Examining Energy Consumption and Carbon Emissions of Microbial Induced Carbonate Precipitation Using the Life Cycle Assessment Method
Bibliographic record
Abstract
Microbial induced carbonate precipitation (MICP) is a new geotechnical engineering technology used to strengthen soils and other materials. Although it is considered to be environmentally friendly, there is a lack of quantitative data and objective evaluation to support conclusions about its environmental impact. In this paper, the energy consumption and carbon emissions of MICP technology are quantitatively analyzed by using the life cycle assessment (LCA) method. The environmental effects of MICP technology are evaluated from the perspectives of resource consumption and environmental impact. The results show that for each tonne of calcium carbonate produced by MICP technology, 1.8 t standard coal is consumed and 3.4 t CO2 is produced, among which 80.4% of the carbon emissions and 96% of the energy consumption come from raw materials. Comparing using MICP with cement, lime, and sintered brick, the current MICP application process consumes less non-renewable resources but has a greater environmental impact. The major environmental impact that MICP has is the production of smoke and ash, with secondary impacts being global warming, photochemical ozone creation, acidification, and eutrophication. In five potential application scenarios of MICP, including concrete, sintered brick, lime mortar, mine cemented backfill, and foundation reinforcement, the carbon emissions of MICP are 3 to 7 times greater than the emissions of traditional technologies. The energy consumption is 15 to 23 times. Based on the energy consumption and carbon emissions characteristics of MICP technology at the current condition, suggestions are given for the future research of MICP.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".