A Spatial Real Options Approach for Modeling Land Use Change: Assessing the Potential for Poplar Energy Plantations in Alberta
Bibliographic record
Abstract
Land use change models based on historical behavior have proven useful in predicting and understanding determinants of change. Such approaches are not possible where land use change data are absent. In this paper, we develop a normative spatial model that considers option values associated with land conversion between agriculture and forestry and their differing time scales. The model is applied in a case study regarding the potential for a cellulosic biofuel industry in Alberta. Results indicate the importance of future patterns of prices in influencing decisions to switch from agriculture to bioenergy production, and suggest that cellulosic plantations are not likely to prevail over agricultural land uses. Les modèles de changement d'affectation des terres basés sur les comportements historiques se sont avérés utiles pour la prédiction et la compréhension des facteurs de changements. Ces approches sont impossibles là où les données concernant les changements d'affectation des terres sont absentes. Cet article présente un modèle normatif spatial qui tient compte des valeurs d'option associées à la conversion des terres agricoles en terres forestières et à leurs différentes échelles de temps. L'application du modèle se fait dans une étude de cas au sujet du potentiel pour une industrie du biocarburant cellulosique en Alberta. Les résultats montrent l'importance de l'influence des futures structures de prix sur le choix de passer de l'agriculture à la production de bioénergie, et suggèrent que les plantations cellulosiques ne l'emporteront pas sur les affectations agricoles des terres.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".