Navigating through green policies: how green technology innovation affects the financial cycle

Abstract With the growing emphasis on sustainable finance and the increasing influence of green technologies on market dynamics, understanding how green technology innovations affect financial volatility has become crucial for accurate financial cycle forecasting. This study investigates how green technology innovation, proxied by green energy patents across upstream, midstream, and downstream stages in the oil production chain, affects the forecasting of the financial cycle. Based on causal analysis, a threshold interval decomposition ensemble (TIDE) model is introduced to predict the interval-valued Chicago Board Options Exchange Volatility Index (VIX) series with non-linear behaviors, with bivariate empirical mode decomposition employed to analyze daily VIX data and forecast lower/upper bounds across short-, medium-, and long-term horizons. Interval predictions are derived from equally weighted component forecasts, and model efficacy is assessed through root mean square error and Diebold-Mariano tests. The main findings are as follows: (1) The green energy patents across different stages of production processes are closely related to the financial cycle both from a causal perspective and in predictions, which enhances the prediction of the medium- and long-term VIX, while having no significant effect in the short term, underscoring the influence of time-compression diseconomies on the financial cycle. (2) There are asymmetrical impacts of green innovation on the financial cycle across the industrial chain, with varying effects observed in upstream crude oil sectors, midstream and downstream diesel operations, and upstream-to-midstream liquefied petroleum gas (LPG) segments, demonstrating that green technology innovations affect VIX differently depending on the specific position within each energy sector’s value chain. (3) Causal analysis shows that green technology innovation increases industrial exposure to the financial cycle in both the short and medium term but has a reducing effect in the long term. However, the external uncertainties brought by climate change, economic policy, and geopolitical risk weaken this linkage, presenting a significant weakening effect in the short term for both climate change and geopolitical risk, and gradually becoming insignificant for the mid- and long-term horizons. This study provides insights for policymakers as well as investors, raising attention regarding the asymmetrical impacts of green innovation on the financial cycle over different time horizons, and the downstream and upstream of the industrial chain, especially in this era of the aforementioned external uncertainties.

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Publication Details

Journal
Humanities and Social Sciences Communications
Published
2026-02-16
DOI
https://doi.org/10.1057/s41599-026-06620-6
Primary Topic
Market Dynamics and Volatility
Type
article
Field-Weighted Citation Impact
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article

Navigating through green policies: how green technology innovation affects the financial cycle

Shengtai Zhang, Mohammad Zoynul Abedin, Zichun Yan, Zehan Wang et al.
Humanities and Social Sciences Communications
Market Dynamics and Volatility
article

Navigating through green policies: how green technology innovation affects the financial cycle

Shengtai Zhang, Mohammad Zoynul Abedin, Zichun Yan, Zehan Wang, Jingjia Zhang, Yuwei Huang
article en

Abstract

Abstract With the growing emphasis on sustainable finance and the increasing influence of green technologies on market dynamics, understanding how green technology innovations affect financial volatility has become crucial for accurate financial cycle forecasting. This study investigates how green technology innovation, proxied by green energy patents across upstream, midstream, and downstream stages in the oil production chain, affects the forecasting of the financial cycle. Based on causal analysis, a threshold interval decomposition ensemble (TIDE) model is introduced to predict the interval-valued Chicago Board Options Exchange Volatility Index (VIX) series with non-linear behaviors, with bivariate empirical mode decomposition employed to analyze daily VIX data and forecast lower/upper bounds across short-, medium-, and long-term horizons. Interval predictions are derived from equally weighted component forecasts, and model efficacy is assessed through root mean square error and Diebold-Mariano tests. The main findings are as follows: (1) The green energy patents across different stages of production processes are closely related to the financial cycle both from a causal perspective and in predictions, which enhances the prediction of the medium- and long-term VIX, while having no significant effect in the short term, underscoring the influence of time-compression diseconomies on the financial cycle. (2) There are asymmetrical impacts of green innovation on the financial cycle across the industrial chain, with varying effects observed in upstream crude oil sectors, midstream and downstream diesel operations, and upstream-to-midstream liquefied petroleum gas (LPG) segments, demonstrating that green technology innovations affect VIX differently depending on the specific position within each energy sector’s value chain. (3) Causal analysis shows that green technology innovation increases industrial exposure to the financial cycle in both the short and medium term but has a reducing effect in the long term. However, the external uncertainties brought by climate change, economic policy, and geopolitical risk weaken this linkage, presenting a significant weakening effect in the short term for both climate change and geopolitical risk, and gradually becoming insignificant for the mid- and long-term horizons. This study provides insights for policymakers as well as investors, raising attention regarding the asymmetrical impacts of green innovation on the financial cycle over different time horizons, and the downstream and upstream of the industrial chain, especially in this era of the aforementioned external uncertainties.

Humanities and Social Sciences CommunicationsVol. 13(1)
Beijing Institute of Technology (CN), Beijing University of Posts and Telecommunications (CN), Nankai University (CN), Swansea University (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 59%
Market Dynamics and Volatility
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