Sustainable Finance and Tax Issues: How Could Advanced ESG Analysis Deter Tax Avoidance?
Bibliographic record
Abstract
Considered by some authors as blindsiding sustainable finance, tax evasion and avoidance represents a measurement problem. This research aims at measuring corporate tax avoidance as a risk continuum and at correlating this measure of risk with financial market risk, measured by average stock price volatility. This research is based on a first set of indicators of this risk continuum, and then improved by an analysis of the literature to take into account the recent implementation of related regulation, notably the GLOBE project of OECD. Indicators are contextualized to understand the complexity of the phenomenon. The risk continuum is broken down into four categories, corresponding to four levels of tax risk. This first test of risk continuum is realized with the Stoxx Europe 50 companies over five semesters, from 2023 to 2025. The average volatility of these categories of risk is measured. The least risky category has a lower volatility and some sectors are identified as specific. Risk factor analysis confirms the results. The last results are put in the perspective of the risk–return arbitrage and show another potential use of these results.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".