Developing A Multi-Tasks Module to Integrate Biodiversity and to Fulfill the Request of the Task Force on Nature-Related Financial Disclosures (TNFD) for Corporate Sustainability- Case Study of A Taiwan Semiconductor Company
Bibliographic record
Abstract
Since the post-2020 Global Biodiversity Framework began, the TNFD framework has gradually gained attention worldwide. By the end of 2022, the Kunming-Montreal Global Biodiversity Framework (GBF) was adopted under the Convention on Biological Diversity (CBD) at COP 15, and in response to the corporate information disclosure requirements, TNFD was officially released in September 2023. Integrating TNFD into enterprises' sustainable development strategies has become an important trend actively pursued by many Taiwanese electronics companies. This study establishes a practical framework with multiple modules, incorporating TNFD and the LEAP approach into the comprehensive value chain of corporates. It also strengthens the collaboration between corporations and local communities to address natural and biodiversity issues. Through a simplified questionnaire model, the study identifies material, natural impacts, and dependencies in the value chain while enhancing stakeholder awareness of nature and biodiversity. Establishing comprehensive and appropriate tools for impact and dependency pathways assists various departments within the company in considering the relationship between nature and biodiversity and understanding the causal effect between corporate activities and biodiversity loss. Ultimately, through workshops, the study collectively analyzes related risks and opportunities of the enterprise and converges strategies across departments. Finally, the operability, compliance, and effectiveness of this practical framework are verified through the actual implementation results of a certain semiconductor company in Taiwan. A case study shows that after deliberation with local communities, the company gradually focused on projects related to local culture and surroundings. The material, natural impacts, and dependencies across the value chain identified through the questionnaire also gradually led the company to understand the current status and preliminary natural risk assessment of its suppliers. Initially, various departments of the case company were less familiar with the connection between their operations and nature. Through tools and workshops, understanding of nature within the company was enhanced; at the same time, the existing operational strategies and natural vision of various internal departments were integrated, gradually incorporating the concepts of nature and biodiversity into the corporate culture.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".