Inclusive policy development from the ground up: Insights from the household water-energy-food nexus
Bibliographic record
Abstract
Despite substantial contemporary research and a growing trend in exploring the water-energy-food (WEF) nexus, most research efforts have been invested in macro-level supply-side infrastructure and policies. However, prioritizing demand-side management policies can provide new opportunities and untapped potential for addressing interconnected resource challenges. Demand management inherently encompasses users’ consumption patterns, behaviors, socio-economic conditions, and choices, thereby necessitating active engagement and participation. Understanding household-level demands is fundamental to assess the demand for and consumption of water, energy, and food, as well as to inform policy decisions. In this context, our study investigated household consumption patterns within the interconnected WEF nexus, including daily practices such as cooking and washing, conservation measures, household governance, and their cross-cutting relationships with climate change. As a case study, we conducted our research in the Jabal Al Natheef neighborhood of Amman City, Jordan. Our findings reveal that households can propose and enact climate-friendly decisions. Significant gender-related differences were also observed in decisions made across WEF household practices. Additionally, households’ perspectives highlighted governance issues and revealed gaps in policy implementation along with the need for more inclusive decision-making processes. Our results underscore the importance of understanding household-level WEF nexus dynamics and daily practices in informing environmental policies, particularly those related to climate action. Such policies are best developed from the bottom-up by incorporating household insights, rather than relying solely on top-down, one-size-fits-all solutions. • Explores household-level perceptions of the Water-Energy-Food (WEF) nexus in marginalized urban areas. • Highlights equity and justice concerns in demand-side management policies for urban resource conservation. • Reveals how socio-economic constraints shape sustainable practices in water, energy, and food use. • Bridges household data with policy recommendations for inclusive and context-sensitive WEF strategies. • Advocates for grounded, community-informed approaches to urban climate adaptation and resource governance.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".