Militarizing the Climate Crisis: An Analysis of the Short-Run and Long-Run Effects of Militarization on Nations’ Carbon Emissions, 1990–2020
Bibliographic record
Abstract
ABSTRACT Building on scholarship in global political economy, historical sociology, and environmental sociology, as well as emerging streams of research on militarization and climate change, we theorize about and successively investigate the short-run and long-run effects of two far-reaching characteristics of militarization on nations’ carbon emissions and the climate crisis in general. We contend that emergent and changing conditions associated with the capital-intensiveness and size of militaries shape path dependencies, which structure short-run and long-run effects on carbon pollution. To test our propositions, we estimate dynamic models of emissions for 104 nations from 1990 to 2020. Overall, the findings confirm our arguments. The short-run and long-run effects of the capital-intensiveness and size of militaries on carbon emissions are positive and nontrivial. Further, their estimated short-run and long-run effects are consistent across three distinct measures of carbon emissions, statistically symmetrical, robust to different modeling techniques, and not sensitive to any nations included in the analysis.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".