Sustainability Practice of a Multinational Oil Company in Nigeria: A Case Study
Bibliographic record
Abstract
Environmental degradation and socioeconomic dilemma continue to affect agricultural productivity in the Niger Delta of Nigeria. Several works of literature confirm the high level of pollution and contamination of land and water as a result of over 50 years of oil production in the region. The effects of environmental pollution continue to aggravate the hardship of the local people, which generates development friction, threaten oil operation, and mutually contrive relational efforts, by so invoking mistrust between oil companies and the host communities. Sustainability programs of oil companies often provide the channel to engage and promote community relations from which projects are conceived and executed. Despite sustainability efforts of oil companies, the region continues to experience oil spills and environmental degradation.Hence, the current research explores the sustainability efforts of a multinational oil company to establish whether the company’s leadership makes environmental considerations and to identify possible corrections that could be adopted to achieve sustainable value. For this purpose, the paper employed a single case study approach using open-ended interview sessions in collecting data. Research data were gathered from a sample of 20 experienced sustainability practitioners of the oil company, partnering nonprofit organizations, and community leaders through face-to-face semi-structured interviews. Data were segmented and categorized. The data analysis process revealed several themes regarding the challenges and shortfalls of sustainability programs in the region. The evidence found suggests that implementing a transparent and inclusive sustainability management system is essential to enable a systems view in contemplating sustainability programs. In so doing, oil MNCs leaders could enable effective environmental consideration in their sustainability programs to help reinvigorate productive agriculture and ensure continuing oil operation.
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 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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".