Does military power shape foreign direct investment’s carbon load displacement? An analysis of carbon emissions in Global South nations, 2000–2020
Bibliographic record
Abstract
Bridging the areas of anthropogenic drivers research in sociology on world-economic integration and militarization, and drawing from macrosociological research on foreign direct investment (FDI), we argue that capital-intensive military power facilitates and supports transnational capital outsourcing their carbon pollution to Global South nations, and this carbon load displacement occurs independent of the overall environmental impacts of the volume of inward FDI. To test these arguments, we create a new measure that quantifies the relative extent to which stocks of inward FDI are sent by nations with more powerful capital-intensive militaries. We use this new variable, along with the well-established measure of inward stocks of FDI as % GDP, as our independent variables of interest in analyses of carbon emissions for a sample of Global South nations from 2000 to 2020. The findings support our arguments. Both primary independent variables have positive short-run and long-run effects on total emissions, emissions per unit of GDP, and per capita emissions. We also find nontrivial evidence of expansion-leaning asymmetry: with few exceptions, increases in both key predictors have larger effects on increasing emissions than proportional decreases in them have in leading to reductions in emissions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".