Biodiversity conservation by Korean corporations towards nature-positive goals
Bibliographic record
Abstract
Abstract Overbalance of ecosystems triggers global biodiversity loss and threatens the sustainability of society by emerging financial risks from the disruption of ecosystem services. Several initiatives and international organizations have developed guidelines on biodiversity conservation to support the increasing demand for the disclosure of nature-positive business practices. However, corporations’ biodiversity-related performances have yet to undergo a comprehensive assessment, either quantitatively or qualitatively. Here we analyze the biodiversity conservation practices, or the evolution of Environmental, Social, and Governance (ESG) management, of the top 200 corporations by market capitalization in South Korea based on their sustainability reports published 2017–2021. We show that the number of corporations issuing sustainability reports doubled in five years, and over 70% issued sustainability reports in 2021. Based on the directionality of the COP15 agreement and the consistency with the targeted ecosystems, we identified that 22% of corporations report engagement with biodiversity conservation without substantive outcomes. The methodology developed can guide major corporations for biodiversity-related disclosures, including those required by the TNFD.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".