Integrating Biodiversity Targets into Corporate Strategies: A Study of Norwegian Companies and the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
This master's thesis investigates how Norwegian businesses are integrating the biodiversity targets of the Kunming-Montreal Framework into their corporate strategies and operations. The Kunming-Montreal Framework, ratified in 2022, emphasizes the private sector's role in biodiversity conservation, particularly through Target 15, which requires businesses to assess, monitor, and disclose their biodiversity impacts.\n\nThe research employs a mixed-method approach, combining qualitative interviews with sustainability professionals and document analysis of corporate sustainability reports. Findings indicate a nascent yet growing commitment among Norwegian businesses to align with the Kunming-Montreal targets. Larger corporations with established sustainability frameworks demonstrate more proactive integration, while small to medium-sized enterprises face challenges due to limited resources and guidance.\n\nKey internal drivers include corporate values, leadership commitment, and existing environmental management systems. External factors such as regulatory frameworks, market pressures, and international norms significantly influence corporate behavior. Despite these efforts, the integration of biodiversity targets remains inconsistent and often lacks specificity, with companies prioritizing carbon footprint reduction over direct biodiversity conservation efforts.\n\nThe thesis underscores the need for more robust frameworks, clearer guidelines, and enhanced stakeholder engagement to fully operationalize these global biodiversity goals. It concludes that while progress is evident, achieving comprehensive integration of biodiversity targets in Norwegian business practices will require sustained focus, resources, and policy support.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".