# Are soil conservation and “No Net Land Take” suitable for urban development? modeling and assessment in two French cases

Jean-Philippe Antoni, Tudal Sinsin, Igor Agbossou, Gilles Vuidel, Edouard Patault

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Jean-Philippe Antoni, Tudal Sinsin, Igor Agbossou, Gilles Vuidel, Edouard Patault. Are soil conservation and “No Net Land Take” suitable for urban development? modeling and assessment in two French cases. City and Environment Interactions, 2025, 28, pp.100243. 10.1016/j.cacint.2025.100243. hal-05268823

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# Are soil conservation and “No Net Land Take” suitable for urban development? modeling and assessment in two French cases

Jean-Philippe Antoni<sup>a, *, e</sup>, Tudal Sinsin<sup>b</sup>, Igor Agbossou<sup>c</sup>, Gilles Vuidel<sup>c</sup>, Edouard Patault<sup>b</sup>

## ARTICLE INFO

### Keywords:

No Net Land Take (NNLT)

Sustainable urban development

Cellular automata

Land use planning

Soil quality

Spatial equity

## ABSTRACT

The No Net Land Take (NNLT) policy seeks to halt the expansion of urbanised land to preserve soil quality and biodiversity. In France, this involves achieving zero net increase in artificial surfaces by 2050, with a mid-term objective of halving land take by 2030. This study examines the implications of NNLT for urban development and environmental preservation through a case study of two southeastern French territories: Grand Rovaltain and Aire Gapençaise. A data-driven cellular automata (CA) simulation model is employed to explore multiple development scenarios. This model integrates soil quality classifications (from 1 to 5) to assess the spatial dynamics of land use under NNLT constraints. The results indicate that while the policy restricts the spatial distribution of new developments, it does not inhibit overall housing and economic growth. Instead, development pressures tend to concentrate in rural and suburban transition zones, particularly those already vulnerable to sprawl. Soil quality thresholds significantly shape the spatial allocation of new growth, reinforcing the role of environmental parameters in guiding sustainable urban planning. The study offers three main contributions. First, it demonstrates the effectiveness of CA-based models in visualising land use outcomes under ecological constraints. Second, it highlights the potential to reconcile socio-economic development with environmental protection. Third, it stresses the importance of territorial dialogue to mitigate spatial inequalities that may emerge from the rigid application of NNLT policy. Overall, the study underscores the value of simulation-based planning tools in informing land use decisions and balancing competing territorial objectives under ambitious sustainability frameworks.

## 1. Introduction

The principles of environmental management that lead to more sustainable development were developed in the 1990s and are now well known and widely accepted in most Western planning policies [1], particularly in transport[2] and building[3]. However, the practical implementation of these principles remains complex. The vast majority of actions focus on reducing the greenhouse gases responsible for current climate change. These mainly concern reducing emissions related to building heating and road transport. In the former, the objective is to enhance the thermal insulation of buildings without compromising their heritage value by reducing energy consumption Copiello et al. [4]. In the latter, the objective is to encourage individuals to reduce their mobility and opt for less polluting and more energy-efficient modes of transport or to increase the proportion of hybrid and electric vehicles in the fleet.

Many regulatory measures, which are sometimes considered restrictive, have been trialled and some are now being implemented to encourage this transition [5].

Over the past decade, the need to conserve biodiversity and soil quality has emerged as an important lever for sustainable development, complementing these principles [6,7]. In this area, a number of measures have been taken to conserve agricultural diversity and the ecosystem services it provides. In the European Union, the objective is to restrict soil sealing and 'land take', which the European Environment Agency defines as 'the coverage of land by urban buildings and infrastructure, as well as urban green spaces and sports and leisure facilities' (Build [8]). In this context, many policies aim to limit urban sprawl in favour of urban renewal [9]. In 2011, the Council and the European Parliament set the 'No Net Land Take' (NNLT) target, and the European Commission extended the NNLT reduction rate to zero by 2050 (European

<footnote><sup>a</sup> ThèMA, UMR 6049 CNRS – Université Bourgogne Europe, Dijon, France</footnote>

<footnote><sup>b</sup> Altereo, Innovation and Digital division, Venelles, France</footnote>

<footnote><sup>c</sup> ThèMA, UMR 6049 CNRS – Université Marie et Louis Pasteur, Besançon, France</footnote>

<footnote>* Corresponding author.
E-mail address: jean-philippe.antoni@u-bourgogne.fr (J.-P. Antoni).</footnote>
Commission, 2011). Each member state is therefore encouraged to incorporate this ambitious goal into its regional development policy, resulting in a variety of approaches[10]. In France, for instance, combatting urban sprawl has been a key objective of planning policy since the 2000 s. However, it remains challenging to implement in a context of growing housing demand, which creates significant economic opportunities for the real estate sector. Against this background, the 2023 law sets a much more ambitious target for 2050: 'Zero Net Artificialization' (ZAN) [11]. Policymakers are ambivalent about this target. Some consider it laudable and urgent to limit the impact of urbanisation on biodiversity[12]. However, others view it as restrictive, drastically reducing opportunities for local economic development and solutions to social needs.

In this context, this paper aims to evaluate the impact of the NNLT objective (ZAN in France) on two specific study areas in south-east France: 'Grand Rovaltain' and 'Aire Gapençaise'. The objective is to measure its impact on both soils and new housing development. To this end, a simulation model based on cellular automata is employed, taking soil quality into account in order to define territorial development scenarios. Section 2 presents the data and materials used in the study, detailing the characteristics of the two study areas and the main land use, driving factor and soil quality datasets. Section 3 outlines the methods, with a focus on the cellular automata modeling approach, its implementation features, and the scenario settings used for the simulation. Section 4 analyses the results of the modeling, including the impacts of NNLT on soil quality and urban development. Section 5 critically discusses these results and reflects on their implications for sustainable urban planning and the broader applicability of the NNLT goal. Finally, Section 6 concludes with key findings and recommendations for future research.

