Effects of Temperature Rise on Clean Energy-Based Capital Market Investments: Neural Network-Based Granger Causality Analysis

Shivam Swarup () and Gyaneshwar Singh Kushwaha
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Shivam Swarup: Department of Management Studies, Maulana Azad National Institute of Technology, Bhopal 462003, India
Gyaneshwar Singh Kushwaha: Department of Management Studies, Maulana Azad National Institute of Technology, Bhopal 462003, India

Sustainability, 2022, vol. 14, issue 18, 1-12

Abstract: During the past 20 years, due to climate change, the government and the private sector have significantly focused on relying on non-fossil fuel-based methods for their energy needs. Climate change-related events, such as unusual weather conditions, abnormal temperature spikes, etc., have an adverse influence on clean energy-based investments. In the given study, we intend to focus on how an incremental temperature rise could affect investors’ perceptions of clean energy assets. To understand the investor-based sentiment on climate change, we utilize prominent clean energy ETFs (exchange traded funds) and consider the temperature’s effect on them. The daily average temperatures of the three most dynamic international financial centers: New York, London and Tokyo, are taken as predictors. Deep learning-based neural networks are applied to understand both the linear and non-linear relationships between the desired variables and identify the causal effects. The results indicate that in almost all the cases with desired lags, there is some sort of non-linear causality, irrespective of linear causality effects. We hope this occurrence can help portfolio managers and environmental professionals in identifying novel climate change-related factors when considering the temperature-related risks.

Keywords: clean energy prices; extreme temperatures; climate mitigation; capital markets; causality (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
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