Quantifying and modelling industrial and commercial land-use demand in France

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Determinants of industrial/commercial land uses are controversial, and few studies have so far considered the factors influencing industrial and commercial developments. The understanding of such dynamics is important to simulate future land-use demand, which is an essential input for land-use modelling applications. The rigorous estimation of demand for industrial and commercial land is also important to support planning policies and decisions, which aim at allocating scarce land resources efficiently. This study uses regional data from 1990 and 2000 to investigate potential driving factors of industrial/commercial land demand for France, and 2012 data for model validation concerning the projections of land demand. A static model and a change model are specified based on the supply and demand relationship of the regional industrial/commercial land market in France. The estimated models indicate that regional characteristics of location and area, mineral resources and infrastructure, and socio-economic factors are critical to understanding industrial/commercial land developments. From regression analysis, static models show better performance over land-use change models in both the estimation and model validation stages. The change models are biased towards unobserved variables and time-lag effects of the changes in explanatory variables. The use of regression approaches is a valuable tool to explore the factors underlying industrial and commercial expansion at regional level, but their usage for long-term projections is subject to high uncertainties.

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  1. 1.

    The land-use models are operating at multiple scales: At the national scale land demand calculations are undertaken and there are regional and local scales to take driving forces into account. Besides top-down allocation, a bottom-up modelling approach is implemented to feed back local changes to the regional and national level (Verburg et al. 2004b). In land-use modelling applications, socio-economic developments at the macro-level impact the demand for land for urban uses, while at the micro-level suitability of locations and actual spatial configuration of land impacts the overall socio-economic developments (van Delden et al. 2011).

  2. 2.

    The NUTS is a spatial coding standard for referencing the administrative divisions of countries for statistical purposes. The standard was introduced by the European Union. There are three levels of NUTS defined representing different levels of local and regional administrative units. NUTS1 refers to major socio-economic regions, while NUTS2 corresponds to basic regions for the application of regional policies. NUTS3 is the smallest regional level applicable for specific diagnoses (Eurostat 2015).

  3. 3.

    A number of extremely small-sized NUTS3 regions were aggregated with the neighbouring regions to form one single region to have relatively homogeneous NUTS areas in the sample. There are four such regions in small size surrounding the city of Paris, which were lumped into one regional area. A further smaller one exists in the Eastern France which was joined to the neighbouring NUTS3 region.

  4. 4.

    Industrial sectors comprise ‘manufacturing, electricity, gas, steam and air conditioning supply, water supply, sewerage, waste management and remediation activities’, commercial sectors are ‘wholesale and retail trade, repair of motor vehicles and motorcycles, accommodation and food service activities, transportation, storage and information and communication’, and finally services sectors cover ‘financial and insurance activities, real estate and renting activities, professional, scientific and technical activities, administrative and support service activities. (The description of the sectors corresponds to NACE Rev.2 classification detailed in Eurostat 2008.)

  5. 5.

    It is expected that the effects of natural limitations can have similar effects as land-use regulations such as open space and other public land limitations. Rose (1989) analysed the restrictive effect of water bodies on the supply of land, as is urban zoning. The study found both types of supply restrictions significant and concluded that the combined effects of these two types of supply constraints can explain 40% of interurban price variations. NATURA2000 areas are used to represent regulations for specific land uses in the study area. NATURA2000 is a EU-wide network of nature protection areas established under the 1992 Habitats Directive of the European Council (Council Directive 92/43/EEC).

  6. 6.

    In this, collinearity problems were initially identified by computing bivariate correlation coefficients (i.e. Pearson’s correlation coefficients) for all the variables listed in Table 2, and next highly correlated variables were identified and dropped from the analysis.


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We would like thank to Chris Jacobs-Crisioni for his invaluable comments and suggestions which had provided significant contribution to the content and exposition of the analysis presented in the paper.

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Correspondence to Eda Ustaoglu.



Tables 8 and 9.

Table 8 Correlation coefficient matrix
Table 9 First-stage regression results for endogenous variables (static model)

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Ustaoglu, E., Batista e Silva, F. & Lavalle, C. Quantifying and modelling industrial and commercial land-use demand in France. Environ Dev Sustain 22, 519–549 (2020) doi:10.1007/s10668-018-0199-7

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  • Industrial and commercial land
  • Land-use demand
  • Regression analysis
  • Model validation
  • France