Analyzing the Macroeconomic and Supply Chain Impacts of CO2 Capture Technology: An Input-Output Framework for the Construction Phase
Bibliographic record
Abstract
Carbon Capture, Utilization, and Storage (CCUS) is considered as a very important technology for reducing greenhouse gas emissions. While reducing the emissions, it enables the existing industries to continue their operations. Most of the previous studies on this topic focus only on the microeconomic analysis of CCUS and its macroeconomic aspects, in particular its construction phase has not been covered. The objective of this paper is to address this gap by evaluating the macroeconomic impact of constructing a CO₂ capture system in Saskatchewan, Canada. This study uses a provincial Input-Output (IO) table for Saskatchewan and the Leontief model to analyze how investment in the construction phase of the capture system, focusing on direct equipment costs, affects industry output, GDP, and employment. The results from this analysis show that an investment of CAD 217.8 million can result in an output increase of approximately $1.83 billion, a GDP impact of $900 million and the creation of about 5,588 jobs. These findings show that the construction of carbon capture projects in a specific region can generate significant benefits across the supply chain and can lead to economic growth in that region. This paper also highlights the steps that should be taken and limitations of such analysis and proposes future directions for addressing these limitations.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".