Abstract
Previous studies point to the impact of energy market on carbon market under normal market conditions, but little is known about the impact under extreme market conditions. Motivated by these concerns, we aim to investigate the extreme risk spillovers to carbon markets from traditional fossil energy and new energy markets in China. After using copula model to obtain nonlinear tail dependence structure between carbon and energy markets, we compute conditional Value-at-Risk (CoVaR) to quantify extreme risk spillovers. The results indicate that (i) the risk spillovers from both traditional fossil energy and new energy markets to carbon markets are obviously larger when extreme events cause large shocks to energy markets; (ii) there are risks in the opposite direction in carbon markets when carbon-intensive energy markets are under extreme market conditions, but the direction of risks in carbon markets is uncertain when low-carbon energy markets are under extreme market conditions; (iii) the extreme risk spillovers to carbon markets from energy markets are regionally heterogeneous not only in magnitude but also in direction; (iv) energy markets prefer to transmit extreme risks to more liquid carbon markets. These new findings have valuable implications for both policymakers and participating enterprises in carbon markets.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Availability of data and materials
The datasets used or analyzed during the current study are available upon request.
Notes
Data comes from the China Emissions Trading website.
Data comes from the National Bureau of Statistics of China.
The data is from the China Emissions Trading website.
References
Abadie A (2002) Bootstrap tests for distributional treatment effects in instrumental variable models. Publ Am Stat Assoc 97(457):284–292
Adrian T, Brunnermeier MK (2016) CoVaR. Am Econ Rev 106(7):1705–1741
Adebayo TS, Onifade ST, Alola AA, Muoneke OB (2022) Does it take international integration of natural resources to ascend the ladder of environmental quality in the newly industrialized countries? Resour Policy 76:102616
Alberola E, Chevallier J, Cheze B (2008) Price drivers and structural breaks in European carbon prices 2005–2007. Energy Policy 36(2):787–797
Alkathery AA, Chaudhuri K (2021) Co-movement between oil price, CO2 emission, renewable energy and energy equities: evidence from GCC countries. J Environ Manage 297:113350
Apergis N, Gozgor G, Lau CKM, Wang S (2020) Dependence structure in the Australian electricity markets: new evidence from regular vine copula. Energy Economics 90:104834
Balclar M, Demirer R, Hammoudeh S, Nguyen DK (2016) Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk. Energy Economics 54:159–172
Cui J, Goh M, Zou H (2020) Information spillovers and dynamic dependence between China’s energy and regional CET markets with portfolio implications: new evidence from multi-scale analysis. J Clean Prod 289:125625
Chang K, Ye ZF, Wang WH (2019) Volatility spillover effect and dynamic correlation between regional emissions allowances and fossil energy markets: new evidence from China’s emissions trading scheme pilots. Energy 185:1314–1324
Diebold FX, Yilmaz K (2012) Better to give than to receive: predictive directional measurement of volatility spillovers. Int J Forecast 28:57–66
Diebold FX, Yilmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econometr 182(1):119–134
Duan K, Ren X, Shi Y, Mishra T, Yan C (2021) The marginal impacts of energy prices on carbon price variations: evidence from a quantile-on-quantile approach. Energy Economics 95:105131
Dutta A, Bouri E, Noor MH (2018) Return and volatility linkages between CO2 emission and clean energy stock prices. Energy 164:803–810
Bollerslev T (1987) A conditionally heteroscedastic time series model for speculative prices and rates of return. Rev Econ Stat 69(3):542–527
Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007
Fan X, Lv X, Yin J, Tian L, Liang J (2019) Multifractality and market efficiency of carbon emission trading market: analysis using the multifractal detrended fluctuation technique. Appl Energy 251:113333
Fan GH, Todorova N (2017) Dynamics of China’s carbon prices in the pilot trading phase. Appl Energy 208:1452–1467
Fang S, Cao G (2021) Modelling extreme risks for carbon emission allowances — evidence from European and Chinese carbon markets. J Clean Prod 316:128023
