Uncovering the land development potential of municipalities in France
Bibliographic record
Abstract
In France, municipalities play a crucial role in land use planning. For this purpose, they have three powers at their disposal: a regulatory power that enables them to define the rules of urbanization and building permits; an incentive power through the tax lever, as well as by playing a role as an animator of the local market with calls for projects, co-financing, and communication campaigns; and finally, power through land ownership, which allows control of the location and destination of a development project. The two primary powers in urban development are regulatory and incentive. However, the success of urban development ultimately depends on the willingness of investors to comply with regulations or withdraw from the market. Additionally, land ownership provides greater control over projects and the allocation of land resources. Municipalities and their groups, as the first public owners on the land markets, have significant resources to guide land use planning. However, in the current economic context, public actors are increasingly selling their land assets, thereby depriving themselves of the power of development. This is due to the spread of the neo-liberal management model and resulting austerity policies (Peck, 2012), which are forcing public authorities to reduce their expenditure and generate new revenue. In a context of rising land prices since the 2000s, they are even more tempted to sell their assets. The privatization of public land is a well-documented phenomenon in Canada, the United Kingdom, Italy, and France, mainly concerning state railway and military land (Adisson and Artioli, 2020; Christophers, 2018; Whiteside, 2020). It is important to question the future ability of municipalities to control the development of their territory without land control as landowners. To provide decision-makers with clarity on this matter, this communication proposes an evaluation of the land ownership of the municipal block that could be utilized for development operations. This will be based on an analysis of land-registry tax-based data, known as les Fichiers Fonciers, at the scale of metropolitan France. The initial stage of this work involves outlining the boundaries and contents of municipal land ownership as it is recorded in the land registry. Municipalities may directly own land parcels, or delegate ownership to a municipal service or external body for development operations. In some cases, plots may be held in multi-ownership by different actors, requiring analysis to determine the level of municipal control. We aim to categorize municipal land ownership into distinct market segments based on Joseph Comby's typology (Comby, 2010), which we have adapted to evaluate the development potential of municipalities through land ownership. It is important to note that owning built heritage and land assigned to a specific use does not necessarily confer the same development potential as owning a vacant plot (Casanova Enault et al., 2021). The plots are qualified based on four determining indicators for their potential mobilization: morphology, topography, use assignment, and state of pollution. No changes in content have been made. This qualification work is carried out on a national scale, at the granularity of the plot, and involves specific methods of spatial database management and automated processing powered by PostgreSQL, PostGIS, and Python, using exogenous public data. Municipal land plots are scored based on their potential for development operations, ranging from 1 for plots with low probability of mobilization to 5 for easily mobilized plots without major constraints. After quantifying and mapping the levels of property concentration and dispersion, we provide an explanatory analysis of their unequal distribution among municipalities. By analyzing the residuals of a Chi-square test of independence, we demonstrate that urban municipalities primarily have land already designated for urban services (such as urban parks and sports fields) for their future development, which raises questions about their future. Although rural municipalities have a higher percentage of non-mobilizable land, such as forests and mountains, suburban municipalities possess significant land reserves that are available for urbanization and are likely to be privatized and developed in the future.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".