Spatio-temporal Dynamics of Water Footprints of Food Consumption in South Korea: A Decomposition Analysis
Bibliographic record
Abstract
South Korea faces severe water stress, as classified by the OECD, with changing dietary patterns significantly impacting water resources. To ensure water conservation and food security, it is crucial to understand the driving factors of the water footprint of food consumption (WFC). This study examined the WFC in South Korea from 2007 to 2023, focusing on how dietary choices impact water use and sustainability, and identified the key driving factors of changes in WFC. Using the logarithmic mean Divisia index (LMDI), this study decomposed these drivers into water footprint intensity, dietary structure, average dietary intake per person, and population effect. Additionally, global and local spatial autocorrelation analyses were used to measure the degree of spatial aggregation and distribution of WFC across administrative units. Results revealed a significant increase in WFC, from 27.6 km 3 in 2007 to 34.7 km 3 in 2023, with an average annual growth of 2%. Among the drivers, water footprint intensity contributed most to the increase in WFC, while average dietary intake per person led to a decrease. Cereals, meats and fish collectively account for more than 76% of the total WFC during the study period. The findings suggest that the drivers influencing the changes in WFC vary across administrative units, underscoring the need for tailored policies and strategies to promote sustainable food consumption practices that could conserve water resources in each administrative unit.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".