Uncovering Spatiotemporally Dependent Uncertainty in SDG 11.3.1 Evaluation for More Scientific and Equitable Land-Use Policies

By providing a globally unified benchmark for urban land-use efficiency (LUE), SDG 11.3.1 establishes a soft constraint rooted in monitoring, comparison, and national commitment, thereby encouraging more sustainable land-use policies. Although extensive assessments have been reported, they often overlook errors originating from data sources, particularly their propagation—and potential amplification—during calculations within specific spatiotemporal contexts. This can introduce significant uncertainty, undermining both the reliability and equity of LUE evaluations and ultimately posing risks to the formulation of sound, sustainable policies. To address this gap, the present study conducts a meticulous examination of biases stemming from data sources, indicator formulation, and spatiotemporal scales, with Yangtze Delta, China, serving as the empirical case. The results indicate that when inappropriate indicators—such as the Land Consumption Rate to Population Growth Rate (LCRPGR)—are employed, even minor inaccuracies in the data sources can be drastically magnified. In contrast, indicators with simpler structures, like built-up area per capita, ensure minimal propagation of input errors. Furthermore, the choice of spatial units and the temporal trend analysis method can significantly influence the assessment results. Therefore, it is essential to move beyond the traditional approach of neglecting data errors and focusing solely on constructing sophisticated algorithms. Instead, the LUE assessment framework must be optimized across data sources, indicator formulas, and spatiotemporal conditions to mitigate error propagation, enhance the reliability of results, and thereby support the formulation of scientifically sound and equitable land-use policies.