Freshwater scarcity and pricing in South Africa: conflicts between conservation and equity in the post-apartheid state
Bibliographic record
Abstract
South Africa faces water scarcity due to the contribution of climatic, geographic, and human variables. As reported by Statistics South Africa, persistent water scarcity and distributional inequity has arisen in a changing political arena from the period of colonization to the most recent chapter of South African governance from 1994 onwards [1]. In the policy context of a state struggling with the legacy of apartheid, conflicts regarding the pricing of freshwater resources have arisen [2]. With discrepancies between the higher price for water required to promote efficiency and conservation, and the alternative pricing system that would meet the South Africa’s responsibility to improve distributional fairness, the most recent challenges took place between 1994 and 2000 [3]. Consequently, the predominant problem linked to South Africa’s freshwater resources is how to allocate water amongst the competing uses of long term environmental and human welfare, without compromising the needs of the country’s urban poor. One perspective, which can provide insight on the issue of water scarcity in South Africa, is free market environmentalism. This branch of economic thought supports a system of water markets with prices that reflect the true cost of providing the resources along with subsidies to address the needs of the poor. Based on an evaluation of the impact of market incentives in South Africa since the 2001 market reforms, it has been determined that a pragmatic, free market environmentalist approach to water can yield economically efficient outcomes for the resource while mitigating distributional equity issues.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".