Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on segmented clean energy indices is asymmetric across different conditional quantiles. Significant predictability of SOI is observed only at the 0.25 and 0.75 quantile levels for all segmented clean energy indices, except for the WilderHill Clean Energy Index and NASDAQ OMX Fuel Cell Index. (ii) The clean energy market is significantly influenced by SOI under bullish market conditions. Impacts of SOI on all clean energy markets are nearly negligible when clean energy indices are at the median and lower quantile levels. (iii) The influence of strong La Niña episodes on segmented clean energy indices is more pronounced than during periods of intense El Niño phenomena. (iv) SOI exhibited a positive correlation at mid-term and long-term frequencies with segmented Clean Energy sectors, excluding bioenergy, for the majority of the sample period. Our conclusions provide deeper insights for investors managing clean energy investments in extreme climate conditions. Additionally, they offer useful information for policymakers to formulate viable economic policies addressing climate change, ensuring energy security, and facilitating a safer transition to clean energy. • The research systematically examines Southern Oscillation Index (SOI)’s influence on segmented clean energy sectors, revealing heterogeneous impacts under various market conditions and extreme weather states. • The study reveals a significant overall Granger causal relationship between SOI and clean energy sectors, but inconsistent across conditional quantiles. • SOI's impact on clean energy indices is asymmetric across market conditions, significant mainly in bullish markets. • Strong La Niña events have a more pronounced influence on segmented clean energy indices than intense El Niño phenomena. • SOI shows positive correlation with most clean energy sectors at mid-term and long-term frequencies, excluding bioenergy.

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Publication Details

Journal
Energy Economics
Published
2024-11-03
DOI
https://doi.org/10.1016/j.eneco.2024.108003
Citations
15
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
16.94

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article

Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

Mohammad Zoynul Abedin, Hongjun Zeng, Vineet Upreti
15 citations
Energy Economics
Market Dynamics and Volatility
16.94
article

Does climate risk as barometers for specific clean energy indices? Insights from quartiles and time-frequency perspective

Mohammad Zoynul Abedin, Hongjun Zeng, Vineet Upreti
article en
15 citations

Abstract

This study presents the first analysis of the nexus between the Southern Oscillation Index (SOI), a measure of climate risk, and segmented clean energy indices (such as solar, renewable, and bioenergy). Our research findings indicate that (i) the Granger quantile causality significance of SOI on segmented clean energy indices is asymmetric across different conditional quantiles. Significant predictability of SOI is observed only at the 0.25 and 0.75 quantile levels for all segmented clean energy indices, except for the WilderHill Clean Energy Index and NASDAQ OMX Fuel Cell Index. (ii) The clean energy market is significantly influenced by SOI under bullish market conditions. Impacts of SOI on all clean energy markets are nearly negligible when clean energy indices are at the median and lower quantile levels. (iii) The influence of strong La Niña episodes on segmented clean energy indices is more pronounced than during periods of intense El Niño phenomena. (iv) SOI exhibited a positive correlation at mid-term and long-term frequencies with segmented Clean Energy sectors, excluding bioenergy, for the majority of the sample period. Our conclusions provide deeper insights for investors managing clean energy investments in extreme climate conditions. Additionally, they offer useful information for policymakers to formulate viable economic policies addressing climate change, ensuring energy security, and facilitating a safer transition to clean energy. • The research systematically examines Southern Oscillation Index (SOI)’s influence on segmented clean energy sectors, revealing heterogeneous impacts under various market conditions and extreme weather states. • The study reveals a significant overall Granger causal relationship between SOI and clean energy sectors, but inconsistent across conditional quantiles. • SOI's impact on clean energy indices is asymmetric across market conditions, significant mainly in bullish markets. • Strong La Niña events have a more pronounced influence on segmented clean energy indices than intense El Niño phenomena. • SOI shows positive correlation with most clean energy sectors at mid-term and long-term frequencies, excluding bioenergy.

Energy EconomicsVol. 140
Swansea University (GB), RMIT University (AU)
RMIT University
Climate action
Openalex Percentile: Top 1%
Market Dynamics and Volatility
16.94
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