CO2 Emissions, Energy Consumption and Economic Growth
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper investigates the role of consumption of both renewable and sustainable energy, as well as alternative and nuclear energy, in mitigating the effects of carbon dioxide (CO2) emissions, based on the Environmental Kuznets Curve (EKC). The papers introduces a novel variable to capture trade openness, which appears to be a crucial factor in inter-regional co-operation and development, in order to evaluate its effect on the environment, The empirical analysis is based on a sample of nine signatories to the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) for the period 1971-2014, which is based on data availability. The empirical analysis is based on several time series econometric methods, such as the cointegration test, two long run estimators, namely the fully modified ordinary least squares (FMOLS) and dynamic ordinary least squares (DOLS) methods, as well as the Granger causality test. There are several noteworthy empirical findings: it is possible to confirm the U-shaped EKC hypothesis for six countries, namely Australia, Canada, Chile, New Zealand, Peru and Vietnam; there is no evidence of the EKC for Mexico; a reverse-shaped EKC is observed for Japan and Malaysia, there are long run relationships among the variables, the adoption of either renewable energy, or alternative energy and nuclear energy, mitigates CO2 emissions, trade openness leads to more beneficial than harmful impacts in the long run, the Granger causality tests show more bi-directional-relationships between the variables in the long run, and the Granger causality tests show more uni-directional-relationships between the variables in the short run.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it