Multi-scenario multi-objective analysis of downscaled shared socio-economic pathways (SSPs) for robust policy development in coupled human-water systems
Bibliographic record
Abstract
Abstract. Shared socio-economic pathways (SSP) scenario analysis is concerned with developing climate change adaptation strategies that perform well across a wide range of plausible future socio-economic and climate change conditions. However, downscaled/localized SSP scenarios, most relevant for regional climate adaptation, are poorly understood in terms of their deep uncertainties and how these scenarios can contribute to the development of robust regional policies in coupled human-water systems. In the present study, we propose a new framework that integrates a multi-scenario multi-objective (meta-criteria) optimization analysis of a set of downscaled/localized SSP storylines with the robust decision-making concept to find optimal robust solutions under deep uncertainty concerning regional climate adaptation. By developing an integrated dynamic simulation-optimization model, potential policy alternatives are investigated, and their robustness evaluated based on four key objectives: farm income, groundwater depletion, soil salinity, and reliability. Scenario-based multi-objective optimization for multiple SSP scenarios is merged into a robust optimization problem and evaluated in parallel. The proposed framework is applied to study potential robust solutions for vulnerabilities of a real-world human-water system in Pakistan's Rechna Doab region that has multiple stakeholders and conflicting objectives. The results revealed Pareto optimal solutions that are both optimally feasible and robustly efficient. The socio-environmental conditions of SSPs have a significant influence on the estimated robustness. The candidate solutions under scenario SSP1 are remarkably comparable to those offered by scenario SSP5, which was deemed to be the best among the SSPs evaluated. SSP3 was the least desirable of the SSP scenarios examined and solutions resulted in undesirable soil salinity, groundwater depletion, and reliability values. By incorporating SSP narratives and quantitative scenario analysis, the proposed framework revealed advantages for integrated dynamic modelling of human-water systems with a high level of uncertainty and complex interconnections to discover robust climate change adaptation solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".