Geography Reference
In-Depth Information
Land use change emerges from the interactions among various components of the
coupled human-landscape system, which then feeds back to the subsequent
development of those interactions (Le et al.
2008
). Currently, there are a lot of
evaluation models to simulate the spatiotemporal process and patterns of land use
change (Deng et al.
2013b
).
8.2.1 Current Models
8.2.1.1 Empirical Statistical Model
There are numerous empirical statistical models applied to land system dynamics
simulation (Verburg et al.
2008
; Liu and Deng
2010
). The empirical statistical
model can provide the information of key driving forces of land use change and
reflect the time lag effect of response. Moreover, the data input could be multi-
scaled. However, certain deficiencies exist in this kind of model, as the model
requires to be driven by the data of exogenous land use change rate and amount.
Besides, the conversion rule of land use should be manually set. Therefore, the
model could not provide references for other regions except for the study area.
8.2.1.2 Econometric Model
Econometric model is a policy evaluation model based on sustainable utilization of
land which can evaluate the influence of policy factors on land use and promote
mixed land for sustainable development. For example, European Commission FP6
framework research programme SENSOR ''Sustainability Impact Assessment:
Tools for Environmental, Social and Economic Effects on Multifunctional Land
Use in European Regions'' aims to develop tools for ex-ante impact assessment for
European policies related to rural land use (Helming et al.
2006
). It includes a
detailed macroeconometric model called NEMESIS, which models cross-sector
impacts, being the major characteristic of this project (Jansson et al.
2008
). As it
applies a cross-sector approach to land use, it is suitable for large region. The
Dynamics of Land System (DLS) model as another representative econometric
model can simulate the dynamics of land system at the fine-grid scale through
analysis of land use allocation constraints, and simulation of land supply and
demand balance. Several case studies show that DLS is able to measure the
influence of natural and socioeconomic driving factors and predict the future
LUCC, which could provide meaningful decision-making information for land use
planning and management (Deng et al.
2010
).
