Relationships among water, food, energy, and ecosystems in the Mid-Latitude Region in the context of sustainable development goals
Bibliographic record
Abstract
The progress of the global effort to achieve Sustainable Development Goals (SDGs) is increasingly impeded by the degradation of critical and fundamental resources such as water, food, energy, and ecosystem services. The Mid-Latitude Region (MLR) of the world is at the forefront of confronting these challenges due to rapid population growth, increasing poverty, and drought and climate change that are exacerbating the transition of semi-arid landscapes to deserts. While scientific studies are accumulating around the water–food–energy–ecosystem nexus, efforts to simulate how the linkages among the elements relate to SDGs are lacking in the MLR. We attempt to review and analyze existing literature about how water–food–energy–ecosystems operate, interact, and relate to SDGs. We identified 37 relationships and ascertained the nature of their interactions, of which 12 are significant and have direct bearings on the SDGs. The findings show that most studies and approaches that address the nexus challenges in the MLR exist in silos. In addition, there is a lack of a scientific approach to quantify how the nexus operates and relates to SDGs. For instance, past studies show that deforestation for agriculture could increase food security. However, there is a weak focus on trade-offs (e.g., loss of ecosystem services due to deforestation). Deforestation is also shown to have a negative relationship with the quantity and quality of water (SDG6) as well as the functionality of an ecosystem (SDG15). Furthermore, the review has indicated a negative relationship between irrigated agriculture and water and a positive relationship with food. This directly implies that water and food issues must be addressed in tandem and not separately if we are to achieve SDGs 2 and 6. The review supports the idea that water, food, energy, and ecosystem services cannot be managed separately, and that future approaches must focus on integrating and optimizing the connections among them to ensure sustainable development.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".