Although economic profitability and food security often outweigh water conservation priorities in arid and semi-arid regions, this study investigates irrigation practices by evaluating water footprint and economic feasibility through a comparative analysis of water-intensive and water-efficient crops. In this context, an optimal irrigation disparity framework integrated with Internet of Things (IoT) and Machine Learning (ML) mechanisms is proposed to evaluate the effectiveness of water conservation, thereby assessing the potential for enhancing economic profitability. IoT-enabled components are employed to monitor real-time environmental—soil moisture, temperature, and weather—conditions between March and November 2023. This data is processed using a hybrid modeling approach that integrates KNN, GBT, and LSTM algorithms to predict both the duration of cultivation and the water requirements. Finally, the predicted parameters are incorporated into a multi-objective framework aimed at minimizing the disparity in water allocation per net benefit. The final results indicate that saffron required substantially less water—ranging from (19.87 to 28.65 ∗ 10⁶ m³)—compared to watermelon, which consumed (34.61 to 47.07 ∗ 10⁶ m³), while achieving a higher average net profit (33 ∗ 10⁹ IRR) relative to watermelon (31 ∗ 10⁹ IRR). Moreover, saffron consistently approached optimal values across disparity-based objective functions, averaging (0.404). These findings emphasize the dual advantages of saffron as a value-added, water-efficient crop in achieving substantial water conservation while enhancing profitability, offering actionable insights for authorities to incentivize water-efficient crop adoption through subsidies, market mechanisms, or regulatory frameworks. These strategies operationalize technical insights into actionable pathways for balancing food security, economic growth, and environmental resilience.

Urban resilience plays a critical role in sustainable regional development. This is particularly so for ecologically vulnerable urban agglomerations undergoing rapid urbanization. This study examines the spatiotemporal development and driving mechanisms of urban resilience in the Chengdu–Chongqing Urban Agglomeration (CCUA) via the perspective of ecological risk. Using panel data from 16 prefecture-level cities during 2010–2023, this study constructs ecological risk and urban resilience indices were constructed based on the entropy weight–TOPSIS method. The coupling coordination degree model was applied to analyze the interactive dynamics between the two subsystems, and a two-way fixed effects panel model was employed to identify the impact of ecological risk on urban resilience and its moderating mechanisms. The results show that urban resilience experienced a foundational stabilization phase followed by gradual improvement, while ecological risk underwent a three-stage transformation characterized by accumulation, stabilization, and decline. The coupling degree between ecological risk and urban resilience remained moderately high, indicating structural tension within the regional system. Econometric analysis indicates that ecological risk significantly suppresses urban resilience. Infrastructure development has a positive direct effect on resilience. However, it negatively moderates the marginal impact of ecological risk, indicating a nonlinear and conditional risk–resilience relationship.