Protector or Polluter? Environmental Impacts of Remittances
Bibliographic record
Abstract
As remittances are largely viewed as potential factor of financial development and economic growth, their role in polluting the environment cannot be denied. In this paper, we investigate the environmental effect of migrant remittance in the global south. By using panel data of 37 countries in the southern hemisphere from 1980 to 2014, results show that remittances worsen the environment. We, therefore, support the remittances-led emission hypothesis. Interestingly, we found that the inflows of remittances do not affect CO2 emissions directly, but indirectly through household consumption, private investment, urbanization and importations. Our results deeply suggest that policymakers in the South should (1) consider remittances as a policy instrument to design strategies related to sustainable and responsible investing, and (2) channel remittances into green consumptions and investments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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".