Fixed capital and growth imperatives: Is commercial aviation trapped in a treadmill?
Bibliographic record
Abstract
Summary: Capital lock-in theory explains how investments in fixed assets create structural inertia in capitalist economies. In this article, we analyze the mechanisms of fixed capital investment to identify and illustrate four forms of lock-in within the commercial aviation industry—spanning aircraft manufacturers, airlines, fuel suppliers, and airports. These capital lock-ins compel firms to continually maintain, upgrade, and expand fixed assets, generating rising costs and requiring ever-higher production levels to sustain profitability. This dynamic produces a structural growth imperative, driving increasing financial and material flows throughout the aviation sector. We argue that this growth imperative anchors aviation within a fossil-fuel-based socio-metabolic regime. Roadmaps to make commercial aviation net-zero face major challenges, including the lack of time needed to mass-produce sustainable aviation fuels, the growing risks of biomass appropriation, and the persistent inability to decouple sectoral growth from greenhouse gas emissions. Consequently, demand-reduction strategies for aviation emissions will remain ineffective unless the supply-side growth imperatives embedded in the capitalist organization of the industry are addressed. The proposed framework also offers. A growth treadmill model explaining how fixed capital lock-in effects drive the capitalist socio-metabolic growth imperative in commercial aviation. This model illustrates how fixed capital investments in aircraft manufacturing, airports, airlines and jet fuel refineries create reinforcing feedback loops that lock the sector into continous expansion. Each subsector faces yield and capacity expansion constraints that compel reinvestment in infrastructure to cover rising costs and maintain competitiveness. These reinvestments increase material and organization complexities, raising the minimum profitability and ecological thresholds requires for survival. As a result, aviation actors are caught in a growth treadmill - where avoiding devaluation of fixed assets and preserving market share necessitate ever-growing passenger and production volumes, reinforcing the socio-metabolic imperative of growth in commercial aviation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".