Proactive management of the economic and environmental impacts of a proposed oil sands mine in Nigeria from the pre-mining phase.
Bibliographic record
Abstract
The mining industry may have adverse impacts on the environment, ecosystems, ecosystem services and the society, all of which are essential to human well-being.The industry has understood that obtaining a formal license to operate from governments, meeting regulatory requirements is no longer enough, and in order to reduce risks for the stakeholders, a social license to operate needs to be obtained.Although impact of mining activities is experienced throughout the life of mine, pre-mining phase was considered in this research because it determines the relationship of the mining industries with the community.Despite the vast abundance of oil sands resources in Nigeria, an economic analysis is yet to be carried out to evaluate the feasibility of a mining operation at different production scales.Most importantly, this resource is associated with natural contamination referred to as bituminous natural effects.Therefore, mining is not only perceived to be an opportunity for both social-economic improvement by the local communities but also as an efficient approach to reduce exposure to bitumen fumes.To address these issues, this research aimed to carry out a pro-active study on some oil sands deposit communities in Nigeria.The methodology used include appraisal of questionnaire to the communities involved, applying an existing methodology of ecosystem services to the oil sands deposits located in Ondo State, Nigeria, modelling of the oil sands resource considering three mining scenarios taking into account synthetic crude oil, bitumen and tar production.A preliminary economic assessment and a sensitivity analysis was also carried out for three different production scales.The results of the research include an evaluation of the social license to operate locally; assessment of the impacts of pre-mining activities on the ecosystem services; and a preliminary economic assessment of three types of oil sands mining operations.The conclusions indicate that the challenges faced by a mining company can be managed if proper measures are taken and if the mining company communicates transparently with the community; in addition mining is perceived as a practical solution to the bituminous natural effects.Also, the ecosystem service assessment was proven to be an appropriate approach for mitigating impacts of pre-mining activities on ecosystem services.There will be opportunities for a public policy with minimum price guarantee so that small-scale mining of oil sands will be able to operate profitably and will help to solve the regional problem of untarred/unpaved roads.This will bring both economic and environmental benefits to both the local communities and the Nigerian government and it can represent an important driver for transformation to sustainability in the region.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".