Guo W (2015) Factors impacting on the price of China’s regional carbon emissions based on adaptive Lasso method. China Popul Resour Environ 25(S1):305–310
Guo LY, Feng C (2021) Are there spillovers among China’s pilots for carbon emission allowances trading? Energy Economics 103:105574
Guo LY, Feng C, Yang J (2022) Can energy predict the regional prices of carbon emission allowances in China? Int Rev Financ Anal 82:102210
Gong X, Shi R, Xu J, Lin B (2021) Analyzing spillover effects between carbon and fossil energy markets from a time-varying perspective. Appl Energy 285:116384
Hammoudeh S, Nguyen DK, Sousa RM (2014) Energy prices and CO2 emission allowance prices: a quantile regression approach. Energy Policy 70(7):201–206
Hanif W, Hernandez JA, Mensi W, Kang SH, Yoon SM (2021) Nonlinear dependence and connectedness between clean/renewable energy sector equity and European emission allowance prices. Energy Economics 101:105409
ICAP (2022) Emissions trading worldwide: status report 2022. International Carbon Action Partnership, Berlin
Ji Q, Liu BY, Zhao WL, Fan Y (2020) Modelling dynamic dependence and risk spillover between all oil price shocks and stock market returns in the BRICS. Int Rev Financ Anal 68:101238
Jian M, He H, Ma C, Wu Y, Yang H (2019) Reducing greenhouse gas emissions: a duopoly market pricing competition and cooperation under the carbon emissions cap. Environ Sci Pollut Res 26(17):16847–16854
Jiang W, Chen Y (2022) The time-frequency connectedness among carbon, traditional/new energy and material markets of China in pre- and post-COVID-19 outbreak periods. Energy 246:123320
Kim HS, Koo WW (2010) Factors affecting the carbon allowance market in the US. Energy Policy 38(4):1879–1884
Kumar S, Managi S, Matsuda A (2012) Stock prices of clean energy companies, oil and carbon markets: a vector autoregressive analysis. Energy Economic 34:215–226
Li X, Hu Z, Cao J (2021) The impact of carbon market pilots on air pollution: evidence from China. Environ Sci Pollut Res 28(44):62274–62291
Lin B, Chen Y (2019) Dynamic linkages and spillover effects between CET market, coal market and stock market of new energy companies: a case of Beijing CET market in China. Energy 172:1198–1210
Lin B, Xu B (2021) A non-parametric analysis of the driving factors of China’s carbon prices. Energy Economics 104:105684
Liu BY, Ji Q, Fan Y (2017) Dynamic return-volatility dependence and risk measure of CoVaR in the oil market: a time-varying mixed copula model. Energy Economics 68:53–65
Liu HH, Chen YC (2013) A study on the volatility spillovers, long memory effects and interactions between carbon and energy markets: the impacts of extreme weather. Econ Model 35:840–855
Marimoutou V, Soury M (2015) Energy markets and CO2 emissions: analysis by stochastic copula autoregressive model. Energy 88:417–429
Mi ZF, Zhang YJ (2011) Estimating the ‘Value at Risk’ of EUA futures prices based on the extreme value theory. Int J Global Energy Issues 35(2–4):145–157
Mu Y, Wang C, Cai W (2018) The economic impact of China’s INDC: distinguishing the roles of the renewable energy quota and the carbon market. Renew Sustain Energy Rev 81:2955–2966
Nie Q, Zhang L, Tong Z, Hubacek K (2022) Strategies for applying carbon trading to the new energy vehicle market in China: an improved evolutionary game analysis for the bus industry. Energy 259:124904
Reboredo JC, Ugolini A (2015) Systemic risk in European sovereign debt markets: a CoVaR-copula approach. J Int Money Financ 51:214–244
Reboredo JC, Rivera-Castro MA, Ugolini A (2016) Downside and upside risk spillovers between exchange rates and stock prices. J Bank Finance 62:76–96
Ren X, Li Y, Yan C, Wen F, Lu Z (2022) The interrelationship between the carbon market and the green bonds market: evidence from wavelet quantile-on-quantile method. Technol Forecast Soc Chang 179:121611
Sklar M (1959) Fonctions de répartition à n dimensions et leurs marges. Publications De L’institut Statistique De L’université De Paris 8:229–231
Sun X, Liu C, Wang J, Li J (2020) Assessing the extreme risk spillovers of international commodities on maritime markets: a GARCH-Copula-CoVaR approach. Int Rev Financ Anal 68:101453
Tan X, Sirichand K, Vivian A, Wang X (2020) How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics. Energy Economics 90:104870
Tan X, Wang X (2017) Dependence changes between the carbon price and its fundamentals: a quantile regression approach. Appl Energy 190:306–325
Tu Q, Mo JL (2017) Coordinating carbon pricing policy and renewable energy policy with a case study in China. Comput Ind Eng 113:294–304
