Green bond vs green stock: which one can resist climate policy uncertainty in China?
DOI: https://doi.org/10.3846/jbem.2026.25508Abstract
This paper applies the time-varying parameter-stochastic volatility-vector auto-regression (TVP-SV-VAR) method to explore the correlations among China’s climate policy uncertainty (CPU), green stock (GS), and green bond (GB). The findings evidence dynamic impacts from CPU to the green assets, indicating that the hedging ability of green assets varies over time. In the short and medium term, the GS may hedge the rising CPU risks effectively while the GB is not. However, in the long term, both the GS and GB may resist the CPU risks, although the GS is found to perform better. Furthermore, the results also suggest that the GS is more reliable when the unexpected shocks happen. Thus, compared to the GB, the GS may possess higher uncertainty risks hedging ability. Nevertheless, the results also suggest that the hedging ability of the GS decreases in recent years. The findings may help investors construct portfolios to hedge CPU risks. Moreover, the results suggest that the government should further promote the standardisation of green investment and reduce the information asymmetry of climate policy, which is critical to improve the performance of green assets.
Keywords:
climate policy uncertainty, China, green stock, green bond, hedging-ability, time-varyingHow to Cite
Share
License
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.

This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Bali, T. G., Peng, L., Shen, Y., & Tang, Y. (2014). Liquidity shocks and stock market reactions. Review of Financial Studies, 27(5), 1434–1485. https://doi.org/10.1093/rfs/hht074
Bouri, E., Iqbal, N., & Klein, T. (2022). Climate policy uncertainty and the price dynamics of green and brown energy stocks. Finance Research Letters, 47, Article 102740. https://doi.org/10.1016/j.frl.2022.102740
Chopra, M., & Mehta, C. (2022). Going green: Do green bonds act as a hedge and safe haven for stock sector risk? Finance Research Letters, 51, Article 103357. https://doi.org/10.1016/j.frl.2022.103357
Doğan, B., Trabelsi, N., Tiwari, A. K., & Ghosh, S. (2023). Dynamic dependence and causality between crude oil, green bonds, commodities, geopolitical risks, and policy uncertainty. Quarterly Review of Economics and Finance, 89, 36–62. https://doi.org/10.1016/j.qref.2023.02.006
Dou, J., Su, C.W., Li, W., & Dou, J. (2025). Green finance and artificial intelligence: Catalysts for promoting sustainability? Economic Analysis and Policy, 88, 13–25. https://doi.org/10.1016/j.eap.2025.08.037
Duan, X., Xiao, Y., Ren, X., Taghizadeh-Hesary, F., & Duan, K. (2023). Dynamic spillover between traditional energy markets and emerging green markets: implications for sustainable development. Resource Policy, 82, Article 103483. https://doi.org/10.1016/j.resourpol.2023.103483
Feng, Y., Xiao, Z., Zhou, J., & Ni, G. (2023). Asymmetrically examining the impact of green finance and renewable energy consumption on environmental degradation and renewable energy investment: The impact of the COVID-19 outbreak on the Chinese economy. Energy Reports, 9, 5458–5472. https://doi.org/10.1016/j.egyr.2023.04.361
Guo, D., & Zhou, P. (2021). Green bonds as hedging assets before and after COVID: A comparative study between the US and China. Energy Economics, 104, Article 105696. https://doi.org/10.1016/j.eneco.2021.105696
Henriques, I., & Sadorsky, P. (2024). Do clean energy stocks diversify the risk of FinTech stocks? Connectedness and Portfolio implications. Global Finance Journal, 62, Article 101019. https://doi.org/10.1016/j.gfj.2024.101019
Huang, F. W., Su, C. W., Yang, S., Qin, M., & Zhang, W. (2025). How do economic policy uncertainty and geopolitical risk affect oil imports? Evidence from China and India. Energy Strategy Reviews, 59, Article 101695. https://doi.org/10.1016/j.esr.2025.101695
Huo, X., Xue, H., & Jiao, L. (2023). Risk management of retrofit project in old residential areas under green development. Energy and Buildings, 279, Article 112708. https://doi.org/10.1016/j.enbuild.2022.112708
Igeland, P., Schroeder, L., Yahya, M., Okhrin, Y., & Uddin, G. S. (2024). The energy transition: The behavior of renewable energy stock during the times of energy security uncertainty. Renewable Energy, 221, Article 119746. https://doi.org/10.1016/j.renene.2023.119746
