In the context of global climate change and rapid urbanization, integrating urban blue-green infrastructure into the built environment is essential for mitigating the urban heat island effect and enhancing climate resilience. Focusing on urban riparian corridors as vital natural cooling systems, this study aims to: (1) quantify their cooling performance in terms of intensity and distance; (2) identify the key landscape drivers and their relative importance; (3) uncover nonlinear relationships and determine ecological thresholds for optimal thermal regulation; and (4) translate these findings into science-based guidelines for climate-adaptive design and sustainable management. Taking 27 representative riparian green spaces in Zhengzhou, China (average area: 17,539 m², range: 10,027–42,690 m²) as a case study, nine key factors characterizing vegetation structure and composition, corridor morphology, and blue-green spatial pattern were used as predictors in a Boosted Regression Tree (BRT) model to analyze their contributions and marginal-effect thresholds. Results show that these corridors provide substantial cooling, with an average intensity of 5.43 °C extending over 215.56 m. Canopy Density, 3D Green Volume per Unit Area, and Green Cover Ratio emerged as the three core drivers, jointly explaining >86% of the cooling performance. The key innovation lies in identifying explicit, design-oriented ecological thresholds—for example, cooling efficacy stabilizes when Green Cover Ratio reaches ~77%, Canopy Density attains 0.7, and the Blue-Green Space Width Ratio approaches 1:1. These thresholds can be directly translated into performance benchmarks for sustainable urban planning and landscape engineering, providing evidence-based parameters for optimizing vegetation structure and spatial configuration. This study demonstrates that applying quantified ecological thresholds can transform riparian corridors into efficient climate-resilient infrastructure, thereby synergistically improving thermal comfort, enhancing ecosystem services, and promoting sustainable land use in urban environments.

Rural dwellings are often self-designed and self-built by their owners, with construction decisions based on experience and imitation of nearby buildings. As existing advanced design methods are often too complex or resource-intensive for rural contexts, balancing cost-efficiency, energy performance, and functional needs remains a challenge. This paper proposes to use the matrix analysis method, which is a relatively simple and easy-to-learn procedure, to identify the optimal design of rural houses. Taking Hebi, located in the Central Plains of China, as an example, field research was carried out, and a baseline model was established. A number of variable models were analysed using the control variable method for building orientation and indoor headroom, and metrics such as energy consumption, uncomfortable hours and construction costs were calculated to screen out effective metrics. Furthermore, by combining matrix analysis with orthogonal tests, the approach enables the development of optimal design solutions more efficiently and with reduced complexity. The results show that the optimised design, generated using the proposed method, significantly improves the indoor thermal environment—reducing energy consumption by 65.26% and uncomfortable hours by 29.22%, with only a 1.3% increase in construction costs. This study contributes to sustainable rural development by proposing a practical framework that guides the design of low-cost and energy-efficient rural housing.