As the science and knowledge service of the European Commission, the Joint Research Centre's mission is to support EU policies with independent evidence throughout the whole policy cycle.
JRC scientists recently published a paper presenting new methods to estimate demand for future industrial and commercial land use at both continental and sub-national scales.
With a rising global human population and fast-paced economic development, increased demand is being put on this non-renewable resource. Land-use change dynamics, driven by technological, economic and demographic processes, impact the landscape and other environmental features by leading to a conversion of natural/semi-natural landscapes into built-up areas.
The JRC study investigates the links between the economic performance of regions and land-use dynamics, and proposes a set of methods for estimating future demand for industrial and commercial land use based on ‘land-use intensity’ measures and economic projections. The validity of the models was estimated by making predictions on land use in 2006, which could then be compared with observational data. The results of the different approaches were compared with trend extrapolations (linear and exponential) and null (no land-use change) models. The tested methods were found to perform substantially better than the null model (no land-use change), with the linear extrapolation and region-specific land-use intensity models proving to be the best overall performers. However, the accuracy of the linear model is expected to decay rapidly with time as it does not take into account any explicit driver of land-use change. As such, the methods based on land-use intensity measurements and economic projections are more suitable for mid to long-term estimations of future demand for industrial and commercial land use. In fact, the land-use intensity approaches gave the best results for many regions and even some whole countries, and were able to generate sensible scenario dependent results on the development of industrial and commercial land across regions.
The authors of this research also carried out an extensive uncertainty analysis of the proposed methods, highlighting that the outputs of such models are sensitive to the quality of the input data, and to the assumptions related to economic growth and to future trends in land-use intensity. The lack of detailed and consistent land-use time series data for Europe was proven to be a major bottleneck for more accurate results.
Currently very little literature which attempts to understand the relationship between economic development and industrial and commercial land-use changes exists. Land-use modelling can help identify the drivers and effects of land-use change, and can therefore be used to inform land-use policy development and implementation, by allowing landscapes to be envisaged under different socio-economic and policy scenarios.
Despite the uncertainties which were identified, the authors argue that the use of straightforward approaches such as those based on land-use intensity are lacking in the literature and are relevant and suitable for studies of large areas for which data are limited.