IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i21p14053-d956118.html
   My bibliography  Save this article

Measuring the Demand Connectedness among China’s Regional Carbon Markets

Author

Listed:
  • Li-Yang Guo

    (School of Economics and Business Administration, Chongqing University, Chongqing 400030, China)

  • Chao Feng

    (School of Economics and Business Administration, Chongqing University, Chongqing 400030, China)

Abstract

After years of emission trading in segmented pilots, China operates a unified market in the power system and plans to involve more industries in the coming future. The aim of this study is to detect the commonalities of transaction behaviors across China’s regional carbon pilots, so as to provide an empirical basis for a future multi-sectoral expansion of national trading. Based on a dataset of daily trading volume in seven regional markets during 2014–2021, the empirical results from connectedness measures show that the total demand connectedness ranges from 10% to 24%, indicating the existence of interactions among China’s regional markets. This not-so-wide range of fluctuation usually shows a trend of rising first and then falling within each year, during which the upward trend is basically related to the accounting, verification and compliance of allowances. After these time nodes, the total connectedness declines. In addition, the directional connectedness could help clarify the specific roles that regional markets play in the variations of total demand connectedness when facing the shocks of these time nodes. Meanwhile, the frequency decomposition reveals that a longer-term component of more than 10 days dominates the connectedness. Based on these findings, some policy implications are provided alongside.

