Oil Change: How the Global Oil and Gas Downturn Transformed Canada’s Energy Industry
Notice bibliographique
Résumé
The oil and gas industry forms a large and integral part of both the Albertan and Canadian economies, contributing tax revenues, generating royalty payments, and employing tens of thousands of people. As the industry is commodity based, its strength and financial stability is dependent on global commodity prices. In late 2014, global oil and natural gas prices fell drastically and are still yet to fully recover. This fall in commodity pricing had a dramatic i mpact on the industry, leading to tens of thousands of job losses, drops in tax and royalty revenues, a fall in corporate profits, and the decline of new investment in Alberta. This new price environment has led to considerable change for the industry in Canada as the market a djusts to changed conditions. The oil and gas industry consists of many firms of different size, with different c haracteristics, and the downturn in oil prices impacted these firms in unique ways. This paper e xamines the impacts of the 2014 downturn in oil prices on the composition and performance of the Canadian oil and gas industry at a firm-level, which helps to form a clearer view of a c onsequential period for the Canadian oil and gas industry. To do this I compile a database of publicly traded oil and gas firms operating in Canada, separate these firms into categories based on production volume, and examine the changing m arket composition. I explore the entry and exit dynamics of firms as they enter the market, grow or decrease in size, and exit the industry through bankruptcy, acquisition, or through f ailure to report their financials. I find that the number of firms operating in the industry d ecreased significantly. Smaller firms made up the vast majority of exits from the market, with the number of larger firms holding almost constant. I examine the production share of each category of firms and show how it changes over the study period and find that industry grew in production volumes, but all the gains were from larger firms as the industry consolidated at the top. I analyse the financial performance of firms using the interest coverage ratio and net-debt-to-cash-flow as measurements of leverage, and the current ratio as a measurement of liquidity. My analysis of financial indicators shows that smaller firms operated closer to the edge with the bulk of the smallest firms operating close to i nsolvency. I then evaluate environmental liability risks by examining firms’ Liability M anagement Ratings as a measure of asset-solvency, and asset retirement provisions to d etermine total outstanding environmental liabilities. I find that while smaller firms are more likely to have insufficient assets to address their reclamation obligations, mid-sized firms were at a higher risk of a high-consequence insolvency in terms of environmental remediation liabilities due to their much greater level of outstanding environmental liabilities. This paper provides data, charts, and analysis that offers a clearer view of the state of the Canadian oil and gas industry and the effect of a consequential period, presenting policymakers with data to better understand the process and outcomes of the downturn.
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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 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,006 |
| Études des sciences et des technologies | 0,006 | 0,002 |
| Communication savante | 0,007 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,001 |
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 ».