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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Predicting Firm's Carbon Emissions to Evaluate Carbon Risks |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Number of Pages | 61 |
| Date | 2022 |
| Abstract Text | The master thesis first summarizes the existing literature and analyzes firms’ carbon emissions and their associated risks. It focuses on two aspects: First, given that not so much data on firm’s emissions is available, the aim is to predict scope 1, 2 and 3 emissions based on other firm characteristics, where no data is available. Carbon emission data paired with firm characteristics (e.g., industry classification, gross margin, capital expenditure, revenue, carbon price in a given country and year, income of country of domicile, etc.), which are both accessible through existing data providers, serve to train a non-parametric machine learning model. The firm characteristics then help to predict the emissions where no data is available. This allows to create a large global data set. Second, in a cross-sectional regression a potential carbon premium will be assessed, in order to explore, whether investors already incorporate carbon risks. Furthermore, it can be tested whether this risk premium can be explained by traditional risk factors or it does hold new information. Thus, this gives a hint whether investors care about carbon risk. |
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