## 2. Data and materials

This section introduces the two territorial development and planning agencies involved in this study: 'Grand Rovaltain' and 'Aire Gapençaise' (2.1). It also presents the geographical data used to model their land use and territorial dynamics (2.2).

### 2.1. Study areas

Both Grand Rovaltain and Aire Gapençaise are located in south-eastern France, in the vicinity of the towns of Valence and Gap, respectively (see Fig. 1). Each area corresponds to a Schéma de Cohérence Territoriale (SCoT), a strategic spatial planning instrument in France aimed at guiding the sustainable development of a defined territory, typically comprising multiple municipalities. SCoTs provide a coherent planning framework across key sectors—including housing, transportation, environmental conservation, and economic development— ensuring alignment with long-term territorial objectives. These two case study areas were selected to capture the diversity of territorial contexts encountered across France. Grand Rovaltain represents a dynamic, peri-urban environment with significant development pressures, whereas Aire Gapençaise is characterised by a more rural, mountainous landscape with different spatial and socio-economic dynamics. This contrast offers a valuable basis for comparative analysis, enabling the exploration of how the NNLT policy may manifest and be implemented under varying geographical and institutional conditions.

The Grand Rovaltain (GR) covers a dynamic urban territory of approximately 1640 km2, comprising 108 municipalities. It has a population of around 316,000, growing at an average rate of 1765 inhabitants per year, equivalent to an annual growth rate of 0.58 %. This demographic expansion is closely linked to both residential demand and the development of economic activities, which are managed under policies aimed at maintaining strict control over land consumption. Residential construction is progressing at an average of 1976 units per year—slightly exceeding the territory's targets of 1900 units annually

for the 2016–2025 period, and 1750 units annually for 2026–2040. This trend reflects sustained urban pressure and signals strong demand within the real estate sector. However, despite proactive planning efforts, vacancy rates have remained persistently high, indicating inefficiencies in the housing market or mismatches between supply and demand. While the urban area of Valence continues to serve as the primary hub for economic and social activity, surrounding suburban and rural municipalities within GR are gaining attractiveness. Notably, the industrial sector—especially the agri-food industry—has experienced marked growth, supporting the expansion of related business services and contributing to the diversification of the local economy.

The Aire Gapençaise (AG) encompasses a predominantly rural territory of approximately 2100 km2, comprising 78 mountain municipalities located in the French Alps. The area has a population of around 78,800 inhabitants, with more than 40,600 (approximately 50 %) residing in the city of Gap. The territory is marked by a pronounced dichotomy between the urban centre and the peripheral zones situated on rugged, high-altitude terrain. Over the past decade, AG experienced a population increase of 6.4 %, corresponding to an average annual growth rate of 0.88 %. However, this growth has decelerated in recent years, declining to 0.44 % between 2013 and 2019. The territory is notable for its rich ecological and landscape value, with approximately 90 % of its surface designated as areas of environmental or scenic interest, all of which are regulated by local urban planning instruments. AG is also a prominent destination for seasonal tourism, offering winter sports such as skiing and a variety of summer mountain leisure activities. These characteristics underscore the strategic importance of preserving the area's natural landscapes and environmental quality, which are central to its attractiveness and long-term development potential.

The juxtaposition of Grand Rovaltain, a rapidly expanding metropolitan area, and Aire Gapençaise, a mountainous territory subject to pronounced spatial constraints, underscores the complexity of implementing the NNLT policy across heterogeneous territorial contexts in France. While alternative study areas could have been considered, the deliberate selection of these two contrasting territories enables a more comprehensive analysis of the policy's implications for soil preservation and the development of new housing. Owing to their distinctive yet complementary characteristics, both territories were particularly well-positioned to serve as pilot sites. Accordingly, they engaged voluntarily as research partners, contributing actively to the study as representative test areas.

### 2.2. Data source

The data used to study and model GR and AG can be divided into three categories. The first category comprises land use data collected from open geographic databases. The second category comprises driving factors that constrain or favour urbanisation in strategic sectors, as provided by the SCoT scheme. The third category comprises soil quality data produced within the MUSE method (described in detail below).

#### 2.2.1. Land use

The first step is to collect all available data describing GR and AG's land use and organise it within a geographic information system (GIS). This data will be used to measure and simulate land use, as well as to distinguish between artificialized and non-artificialized parcels (the former being residential, industrial or mixed, and the latter being natural, agricultural or forest land). This data is created using the graphic and topographic database 'BD Topo®<fnref n="1" />, which is created and regularly updated by the National Geographic Institute (IGN). It delineates parcels and associated buildings across the entirety of France. As a 3D vector-based representation of territory and infrastructure, including buildings, roads, activity zones, forests and water surfaces, it enables the

<footnote n="1">¹ https://geoservices.ign.fr/bdtopo (only in French).</footnote>
<figure><img src="image_2.png" /><figcaption>Fig. 1. Land use in “Grand Rovaltain” (GR) and “Aire Gapençaise” (AG).</figcaption></figure>

characterisation of both artificialized and non-artificialized land, categorised under a suitable thematic structure with metric precision usable at scales ranging from 1:2000 to 1:50,000. Additionally, the 'Graphical Parcel Register' (RPG),<fnref n="2" /> which is typically employed as a European standard for allocating Common Agricultural Policy (CAP) subsidies, has been utilised to accurately identify agricultural lands. Combining “BD Topo®” and the “Graphical Parcel Register” with the “Land Files” (a national database providing information on the use of cadastral parcels based on tax declarations) yields a land use map comprising seven relevant categories, as illustrated in Fig. 1.

