Exploring the Long-Term Relationship Between Freshwater Withdrawals and Agricultural Output in Azerbaijan: Evidence from ARDL and Cointegration Analysis (2000-2021)
Bibliographic record
Abstract
Effective management of water resources plays a critical role in maintaining agricultural productivity, especially in regions that face increasing water scarcity.In the case of Azerbaijan, where agriculture constitutes a significant component of the national economy and freshwater resources are under growing pressure, it is essential to understand the relationship between water consumption and agricultural output for informed policy development.This study explores the long-term equilibrium association between annual freshwater withdrawals and agricultural gross domestic product for the period from 2000 to 2021, based on annual time series data.The autoregressive distributed lag bounds testing approach indicates the presence of a cointegrated relationship, as the computed F-statistic of 4.986 exceeds the upper bound critical values at both the five percent and ten percent significance levels.Subsequent analysis using the fully modified ordinary least squares method identifies a statistically significant and positive long-run relationship, showing that a one percent increase in agricultural output leads to a 0.025 percent rise in freshwater usage.The Engle-Granger cointegration test further validates this finding, yielding a tau-statistic p-value of 0.015, which confirms the existence of a stable long-term connection between the variables.These findings highlight the importance of implementing coherent water and agricultural policies in Azerbaijan, including measures to improve irrigation efficiency, invest in waterconserving technologies, and ensure that agricultural development is aligned with the principles of sustainable water resource management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".