Beschreibung:
This paper examines the environmental impact of green assets using machine learning and impulse responses by local projections. We consider a series of green assets from various classes, namely firms providing renewable energy and carbon offset solutions, carbon and sustainable investing ETFs and green cryptocurrencies. We examine whether asset prices, returns and trading volumes have an impact on environmental indicators such as temperature (global mean and anomalies) and greenhouse gas concentration. Our results indicate that adoption of these green assets does not have a significant environmental impact, suggesting that they should not be used as substitutes for real climate action. Our work serves as a cautionary tale on the nexus between green assets and environmental indicators and our results can be used by governments and corporations when formulating climate and ESG strategies