### 2.2.2. Driving factors

For simulations of territorial dynamics to be consistent with the economic, demographic and environmental development objectives set out in the ScoTs' schemes, land use is not the only information needed. Realistic simulations must also be based on the normative territorial planning rules established by current regulations. These rules can be translated into spatial data and incorporated into a model of future trajectories. As detailed in Table 1, the key elements of the rules include: (i) development areas located within urban envelopes, which are designed to avoid urban sprawl and make housing more compact; (ii) non-buildable zones, including ecological corridors, green and blue frames, urban prescriptions, public utility easements and risk zones, such as areas at risk of flooding, fire and landslides; and (iii) adjustable areas, which depend on soil quality.

### 2.2.3. Soil quality

Finally, in addition to land use and driving factors, soil quality must be considered as an adjustment variable. In the context of soil conservation and the implementation of the NNLT policy in urban development, soil quality analysis is indeed a pivotal component. Soils are fundamental to sustainable development due to their inherent characteristics and capacity to perform a variety of ecological functions[13]. A classification of soil quality can be made using the MUSE<fnref n="3" /> method [14]; Colsaet et al., 2018) from four major indicators chosen for their

<table><caption>**Table 1**
Driving factors: development areas and non-buildable zones.</caption><thead><tr><th>Driving factors</th><th>Data name</th><th>Description</th></tr></thead><tbody><tr><td>Development zones</td><td>Urban Envelope</td><td>In the development and evolution of urban planning documents, the existing urban envelope and frontage are used to distinguish between the densification and expansion of existing urban areas, particularly since the French ALUR law of 2014.</td></tr><tr><td>Non-buildable zones</td><td>Non-building areas</td><td>These zones are defined in the local regulatory planning document. They include urban prescriptions such as public utility easements (to promote the public interest), risk zones (e.g. fire or flood risk areas) and protected areas (e.g. green and blue corridors and zones of biological interest). These zones affect the potential for development and broader land use.</td></tr><tr><td>Adjustable</td><td>Activity Zones</td><td>These zones are specifically designated areas for the development of infrastructure and economic activities. We do not simulate housing in these zones.</td></tr><tr><td></td><td>Soil quality</td><td>These zones are detailed below. See Section 2.2.3.</td></tr></tbody></table>

ecological relevance and their ability to provide essential information on the functional potential of soils[15,16]: agronomic potential (capacity of soils to produce biomass, influenced by texture and pH), infiltration (ability of soils to absorb and regulate water flows), carbon stock (measure of organic carbon in the soil, crucial for CO₂ sequestration.) and biodiversity (soil microbiomes and fauna.). This method classifies each part of the territory according to five indexes, ranging from very low multifunctionality (index 1) to very high multifunctionality (index 5). The description and potential of each index is provided in Table 2, and the resulting map is shown in Fig. 2.

The MUSE method's typology is essential for recognising the diversity and multifunctional potential of soils in urban and peri-urban contexts. However, while it is specific to the “Grand Rovaltain” and “Aire Gapençaise” areas, it may be subject to variations and discussions based on different local and regional characteristics and databases. Here, it is only used to help decision-makers consider options for

<footnote n="2">² https://geoservices.ign.fr/rpg (only in French).</footnote>

<footnote n="3">³ https://www.cerema.fr/fr/mots-cles/projet-muse (only in French).</footnote>
<table><caption>**Table 2**
Soil multifunctionality quality index.</caption><thead><tr><th>Soil quality index</th><th>Meaning</th><th>Description</th><th>Potential use</th></tr></thead><tbody><tr><td>1</td><td>Very low multifunctionality</td><td>Soils at this level have a very limited ability to fulfil their ecological functions. They are often subject to significant physical or chemical constraints, such as hydromorphism, high acidity or compaction. These constraints diminish their productivity and ability to sustain biodiversity.</td><td>The application of these soils to agriculture or other intensive land use is not favourable. Efforts to restore these lands could focus on ecological rehabilitation or establishing them as controlled biodiversity reserves.</td></tr><tr><td>2</td><td>Low multifunctionality</td><td>These soils demonstrate limited functionality, usually due to one or two compromised characteristics, such as reduced carbon reservoir potential or insufficient water retention capacity.</td><td>This is appropriate for moderate forestry initiatives or the implementation of minimal intervention landscaping elements.</td></tr><tr><td>3</td><td>Medium multifunctionality</td><td>Soils in this category exhibit a balance between limitations and advantages, enabling them to support moderate levels of biomass and biodiversity. They have a reasonable capacity for storing carbon.</td><td>These soils are versatile by nature and are well-suited to sustainable agricultural endeavours, the development of urban green spaces and agroecological projects.</td></tr><tr><td>4</td><td>High multifunctionality</td><td>These soils are characterized by physicochemical properties that are conducive to most ecological functions. They offer commendable water retention, substantial carbon storage potential, and support diverse biodiversity.</td><td>These soils are characterised by physicochemical properties that support most ecological functions. They offer excellent water retention and substantial carbon storage potential, supporting diverse biodiversity.</td></tr><tr><td>5</td><td>Very high multifunctionality</td><td>These soils exhibit peak capacities across all assessed ecological functions and are devoid of major limitations. They benefit from sustainable management practices that have already been established.</td><td>These soils are essential for conservation purposes and are particularly well-suited to nature reserves, pioneering agroforestry and preserving strategic ecosystems.</td></tr></tbody></table>

sustainable soil management and integrate soil quality into planning processes.

