Oil Change: How the Global Oil and Gas Downturn Transformed Canada’s Energy Industry
Bibliographic record
Abstract
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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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".