Ivanovski, K., & Marinucci, N. (2021). Policy uncertainty and renewable energy: Exploring the implications for global energy transitions, energy security, and environmental risk management. Energy Research & Social Science, 82, Article 102415. https://doi.org/10.1016/j.erss.2021.102415
Kanamura, T. (2020). Are green bonds environmentally friendly and good performing assets? Energy Economics, 88, Article 104767. https://doi.org/10.1016/j.eneco.2020.104767
Kocaarslan, B., & Soytas, U. (2021). Reserve currency and the volatility of clean energy stocks: The role of uncertainty. Energy Economics, 104, Article 105645. https://doi.org/10.1016/j.eneco.2021.105645
Lee, K., & Cho, J. (2023). Measuring Chinese climate uncertainty. International Review of Economics & Finance, 88, 891–901. https://doi.org/10.1016/j.iref.2023.07.004
Li, X., & Su, C. W. (2024). Evaluating the impact of multiple uncertainty shocks on China’s airline stocks volatility: A novel joint quantile perspective. Journal of Air Transport Management, 121, Article 102688. https://doi.org/10.1016/j.jairtraman.2024.102688
Liu, Y., Zhao, H., & Li, X. (2024). Environmental policy effects of the carbon tax, subsidy, and policy combinations of China’s textile industry: Evidence from the DSGE model. Journal of Cleaner Production, 439, Article 140791. https://doi.org/10.1016/j.jclepro.2024.140791
Pham, L. (2021). Frequency connectedness and cross-quantile dependence between green bond and green equity markets. Energy Economics, 98, Article 105257. https://doi.org/10.1016/j.eneco.2021.105257
Pham, L., & Nguyen, C. P. (2022). How do stock, oil, and economic policy uncertainty influence the green bond market? Finance Research Letters, 45, Article 102128. https://doi.org/10.1016/j.frl.2021.102128
Qin, M., Zhu, Y., Xie, X., Shao, X., & Lobont, O.-R. (2024a). The impact of climate risk on technological progress under the fourth industrial era. Technological Forecasting and Social Change, 202, Article 123325. https://doi.org/10.1016/j.techfore.2024.123325
Qin, M., Wan, Y., Dou, J., & Su, C. W. (2024b). Artificial Intelligence: Intensifying or mitigating unemployment? Technology in Society, 79, Article 102755. https://doi.org/10.1016/j.techsoc.2024.102755
Razzaq, A., Sharif, A., Ozturk, I., & Yang, X. (2023). Central inspections of environmental protection and transition for low-carbon Chinese cities: Policy intervention and mechanism analysis. Energy Economics, 124, Article 106859. https://doi.org/10.1016/j.eneco.2023.106859
Ren, S., Hao, Y., & Wu, H. (2022a). The role of outward foreign direct investment (OFDI) on green total factor energy efficiency: Does institutional quality matters? Evidence from China. Resource Policy, 76, Article 102587. https://doi.org/10.1016/j.resourpol.2022.102587
Ren, X., Li, Y., Cheng, Y., Wen, F., & Lu, Z. (2022b). The interrelationship between the carbon market and the green bonds market: Evidence from wavelet quantile-on-quantile method. Technological Forecasting and Social Change, 179, Article 121611. https://doi.org/10.1016/j.techfore.2022.121611
Saeed, T., Bouri, E., & Tran, D. K. (2020). Hedging strategies of green assets against dirty energy assets. Energies, 13(12), Article 3141. https://doi.org/10.3390/en13123141
Sims, C. A. (1986). Are forecasting models usable for policy analysis? Quarterly Review, 10, 2–16. https://doi.org/10.21034/qr.1011
Su, C.-W., Yang, S., Peculea, A. D., Biţoiu, T. I., & Qin, M. (2024a). Energy imports in turbulent eras: Evidence from China. Energy, 306(15), Article 132586. https://doi.org/10.1016/j.energy.2024.132586
Su, C. W., Liu, X., Vătavu, S., & Dumitrescu Peculea, A. (2024b). Will peer-to-peer online lending affect the effectiveness of monetary policy?. Technological and Economic Development of Economy, 31(1), 1–22. https://doi.org/10.3846/tede.2024.19334
Su, C. W., Song, X. Y., Dou, J., & Qin, M. (2025). Fossil fuels or renewable energy? The dilemma of climate policy choices. Renewable Energy, 238, Article 121950. https://doi.org/10.1016/j.renene.2024.121950
Song, X. Y., Su, C. W., & Qin, M. (2025a). How geopolitical risk affects the market performance of airline stocks? Transport Policy, 172, Article 103778. https://doi.org/10.1016/j.tranpol.2025.103778
