Systemic Tail Dependence Between Biodiversity, Clean Energy, and Financial Transition Assets: A Partial Correlation-Based Network Approach

This study investigates the systemic tail dependence among biodiversity, clean energy, and financial transition assets using a novel partial correlation-based network approach. Analyzing eleven indices from 2019 to 2025, we capture dynamic connectedness across normal and extreme market conditions. Empirical findings indicate that clean energy assets form a central hub of connectedness, while biodiversity-linked instruments increasingly influence systemic behavior under stress. Events such as the COVID-19 vaccine rollout, the Russia–Ukraine war, and El Niño intensify these dynamics. Compared to the traditional Generalized Forecast Error Variance Decomposition (GFEVD) framework, our approach better detects short-term shocks, offering actionable insights for climate-aware investment and risk management.

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Publication Details

Journal
Sustainability
Published
2025-07-18
DOI
https://doi.org/10.3390/su17146568
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
0.00

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article

Systemic Tail Dependence Between Biodiversity, Clean Energy, and Financial Transition Assets: A Partial Correlation-Based Network Approach

Mohammed Alhashim, Nader Naifar
Sustainability
Market Dynamics and Volatility
article

Systemic Tail Dependence Between Biodiversity, Clean Energy, and Financial Transition Assets: A Partial Correlation-Based Network Approach

Mohammed Alhashim, Nader Naifar
article en

Abstract

This study investigates the systemic tail dependence among biodiversity, clean energy, and financial transition assets using a novel partial correlation-based network approach. Analyzing eleven indices from 2019 to 2025, we capture dynamic connectedness across normal and extreme market conditions. Empirical findings indicate that clean energy assets form a central hub of connectedness, while biodiversity-linked instruments increasingly influence systemic behavior under stress. Events such as the COVID-19 vaccine rollout, the Russia–Ukraine war, and El Niño intensify these dynamics. Compared to the traditional Generalized Forecast Error Variance Decomposition (GFEVD) framework, our approach better detects short-term shocks, offering actionable insights for climate-aware investment and risk management.

SustainabilityVol. 17(14)
Imam Mohammad ibn Saud Islamic University (SA), King Saud University (SA)
Imam Mohammed Ibn Saud Islamic University, Al-Imam Muhammad Ibn Saud Islamic University
Industry, innovation and infrastructure
Openalex Percentile: Top 72%
Market Dynamics and Volatility
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