Land Stewardship and Development Behaviors Under an Ecological-Impact-Weighted Land Value Tax Scheme: A Proof-of-Concept Agent-Based Model
Bibliographic record
Abstract
Sprawling land development patterns have exacerbated ecological degradation, social fragmentation, and public health problems. Perverse incentives arise from the ability to privatize collectively created value in land rents and socialize ecological costs. Land value taxation (LVT) has been shown to encourage urban infill development by reducing or eliminating rent-seeking behavior in land markets. However, despite its purported benefits, this tax reform is value monistic in its definition of optimal land use and, therefore, does little to address the lack of non-market information to inform land use decisions. We propose an ecological-impact-weighted land value taxation policy (ELVT) which incorporates the ecological footprint of land use into one’s land value tax burden. We test both proposed policies (LVT and ELVT) relative to a “status quo” (SQ) property tax scheme, utilizing a conceptual spatially explicit agent-based model of land use behaviors and housing development. Our findings suggest that both tax interventions can increase the capital intensity and decrease the land intensity of housing development. Furthermore, both tax interventions can lead to a net profit loss for speculators and a decrease in the average housing unit price. The ELVT scheme is shown to significantly increase urban nature provisions and dampen the loss of ecological value across a region.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".