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Time-varying relatedness and structural changes among green growth, clean energy innovation, and carbon market amid exogenous shocks: A quantile VAR approach

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  • Hossain, Mohammad Razib
  • Ben Jabeur, Sami
  • Si Mohammed, Kamel
  • Shahzad, Umer

Abstract

Green growth has become a critical driver in sustainability science. However, attaining a steady state of green growth can be challenging in the face of exogenous economic shocks. As such, this study assesses the time-varying relatedness among the USA's green economy (OMXGE), carbon markets (CCAI), green energy (ECO), natural resource markets (SPNGR), and sustainability index (DJS) considering multiple economic traumatism (i.e., the Brexit, COVID-19, Russia-Ukraine conflict). By deploying the quantile VAR (QVAR) and “Wavelet Local Multiple Correlations (WLMC)” approaches, we find: 1) Without the quantile effect, we note strong total interconnectedness among the designated series, which is close to 93 %. 2) From the QVAR findings, we note a high shock-receiving tendency in ECO and CCAI during the Brexit referendum, the first wave of COVID-19, and the Ukraine-Russia conflict, making these markets attractive for further investment during turbulent times. 3) DJS and OMXGE are temporary shock receivers and predominant shock transmitters, indicating that the green economy and sustainability indicators are not robust enough to absorb shocks during economic shocks. 4) Wavelet coherence findings further validate that the green economy is not a safe haven asset, whereas the carbon market has a strong shock absorption capacity and can provide strong hedging support. The underlying findings reveal that Washington's policy toward a renewable-based equity market should be more liberal to ramp up investment in renewables.

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  • Hossain, Mohammad Razib & Ben Jabeur, Sami & Si Mohammed, Kamel & Shahzad, Umer, 2024. "Time-varying relatedness and structural changes among green growth, clean energy innovation, and carbon market amid exogenous shocks: A quantile VAR approach," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524005031
    DOI: 10.1016/j.techfore.2024.123705
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