Issues Impacting Sustainability in the Oil and Gas Industry
Bibliographic record
Abstract
<p>This research paper explores the concept of sustainability and the role played by O&amp;G industry in achieving sustainable development. The authors bring a rational approach in defining the key issues for the O&amp;G sector that affect sustainability as well as try to devise the inherent risks as well as mitigation approaches adopted by these companies. Sustainability is a topic gaining fast repute today. As new conventional oil and gas sources decline, unconventional sources, including shale gas in the US, oil sands in Canada, coal seam gas in Australia, and deep-water offshore wells in Brazil, West Africa and Asia have been identified as key areas with significant reserves potential. Despite the growth potential, sustainability risks such as climate change, safety risks, and community disagreements exert pressure on the economic feasibility of these opportunities.</p><p>The three components of sustainable development: economic, environmental and social, often referred to as the ‘Triple Bottom Line’ or TBL, can be used in evaluating a company’s performance in financial, environmental and social dimensions. These three dimensions of sustainable development, as explained by John Elkington and adopted by Shell’s first sustainability report in 1997, are also commonly referred to as the 3Ps: People, Planet and Profit.</p><p>The paper also focuses on analyzing the various threats that could obstruct sustainable development being carried out by companies in the oil and gas industry. The importance of sustainable economic growth with regards to the oil and gas industry has also been highlighted. The 3Ps explained above can be used to categorize the key issues/risks that impact sustainability. The researchers concluded that the sustainability programs followed by oil and gas industry are not satisfactory; however there is strong evidence of improvement in near future. Towards the end, the researchers have tried to list the Strategies and Methodologies for enhancing the effectiveness of sustainability strategies and programs for the sector.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".