### 3. Methods

The methodological approach adopted in this study consists of five successive stages (see Fig. 3): (1) Review of the literature on spatial modelling techniques in geosimulation, with a focus on methods applicable to land-use planning under sustainability constraints, (2) Development of a modelling framework based on cellular automata

(Lucsim), designed to incorporate indicators of soil multifunctionality in spatial simulations, (3) Co-construction of land-use scenarios in collaboration with local stakeholders from the two pilot territories (Grand Rovaltain and Aire Gapençaise), integrating both climate change adaptation and mitigation strategies, (4) Simulation and evaluation of territorial development trajectories aligned with the No Net Land Take (NNLT) objective, aimed at identifying trade-offs between environmental conservation and socio-economic development, (5) Impact analysis of simulated trajectories, with particular attention to soil quality thresholds, to inform decision-making processes and support the formulation of sustainable urban planning strategies Fig. 4.

The following section outlines the methodology employed to model prospective land-use change scenarios under the No Net Land Take (NNLT) constraints, projected to the year 2050. The approach consists of two main components. First, we present the underlying logic and operational structure of the cellular automata model integrated within the broader modelling framework. This model forms the core of the simulation process, enabling the dynamic representation of spatial interactions and land-use transitions over time. Second, we detail the scenarios developed to explore the effects of varying urbanisation constraints and their implications for soil quality. These scenarios are designed to reflect different trajectories of territorial development, taking into account both regulatory and ecological considerations. The cellular automata model serves as the analytical engine that integrates all relevant data inputs and spatial constraints described in Section 2, including land-use designations, soil quality classifications, and planning regulations. The resulting simulations, presented in Section 4, provide a basis for assessing the spatial outcomes and trade-offs associated with the implementation of the NNLT policy.

#### 3.1. Modeling process

The centrepiece of the modeling process is a geographic cellular automaton (CA), i.e. a dynamic model for simulating changes in land use and land cover (LULC). As the name suggests, this type of model is based on a series of cellular grids. The resolution of these grids must be consistent and realistic in order to simulate or plan future urbanisation projects.

##### 3.1.1. CA modeling features

A variety of approaches and methods have been developed to model urban systems and shift towards sustainable and efficient planning. These include the Markov chain, logistic regression, potential models and cellular automata[17,18], Priem and Canters [19,20]. These models and methods can be used in isolation or in combination with one another, or alongside other statistical models. The flexibility of CA-based models, coupled with their capacity to integrate GIS technologies in the modeling of intricate urban systems, has led to their widespread application in urban growth modeling[21,22,23]. In geography, the territorial dimension of cellular automata was supported by Tobler [24] through his pioneering work in cellular geography. Several approaches and models have since been proposed to modify standard CA in order to simulate land use changes from a bottom-up perspective, particularly in urban and suburban areas.

By definition, a cellular automaton (CA) is a discrete computer model composed of a cellular grid, where each cell is classified as one of a number of predefined states. In geographical and land planning applications, these states typically correspond to land use categories, such as housing, activity zones and mixed zones (see Fig. 1). Within the CA grid, the state of each cell can evolve over time according to the states of neighbouring cells. Transition rules are then applied iteratively as many times as desired. The main objective is to identify suitable locations for future affordable housing and activity projects that will best sustain natural resources, particularly in contexts of land shortage due to urbanisation, while promoting urban development and comfortYigitcanlar and Teriman [25]. The generated outcomes will also demonstrate the
<figure><img src="image_3.png" /><figcaption>Fig. 2. Driving factors in “Grand Rovaltain” and “Aire Gapençaise”.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 3. Global modeling process.</figcaption></figure>

role and impact of spatial decision-making behaviours in reshaping
landscape structure.

In this study, we used LucSim<fnref n="4" /> CA software [26] to simulate the designed scenarios. It is a geographical cellular automata whose major functionality is based on P. Torrens’s definition[27]. The software is based on the assumption that the state of each cell is determined by its neighbourhood. In our case, this neighbourhood is defined as the LULC categories of the surrounding cells within a given radius.

$$
\forall i \in E, V_i, k = f (Vik (t), \Omega_i (t)).
$$

Where.

$$
\Omega i = f V rk = 1, V rk = 2, \dots, V rk=N \text{ and } r \in \{0, \dots, \infty\}.
$$

where *E* is a set of cells that can undergo a transition (i.e are not
locked), *V*ᵢ is the land use of the cell i, Ωᵢ is the neighbourhood of the cell
i within a radius *r* (at time *t*), and *Crn* is the number of cells with a land
use *S* within a radius *r*, at time *t* [28]. To guide CA during simulation,

particular sets of transition rules were defined to achieve the goals of
each scenario.

