Addressing the Challenges of Economic Water Scarcity in Kenya: Multi-Barrier and Multilateral Integrated Approach Systems for Sustainable Access to Safe Drinking Water A Review
Bibliographic record
Abstract
Accessibility to clean drinking water is essential to the quality of life of all humans. Water is also important in national development of a country. Kenya is faced with both physical water scarcity and economic water scarcity. Physical water scarcity is due to increased water demand, limited supply of water and low rainfall whereas economic water scarcity is due to lack of sufficient financial capability to support water infrastructure. Depending on the water use, there are various techniques available for removal of harmful bacteria, water softening and removing organic particulates and other contaminants from drinking water. They range from simple to sophisticated technologies. This review focused on the economic water scarcity in Kenya which is a developing country. Aspects reviewed included: water poverty index and effects of economic water scarcity. The review methodology tool used was the water poverty index assessment tool (WPI) at which elemental parameters related to water were identified and discussed. The outcome of this research was expected to contribute in the development of affordable and sustainable purification technologies and policies that can be used to deliver safe and clean drinking water. The assessment using the Water Poverty Index (WPI) found Kenya’s WPI to be low in the range of 38-45 %, which is characterised as being severe in regard to the global water stress. Kenya’s safe yield of surface water resources and ground water was estimated to be 7.4 BCM per annum and 1.0 BCM per annum respectively. Water abstractions is estimated at 1.6 BCM per annum representing 13-19% of the available water. Challenges identified include: undeveloped water resources, low financial and institutional capacity, limited access and increased demand of water in agricultural activities such as irrigation with a declining total annual renewable water per capita in the last 50 years. Recommendations proposed as solutions that can be adopted to address economic water scarcity and consequently water stress in Kenya, include exploring sustainable low-cost purification technologies, adopting multi-barrier integrated approach system and the use of multilateral economic groups support to address economic water scarcity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".