VALIDATING ESG-ERM INTEGRATION IN OIL AND GAS: A MULTI-COUNTRY EMPIRICAL STUDY
Bibliographic record
Abstract
The oil and gas sector is increasingly exposed to complex risks such as climate change, regulatory pressures, and shifting stakeholder expectations.While traditional Enterprise Risk Management (ERM) frameworks have primarily focused on financial and operational risks, these models often fail to capture Environmental, Social, and Governance (ESG) risks that influence long-term corporate sustainability.This study examines the effectiveness of integrating ESG considerations into ERM systems across publicly listed oil and gas companies in the United States, Canada, Norway, and the United Arab Emirates countries selected for their distinct regulatory and ESG maturity levels.Using a quantitative, cross-sectional design and data from 2022-2023, the study evaluates the impact of ESG-ERM integration on financial performance (ROA), operational performance (incident rates and downtime), and ESG metrics (scores and carbon intensity).Results show that firms with higher levels of ESG-ERM integration consistently outperform their peers across all performance dimensions, particularly in countries with stricter regulatory environments and strong stakeholder engagement.The findings offer compelling evidence that ESG-ERM integration not only strengthens risk resilience but also drives sustainable value creation.The study concludes with recommendations for aligning national ESG policies with corporate risk frameworks to enhance the industry's overall sustainability and governance practices.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".