Abstract
Current developments in the field of land use modelling point towards greater level of spatial and thematic resolution and the possibility to model large geographical extents. Improvements are taking place as computational capabilities increase and socioeconomic and environmental data are produced with sufficient detail. Integrated approaches to land use modelling rely on the development of interfaces with specialized models from fields like economy, hydrology, and agriculture. Impact assessment of scenarios/policies at various geographical scales can particularly benefit from these advances. A comprehensive land use modelling framework includes necessarily both the estimation of the quantity and the spatial allocation of land uses within a given timeframe. In this paper, we seek to establish straightforward methods to estimate demand for industrial and commercial land uses that can be used in the context of land use modelling, in particular for applications at continental scale, where the unavailability of data is often a major constraint. We propose a set of approaches based on ‘land use intensity’ measures indicating the amount of economic output per existing areal unit of land use. A base model was designed to estimate land demand based on regional-specific land use intensities; in addition, variants accounting for sectoral differences in land use intensity were introduced. A validation was carried out for a set of European countries by estimating land use for 2006 and comparing it to observations. The models’ results were compared with estimations generated using the ‘null model’ (no land use change) and simple trend extrapolations. Results indicate that the proposed approaches clearly outperformed the ‘null model’, but did not consistently outperform the linear extrapolation. An uncertainty analysis further revealed that the models’ performances are particularly sensitive to the quality of the input land use data. In addition, unknown future trends of regional land use intensity widen considerably the uncertainty bands of the predictions.
Highlights
The expansion of industrial and commercial land is poorly understood
Model 1 seems to be the best performer, as it scored best for Average Absolute Error (AAE) and Total Absolute Error (TAE), and showing one of the lowest relative differences. This model shows the narrowest distribution of errors. This indicates that the models that incorporated the economic output as driver for land use change were not able to perform better than trend extrapolations
Among the models that use gross value added (GVA) as driver of land use change, model 3 stands out, as its predictions are overall closer to the known land use in 2006 than predictions from the others
Summary
The expansion of industrial and commercial land is poorly understood. Much research focuses on sector-specific dynamics and aspects such as industry location, productivity and employment [1,2,3]. The relation with land use change, is hardly studied This is an important aspect of the potential impact of economic development on the landscape and other environmental conditions. The development of certain economic activities requires the conversion of land from natural/semi-natural to artificial covers, often irreversibly. These dynamics are difficult to grasp: they relate to global technological and economic processes (e.g. deindustrialization of developed countries, outsourcing of production to cheap-labour countries, increased importance of information and communication technologies) as well as regional and local dynamics reflected in, for example, regional competitiveness and specialization, agglomeration economies and the performance of individual firms [4,5,6,7,8,9]
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