Human Activities and Global Warming: A Cointegration Analysis
Bibliographic record
Abstract
Do human activities indeed cause global warming? This paper attempts to answer this question by reexamining the time series properties of climate variables and the existence of long-run relationships between them. Double unit root testing shows that most of the radiative forcings of greenhouse gases are integrated of order two. We then apply an I(1) and I(2) cointegrating rank analysis to identify the presence of I(2) components. After identifying a linear combination of I(2) variables that cointegrates to an I(1) process, we proceed with the I(1) cointegrating analysis and we identify two possible cases with different rank specifications. Estimation of the equation for temperature suggests that this variable reacts significantly to the radiative forcings of greenhouse gases in the long-run. This evidence allow us to conclude that human activities a¤ect temperature variations. / Est-ce que l’activité humaine entraîne effectivement le réchauffement du globe? Ce travail tente de répondre à cette question en ré-examinant les propriétés des séries chronologiques des variables climatiques, ainsi que la possibilité de l’existence à long terme d’une relation entre elles. Des tests de racines unitaires doubles démontrent que la variable qui représente la quantité de radiations causées par les gaz à effet de serre est intégrée d’ordre deux. Suite aux tests de racines unitaires, nous analysons la cointégration selon le rang pour des variables intégrés d’ordre I(1) et I(2) afin d’identifier la présence des composantes I(2). Après avoir identifié la combinaison linéaire de variables I(2) qui, par la cointégration résulte en un processus I(1), nous procédons à l’analyse de cointégration I(1) et nous identifions deux cas possibles avec des spécifications de rangs différentes. L’estimation de l’équation de la température nous amène à croire que cette variable réagie significativement aux radiations causées par les gaz à effet de serre à long terme. Les évidences nous permettent de conclure que l’activité humaine affecte les variations de la température.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".