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Record W3025422644 · doi:10.11575/prism/37817

Oil Change: How the Global Oil and Gas Downturn Transformed Canada’s Energy Industry

2019· article· en· W3025422644 on OpenAlexaboutno aff
Mitchell Boyne

Bibliographic record

VenueUniversity of Calgary · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Policy and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum industryFossil fuelEconomyEconomicsBusinessNatural resource economicsEnvironmental scienceEngineeringWaste managementEnvironmental engineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.907
Threshold uncertainty score0.208

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.194
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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