Economic feasibility of microalgae as a biological carbon capture solution : financial comparison with traditional chemical carbon capture technologies
Bibliographic record
Abstract
Despite the growing affordability of renewable energy, fossil fuels remain the largest source of global power supply, providing two-thirds of the world’s electricity. This is especially true in developingcountries that lack the infrastructure for large-scale adoption of renewable technologies. This highlights the crucial role of carbon capture in mitigating the effect of greenhouse gases on the environment, while non-renewable energy sources remain dominant. \n \nThis thesis evaluated the economic viability of microalgae as a biological method for carbon capture by drawing comparisons with chemical technologies used today. \n \nThe study conducted a comprehensive analysis of literature reviews, simulations, small-scale pilot tests, and real-life projects to thoroughly examine the costs per tonne of CO2 captured using chemical capture technologies and microalgae. Chemical methods were categorized based on their CO2 source: either from effluent gas or directly from the atmosphere. For each category, notable real-life projects were examined, including the Quest project in Alberta, Canada, and the Orca plant in Iceland. \n \nThe findings indicated that the cost of capturing CO2 from effluent gas typically ranges from $40 to $80 per tonne, whereas costs for atmospheric capture are higher, ranging from $100 to $300 per tonne, however, real-life projects for both methods have higher costs, closer to $100 and $1000 respectively. For Microalgae, secondary sources suggest that the costs per tonne for microalgae capture are significantly higher than those for chemical methods, ranging from $800 to $1600, and even under the most favourable conditions are not expected to drop below $225. \n \nThe adoption of microalgae as a biological capture method is highly dependent on the market value of the biomass produced, which could help offset the high capital and capture costs. In its current stage, however, microalgae cannot financially compete with chemical carbon capture technologies.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".