Integrating renewables in mining
Bibliographic record
Abstract
Mining activities are energy-intensive and rely largely on fossil fuels to meet their energy demands. This exposes the mining sector to potential policy and regulatory risks, stemming from government efforts to shift the global economy to a low-emission development pathway, as envisaged by the Paris Agreement. At the same time, renewables have become an increasingly cost-competitive source of power generation. This has resulted in a business case for the adoption of solar and wind energy solutions in the mining sector, to reduce costs as well as carbon footprint of operations. The sector’s energy transition also presents an opportunity for resource-rich countries, including developing economies, to foster the synergistic development of higher value added domestic activities in the renewable energy sector. The shift of the mining industry to low-carbon energy has the potential to contribute to advancing the climate and sustainable development agenda, while also pursuing economic diversification objectives. However, the integration of new technologies into conventional power systems comes with risks and challenges. This paper aims to enhance the understanding of the key drivers for, and obstacles to, renewable energy integration in mining operations, based on a review of over 30 existing projects worldwide. The analysis identifies a need for an enabling policy environment, encompassing among others a competitive energy market structure and adequate energy infrastructure, to overcome current challenges and support the synergies between the development of the mining and renewable energy sectors.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".