Comparing Environmental Financial Guarantee Schemes in Kenya and South Africa
Bibliographic record
Abstract
Kenya and South Africa have enacted some laws that inculcate economic incentives schemes as key elements of their environmental regulatory terrain. While Kenya has advanced the use of Environmental Performance Deposits (EPDBs), South Africa has adopted the use of the Financial Provisioning Regulations, applied specifically for the upstream mining sector. This article reviews the use of financial assurance schemes in environmental management and their specific application to the upstream mining sectors in the two countries. The data used in the analysis is from literature review, key informant interviews, interview schedules, and focus group discussions. Results indicate that while the use of financial provisioning is a well-established practice in South Africa, in Kenya only one company has deposited funds to the environmental regulator as a security for good environmental practices. A comparative analysis of the regulatory framing for financial assurance instruments in Kenya and South Africa demonstrates some similarities in terms of requirement for public participation, requirement for periodic review of the bonds, use of the bonds for environmental rehabilitation; and some differences such as requirement for use of cash and/or financial instruments, how to treat the accrued interest from the deposited funds, and how the deposit bond amounts are set. Largely, both countries acknowledge the importance of economic incentives in their environmental management frameworks. The article recommends strengthened regional cooperation to enhance the application of financial assurance in the law for effective environmental management in Africa.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".