The moderating effects of environmental and regulatory quality on financial development to promote sustainable FDI inflows in Canada
Bibliographic record
Abstract
Abstract Stimulating sustainable FDI through the connection of financial development and its moderating relationships with environmental quality and regulatory quality emerges as a crucial agenda nowadays. This study investigates into this relationship using quarterly data from 1990Q1 to 2022Q4 for Canada; employing ARDL bound tests, Granger Causality, and FM-OLS econometric models. Foreign direct investment is the dependent variable of this study. The findings confirm significant long-run relationships among financial development, stock market development, and the moderating effects of environmental and regulatory quality on FDI inflows in the Canadian economy. Conversely, in the short run, financial development, stock market development, and economic growth exhibit bidirectional causal links with FDI, while environmental quality, regulatory quality, and trade openness demonstrate unidirectional causal links with FDI. The error correction mechanism indicates that all variables quickly return to equilibrium except trade openness. Robustness checks further confirm that all the variables have fully modified co-integrating relationship with FDI inflows including the moderating effects of environmental and regulatory quality which is the innovation in the FDI-Growth existing literature. Thus, policymakers are urged to prioritize environmental quality and regulatory quality, alongside other significant explanatory variables identified in this study to promote sustainable FDI inflows.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".