Song, X.Y., Su, C.W., & Qin, M. (2025b). Geopolitical storm approaches: Can defence stocks serve as safe haven. Defence and Peace Economics. https://doi.org/10.1080/10242694.2025.2573012
Tang, Y., Wang, B., Pan, N., & Li, Z. (2023). The impact of environmental information disclosure on the cost of green bond: Evidence from China. Energy Economics, 126, Article 107008. https://doi.org/10.1016/j.eneco.2023.107008
Tian, H., Long, S., & Li, Z. (2022). Asymmetric effects of climate policy uncertainty, infectious diseases-related uncertainty, crude oil volatility, and geopolitical risks on green bond prices. Finance Research Letters, 48, Article 103008. https://doi.org/10.1016/j.frl.2022.103008
Tiwari, A. K., Abakah, E. J. A., Gabauer, D., & Dwumfour, R. A. (2022). Dynamic spillover effects among green bond, renewable energy stocks and carbon markets during COVID-19 pandemic: Implications for hedging and investments strategies. Global Finance Journal, 51, Article 100692. https://doi.org/10.1016/j.gfj.2021.100692
Wan, D., Xue, R., Linnenluecke, M., Tian, J., & Shan, Y. (2021). The impact of investor attention during COVID-19 on investment in clean energy versus fossil fuel firms. Finance Research Letters, 43, Article 101955. https://doi.org/10.1016/j.frl.2021.101955
Wang, J., Mishra, S., Sharif, A., & Chen, H. (2024). Dynamic spillover connectedness among green finance and policy uncertainty: Evidence from QVAR network approach. Energy Economics, 131, Article 107330. https://doi.org/10.1016/j.eneco.2024.107330
Wang, K.-H., Jiang, X.-Y., & Li, X. (2025a). Digital revolution meets ESG: Can AI, blockchain and cloud computing enhance ESG performance? Oeconomia Copernicana, 16(2), 593–641. https://doi.org/10.24136/oc.3629
Wang, K.-H., Li, S.-M., Lobont, O.-R., & Moldovan, N.-C. (2025b). Is green innovation the “Golden Ticket” in achieving energy security and sustainable development? Economic Analysis and Policy, 87, 297–314. https://doi.org/10.1016/j.eap.2025.06.016
Wang, X. Q., Jin, W., Xu, B., & Wang, K. (2025a). Volatility in carbon futures amid uncertainties: Considering geopolitical and economic policy factors. Journal of Futures Markets, 45(4), 308–325. https://doi.org/10.1002/fut.22565
Wang, X. Q., Wang, K., Xu, B., & Jin, W. (2025b). Digitalisation and technological innovation: Panaceas for sustainability?. International Journal of Production Research, 63(16), 6071–6088.
Wang, X. Q, Wang, K.-H., Safi, A., & Umar, M. (2025c). How is artificial intelligence technology transforming energy security? New evidence from global supply chains. Oeconomia Copernicana, 16(1), 15–38. https://doi.org/10.24136/oc.3488
Wei, P., Qi, Y., Ren, X., & Gozgor, G. (2023). The role of the COVID-19 pandemic in time-frequency connectedness between oil market shocks and green bond markets: Evidence from the wavelet-based quantile approaches. Energy Economics, 121, Article 106657. https://doi.org/10.1016/j.eneco.2023.106657
Wen, C. P., Wang, K. H., Su, C. W., Li, X., & Wang, Z. S. (2025). Navigating China’s green bonds: Insights from cryptocurrency price, oil price, and economic policy uncertainty. International Review of Economics & Finance, 102, Article 104324. https://doi.org/10.1016/j.iref.2025.104324
Wu, R., & Liu, B.-Y. (2023). Do climate policy uncertainty and investor sentiment drive the dynamic spillovers among green finance markets? Journal of Environmental Management, 347, Article 119008. https://doi.org/10.1016/j.jenvman.2023.119008
Yang, K., Wei, Y., Li, S., & He, J. (2021). Geopolitical risk and renewable energy stock markets: An insight from multiscale dynamic risk spillover. Journal of Cleaner Production, 279, Article 123429. https://doi.org/10.1016/j.jclepro.2020.123429
Ziadat, S. A., Mensi, W., Al-Kharusi, S., Vo, X. V., & Kang, S. H. (2024). Are clean energy markets hedges for stock markets? A tail quantile connectedness regression. Energy Economics, 136, Article 107757. https://doi.org/10.1016/j.eneco.2024.107757
View article in other formats
Published
Issue
Section
Copyright
Copyright (c) 2026 The Author(s). Published by Vilnius Gediminas Technical University.
License

This work is licensed under a Creative Commons Attribution 4.0 International License.