Global influences of the oil market through production governance, and quality of life variables: analysis from 1990 to 2020
Notice bibliographique
Résumé
It is evident that the petroleum market encompasses a range of political, demographic, cultural, and predominantly economic influences, necessitating a careful examination of certain variables that allow for a detailed analysis of scenarios and practices, taking into consideration the historical and environmental trajectories of each country. This research aims to evaluate the correlation between governance, production and quality of life criteria in countries considered to be the largest oil producers worldwide. Utilizing data gathered from the World Bank Group, International Energy Agency (IEA), and United Nations Development Programme (UNDP) databases, this research facilitates statistical analysis to ascertain the variables that most significantly accentuate or stagnate within this market, thereby directly impacting these nations. These variables have been categorized based on characteristics belonging to three principal groups that align with the research scope: Governance, Production, and Quality of Life in the petroleum market. This allows for the application of statistical techniques that correlate the actions within this market. As a statistical method, after defining the methodology of this study, variable separations along research axes were conducted, encompassing Spearman correlation analysis, Shapiro-Wilk tests, Analysis of Variance (ANOVA), and finally, canonical distribution verification, to achieve a better conception of these axes. Notable findings within the Production axis reveal that the variables exhibit normality, particularly when interconnected with Oil Profitability, considering the percentage index of Gross Domestic Product (GDP), Consumption of energy derived from fossil fuels (% of total); there is a positive correlation between Crude Oil, Gasoline, Diesel, and Fuel Oil Production and potential hindrances related to indicators and their relationship with the commercial US Dollar (USD), as discussed in economic literature. The statistical relationship for the Production variables was deemed significant (p = 0.842). Subsequently, within the Governance axis, specific highly significant correlations were observed, such as between the variables Government Effectiveness (Estimate), General government final consumption expenditure (US$ at current prices), and Political Stability and Absence of Violence/Terrorism. Once again, the statistical significance for the summarized model was affirmed by the R-squared value (R = 0.951), rendering this discussion noteworthy for the petroleum market as well. Similarly, within the Quality of Life-oriented analysis axis, the most significant correlation finding, although the correlation overall exhibited p = 0.107 and emphasized a moderate-to-strong significance, can be synthesized through the correlation between Life Expectancy and Human Development Index (HDI) as one of the most evident points within this axis. Following this examination of axes, the alignment of canonical data became possible through the verification of the relationship between variables and the investigated countries, allowing for a general understanding that China, the United States, and Canada exhibited the most significant outliers when correlating the variables of Production, Governance, and Quality of Life. Theoretical and practical implications of this research can be described as follows: in terms of literary intersection and comprehension of axes and elements that support the development of countries and this market as a whole; and through a more robust statistical understanding and the establishment of policies and tools that can assist in meeting the needs of this market on a regional and global scale. It is important to recognize and align the complexity of correlations between these variables and the research subjects, where intervening variables can serve as a framework and continuity (or discontinuity) of the research, such as political, demographic, geographical, and diplomatic aspects that are intrinsically linked to this market.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».