Sustainable Energy Transition for the Mining Industry: A Bibliometric Analysis of Trends and Emerging Research Pathways
Bibliographic record
Abstract
The mining industry (MI), one of the largest energy consumers globally, is under increasing pressure to transition towards more sustainable energy systems. This paper explores the current trends in sustainable energy transition (SET) in mining operations, focusing on integrating renewable energy, decarbonization efforts, economic and technological enablers, and sustainability frameworks. Through a systematic literature review utilizing bibliometric tools such as Scopus and VOSviewer 1.6.20, this study identifies key themes, trends, and challenges shaping the future of energy transition in mining. Despite advancements in renewable technologies such as solar, wind, and hydrogen, the MI faces significant barriers, including high upfront costs, logistical challenges in remote operations, and inconsistent regional decarbonization policies. The review highlights the importance of global regulatory alignment, technological innovation, and financial mechanisms to overcome these challenges and accelerate the industry’s shift towards clean energy. Future research directions address gaps in renewable energy deployment, energy efficiency, and sustainability practices in the mining sector. This study aims to contribute to the academic discourse and provide actionable insights for industry stakeholders striving to achieve a SET.
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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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.167 | 0.272 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".