Water Sector Assumptions for the Shared Socioeconomic Pathways in an Integrated Modeling Framework
Bibliographic record
Abstract
Abstract The Shared Socioeconomic Pathways (SSPs) were developed without explicit assumptions for the future of the water sector; therefore, projections of future water demands based on the SSPs often lack a treatment of water technology assumptions that is consistent with the SSP storylines. This study has developed a set of qualitative and quantitative assumptions for future water sector technological advancements in the agricultural, electricity, manufacturing, and municipal sectors within the SSPs and then applied the resulting scenarios to an integrated assessment model to permit analysis of future water demand in a water‐constrained world. These scenarios are then compared to another set that excludes the adoption of water‐efficient technologies. Water demand impacts of individual SSP assumption categories are analyzed to determine scenario‐by‐scenario changes. By 2100, global annual water demands range from 3,560 to 6,600 km 3 . The results show that (1) technological change in the water sector can act to reduce water demand in a water limited world by up to 32% in 2100 in the SSP scenarios, (2) the most sustainable scenario produces end‐of‐century water withdrawals lower than 2010 values, (3) low‐income regions will likely be one of the largest drivers of future water demands and exhibit the greatest sensitivity to highly‐efficient water technologies, and (4) nonwater sector SSP assumptions have significant and differing impacts on demands across SSP scenarios that act to alter global water demands.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".