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

USTAOĞLU E., Batista e Silva F., Lavalle C.

Environment, Development and Sustainability, cilt.22, sa.1, ss.519-549, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10668-018-0199-7
  • Dergi Adı: Environment, Development and Sustainability
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, International Bibliography of Social Sciences, PASCAL, ABI/INFORM, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Business Source Elite, Business Source Premier, CAB Abstracts, Geobase, Greenfile, Index Islamicus, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.519-549
  • Anahtar Kelimeler: Industrial and commercial land, Land-use demand, Regression analysis, Model validation, France
  • Abdullah Gül Üniversitesi Adresli: Evet

Özet

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.