ESG mapping of the Australian mining sector – The state of play on mobilising spatial datasets for decision making
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.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".