Suggested Citation

  • Li-Yang Guo & Chao Feng, 2022. "Measuring the Demand Connectedness among China’s Regional Carbon Markets," IJERPH, MDPI, vol. 19(21), pages 1-16, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14053-:d:956118
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/21/14053/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/21/14053/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2020. "Dynamic frequency connectedness between oil and natural gas volatilities," Economic Modelling, Elsevier, vol. 84(C), pages 181-189.
    2. Balcılar, Mehmet & Demirer, Rıza & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2016. "Risk spillovers across the energy and carbon markets and hedging strategies for carbon risk," Energy Economics, Elsevier, vol. 54(C), pages 159-172.
    3. Xiqiang Xia & Mengya Li & Biao Li & Hao Wang, 2021. "The Impact of Carbon Trade on Outsourcing Remanufacturing," IJERPH, MDPI, vol. 18(20), pages 1-18, October.
    4. Moreno, Blanca & García-Álvarez, María Teresa & Fonseca, Ana Rosa, 2017. "Fuel prices impacts on stock market of metallurgical industry under the EU emissions trading system," Energy, Elsevier, vol. 125(C), pages 223-233.
    5. Wang, Gang-Jin & Xie, Chi & Zhao, Longfeng & Jiang, Zhi-Qiang, 2018. "Volatility connectedness in the Chinese banking system: Do state-owned commercial banks contribute more?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 57(C), pages 205-230.
    6. Ehlers, Torsten & Packer, Frank & de Greiff, Kathrin, 2022. "The pricing of carbon risk in syndicated loans: Which risks are priced and why?," Journal of Banking & Finance, Elsevier, vol. 136(C).
    7. Koop, Gary & Pesaran, M. Hashem & Potter, Simon M., 1996. "Impulse response analysis in nonlinear multivariate models," Journal of Econometrics, Elsevier, vol. 74(1), pages 119-147, September.
    8. Pesaran, H. Hashem & Shin, Yongcheol, 1998. "Generalized impulse response analysis in linear multivariate models," Economics Letters, Elsevier, vol. 58(1), pages 17-29, January.
    9. Ferrer, Román & Shahzad, Syed Jawad Hussain & López, Raquel & Jareño, Francisco, 2018. "Time and frequency dynamics of connectedness between renewable energy stocks and crude oil prices," Energy Economics, Elsevier, vol. 76(C), pages 1-20.
    10. Kumar, Surender & Managi, Shunsuke & Matsuda, Akimi, 2012. "Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis," Energy Economics, Elsevier, vol. 34(1), pages 215-226.
    11. Susheng Wang & Gang Chen & Xue Han, 2021. "An Analysis of the Impact of the Emissions Trading System on the Green Total Factor Productivity Based on the Spatial Difference-in-Differences Approach: The Case of China," IJERPH, MDPI, vol. 18(17), pages 1-18, August.
    12. Guo, Li-Yang & Feng, Chao, 2021. "Are there spillovers among China's pilots for carbon emission allowances trading?," Energy Economics, Elsevier, vol. 103(C).
    13. Tsouknidis, Dimitris A., 2016. "Dynamic volatility spillovers across shipping freight markets," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 91(C), pages 90-111.
    14. Yang, Jun & Hao, Yun & Feng, Chao, 2021. "A race between economic growth and carbon emissions: What play important roles towards global low-carbon development?," Energy Economics, Elsevier, vol. 100(C).
    15. Chai, Shanglei & Yang, Xiaoli & Zhang, Zhen & Abedin, Mohammad Zoynul & Lucey, Brian, 2022. "Regional imbalances of market efficiency in China’s pilot emission trading schemes (ETS): A multifractal perspective," Research in International Business and Finance, Elsevier, vol. 63(C).
    16. Zhang, Wei & Li, Jing & Li, Guoxiang & Guo, Shucen, 2020. "Emission reduction effect and carbon market efficiency of carbon emissions trading policy in China," Energy, Elsevier, vol. 196(C).
    17. Fang Zhang & Zhengjun Zhang, 2020. "The tail dependence of the carbon markets: The implication of portfolio management," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-17, August.
    18. Diebold, Francis X. & Yilmaz, Kamil, 2015. "Financial and Macroeconomic Connectedness: A Network Approach to Measurement and Monitoring," OUP Catalogue, Oxford University Press, number 9780199338306.
    19. Chen, Zhongfei & Zhang, Xiao & Chen, Fanglin, 2021. "Do carbon emission trading schemes stimulate green innovation in enterprises? Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    20. Zhao, Lili & Wen, Fenghua & Wang, Xiong, 2020. "Interaction among China carbon emission trading markets: Nonlinear Granger causality and time-varying effect," Energy Economics, Elsevier, vol. 91(C).
    21. Kim, Pyung & Bae, Hyunhoe, 2022. "Do firms respond differently to the carbon pricing by industrial sector? How and why? A comparison between manufacturing and electricity generation sectors using firm-level panel data in Korea," Energy Policy, Elsevier, vol. 162(C).
    22. Diebold, Francis X. & Yilmaz, Kamil, 2012. "Better to give than to receive: Predictive directional measurement of volatility spillovers," International Journal of Forecasting, Elsevier, vol. 28(1), pages 57-66.
    23. Chang, Kai & Ye, Zhifang & Wang, Weihong, 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, Elsevier, vol. 185(C), pages 1314-1324.
