Biodiversity conservation activities for nature-positive goals: Cases of Korean companies
Bibliographic record
Abstract
Corporation operations and other anthropogenic activities threaten global biodiversity and ecosystem services. They also raise financial risks for the sustainability of society. International organizations and initiatives have developed guidelines on the disclosure of nature-positive business practices to support the conservation of biodiversity. However, biodiversity-conserving performances of corporations have yet to undergo a comprehensive assessment, either quantitatively or qualitatively. Here, we analyzed the biodiversity conservation activities, or the evolution of Environmental, Social, and Governance (ESG) management, of the top 200 corporations in South Korea based on their sustainability reports covering years 2017 to 2021. The number of corporations issuing sustainability reports doubled in the five-year period, and over 70% issued sustainability reports in 2022. Based on the 15th Conference of the Parties to the United Nations Convention on Biological Diversity (COP15) Global Biodiversity Framework’s directionality and consistency with the targeted ecosystems, 22% of the corporations reported engagement with biodiversity conservation without substantive outcomes. The methodology developed in this paper can guide major corporations on biodiversity-related disclosures, including those required by the Taskforce on Nature-related Financial Disclosures (TNFD).
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".