Exergy-Based Sustainability Assessment of Gold Mining in Colombia: A Comparative Analysis of Open-Pit and Alluvial Mining
Bibliographic record
Abstract
Thermodynamic methods such as exergy analysis enable the evaluation of environmental load (environmental impacts) by quantifying entropy generation and exergy destruction associated with using renewable and non-renewable resources throughout a production system. Based on the principle that environmental impacts occur when exergy is dissipated into the environment, this study applies exergy analysis as a tool for assessing the sustainability of gold mining in Colombia. Two extraction technologies—open-pit and alluvial mining—are evaluated by calculating exergy efficiencies, cumulative exergy demand (CExD), and associated environmental impacts. The results reveal significant differences between the two methods: open-pit mining is heavily dependent on fossil fuels (53% of input exergy), with 99.62% of total exergy destroyed, resulting in an exergy efficiency of just 0.37% and a sustainability index (SI) of 1.00. In contrast, alluvial mining relies predominantly on water (94%), with 69% of input exergy destroyed, an exergy efficiency of 31%, and an SI of 1.46. Four strategies are proposed to reduce environmental burdens: improving efficiency, minimizing exergy losses, integrating renewable energy, and adopting circular economy principles. This study presents the first application of exergy analysis to comprehensively assess the exergy cost of gold production, from extraction through refining, casting, and molding, highlighting critical exergy hotspots and offering a thermodynamic foundation for optimizing resource use in mineral processing.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".