The effect of FDI on environmental emissions: Evidence from a meta-analysis
Bibliographic record
Abstract
One important and frequently-raised issue about foreign direct investment (FDI) is the potentially negative consequences for the environment. The potential environmental cost due to increased emissions may undermine the economic gains associated with increases in FDI inflow. Although the literature is dominated with this adverse view of FDI on the environment, there is also a possibility that FDI can contribute to a cleaner environment, especially, if FDI comes with green technologies and this creates spillovers for domestic industries. Theoretically, the effect of FDI on the environment can be negative or positive. To deal with the theoretical ambiguity about the FDI-environment nexus, many empirical studies have been conducted but their results only reinforce the controversy as they produce contrasting results. We conduct a meta-analysis of the effect of FDI on environmental emissions using 65 primary studies that produce 1006 elasticities. Our results show that the underlying effect of FDI on environmental emissions is close to zero, however, after accounting for heterogeneity in the studies, we find that FDI significantly reduces environmental emissions. Results remain robust after disaggregating the effect for countries at different levels of development as well as for different pollutants.
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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 | 0.017 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.033 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".