Is There a Relationship between CO2 Emissions by Sources, Electricity Consumption and Economic Growth in Côte d’Ivoire? Evidence from an ARDL Investigation
Bibliographic record
Abstract
This paper investigates the impact of different sources of CO2 emissions together with electric power consumption on per capita GDP in Côte d’Ivoire with data ranging from 1970 to 2019. We found cointegration relationships between per capita GDP and the variables of interest. CO2 emissions, CO2 emissions from liquid fuel consumption, electric power consumption and investment have positive impact on per capita GDP. A simultaneous increase of electric power consumption and CO2 emissions in the long run has a negative impact on per capita GDP. Causality runs from CO2 emissions, electric power consumption and investment to per capita GDP. In the short run, electric power consumption positively impacts per capita GDP. Labor force has a negative impact on per capita GDP. Causality runs from electric power consumption to per capita GDP. Similar results are obtained when CO2 emissions from liquid fuel consumption are used in place of global CO2 emissions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".