Tradeoffs and Synergies Across Global Climate Change Adaptations in the Food‐Energy‐Water Nexus
Bibliographic record
Abstract
Abstract Food‐energy‐water (FEW) systems are increasingly vulnerable to natural hazards and climate change risks, yet humans depend on these systems for their daily needs, wellbeing, and survival. We investigated how adaptations related to FEW vulnerabilities are occurring and what the global community can learn about the interactions across these adaptations. We conducted a global analysis of a data set derived from scientific literature to present the first large scale assessment ( n = 1,204) of evidence‐based FEW‐related climate adaptations. We found that the most frequently reported adaptations to FEW vulnerabilities by continent occurred in Africa ( n = 495) and Asia ( n = 492). Adaptations targeting food security were more robustly documented than those relevant to water and energy security, suggesting a greater global demand to address food security. Determining statistically significant associations, we found a network of connections between variables characterizing FEW‐related adaptations and showed interconnectedness between a variety of natural hazards, exposures, sectors, actors, cross‐cutting topics and geographic locations. Connectivity was found between the vulnerabilities food security, water, community sustainability, and response to sea level rise across cities, settlements, and key infrastructure sectors. Additionally, generalized linear regression models revealed potential synergies and tradeoffs among FEW adaptations, such as a necessity to synergistically adapt systems to protect food and water security and tradeoffs when simultaneously addressing exposures of consumption and production vs. poverty. Results from qualitative thematic coding showcased that adaptations documented as targeting multiple exposures are still limited in considering interconnectivity of systems and applying a nexus approach in their responses. These results suggest that adopting a nexus approach to future FEW‐related adaptations can have profound benefits in the management of scarce resources and with financial constraints.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 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".