3.1.2. Cellular grids and global process

Using LucSim requires the geographical information collected on
each study area (see Section 2) to be transformed into raster grids in
order to make it compatible with the model. The remaining question is
to determine the most relevant cell size for containing this information,
i.e. the model’s granularity. Currently, there is no definitive method for
determining this, and CA approaches described in the literature use sizes
ranging from over a dozen kilometres to just a few metres. In this study,
15 x 15 m cells were empirically chosen to optimise the granularity of
territorial observation and computation time for CA simulations. This
size roughly corresponds to that of an individual house and garden, i.e.
one of the smallest elements of the urban fabric that can be considered
when measuring urban growth and development. All grids were ob-
tained using the Graphab add-on for QGIS[29] from the data described
in Section 2. The result is a set of georeferenced TIFF images. The land

<footnote n="4">⁴ https://thema.univ-fcomte.fr/productions/software/lucsim/.</footnote>
## Grand Rovaltain (GR) Goal: 29931 cells in 2050

### Scenario A

<figure><img src="image_5.png" /></figure>

## Aire Gapençaise (AG) Goal: 9988 cells in 2050

### Scenario A

<figure><img src="image_6.png" /></figure>

### Scenario B

<figure><img src="image_7.png" /></figure>

### Scenario B

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Simulated urban development (2030–2050) under soil quality constraints and NNLT Policy in GR and AG.</figcaption></figure>

use image contains seven categories, coded from 1 to 7 as shown in Fig. 1. The image describing soil quality contains five categories, coded from 1 to 5 (see Table 2). Other images containing 'development areas' and 'non-buildable' zones (see Table 1) are binary coded: 1 if building is allowed or favoured, and 0 if non-buildable. Fig. 3 shows how the global modeling process integrates driving factors and scenarios in the cellular automaton to produce results adjusted for soil quality.

## 3.2. Scenarios settings

The first scenario involves simulating a decrease in soil artificialization until 2050. As set out in the NNLT objective of the European Commission and in France, this decrease is calculated by halving the level of artificialization measured over the 2010–2020 period for each subsequent period. Table 3 shows the results of this calculation, i.e. the number of hectares available for development according to the objective, converted into the number of 15x15 m cells.

Artificialization in Grand Rovaltain is measured by the National Artificialization Observatory (https://artificialisation.developpement-durable.gouv.fr/). In Aire Gapençaise, it was measured by the authors through GIS requests.

According to Table 3, the scenario implemented in LucSim in Gap, for instance, will allow 14,368 cells to transition from non-artificialized land use to new residential or activity cells in the first period, 7134 cells in the second period, and so on. A geographical rule can then be implemented in the CA to simulate the transition from non-artificialized land use to 'New_Habitat'. This assumes that transition is only possible within the 'urban envelope' if the one-cell neighbourhood is already urbanised, and that the cells to be artificialized correspond to at least a continuous set of four cells, in order to avoid isolated cells and enable

the construction of realistic housing projects. Moreover, this can occur in two scenarios:

* Scenario A: with the urban envelope constraint only.

* Scenario B: with all constraints defined as 'non-buildable zones' in Table 1.

Furthermore, both scenarios take soil quality into account, considering the quality index presented in Table 2. Initially, the model only permits transitions in areas with a soil quality index of ≤ 2. If the required number of cells cannot be reached in these areas, it will gradually allow transitions for indexes 3, 4, and finally 5 if necessary.

## 4. Results

This section presents the outcomes of the simulations that were conducted for the two study areas (Grand Rovaltain and Aire Gapençaise). These simulations aimed to assess the impact of soil quality constraints and other regulatory factors on the projected urban development. The simulations evaluated development milestones for 2030, 2040 and 2050, with urbanisation targets of 16,207, 25,356 and 29,931 cells for Grand Rovaltain and 5707, 8561 and 9988 cells for Aire Gapençaise. These scenarios integrate soil conservation principles and the NNLT objective, offering insights into managing urban growth within varying soil quality and non-buildable zone constraints.

### 4.1. Scenarios A constrained by urban envelope only

In scenarios limited solely by the urban envelope, the two study areas exhibited distinct development trends influenced by soil quality

<table><caption>**Table 3**
Measured and planned soil artificialization until 2050.</caption><thead><tr><th></th><th></th><th>2010-2020 (measured)*</th><th>2020-2030 (planned)</th><th>2030-2040 (planned)</th><th>2040-2050 (planned)</th><th>After 2050</th></tr></thead><tbody><tr><td rowspan="2">Grand Rovaltain</td><td>ha</td><td>729</td><td>365</td><td>206</td><td>103</td><td>0</td></tr><tr><td>cells</td><td>-</td><td>16,207</td><td>9149</td><td>4575</td><td>0</td></tr><tr><td rowspan="2">Aire Gapençaise</td><td>ha</td><td>257</td><td>128</td><td>64</td><td>32</td><td>0</td></tr><tr><td>cells</td><td>-</td><td>5707</td><td>2854</td><td>1427</td><td>0</td></tr></tbody></table>
thresholds.

* Soil Quality = 1 (lowest quality): Under this stringent constraint, both study areas displayed limited development potential. Aire Gapençaise showed a gradual increase in potential urbanisation cells, rising from 5707 in 2030 to 8669 by 2050. Grand Roaltain, however, exhibited a static capacity of 6413 cells throughout the entire simulation period, indicating restricted flexibility under this scenario.

* Soil Quality $\le$ 2 or 3: When the constraint was slightly relaxed, the potential for urbanisation expanded significantly. In Grand Roaltain, for example, development increased from 16,207 cells in 2030 to 20,643 by 2040, remaining stable until 2050. Aire Gapençaise followed a similar upward trend, with the number of cells increasing from 5707 in 2030 to 8669 by 2050.

* Soil quality $\le$ 4 (high quality): Expanding the threshold to include higher-quality soils resulted in substantial increases in development capacity. Grand Roaltain reached its full development targets, achieving 25,356 cells by 2040 and 29,931 by 2050. Aire Gapençaise also demonstrated improved capacity, peaking at 9988 cells by 2050. This indicates that urbanisation targets can be met under less restrictive soil quality constraints.

