, Qiao Mei
AU - Wei, Yi Ming
AU - Yan, Song Yang
AU - Wang, Xiang Yu
AU - Li, De Hua
AU - Yi, Chen
AU - Ji, Chang Jing
N1 - Publisher Copyright:
© 2025 The Authors
PY - 2025/5
Y1 - 2025/5
N2 - 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.
AB - 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.
KW - Carbon pricing
KW - Dynamic correlation
KW - New energy stock market
KW - Price volatility risk
KW - Volatility spillover effect
UR - http://www.scopus.com/inward/record.url?scp=105002309914&partnerID=8YFLogxK
U2 - 10.1016/j.esr.2025.101718
DO - 10.1016/j.esr.2025.101718
M3 - Article
AN - SCOPUS:105002309914
SN - 2211-467X
VL - 59
JO - Energy Strategy Reviews
JF - Energy Strategy Reviews
M1 - 101718
ER -
