Measuring food‐energy‐water nexus footprint using a systematic input–output approach: A case study of Pune district
Bibliographic record
Abstract
Abstract The advent of climate change with the recognition of interlinkages between Food‐Energy‐Water (FEW) resource security has brought forth a renewed emphasis on ascertaining the FEW footprints of varied policy interventions. The nexus approach through an IO framework explores the interplays and synergies between sectoral flows and their FEW footprints. India is currently undergoing great transformations under policy interventions on both the economic and environmental front. The country has been attracting investments to expand its manufacturing base, while also aiming to transition into a greener economy. The district of Pune has a balanced and diversified economic profile, spread across manufacturing, as well as knowledge‐based tertiary sectors. However, rapid urbanization has put an undue burden on its FEW resources, challenging urban sustainability. Hence cohesive strategies are of priority to ensure sustainable and equitable development. Toward this end, the current study presents the first‐ever district‐level economy‐wide FEW nexus study in India. Results indicate that the most resource‐intensive sectors in Pune district include the food processing sector, motor vehicles, and electrical engineering and instruments. Based on this comprehensive footprint analysis, priority sectors are identified to pursue sector‐wise efficiency analysis.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".