ESG Investing in “White Gold”: The Case of Lebanese Salinas
Bibliographic record
Abstract
Lebanese sea salt is historically known as “white gold”. Traditional coastal sea salt production now survives mainly in the coastal city of Anfeh, and is facing various constraints due to regulations, as well as environmental threats which affect the quality of the sea salt. This research points out the case of Lebanese Salinas that invested in ESG to improve the salt quality through social implications and diverse environmental techniques. Based on ESG investments and innovation theory, the main objectives of this research action project were to: create a plastic-free area and implement plastic-free sea salt production at 10 Salinas, using a local innovative tool to filter sea water that consists of a windmill, pump, metallic tube, and filter, which is placed on the main basin of a Salina to prevent the leakage of microplastics into the water used in sea salt extraction, to obtain a plastic-free sea salt. This would create a sustainable, ecofriendly process via the sorting of plastics at the source, clean-up activities, awareness activities, and incentive activities, resulting in the production of better sea salt and the promotion of local products and coastal tourism. The goal of the study was to implement methods that were recommended in the “S.O.S. (Save our Salt)” initiative, which was put into place by the Green Community NGO to protect Lebanese sea salt production and guarantee a reduction in the amount of these microparticles in sea salt. Data gathered from the project, as well as from in-person interviews and follow-ups with the project team, were used to conduct the empirical analysis. The amount of plastic that was present was reduced, resulting in one of the best sea salts in the area. Findings aligned with ESG investment for an increasing and sustainable firm performance and have several practical implications for many stakeholders, both internally and externally, including managers, investors, lenders, policymakers, government, and the public. Our results highlight the significance of formulating regulations for Lebanese Salinas to collectively handle production risks and enhance technical efficacy, and for regulators to lessen marine pollution.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".