Convergence of aggregate and sectoral nitrogen oxides in G7 countries for 1750–2019: Evidence from a new panel Fourier threshold unit root test
Bibliographic record
Abstract
One of the major pollutants in the atmospheric space is nitrogen oxide (NOx) emissions is, and industrialized countries are responsible for a substantial share of this air pollutant at the global level. Among developed countries, G7 countries (Canada, France, Germany, Italy, Japan, UK, and the US) were responsible for 12% of the global nitrogen oxide emissions in 2019. Nitrogen oxide emissions increased by almost 280 times between 1750 and 2019 in this set of countries. For this reason, this study concentrates on this group of countries. Besides, it is rational to test the convergence of NOx emissions at the sectoral level for designing effective environmental policies. Therefore, this paper aims to explore the convergence pattern of aggregate and sectoral NOx emission levels in G7 countries covering long-span data, from 1750 to 2019. Agriculture, energy production, industry, residential-commercial-other (RCO), transport, and waste are the main sectors where nitrogen oxides are emitted from. For this purpose, we utilize the panel Fourier threshold unit root test, which allows for the possibility of nonlinearity in the series. Besides, the test is able to capture gradual structural changes through the Fourier approximation. The empirical results derived from the analysis indicate that all the nitrogen oxide series have nonlinear behavior. In addition, this study highlights that the convergent behavior of sectoral nitrogen oxide displays at least partial convergence for the countries examined. Besides, global (full) convergence is confirmed for agriculture, energy production, and transport sectors for the period studied. According to the empirical findings, it can be said that policymakers in G7 countries should prioritize the emission allocation policies.
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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 | 0.001 | 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 | 0.001 |
| 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".