A decision support system for urban ecological sustainability: Optimizing spatial trade-offs among environmental effectiveness, social equity, and economic viability

• A spatial DSS supports ecological sustainability across land systems • The framework translates ecological metrics into actionable landuse strategies • Context-sensitive accounting weights ecological footprints by social-ecological vulnerability • Spatial optimization exposes efficiency-equity-equality trade-offs • Spatial targeting improves ecological efficiency by 32% over uniform land treatment Cities disproportionately concentrate environmental burdens, yet existing environmental accounting frameworks often treat urban systems as spatially homogeneous and overlook variation in land quality, management practices, and social vulnerability. This oversimplification limits the capacity of sustainability assessments to translate ecological indicators into spatially actionable strategies that indicate where to intervene. The fundamental gap lies in the disconnect between environmental assessment, spatial planning, and trade-off management. This research presents an integrated decision support system addressing this gap through multi-objective spatial optimization and context-sensitive ecological footprint accounting. The framework incorporates fine-resolution analysis at planning-relevant scales; context-sensitive weighting integrating environmental degradation and socioeconomic vulnerability indicators; spatially explicit multi-objective optimization balancing environmental effectiveness, social equity, and economic viability through Pareto analysis; and graph-theoretic spatial coherence ensuring feasible interventions. Applied to the Netherlands, the framework demonstrates substantial improvements in sustainability planning. By incorporating local environmental and social conditions into footprint accounting, it distinguishes unsustainable from sustainable land uses, enabling 32% greater targeting efficiency than approaches treating all land as equivalent. When allocating interventions across land types, the framework achieves 57% reduction in ecological deficit within realistic budget constraints, though Pareto analysis shows that each 10% environmental gain requires approximately 9% trade-off in social equity. This approach demonstrates that environmental footprint accounting, traditionally limited to performance monitoring, can guide spatially explicit decision-making when weighted by local conditions and embedded within multi-objective optimization frameworks. Requiring widely available spatial data (land cover, environmental quality, socioeconomic conditions), the methodology enables cities worldwide to translate sustainability commitments into spatially targeted interventions.