P.I.: Silvia Muzzioli 

Budget: € 265.703,00 Euro

Funded by: MIUR - Ministero dell’Istruzione, dell’Università e della Ricerca

National Departments involved: University of Modena and Reggio Emilia, University of Pavia, Mediterranean University of Reggio Calabria, University of Palermo.

International Departments involved: University of  Ghent, University of Pyraeus

ABSTRACT:

Green investments represent one of the main and most impactful challenges for the future sustainable growth of EU countries. Since the relevance and the sign of climate risk factors and sustainability scores in explaining future stock returns are often debated and inconsistent, it is essential to provide investors with a better understanding of the relationship between climate risks and the cross-section of stock returns, in order to encourage informed investment decisions in environmentally sustainable firms. Despite recent progress, the currently available ESG information remains limited in its ability to support long-term value creation and international climate-related objectives. In particular, ESG scores are available for only a limited number of firms, and ESG data are typically reported at a low frequency (quarterly or annually). When available, ESG scores provided by different information providers (e.g., Bloomberg, Reuters, S&P Global) often diverge significantly, creating confusion among investors. Moreover, at the individual firm level, there may be a tendency to disclose only partial information, emphasizing environmental dimensions where the company performs well while neglecting weaker areas, thus contributing to greenwashing practices. To address these gaps, the project aims to deepen the understanding of the relationship between climate risk and market returns by aggregating multiple information sources to mitigate greenwashing and by employing appropriate econometric techniques to manage data uncertainty. The objectives of the project are manifold. First, to propose an innovative theoretical framework for the relationship between climate risk and the cross-section of stock returns. Second, to measure firms’ exposure to climate risk for a large sample of European equities by combining various sources of information such as ESG ratings, firms’ exposure to climate-related news, financial statements, green bonds issued by companies, carbon footprint data, CO₂ emission reports, and potential environmental litigation records. Third, to investigate the climate risk premium by constructing portfolios that reflect different levels of exposure to climate risk. Fourth, to evaluate the properties of green and brown portfolios in terms of diversification, tail risk, and potential spillover effects. Finally, to assess the predictive power of climate risk for future stock returns using advanced machine learning techniques suitable for large datasets with mixed frequencies. The expected results of the project will have significant implications for investors, firms, and policymakers, and more broadly for the European Union, as they are crucial to effectively directing private financial resources toward activities aimed at climate change mitigation and adaptation.