Market Impacts on Land-Use Change: An Agent-Based Experiment
Bibliographic record
Abstract
Land-use change in a market economy, particularly at the urban–rural fringe in North America, is shaped through land and housing markets. Although market activities are at the core of economic studies of land-use change, many market elements are neglected by coupled human–environment models. We scrutinized the effects of the level of detail of market representation using an abstract, agent-based model of land-use change. This model includes agents representing land buyers and sellers and their respective market-based decision-making behaviors. Our results show that although incorporating key market elements, particularly budget constraints and competitive bidding, in land-use models generally alters projected land-use patterns, their impacts differ significantly depending on the level of detail of market representation. Consistent with theories of land change, our research confirms that budget constraints can considerably reduce the projected quantity of land-use change. The effects of competitive bidding, however, are more complex and depend on buyers’ budgets, their relative preferences for proximity versus open-space amenities, and the size of neighborhoods. Market competition might reduce or increase the quantity of land-use change and the degree of sprawl in the simulated landscapes. Because of the strong effects of market elements on resulting patterns, adequate representation of the structure of markets is important for capturing and characterizing the complexity inherent in coupled human–environment systems.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".