ESG mapping of the Australian mining sector – The state of play on mobilising spatial datasets for decision making
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The global energy transition will drive increased demand for a broad range of mined minerals. Australia is well positioned to support the global energy transition, given its mature mining sector and rich and diverse mineral resources. The potential growth in the mining sector represents an economic opportunity, however, navigating the associated environmental, social, and governance (ESG) risks remains a challenge. A step towards improved ESG credentials across the Australian mining sector is for mine developers, regulators, communities, investors and other industry stakeholders to be capable of integrating diverse types of ESG data into decision-making processes. This paper establishes the foundations for applying ESG mapping, a research technique that mobilises spatial data to analyse and compare extractive locations in terms of factors relevant to mining and exploration, at the scale of Australia. To do so, the paper first critically reviews 33 spatial ESG datasets available at national scale across six main themes: people, land uses, water resources, extreme events, nature conservation, and governance. The paper then provides two proof-of-concept applications of ESG mapping to the Australian mining context and draws on these preliminary applications to propose a program of research aiming to fully utilise this technique to inform decision makers. • ESG mapping analyses and compares extractive locations across large scales. • The paper critically reviews 33 spatial ESG datasets available at the scale of Australia. • It identifies steps to using ESG mapping as a decision-support tool and provides two proof-of-concept applications. • One application tests the overlap between land tenure and mining project delays. • The second application aggregates ESG datasets into a composite measure of vulnerability.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it