Biodiversity disclosure in the European finance sector
Bibliographic record
Abstract
As the significant environmental, social, and economic consequences of biodiversity loss become more clearly recognized, biodiversity management has become an increasingly important issue for the financial sector. According to the Global Risk Report 2023, biodiversity loss will be the fourth most significant risk worldwide over the next ten years. The financial sector plays a crucial role in supporting and financing business activities that impact biodiversity. Financial institutions can help ensure that biodiversity is protected as they strongly influence management activities and practices in the economy through capital allocation. Based on institutional theory, this study aims to illuminate a disclosure level on biodiversity (risks) in the financial sector. Using content analysis, we empirically investigate non-financial reports by the European financial industry. To evaluate drivers, we present and compare specific disclosure standards and regulations concerning biodiversity. Overall, the disclosure quality differs in scope and level across the sampled companies. The results show the relevance of evolving disclosure frameworks like the EU taxonomy, Sustainable Finance Disclosure Regulation, and Corporate Sustainability Reporting Directive. This study contributes to improving biodiversity management and disclosure by presenting a better understanding of biodiversity activities and risks within the financial sector as a mediating agent. • Current requirements and initiatives for biodiversity disclosure in financial sector. • Empirical research by content analysis of non-financial report for 24 financial institutions. • Research and practice on biodiversity reporting in financial sector is in an early stage. • Identification of recommendations for enhancing the quality of biodiversity reporting. • Institutional theory is evident for a sustainability transition in financial sector.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".