Water–energy nexus for water distribution systems: a literature review
Bibliographic record
Abstract
Water and energy are interdependent on each other. Energy is required to supply water to a system while, at the same time, water is needed for power generation in any natural or artificial system. This relationship is often called the water–energy nexus (WEN). In a water supply system, energy is consumed for source water extraction, transmission, treatment, and distribution. About 7%–8% of the world’s total generated energy is used for drinking water production and distribution. A major portion of this energy is used for distribution, i.e., pumping, chlorination, and maintenance activities, and hence is the focus of this review. Most of the world’s energy is generated by fossil fuels (oil, gas, and coal), which results in greenhouse gas (GHG) emissions. Here we review studies conducted to assess and evaluate the energy consumption and the related GHG emissions in water distribution systems (WDSs). This review covers the basic concepts and studies on WEN, energy saving solutions, renewable energy resources for water pumping, optimization of design, and the life cycle assessment (LCA) of large WDSs. Most of the reviewed studies suggest a trade-off between energy cost and the associated GHG emissions when selecting fixed-speed pumps over variable-speed pumps for large WDSs. To mitigate CO2 emissions, renewable energy resources like solar, wind, and mini-water turbines for water pumping have been discussed and mini-water turbines were found to be energy efficient solutions. The energy-focused LCA model has been studied to investigate the environmental impacts, GHG emissions, operational energy, and various life cycle stages of pipe manufacturing (embodied energy) in the network. Case studies of real world WDSs are reviewed and the potential research gaps are identified. Most life cycle studies have focused on the areas of pipe replacement, the life cycle cost of the system, the operational energy, and the reduction of GHG emissions, whereas less attention has been paid to the geographical and socio-economic issues along with the areas of human health, water resource diversity, and the hydraulic characteristics of WDSs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".