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Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?

Author

Listed:
  • Chi-Wei Su

    (School of Businesses, Wuchang University of Technology, Wuhan 430223, China)

  • Yuru Song

    (Graduate Academy, Party School of the Central Committee of the Communist Party of China (National Academy of Governance), Beijing 100091, China)

  • Hsu-Ling Chang

    (Department of Accounting, Ling Tung University, Taichung 408284, Taiwan, China)

  • Weike Zhang

    (School of Public Administration, Sichuan University, Chengdu 610065, China)

  • Meng Qin

    (School of Marxism, Qingdao University, Qingdao 266071, China)

Abstract

Investigating the essential impact of the cryptocurrency market on carbon emissions is significant for the U.S. to realize carbon neutrality. This exploration employs low-frequency vector auto-regression (LF-VAR) and mixed-frequency VAR (MF-VAR) models to capture the complicated interrelationship between cryptocurrency policy uncertainty (CPU) and carbon emission (CE) and to answer the question of whether cryptocurrency policy uncertainty could facilitate U.S. carbon neutrality. By comparison, the MF-VAR model possesses a higher explanatory power than the LF-VAR model; the former’s impulse response indicates a negative CPU effect on CE, suggesting that cryptocurrency policy uncertainty is a promoter for the U.S. to realize the goal of carbon neutrality. In turn, CE positively impacts CPU, revealing that mass carbon emissions would raise public and national concerns about the environmental damages caused by cryptocurrency transactions and mining. Furthermore, CPU also has a mediation effect on CE; that is, CPU could affect CE through the oil price (OP). In the context of a more uncertain cryptocurrency market, valuable insights for the U.S. could be offered to realize carbon neutrality by reducing the traditional energy consumption and carbon emissions of cryptocurrency trading and mining.

Suggested Citation

  • Chi-Wei Su & Yuru Song & Hsu-Ling Chang & Weike Zhang & Meng Qin, 2023. "Could Cryptocurrency Policy Uncertainty Facilitate U.S. Carbon Neutrality?," Sustainability, MDPI, vol. 15(9), pages 1-15, May.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:9:p:7479-:d:1138222
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    References listed on IDEAS

    as
    1. Andrea Silvestrini & David Veredas, 2008. "Temporal Aggregation Of Univariate And Multivariate Time Series Models: A Survey," Journal of Economic Surveys, Wiley Blackwell, vol. 22(3), pages 458-497, July.
    2. Ghabri, Yosra & Ben Rhouma, Oussama & Gana, Marjène & Guesmi, Khaled & Benkraiem, Ramzi, 2022. "Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions," International Review of Financial Analysis, Elsevier, vol. 82(C).
    3. J. Isaac Miller, 2014. "Mixed-frequency Cointegrating Regressions with Parsimonious Distributed Lag Structures," Journal of Financial Econometrics, Oxford University Press, vol. 12(3), pages 584-614.
    4. Jana, Rabin K. & Ghosh, Indranil & Wallin, Martin W., 2022. "Taming energy and electronic waste generation in bitcoin mining: Insights from Facebook prophet and deep neural network," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
    5. Ghysels, Eric & Hill, Jonathan B. & Motegi, Kaiji, 2016. "Testing for Granger causality with mixed frequency data," Journal of Econometrics, Elsevier, vol. 192(1), pages 207-230.
    6. Su, Chi-Wei & Qin, Meng & Tao, Ran & Umar, Muhammad, 2020. "Financial implications of fourth industrial revolution: Can bitcoin improve prospects of energy investment?," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    7. Sarkodie, Samuel Asumadu & Ahmed, Maruf Yakubu & Leirvik, Thomas, 2022. "Trade volume affects bitcoin energy consumption and carbon footprint," Finance Research Letters, Elsevier, vol. 48(C).
    8. Götz, Thomas B. & Hecq, Alain & Smeekes, Stephan, 2016. "Testing for Granger causality in large mixed-frequency VARs," Journal of Econometrics, Elsevier, vol. 193(2), pages 418-432.
    9. Kim, Daehan & Ryu, Doojin & Webb, Robert I., 2023. "Determination of equilibrium transaction fees in the Bitcoin network: A rank-order contest," International Review of Financial Analysis, Elsevier, vol. 86(C).
    10. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    11. Wang, Kai-Hua & Su, Chi-Wei & Umar, Muhammad, 2021. "Geopolitical risk and crude oil security: A Chinese perspective," Energy, Elsevier, vol. 219(C).
    12. Li, Jingming & Li, Nianping & Peng, Jinqing & Cui, Haijiao & Wu, Zhibin, 2019. "Energy consumption of cryptocurrency mining: A study of electricity consumption in mining cryptocurrencies," Energy, Elsevier, vol. 168(C), pages 160-168.
    13. Qin, Meng & Zhang, Xiaojing & Li, Yameng & Badarcea, Roxana Maria, 2023. "Blockchain market and green finance: The enablers of carbon neutrality in China," Energy Economics, Elsevier, vol. 118(C).
    14. Anwer, Zaheer & Farid, Saqib & Khan, Ashraf & Benlagha, Noureddine, 2023. "Cryptocurrencies versus environmentally sustainable assets: Does a perfect hedge exist?," International Review of Economics & Finance, Elsevier, vol. 85(C), pages 418-431.
    15. Motegi, Kaiji & Sadahiro, Akira, 2018. "Sluggish private investment in Japan’s Lost Decade: Mixed frequency vector autoregression approach," The North American Journal of Economics and Finance, Elsevier, vol. 43(C), pages 118-128.
    16. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2021. "Bitcoin-energy markets interrelationships - New evidence," Resources Policy, Elsevier, vol. 70(C).
    17. Baur, Dirk G. & Oll, Josua, 2022. "Bitcoin investments and climate change: A financial and carbon intensity perspective," Finance Research Letters, Elsevier, vol. 47(PA).
    18. Tee, Chwee-Ming & Wong, Wai-Yan & Hooy, Chee-Wooi, 2023. "Economic policy uncertainty and carbon footprint: International evidence," Journal of Multinational Financial Management, Elsevier, vol. 67(C).
    19. Lucey, Brian M. & Vigne, Samuel A. & Yarovaya, Larisa & Wang, Yizhi, 2022. "The cryptocurrency uncertainty index," Finance Research Letters, Elsevier, vol. 45(C).
    20. Pham, Linh & Karim, Sitara & Naeem, Muhammad Abubakr & Long, Cheng, 2022. "A tale of two tails among carbon prices, green and non-green cryptocurrencies," International Review of Financial Analysis, Elsevier, vol. 82(C).
    21. Yang, Lu & Hamori, Shigeyuki, 2021. "The role of the carbon market in relation to the cryptocurrency market: Only diversification or more?," International Review of Financial Analysis, Elsevier, vol. 77(C).
    22. Hu, Jinyan & Wang, Kai-Hua & Su, Chi Wei & Umar, Muhammad, 2022. "Oil price, green innovation and institutional pressure: A China's perspective," Resources Policy, Elsevier, vol. 78(C).
    Full references (including those not matched with items on IDEAS)

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