Bibliographic record
Abstract
This DPhil is motivated by the impact that public data can have on the practise of sustainable finance. Disclosure guidance for environmental information is increasingly being used to compel companies to better manage their environmental risk exposure and environmental impacts. Data-driven technologies can ‘unravel’ company information related to environmental risk and are changing the information asymmetries between company managers, their investors, and society. Three novel and rigorous data science papers have been prepared which provide new access to company environmental risk information. The first paper develops a global inventory of photovoltaic solar energy facilties using machine learning and earth observation and then uses this dataset to assess land cover impacts due to renewable energy siting decisions. The second paper develops asset-level data arrangements of the coal, oil, and gas supply chains, and identifies concentrations of environmental transition risk in the complex network topology. The third paper advances earth observation and computer vision technical methods, assessing the power of these techniques for producing company asset-level data even with limited training data. In developing these papers, this DPhil has identified the opportunities and limits that technology might have in the provision of information for public and private governance of environmental risk and impact. These limits add clarity to the role that policy must play in meeting the diverse information needs of both company shareholders and stakeholders. A discussion chapter provides commentary on these limits; on how the needs of both shareholders and stakeholders can be met with company risk disclosure at the asset-level; and how innovation might be fostered in markets for environmental information. Policy recommendations are summarised and an agenda for further research is provided.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".