Implementation of risk management and corporate sustainability in the Canadian oil and gas industry
Bibliographic record
Abstract
Purpose With the increasingly complex global environment companies are facing increased regulations. Financial and social risks are often overlooked but the key in establishing the necessary framework for risk management. Under pressure(s) from the media, public and government, the current companies within the oil and gas fields have taken precautionary steps to reduce their carbon footprint and have allowed technological innovations to take a proactive role in maintaining efficiency and sustainability. The purpose of this paper is to propose a framework outlining how organizations are implementing risk assessment and analysis to determine sustainable operations and methods in developing low-risk outcomes. Design/methodology/approach The authors used a case study approach to develop and illustrate the risk management framework. Findings This study provides a theoretical framework for analyzing and reducing risk within the oil and gas sector through explaining various means of innovation and sustainability. Risk integration and mitigation are modeled and quantified within an evolutionary framework. The case study illustrates the risk management techniques currently used in a corporate setting. Originality/value Using innovation and sustainable technologies, organizations can take a proactive role in reducing risk in the oil and gas industry in northern Alberta. Providing shareholders with an innovative framework dealing with strategic implications to reduce risk in compliance with operational costs.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".