Gender diversity and climate disclosure: a tcfd perspective
Bibliographic record
Abstract
Abstract The paradigm of corporate environmental disclosures aimed at investors developed in 2017 with the Task Force on Climate-related Financial Disclosures (TCFD) recommendations. Existing literature on social responsibility disclosures points to gender diversity on the board of directors as an influencing factor. This study aims to assess the influence of gender diversity in climate-related financial disclosures, as recommended by the TCFD based on a sample of 27 companies operating within the sectors of electricity, oil, coal and gas, water, and alternative energy that have announced their adherence to the recommendations from 2017 to 2021. By applying a linear regression model, the results indicate the presence of a positive association between the level of TCFD disclosures and board gender diversity, as well as other factors, such as company size, CEO duality, and general liquidity. However, the influence of board gender diversity on corporate reporting based on the TCFD recommendations suggests that the commitment of boards to the reporting of climate change risks and opportunities is not significantly dependent on gender diversity, as the presence of women in the Boards is favorable for the reporting but without a significant impact on the level of disclosures. This research offers insights into sustainability reporting practices, focusing on a relatively new perspective of reporting climate-related financial topics and their determinants. The findings hold implications for organizational leaders and stakeholders, mainly investors, as these recent sustainable reporting practices are challenging but also bring new opportunities related to transparency towards climate-related issues.
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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 | 0.008 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".