Conventional applications of the CLUE-S model rely on a static driver assumption, using driver data and their associated coefficients from a base year to simulate land-use patterns for a future target year—an approach that implicitly assumes temporally invariant human–land relationships. To address this limitation, this study introduces and compares two simulation models: the Baseline-Driven Pattern (BDP), which follows the conventional protocol by employing base-year drivers to project future land use, and the Target-Driven Pattern (TDP), which instead utilizes driver data and coefficients that correspond synchronously to the target year, thereby capturing the dynamic evolution of driving mechanisms over time. In terms of implementation, the TDP involves updated driver datasets and regression coefficients, enabling a more accurate spatial allocation of land-use demand. Comparative experimental results from Xiamen in China demonstrate that the TDP achieves higher simulation accuracy than the BDP simulation, with notably greater sensitivity to dynamic factors such as transportation infrastructure and policy boundaries. For study periods 1989–2000 and 2000–2010, the accuracy of TDP simulation for all land-use types surpasses that of BDP simulation. As time progresses, the advantage of TDP simulation over BDP simulation becomes more pronounced, resulting in a significant improvement in the simulation accuracy. These findings confirm that the temporal alignment between driver data and the simulation period is a critical determinant of CLUE-S simulation accuracy. This methodological refinement holds significant implications for model-based land-use planning: it allows simulation procedures to explicitly incorporate future driver conditions articulated in planning documents. Moreover, it equips decision makers with a more realistic simulation tool for evaluating the land-use consequences of alternative planning interventions in scenario-based analyses.
Zhang et al. (Sun,) studied this question.