Uddin GS, Hernandez JA, Shahzad S, Kang SH (2020) Characteristics of spillovers between the US stock market and precious metals and oil. Resour Policy 66:101601
Wang Y, Guo Z (2018) The dynamic spillover between carbon and energy markets: new evidence. Energy 149:100692
Wang G, Zhang Q, Su B, Shen B, Li Y, Li Z (2021) Coordination of tradable carbon emission permits market and renewable electricity certificates market in China. Energy Economics 93:105038
Wang X, Yan L (2022). Measuring the integrated risk of China’s carbon financial market based on the copula model. Environ Sci Pollut Res
Wen XQ, Guo YF, Wei Y, Huang DS (2014) How do the stock prices of new energy and fossil fuel companies correlate? Evidence from China. Energy Economics 41:63–75
Wen F, Zhao H, Zhao L, Yin H (2022) What drive carbon price dynamics in China? Int Rev Financ Anal 79:101999
Wu R, Qin Z (2021) Assessing market efficiency and liquidity: evidence from China’s emissions trading scheme pilots. Sci Total Environ 769:144707
Wu R, Qin Z, Liu BY (2022a) A systematic analysis of dynamic frequency spillovers among carbon emissions trading (CET), fossil energy and sectoral stock markets: evidence from China. Energy 254:124176
Wu Z, Fan X, Zhu B, Xia J, Zhang L, Wang P (2022b) Do government subsidies improve innovation investment for new energy firms: a quasi-natural experiment of China’s listed companies. Technol Forecast Soc Chang 175:121418
Xiao Z, Ma S, Sun H, Ren J, Feng C, Cui S (2022) Time-varying spillovers among pilot carbon emission trading markets in China. Environ Sci Pollut Res
Xu Y (2021) Risk spillover from energy market uncertainties to the Chinese carbon market. Pac Basin Financ J 67:101561
Yang G, Zha D, Zhang C, Chen Q (2020) Does environment-biased technological progress reduce CO2 emissions in APEC economies? Evidence from fossil and clean energy consumption. Environ Sci Pollut Res 27(17):20984–20999
Yuan N, Yang L (2020) Asymmetric risk spillover between financial market uncertainty and the carbon market: a GAS-DCS–copula approach. J Clean Prod 259(1):120750
Zeng S, Nan X, Chao CJ (2017) The response of the Beijing carbon emissions allowance price (BJC) to macroeconomic and energy price indices. Energy Policy 106:111–121
Zeng SH, Jiang CX, Ma C, Su B (2018) Investment efficiency of the new energy industry in China. Energy Economics 70:536–544
Zhang YJ, Sun YF (2016) The dynamic volatility spillover between European carbon trading market and fossil energy market. J Clean Prod 112:2654–2663
Zhang Y, Zhang Q, Pan B (2019) Impact of affluence and fossil energy on China carbon emissions using STIRPAT model. Environ Sci Pollut Res 26(18):18814–18824
Zhang C, Yang Y, Yun P (2020) Risk measurement of international carbon market based on multiple risk factors heterogeneous dependence. Financ Res Lett 32:101083
Zhao L, Liu W, Zhou M, Wen W (2021) Extreme event shocks and dynamic volatility interactions: the stock, commodity, and carbon markets in China. Finance Research Letters:102645
Zhu B, Huang L, Yuan L, Ye S, Wang P (2020) Exploring the risk spillover effects between carbon market and electricity market: a dimensional empirical mode decomposition based conditional value at risk approach. Int Rev Econ Financ 67:163–175
Zhu B, Ye S, Wang P, Chevallier J, Wei YM (2021) Forecasting carbon price using a multi-objective least squares support vector machine with mixture kernels. J Forecast 41(1):100–117
Zhu B, Xu C, Wang P, Zhang L (2022) How does internal carbon pricing affect corporate environmental performance? J Bus Res 145:65–77
Funding
This work was supported by the National Natural Science Foundation of China (No. 72071008).
Author information
Authors and Affiliations
Contributions
Ruirui Wu: conceptualization, methodology, software, data curation, writing—original draft. Zhongfeng Qin: conceptualization, methodology, supervision, writing—reviewing and editing.
Corresponding author
Ethics declarations
Ethics approval
Not applicable.
Consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Responsible Editor: Roula Inglesi-Lotz.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Wu, R., Qin, Z. Assessing the extreme risk spillovers to carbon markets from energy markets: evidence from China. Environ Sci Pollut Res 30, 37894–37911 (2023). https://doi.org/10.1007/s11356-022-24610-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11356-022-24610-4