Nexus between Nitrous Oxide Emissions and Agricultural Land Use in Agrarian Economy: An ARDL Bounds Testing Approach
Bibliographic record
Abstract
This paper analyses the relationship between Nitrous Oxide emissions, agricultural land use, and economic growth in Pakistan. Agriculture largely contributes to Nitrous Oxide emissions. Hence, models of agriculture induced Nitrous Oxide emissions are estimated in addition to models of total Nitrous Oxide emissions. Estimated models accommodate more flexible forms of relationship between economic growth and emissions than those of the widely adopted models in testing the Environmental Kuznets Curve. The Auto-Regressive Distributed Lag (ARDL) bounds testing approach to co-integration and the vector error correction model approach is applied to test the Environmental Kuznets’s Curve hypothesis for Pakistan and to detect the directions of causality among variables using the time series data for the period 1971 to 2012. Results indicate that an N-shaped rather than an inverted U-shaped relationship exists in the case of Pakistan. The tipping values for total Nitrous Oxide emissions and agriculturally induced Nitrous Oxide emissions indicate that Pakistan passes through a phase of increasing environmental degradation. Increases in agricultural land use and per capita energy use will increase the level of Nitrous Oxide emissions. However, controlling Nitrous Oxide emissions from agricultural land use and per capita, energy use without adversely affecting economic development will be a serious policy challenge for Pakistan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".