## 4.2. Scenarios B constrained by urban envelope and non-buildable zones

Adding non-buildable zones as an additional constraint significantly altered the development potential of both study areas.

* Soil Quality = 1 (lowest quality): In this scenario, both Grand Roaltain and Aire Gapençaise experienced a notable decrease in development capacity. Aire Gapençaise maintained a maximum of 8,564 cells from 2030 to 2050, whereas Grand Roaltain consistently achieved only 4810 cells throughout this period.

* Soil quality $\le$ 2 or 3: With slightly relaxed constraints, Grand Roaltain exhibited moderate growth, reaching 16,207 cells in 2030, increasing marginally to 17,369 by 2040 and 2050. Aire Gapençaise exhibited stagnation, achieving a maximum of approximately 8564 cells across all time frames.

* Soil Quality $\le$ 4 (high quality): Increasing the soil quality threshold significantly improved developmental outcomes. Grand Roaltain achieved its full development targets, reaching 29,931 cells by 2050 despite constraints in non-buildable zones. Aire Gapençaise also demonstrated enhanced potential, achieving 9871 cells by 2050.

The results highlight significant differences between the two study areas. For Grand Roaltain, meeting urban development targets was feasible under scenarios allowing for moderate soil quality thresholds ($\le$4) and incorporating non-buildable zone constraints. In contrast, Aire Gapençaise required an extended soil quality threshold of $\le$ 5 to fully achieve its urbanisation targets by 2050.

## 4.3. Synthesis

In both cases, the results show significant local disparities. Fig. 5 shows the urbanisation rate (in percentage terms) of the urban envelope at commune level. With a rate of 100 % (as seen in one commune in the north-east of the Grand Roaltain region), the simulation shows that all available land in the commune has been urbanised. In contrast, a rate of 0 % (observed in several communes in the north-west of Grand Roaltain) indicates that the commune was not developed at all, despite having significant potential. In the first case, it is therefore possible to identify municipalities that have excessive urban development. In the second case, certain municipalities seem to have been overlooked by development projects. The maps show that the areas in greatest demand are neither the densest urban areas nor the most rural. They lie somewhere in between, in a large suburban area that is already experiencing urban sprawl.

These findings emphasise the importance of incorporating soil quality metrics and spatial constraints into urban planning. Although strict soil protection measures can limit development potential, relaxing these constraints in a controlled manner enables urbanisation goals to be achieved without compromising regulatory principles. The results provide valuable insights into optimising land use strategies within the context of NNLT policies and sustainable urban growth. They emphasise the importance of balancing urban growth objectives with strict resource conservation protocols. The analysis emphasises the strategic importance of careful land management as a foundation for resilient

<figure><img src="image_9.png" /><figcaption>Fig. 5. Maps of urban development (2030–2050) under soil quality constraints and NNLT Policy in RG and AG.</figcaption></figure>
development pathways in the face of environmental and regulatory challenges. Achieving this balance is essential for sustainable urban development while adhering to strict conservation practices. The subsequent discussion section will facilitate further analysis of the strategic and policy implications and encourage dialogue, offering insights that will benefit policymakers and stakeholders involved in land use and urban planning processes.

# 5. Discussion

This section presents the results and conclusions of the study. It begins by outlining the methodological advantages of using cellular automata for the co-construction of geoprospective scenarios. Following this, it offers a critical analysis of No Net Land Take (NNLT) policies, examining their implications for spatial planning and environmental protection.

## 5.1. Cellular automata as decision support models

The choice of methodology for simulating urbanisation dynamics is of strategic importance in terms of both the robustness of the results and their ability to inform spatial planning policies. This study CA within an explicit spatial modeling framework to represent land artificialization scenarios coherently. This framework integrates the spatial and regulatory dimensions of urban development and the ecosystem services provided by soils. Unlike machine learning approaches, which are widely promoted for short-term land use prediction, CA provide a more suitable framework for incorporating decision rules based on environmental constraints (e.g. topography and ecology), socio-economic constraints (e.g. land pressure and urban attractiveness) and political constraints (e.g. visions and strategies).

Although artificial intelligence methods are efficient for tasks such as land cover change detection or image-based classification, they often struggle to model the complex, non-linear and systemic interactions between the driving factors of soil artificialization. In this regard, CA are advantageous in that they rely on spatial and transitional logic: the state of a cell (or spatial unit) depends on both its intrinsic characteristics and the conditions in its neighbourhood. The ability to simulate the self-organisation of territorial dynamics has been well demonstrated in the foundational work of White and Engelen (1993), who emphasise its relevance for fine-scale urban modeling.

Furthermore, while traditional statistical approaches (e.g. logistic and spatial regression models, and agent-based models) are effective at identifying explanatory factors, they often lack the ability to simulate dynamic spatial projections or capture spatial feedback loops over time [30]. Therefore, CA-based modeling is employed as a decision support system. This is particularly appropriate in contexts where the goal is to simulate urban trends and co-construct forward-looking land-use trajectories that align with NNLT objectives. The model enables political and ecological hypotheses to be contrasted and the associated trade-offs to be made explicit.

## 5.2. NNLT and sustainable growth

This research examines the impact of NNLT policy on urban development, as simulated using computer-aided methods, through two contrasting French case studies. The results show that soil quality significantly affects urbanisation potential. High-quality soils provide essential ecosystem services, justifying their conservation. Furthermore, incorporating urban envelope constraints helps channel growth into designated areas, encouraging densification and more efficient infrastructure management. Combined with non-buildable zones, these constraints act synergistically to protect sensitive land and direct development to more suitable areas, thereby enhancing the coherence of sustainable planning strategies. These findings **emphasise** the importance of integrating environmental considerations with the demands of

urban growth, requiring collaboration between planners, ecologists, and policymakers.