    24. Tan, Xueping & Sirichand, Kavita & Vivian, Andrew & Wang, Xinyu, 2020. "How connected is the carbon market to energy and financial markets? A systematic analysis of spillovers and dynamics," Energy Economics, Elsevier, vol. 90(C).
    25. Zhang, Yue-Jun & Wang, Wei, 2021. "How does China's carbon emissions trading (CET) policy affect the investment of CET-covered enterprises?," Energy Economics, Elsevier, vol. 98(C).
    26. Maghyereh, Aktham I. & Awartani, Basel & Bouri, Elie, 2016. "The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 57(C), pages 78-93.
    27. Kang, Sang Hoon & Tiwari, Aviral Kumar & Albulescu, Claudiu Tiberiu & Yoon, Seong-Min, 2019. "Exploring the time-frequency connectedness and network among crude oil and agriculture commodities V1," Energy Economics, Elsevier, vol. 84(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Guo, Li-Yang & Feng, Chao, 2021. "Are there spillovers among China's pilots for carbon emission allowances trading?," Energy Economics, Elsevier, vol. 103(C).
    2. Wenting Zhang & Xie He & Tadahiro Nakajima & Shigeyuki Hamori, 2020. "How Does the Spillover among Natural Gas, Crude Oil, and Electricity Utility Stocks Change over Time? Evidence from North America and Europe," Energies, MDPI, vol. 13(3), pages 1-26, February.
    3. Antonakakis, Nikolaos & Cunado, Juncal & Filis, George & Gabauer, David & Perez de Gracia, Fernando, 2018. "Oil volatility, oil and gas firms and portfolio diversification," Energy Economics, Elsevier, vol. 70(C), pages 499-515.
    4. Su, Chi-Wei & Pang, Li-Dong & Qin, Meng & Lobonţ, Oana-Ramona & Umar, Muhammad, 2023. "The spillover effects among fossil fuel, renewables and carbon markets: Evidence under the dual dilemma of climate change and energy crises," Energy, Elsevier, vol. 274(C).
    5. Lovcha, Yuliya & Perez-Laborda, Alejandro, 2022. "Long-memory and volatility spillovers across petroleum futures," Energy, Elsevier, vol. 243(C).
    6. Zhu, Huiming & Chen, Weiyan & Hau, Liya & Chen, Qitong, 2021. "Time-frequency connectedness of crude oil, economic policy uncertainty and Chinese commodity markets: Evidence from rolling window analysis," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    7. Yang Liu & Xueqing Yang & Mei Wang, 2021. "Global Transmission of Returns among Financial, Traditional Energy, Renewable Energy and Carbon Markets: New Evidence," Energies, MDPI, vol. 14(21), pages 1-32, November.
    8. Liu, Jianing & Man, Yuanyuan & Dong, Xiuliang, 2023. "Tail dependence and risk spillover effects between China's carbon market and energy markets," International Review of Economics & Finance, Elsevier, vol. 84(C), pages 553-567.
    9. Foglia, Matteo & Addi, Abdelhamid & Angelini, Eliana, 2022. "The Eurozone banking sector in the time of COVID-19: Measuring volatility connectedness," Global Finance Journal, Elsevier, vol. 51(C).
    10. Matteo Foglia & Eliana Angelini, 2020. "Volatility Connectedness between Clean Energy Firms and Crude Oil in the COVID-19 Era," Sustainability, MDPI, vol. 12(23), pages 1-22, November.
    11. Tiantian Liu & Shigeyuki Hamori, 2020. "Spillovers to Renewable Energy Stocks in the US and Europe: Are They Different?," Energies, MDPI, vol. 13(12), pages 1-28, June.
    12. Yijin He & Tadahiro Nakajima & Shigeyuki Hamori, 2019. "Connectedness Between Natural Gas Price and BRICS Exchange Rates: Evidence from Time and Frequency Domains," Energies, MDPI, vol. 12(20), pages 1-28, October.
    13. Dai, Zhifeng & Zhu, Haoyang & Zhang, Xinhua, 2022. "Dynamic spillover effects and portfolio strategies between crude oil, gold and Chinese stock markets related to new energy vehicle," Energy Economics, Elsevier, vol. 109(C).
    14. Duan, Xiaoping & Xiao, Ya & Ren, Xiaohang & Taghizadeh-Hesary, Farhad & Duan, Kun, 2023. "Dynamic spillover between traditional energy markets and emerging green markets: Implications for sustainable development," Resources Policy, Elsevier, vol. 82(C).
    15. Umar, Muhammad & Farid, Saqib & Naeem, Muhammad Abubakr, 2022. "Time-frequency connectedness among clean-energy stocks and fossil fuel markets: Comparison between financial, oil and pandemic crisis," Energy, Elsevier, vol. 240(C).
    16. Asadi, Mehrad & Pham, Son D. & Nguyen, Thao T.T. & Do, Hung Xuan & Brooks, Robert, 2023. "The nexus between oil and airline stock returns: Does time frequency matter?," Energy Economics, Elsevier, vol. 117(C).
    17. Evrim Mandacı, Pınar & Cagli, Efe Çaglar & Taşkın, Dilvin, 2020. "Dynamic connectedness and portfolio strategies: Energy and metal markets," Resources Policy, Elsevier, vol. 68(C).
    18. Adekoya, Oluwasegun B. & Oliyide, Johnson A. & Noman, Ambreen, 2021. "The volatility connectedness of the EU carbon market with commodity and financial markets in time- and frequency-domain: The role of the U.S. economic policy uncertainty," Resources Policy, Elsevier, vol. 74(C).
    19. Yi Yao & Lixin Tian & Guangxi Cao, 2022. "The Information Spillover among the Carbon Market, Energy Market, and Stock Market: A Case Study of China’s Pilot Carbon Markets," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    20. Kinkyo, Takuji, 2021. "Region-wide connectedness of Asian equity and currency markets," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:14053-:d:956118. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.