Biodiversity in energy-intensive industry
Bibliographic record
Abstract
Biodiversity, the variety of life on Earth, is fundamental to human well-being, providing essential services such as food, clean water, medicine, and climate regulation. However, biodiversity is under unprecedented threat due to human activities. There also industries, which are crucial for economic development, are significant contributors to environmental degradation, leading to habitat loss, pollution, and climate change. The aim of this thesis was to explore the intersection of biodiversity and energy-intensive industries, focusing on the ways to integrate biodiversity considerations into industrial practices. \n \nThrough a case study of a client company, the thesis examines a pilot initiative designed to in-corporate biodiversity into its operational framework. In energy-intensive industries, the climate topic is critical due to the significant emissions these sectors generate and the substantial risks they face from both the physical impacts of climate change and the transition to a low-carbon economy. Many energy-intensive companies have been taking climate actions for a long time, while biodiversity is clearly a newer issue for companies to manage. In this thesis case study, biodiversity was examined as a separate topic from climate so that it would receive sufficient attention in the company. TNFD (Taskforce on Nature-Related Financial Disclosure) LEAP (Locate, Evaluate, Assess and Prepare) approach was used to evaluate, disclose, and address company’s dependencies, impacts, risks, and opportunities related to nature. LEAP approach is aligned with the goals and targets of the Kunming-Montreal Global Biodiversity Framework and accommodates the various approaches to materiality currently in use. \n \nBiodiversity is a complex topic for several reasons, ranging from the intricate relationships between species and ecosystems to the social, economic, and political factors that influence how biodiversity is understood and managed. The thesis was bringing out the multidimensionality related to the topic and encouraging company to develop biodiversity-positive strategy as biodiversity should not be seen only an ethical responsibility for companies but also a smart business strategy to ensure the sustainable future. Biodiversity is constantly evolving due to environmental changes, species interactions, and human influence. Ecosystems are dynamic, with species populations, habitats, and environmental conditions fluctuating over time. Therefore, biodiversity should not be seen as a onetime project inside the companies and thesis also emphasized the need to integrate biodiversity management into the company's processes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".