However, the scenarios show that restricting development to marginal soils severely limits urban potential. In contrast, including medium-quality soils enables urban objectives to be met, particularly in the Grand Rovaltain area. Strict conservation measures can significantly restrict urban growth and economic potential. This highlights the need for strategic trade-offs when identifying areas for development. Grand Rovaltain benefits from greater flexibility, whereas Aire Gapençaise, which has tighter spatial and environmental constraints, requires more adaptive measures. The results show that areas most exposed to urbanisation are in suburban and rural zones, which are already main targets of urban sprawl.

Two key conclusions can be drawn from this. Firstly, while integrating soil quality and NNLT considerations does influence the location of urban development, it does not fundamentally challenge the existing pattern of urban sprawl. As Fig. 5 shows, urbanisation continues to expand beyond major urban centres (which are already densely built up) and avoids the most rural areas (which tend to be less dynamic). Secondly, these findings reveal spatial inequalities between municipalities that are highly sought after for urban development and those excluded from any real prospects for economic growth. This situation could lead to political tensions and requires the implementation of compensation policies between municipalities using existing regulatory planning tools. However, while such compensation aims to be more spatially equitable, there is a strong likelihood that it could significantly undermine efforts to preserve soil quality and concentrate urban development within relatively compact urban areas.

This confirms the need for the implementation of NNLT policies to be context-sensitive. In order to avoid unintended consequences, such as housing shortages or economic stagnation, policymakers must also consider socio-economic factors, including housing demand and local development needs. This analysis yields several actionable recommendations:

* Adopt flexible soil quality thresholds allows for development on soils of moderate quality, thereby alleviating spatial constraints without undermining conservation objectives. Such flexibility can serve as an important mechanism to mitigate political tensions and territorial inequalities by facilitating dialogue and enabling context-specific adaptations. This approach helps to ensure that the implementation of the No Net Land Take (NNLT) policy does not inadvertently exacerbate regional disparities. We fully acknowledge that, if left unregulated, this flexibility could contribute to an “eco-deficit cycle,” ultimately jeopardizing long-term environmental sustainability. However, our intention in advocating for adjustable soil quality thresholds is not to promote unchecked development that disregards ecological limits. Rather, it represents a pragmatic and transitional strategy to support the effective implementation of NNLT in regions experiencing complex demographic and economic growth pressures.

* Couple the flexibility of soil quality thresholds with more robust and long-term strategies aimed at preserving soils and biodiversity. As emphasized by Vuong et al. [31], policies must evolve beyond simply offsetting environmental impacts to actively generating net ecological gains through soil restoration, biodiversity enhancement, and the promotion of multifunctional land uses. Within this framework, flexible thresholds are not an end in themselves but serve as a transitional measure toward embedding development within a proactive eco-surplus approach. This approach enhances the long-term sustainability and resilience of NNLT implementation, particularly in rapidly growing territories facing complex environmental and socio-economic pressures.

* Provide innovative pathways to reconcile economic development with ecological conservation by transforming ecological values into restoration incentives, thereby fostering an eco-surplus trajectory
and promoting more balanced territorial decision-making. To prevent urbanization from descending into an eco-deficit spiral—resulting in immiserizing growth driven by ecosystem degradation—it is instructive to consider the semiconducting principle of environmental and monetary exchange[32]. Given that soil quality can be enhanced through appropriate restoration techniques, establishing mechanisms that translate ecological improvements (e. g., soil quality enhancement) into monetary incentives could incentivize businesses to invest in nature restoration and steer development toward an eco-surplus pathway. For such mechanisms to be effective and sustainable, it is essential to elevate the Nature Quotient (NQ)—a form of ecological intelligence that enables individuals and communities to understand, adapt to, and harmonize with complex natural systems [33]. Strengthening NQ across the population is therefore a critical prerequisite for fostering widespread engagement in ecological restoration and achieving long-term sustainable development.

* Prioritizing urban renewal and densification over greenfield expansion is essential to minimizing soil sealing and aligning urban growth with sustainability principles. By concentrating development within existing urban areas, this approach helps preserve natural and agricultural lands, reduces habitat fragmentation, and promotes more efficient land use patterns conducive to sustainable territorial development.

* Developing region-specific strategies is crucial for the effective implementation of the No Net Land Take (NNLT) policy. By tailoring approaches to local land-use patterns, environmental conditions, and socio-economic contexts, policymakers can achieve more equitable and context-sensitive outcomes. Such customization enhances the relevance and feasibility of interventions, helps address regional disparities, and fosters stakeholder engagement, ultimately improving the policy's overall effectiveness and sustainability.

## 6. Perspectives

A critical examination of No Net Land Take (NNLT) policies reveals potential unintended consequences despite their objectives to preserve soil quality and limit urban sprawl. By constraining development within densely populated urban cores where infrastructure is already established, NNLT policies may inadvertently incentivize growth at the urban periphery. This spatial displacement can exacerbate rather than mitigate urban sprawl, increasing reliance on private vehicles and consequently elevating greenhouse gas emissions due to longer commuting distances. For instance, restrictions on high-density housing developments in city centres may force residents to seek accommodation further from employment hubs, thereby extending travel times and raising transport-related emissions. This paradox highlights the importance of critically assessing whether NNLT policies align with urban planning frameworks that promote compact, accessible cities, such as the short-distance city [34] or the 15-minute city concept [35]. These models emphasize sustainable mobility and support broader climate change mitigation objectives. To address these challenges, policymakers should incorporate comprehensive mobility assessments into NNLT planning processes. Such integration ensures that land-use decisions are coordinated with existing and planned transportation infrastructure, thereby fostering sustainable urban growth patterns that reconcile environmental conservation with accessibility and social equity. Several avenues emerge for advancing research and practice in the field.

