The Asymmetric Effects of Extreme Climate Risk Perception on Coal Futures Return Dynamics: Evidence from Nonparametric Causality-In-Quantiles Tests

Gao, Wang; Wei, Jiajia; Yang, Shixiong

doi:10.3390/su15108156

Cited by 2 publications

(1 citation statement)

References 85 publications

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“…Wind energy, a form of renewable energy, is a pivotal driver for achieving sustainable societal development, as substantiated by numerous studies highlighting its role in the transition towards cleaner energy sources [39]. With the intensified global regulation of traditional energy sources [40], China's energy structure is shifting from coal reliance towards clean wind energy, a trend supported by recent policy initiatives and investments in the sector [41]. Additionally, with the maturation of wind power technology and business models in China [42], coupled with policy support [43], the wind energy sector is undergoing rapid expansion, emerging as a new growth engine for the national economy, as documented in recent economic analyses [44].…”

Section: Industry Domain Selectionmentioning

confidence: 99%

A New Perspective on Financial Risk Prediction in a Carbon-Neutral Environment: A Comprehensive Comparative Study Based on the SSA-LSTM Model

Wang,

Huang

2023

Sustainability

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Climate change is widely acknowledged as the paramount global challenge of the 21st century, bringing economic, social, and environmental impacts due to rising global temperatures, more frequent extreme weather events, and ecosystem disturbances. To combat this, many countries target net-zero carbon emissions by 2050, reshaping both the financial system and consumption patterns. This transition has sharpened the financial sector’s focus on climate-related risks, making the carbon footprint, environmental benefits of investments, and sustainability of financial products critical to investors’ decisions. However, conventional risk prediction methods may not fully capture these climate-associated risks in a carbon-neutral setting. Emerging from this context is the need for innovative predictive tools. Recently, Long Short-Term Memory networks (LSTM) have gained prominence for their efficacy in time-series forecasting. Singular Spectrum Analysis (SSA), effective for extracting time series patterns, combined with LSTM as SSA-LSTM, offers a potentially superior approach to financial risk prediction. Our study, focusing on a case study of the wind energy sector in China, situates itself within the growing body of research focusing on the integration of environmental sustainability and financial risk management. Leveraging the capabilities of SSA-LSTM, we aim to bridge the gap in the current literature by offering a nuanced approach to financial risk prediction in the carbon-neutral landscape. This research not only reveals the superiority of the SSA-LSTM model over traditional methods but also contributes a robust framework to the existing discourse, facilitating a more comprehensive understanding and management of financial risks in the evolving carbon-neutral global trend.

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