Evaluation of Development Policies of a Tourist Territory Confronted with Intensive Urban Development using a Multi-Agent Modeling
Bibliographic record
Abstract
This article employs an agent-based model to quantify how five alternative policy packages reshape land prices, urban form and ecological pressure in Corsica-a Mediterranean island where tourism intensifies competition for scarce coastal land. The model couples housing and tourist-rental markets with heterogeneous household and investor behaviour calibrated on 2010-2022 micro-data. We compare a Business-as-Usual baseline with: (i) a blanket ban on tourist-rental investments (BTRI); (ii) a 20 % tax on tourist-rental incomes (TTRI); (iii) a coastal-setback zoning that restricts new tourist-rental investments to sites located between 1 km and 50 km from the shoreline; and (iv) a CBD-buffer zoning that applies the same 1-50 km distance rule around the central business district. Linear-regression analysis of 2 500 Monte-Carlo runs shows that, ceteris paribus, the TTRI and Coastal-Zoning scenarios cut mean simulated coastal land prices by 12-18 % and reduce intra-island price inequality while preserving aggregate housing-stock growth. Conversely, the blanket ban displaces development towards ecologically sensitive upland areas and erodes local tax revenues. These findings highlight the importance of combining market-based and spatial instruments to reconcile economic vitality with ecological integrity in tourist territories. Beyond Corsica, the modelling framework is transferable to islands and coastal regions facing similar land-use tensions.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".