First, future research should incorporate transportation planning dimensions into land-use simulation models, recognizing mobility and accessibility as critical drivers of urban growth dynamics. Integrating transport policy scenarios—such as multimodal accessibility improvements, public transit expansions, or strategies to reduce car dependency—into NNLT modeling frameworks would provide a more comprehensive understanding of the interactions between land conservation and mobility strategies. In alignment with Almatar [36], who

highlighted the potential of transit-oriented development (TOD) in Riyadh to promote compact and accessible urban forms, forthcoming applications of the NNLT framework should explicitly embed TOD principles to harmonize soil preservation with sustainable mobility goals.

Second, addressing spatial justice and territorial equity under NNLT constraints warrants further exploration. Our findings confirm that suburban transition zones often concentrate development pressures, exacerbating socio-economic disparities among municipalities. Future investigations should evaluate compensation mechanisms and planning instruments that reconcile ecological imperatives with equitable economic opportunities, while ensuring the long-term resilience of infrastructure and services. Drawing on Almatar's [37] work emphasizing the role of green mobility in reducing carbon emissions and facilitating renewable energy transitions in Saudi cities, subsequent research should explore the synergistic coupling of NNLT policies with green mobility strategies to optimize co-benefits for climate mitigation and energy efficiency.

Finally, expanding the modeling framework to integrate socio-technical transitions and participatory approaches offers promising potential. Involving stakeholders—including residents, local authorities, and private sector actors—in co-designing urban scenarios can enhance the legitimacy, acceptability, and practicality of NNLT strategies. As demonstrated by Almatar [38] in the context of Riyadh's public transportation implementation, urban mobility strategies must address institutional and governance challenges. Incorporating these dimensions into NNLT models would increase their policy relevance and applicability. Moving towards integrated frameworks that combine soil conservation, transport policy, and governance innovation will yield more robust decision-support tools. Consequently, future research should strive to bridge ecological objectives with pragmatic urban development needs, situating the NNLT policy within the broader agenda of sustainable, climate-resilient, and inclusive cities.

## 7. Conclusion

Although the NNLT policy poses significant challenges, particularly in balancing soil conservation with the demands of urbanisation, this study shows that it is feasible under carefully calibrated scenarios. Leveraging data-driven tools such as cellular automata modeling enables policymakers and urban planners to navigate the complexities of sustainable land use management, fostering urban development that aligns with environmental and socio-economic objectives. Integrating soil conservation principles with NNLT policies provides a viable approach to sustainable urban development. However, it also reveals potential spatial inequalities that could lead to political tensions at the territorial level, challenging some initial assumptions. The results of this study provide a basis for informed decision-making in urban planning, emphasising the need for balanced approaches that respect human and ecological needs alike.

While the findings offer valuable and actionable insights, several limitations warrant consideration. The current reliance on static soil quality thresholds and predefined urban envelopes may insufficiently represent the adaptive strategies employed by local stakeholders responding to housing demand, market dynamics, and mobility constraints. To enhance the robustness and predictive accuracy of future simulations, it is essential to incorporate dynamic behavioral responses and cross-sectoral feedback mechanisms, particularly those related to transportation, energy systems, and ecosystem services. Integrating these complexities will better reflect real-world interactions and improve the applicability of modeling outcomes for policy and planning.

Further research is necessary to refine the models used in this study, particularly by incorporating dynamic feedback mechanisms to improve the prediction of long-term policy intervention impacts on urban systems and ecological processes. Engaging with a broader dataset across varied study areas would also help to generalise these findings and
enhance their applicability in different contexts. While the modeling approach is robust, it relies on assumptions regarding soil quality thresholds, urban envelopes and non-buildable zones, which may not fully capture real-world complexity. Future studies could integrate additional variables, such as economic viability, demographic trends, road networks and infrastructure requirements, to make the simulations more realistic and applicable

Looking ahead, this study opens several promising research avenues. Comparative analyses across European and international contexts are needed to assess the transferability of the proposed approach and to understand how diverse governance structures influence the implementation of No Net Land Take (NNLT) policies. Additionally, integrating geosimulation models with multi-criteria decision analysis could facilitate the development of adaptive, context-sensitive policies that effectively balance ecological preservation, housing affordability, and sustainable mobility. Ultimately, the ambitious goals of NNLT can only be realized if the policy is embedded within integrated territorial strategies that address not only land conservation but also the broader imperative of fostering compact, accessible, and resilient urban futures.

### CRediT authorship contribution statement

Jean-Philippe Antoni: Writing - original draft, Supervision, Conceptualization. Tudal Sinsin: Methodology, Data curation. Igor Agbossou: Methodology, Formal analysis. Gilles Vuidel: Validation, Software. Edouard Paultait: Supervision, Project administration, Funding acquisition.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The authors would like to thank all the members of the consortium (Altereo, Cerema, ThéMA, SCOT Grand Rovaltain and SCOT Aire Gapençaise) who worked on the MALTOSE project, which was funded by the French Energy Agency (ADEME) as part of the second edition of the PACT²e Call for Research Projects (convention no. 22DAD0039). 22DAD0039).

### Data availability

Data will be made available on request.

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