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Crypto currency and green investment impact on global environment: A time series analysis

Author

Listed:
  • Ye, Wang
  • Wong, Wing-Keung
  • Arnone, Gioia
  • Nassani, Abdelmohsen A.
  • Haffar, Mohamed
  • Faiz, Muhammad Fauzinudin

Abstract

Climate change has become a central theme in both national and international forums in recent decades. In this regard, the argument has quickly moved and centered on the role of crypto currencies, in addition to the fundamental culprits of ecological destruction, such as fossil fuels, agricultural, and industrial pollution. The aim of the present study is to assess the role of asymmetries in determining the relationship between blockchain and green investment with the environment using the Non-linear Autoregressive Distributive Lag (NARDL) technique. The data from the United States of America (USA) is used over the period from 2011 to 2020. The findings reveal that, contrary to common belief, there is asymmetric relation between crypto currencies and biofuel usage in both the short and long run. Similarly, asymmetry also exists between renewable energy use and consumption of biofuel. Further, there is a strong coherence among the concerned variables is also proved in this study. Therefore, the study implies that assuming symmetric and weak coherence relationships between blockchain technology and green investment in the global environment produce biased and misleading findings which are not a true representation of the real-world scenario. Based on this the study suggests that policymakers and environmentalists may strive to achieve low carbon emissions using environment-friendly technology and less energy use. Lastly, the negative nonlinear impacts of blockchain technology and green investment must be considered in the carbon emissions released in the USA economy.

Suggested Citation

  • Ye, Wang & Wong, Wing-Keung & Arnone, Gioia & Nassani, Abdelmohsen A. & Haffar, Mohamed & Faiz, Muhammad Fauzinudin, 2023. "Crypto currency and green investment impact on global environment: A time series analysis," International Review of Economics & Finance, Elsevier, vol. 86(C), pages 155-169.
    • Handle: RePEc:eee:reveco:v:86:y:2023:i:c:p:155-169
    DOI: 10.1016/j.iref.2023.01.030
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