Three Essays on Sustainable Finance: Canadian Physical Costs of Climate Change, Global Carbon Prices and the Costs of Climate Change, and, Environmental and Disclosure Performance: A Study of CA 100+ Companies
Bibliographic record
Abstract
Sustainable finance has been increasingly gaining prominence in mainstream financial studies. The consideration of sustainability concerns within the sphere of finance has significant economic implications for society, investors, and beyond. In the past decade, the severity of climate change and its associated costs has spurred conversations about how to safeguard our prosperity in the face of this growing concern. These questions form the fundamental motivation for the study of how global climate change influences economic outcomes and firm performance and cost of equity. Specifically, this thesis is motivated by areas within sustainable finance that demand further research. The results from the first essay provide insight into the economic costs to Canada across several temperature outcomes leading into 2100. Using the Dynamic Integrated Climate and Economic (DICE) model, we project that climate change mitigation efforts that lead to a 2oC outcome more than pay for themselves in avoided climate costs from physical damages. The second essay investigates the climate change outcomes under varying global average carbon prices. We find that a global carbon price, while playing a critical role, will not be sufficient to meet our Paris Climate Agreement goals of limiting our global temperature increase to 1.5-2oC above pre-industrial levels by 2100. We also project significant differences in global physical costs, highlighting the urgency of taking action to mitigate global warming. The third essay investigates how environmental and disclosure performance influences firm’s return performance and cost of equity. We examine CA 100+ and find that they outperform other world indices in returns at a lower standard deviation, indicating stochastic dominance performance. We further find that the 2022 cost of equity (CoE) estimates for CA 100+ firms are in line with expectations after adjusting for country and industry impacts. We also find that, among CA 100+ firms, those that performed the worst environmentally and regarding disclosures had a lower CoE, contrary to expectations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".