The Impact of Green Finance Investments on Carbon Emissions Reduction: A Finding of High-Performance Stocks in the S&P Global Clean Energy Index Using Machine Learning with Bayesian Additive Regression Trees

This study aims to verify the effectiveness of carbon emissions reduction through investments in Green finance, focusing on the S&P Global Clean Energy Index as a representative of the global Green finance sector. Specifically, the research identifies constituent stocks within this index that hold the potential to serve as prototypes for carbon emissions reduction initiatives. Utilizing monthly data for 99 stocks, the S&P Global Clean Energy Index, and industrial carbon emissions, this study employs the BART machine model, a novel approach combining Bayesian additive regression trees (BART) and machine learning. The findings of the study reveal a significant relationship between the log prices of AVANGRID Inc.’s stocks and carbon emissions. Notably, as the log prices of these stocks increase, carbon emissions demonstrate a corresponding increase. This effect is particularly pronounced when their price ranges from 44.70 to 46.99 US dollars. This suggests a substantial impact of AVANGRID Inc.’s stock prices on the rise in carbon emissions. Furthermore, the company's significant investments in cutting-edge technology to boost grid capacity in New York for renewable energy adoption in 2023 may result in short-term cost increases, potentially impacting profits, and investor outlook. However, these initiatives are expected to yield long-term environmental benefits and sustainable returns, positively influencing both the company's stock price and shareholder value in the future.