Asymmetric effect of financial globalization on carbon emissions in G7 countries: Fresh insight from quantile-on-quantile regression
Bibliographic record
Abstract
Being among the highest emitters of greenhouse gases globally, the G7 countries have pledged to halve their carbon emissions by 2030, relative to 2010. This is in clear recognition of the need to transit from carbon energy to more sustainable solutions that are climate-friendly. In view of this, understanding how financial globalization contributes to the realization of those pledges becomes necessary. In this paper, we introduce two major innovations to the literature on financial globalization and environmental degradation. First, in terms of methodology, we apply the quantile-on-quantile regression (QQR) approach with a nonparametric technique over the period 1970Q1–2018Q4. The combination of these techniques has so far received limited attention in the literature. Second, we test for an asymmetric nexus between financial globalization and carbon emission in the G7 economies—Canada, France, Germany, Italy, Japan, the United Kingdom and the United States—as they present an interesting area of research focus. Empirical results from the QQ regression show an emission-increasing effect of financial globalization on environmental degradation in the G7 nations. Furthermore, in order to assess the causal effect of financial globalization on environmental degradation, we apply the nonparametric causality technique. Overall, results from the nonparametric estimations show that financial globalization significantly predicts variation in environmental degradation across quantiles. From a policy standpoint, economic and political frameworks in these nations should be directed towards enhancing higher financial inflows that are in line with the stated economic and environmental policies, among other policy suggestions.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".