Mining and the Sustainable Development Goals: A Systematic Literature Review
Bibliographic record
Abstract
193 United Nations members are signatories of the 17 Sustainable Development Goals (SDGs). Even though it does not make it legally binding to the country members, the SDGs establishment incites national and managerial frameworks to achieve the SDGs. The mining industry inserts itself in this context by its global presence and frequent location within ecologically sensitive and less developed areas. This paper aims to consolidate the state of academic research on mining, sustainability and sustainable development, by organizing the results of previous studies within a systematic review on the SDGs set. To do so, the ISI Web of Science TM Core Collection database was chosen as a database of record, as it is one of the most widespread databases of academic journals. We have used all years available in the ISI database, from 1945 to 2016 (for complete years). The systematic review process comprised of five steps: (i) to search terms [(“sustainability” or “sustainable development”) and mining] on the database and to apply filters of criteria; (ii) organizing papers; (iii) metrics and relations between papers and authors; (iv) classification of the results through content analysis techniques; and (v) synthesis. The results were divided in two groups: the highly cited and the most recent papers, to include papers that have academic impact and those which show the newest contributions to the field. The results showed that, in spite of a growing amount of publications in the past years that relates to mining and sustainability, the main focus of these publications are still on the environmental dimensions of the UN goals. This suggests that more practical and academic work in the mining sector are required to fill in the blank spaces regarding the other set of goals that compose the SDGs framework.
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) | 0.000 | 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 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".