[go: up one dir, main page]
Skip to main content

Advertisement

SpringerLink
Log in

Assessing the extreme risk spillovers to carbon markets from energy markets: evidence from China

  • Research Article
  • Published:
Environmental Science and Pollution Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Explore related subjects

Find the latest articles, discoveries, and news in related topics.

Availability of data and materials

The datasets used or analyzed during the current study are available upon request.

Notes

  1. Data comes from the China Emissions Trading website.

  2. Data comes from the National Bureau of Statistics of China.

  3. 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

    Google Scholar 

  • Adrian T, Brunnermeier MK (2016) CoVaR. Am Econ Rev 106(7):1705–1741

    Google Scholar 

  • 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

    Google Scholar 

  • Alberola E, Chevallier J, Cheze B (2008) Price drivers and structural breaks in European carbon prices 2005–2007. Energy Policy 36(2):787–797

    Google Scholar 

  • 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

    CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Diebold FX, Yilmaz K (2012) Better to give than to receive: predictive directional measurement of volatility spillovers. Int J Forecast 28:57–66

    Google Scholar 

  • Diebold FX, Yilmaz K (2014) On the network topology of variance decompositions: measuring the connectedness of financial firms. J Econometr 182(1):119–134

    Google Scholar 

  • 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

    Google Scholar 

  • Dutta A, Bouri E, Noor MH (2018) Return and volatility linkages between CO2 emission and clean energy stock prices. Energy 164:803–810

    Google Scholar 

  • Bollerslev T (1987) A conditionally heteroscedastic time series model for speculative prices and rates of return. Rev Econ Stat 69(3):542–527

    Google Scholar 

  • Engle RF (1982) Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica 50:987–1007

    Google Scholar 

  • 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

    Google Scholar 

  • Fan GH, Todorova N (2017) Dynamics of China’s carbon prices in the pilot trading phase. Appl Energy 208:1452–1467

    Google Scholar 

  • Fang S, Cao G (2021) Modelling extreme risks for carbon emission allowances — evidence from European and Chinese carbon markets. J Clean Prod 316:128023

    Google Scholar 

  • 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

    Google Scholar 

  • Guo LY, Feng C (2021) Are there spillovers among China’s pilots for carbon emission allowances trading? Energy Economics 103:105574

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Hammoudeh S, Nguyen DK, Sousa RM (2014) Energy prices and CO2 emission allowance prices: a quantile regression approach. Energy Policy 70(7):201–206

    Google Scholar 

  • 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

    Google Scholar 

  • ICAP (2022) Emissions trading worldwide: status report 2022. International Carbon Action Partnership, Berlin

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  Google Scholar 

  • Kim HS, Koo WW (2010) Factors affecting the carbon allowance market in the US. Energy Policy 38(4):1879–1884

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  Google Scholar 

  • 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

    Google Scholar 

  • Lin B, Xu B (2021) A non-parametric analysis of the driving factors of China’s carbon prices. Energy Economics 104:105684

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Marimoutou V, Soury M (2015) Energy markets and CO2 emissions: analysis by stochastic copula autoregressive model. Energy 88:417–429

    CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Reboredo JC, Ugolini A (2015) Systemic risk in European sovereign debt markets: a CoVaR-copula approach. J Int Money Financ 51:214–244

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Tan X, Wang X (2017) Dependence changes between the carbon price and its fundamentals: a quantile regression approach. Appl Energy 190:306–325

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Wang Y, Guo Z (2018) The dynamic spillover between carbon and energy markets: new evidence. Energy 149:100692

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Wen F, Zhao H, Zhao L, Yin H (2022) What drive carbon price dynamics in China? Int Rev Financ Anal 79:101999

    Google Scholar 

  • Wu R, Qin Z (2021) Assessing market efficiency and liquidity: evidence from China’s emissions trading scheme pilots. Sci Total Environ 769:144707

    CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    CAS  Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Zeng SH, Jiang CX, Ma C, Su B (2018) Investment efficiency of the new energy industry in China. Energy Economics 70:536–544

    Google Scholar 

  • Zhang YJ, Sun YF (2016) The dynamic volatility spillover between European carbon trading market and fossil energy market. J Clean Prod 112:2654–2663

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • 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

    Google Scholar 

  • Zhu B, Xu C, Wang P, Zhang L (2022) How does internal carbon pricing affect corporate environmental performance? J Bus Res 145:65–77

    Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China (No. 72071008).

Author information

Authors and Affiliations

  1. School of Economics and Management, Beihang University, Beijing, 100191, China

    Ruirui Wu & Zhongfeng Qin

  2. Key Laboratory of Complex System Analysis, Management and Decision (Beihang University), Ministry of Education, Beijing, 100191, China

    Zhongfeng Qin

Authors
  1. Ruirui Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhongfeng Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Ruirui Wu: conceptualization, methodology, software, data curation, writing—original draft. Zhongfeng Qin: conceptualization, methodology, supervision, writing—reviewing and editing.

Corresponding author

Correspondence to Zhongfeng Qin.

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.

Appendix

Appendix

Table 9

Table 9 The AIC values of ARMA(\(p,q\)) model
Full size table

Table 10

Table 10 Summary statistics of VaR and CoVaR for carbon markets conditional on energy markets
Full size table
Fig. 3
figure 3

The changes in daily prices for three carbon pilots (unit: Yuan)

Full size image

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.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11356-022-24610-4

Keywords

Search

Navigation