This study proposes the Sponge City+ parametric design toolkit, which integrates low-impact development (LID) measures into urban design to support compliance checking, runoff risk analysis, and optimization of design alternatives. Compliance is evaluated using the annual runoff volume capture ratio (AVCR) calculated via the Volume Method, which is the core criterion in sponge city standards. The toolkit combines a measures database, runoff volume control functions, and runoff simulation functions to evaluate and compare design alternatives. Its applicability was tested through case studies of three university campuses in China. These cases were used to: (1) conduct a sensitivity analysis of the toolkit’s response to different LID strategies, ranking three typical LID measures (sunken green spaces > permeable pavements > green roofs) in terms of their contribution to runoff control; (2) perform multi-objective optimization considering cost, runoff control, and peak reduction, which, under ordinary PC computational capacity, efficiently identified 27 qualified solutions out of more than 5000 samples, thereby providing a broader set of design choices while ensuring compliance with runoff control requirements; and (3) demonstrate a design optimization process based on runoff visualization, where human–computer interaction helped avoid potential flood risks during the early design stage. This study demonstrates the potential of a parametric workflow to bridge disciplinary boundaries and support the achievement of global sustainability goals.

In the subtropical regions of southern China, springtime is often characterized by persistently high humidity, leading to frequent condensation on building envelopes and interior surfaces. Top-floor rooms are particularly vulnerable due to their direct exposure to outdoor conditions through walls and the roof, making condensation prevention a critical concern. This study is grounded in the residential habits and spatial preferences of southern Chinese residents and evaluates three roof configurations—standard, thickened, and green roofs—using EnergyPlus (v22.1.0) simulation software to analyze their effects on indoor relative humidity levels in top-floor spaces. The results demonstrate that green roof systems significantly reduce indoor relative humidity, especially in high-rise residential buildings. Taking a 30-story residential building as an example, with a conventional roof, the indoor relative humidity remains at 100% for extended periods during high-risk condensation intervals, resulting in surface condensation. In contrast, when a green roof with a soil depth of 70 cm and daylilies at a height of 100 cm is used, the peak indoor maximum relative humidity is reduced by 10–40%, and the duration of condensation decreases to zero. Among the factors involved in green roofs, including plant height, soil depth, and leaf area index (LAI), soil depth shows a significant negative correlation with the maximum indoor relative humidity (correlation coefficient r = −0.987, p < 0.01), while the LAI exhibits a positive correlation with the maximum indoor relative humidity (r = 0.180, p < 0.05). Selecting plant species with a low LAI and increasing soil depth are effective passive strategies for humidity control and condensation prevention. These findings establish a basis for optimizing building environmental models and propose passive design strategies to enhance overall performance. In addition, the study highlights how roof greening contributes to global sustainability goals, particularly SDG 3 (Good Health and Well-being), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by improving indoor comfort, enhancing resilience, and reducing climate-related risks.

Northwestern Sichuan Province in China, characterized by its unique geography and rich ethnic culture, hosts numerous Tibetan–Qiang Traditional Villages (TTVNS) of significant conservation value. These villages exhibit pronounced macro-level spatial inequalities, making the study of their spatial differentiation and the quantification of driving mechanisms essential for implementing targeted conservation strategies. This study examines 412 TTVNS using Kernel Density Estimation (KDE), accessibility-based clustering, Optimal Parameter Geographical Detector (OPGD), Multiscale Geographically Weighted Regression (MGWR), and Random Forest with SHAP (RF–SHAP) to identify spatial patterns and quantify the mechanisms influencing them. The results indicate that: (1) TTVNS display a “multi-core clustering with contiguous distribution” pattern, forming two northeast–southwest aggregation belts, with significant positive spatial autocorrelation (Moran’s I = 0.175); (2) in single-factor detection, Cultivated Land (X3, q = 0.3019), Population (X19, q = 0.2510), and Road Density (X13, q = 0.2303) are the strongest explanatory factors, while the strongest interaction occurs between Temperature (X9) and Population (X19, q = 0.406), demonstrating that spatial differentiation is driven by the coupling of environmental suitability and social support rather than any single factor; (3) model comparison shows that MGWR substantially outperforms OLS and GWR, with R² increasing from 0.3049 and 0.3246 to 0.6568, confirming pronounced spatial non-stationarity in the driving mechanisms of TTVNS; (4) different categories of drivers exhibit distinct pathways: nature and geography influence aggregation through cultivated land, river systems, and temperature; location and transportation shape a “node–corridor” settlement pattern along valley corridors; humanities and religion affect clustering through the coupling of religious nodes and local social structures; society and economy reshape contemporary spatial patterns primarily via tourism development and population dynamics. These findings demonstrate that the spatial differentiation of TTVNS emerges under long-term natural constraints and religious-cultural networks, and continues to be reshaped by modern transportation and tourism development. The study provides a quantitative basis for designing differentiated and context-specific conservation strategies.

Sponge city theory utilizes policy-driven design to mitigate urban flooding and shape urban development. However, existing approaches primarily focus on micro-scale landscape facilities and performance assessments, overlooking the integration of macro-scale urban design strategies. This study develops a parametric design framework linking the quantitative control requirements of sponge city policies to urban morphological elements, establishing a "form-performance" correlation model. Key design indicators (green space, roof, water body, pavement, and their sub-indicators) were identified, and sensitivity analysis evaluated their responses to varying plot ratios and site scales. Results indicate that policy-guided stormwater runoff reduction strategies differ significantly by development density: high-density projects favor increased green roof ratios, while low-density projects are better suited for permeable pavements. Larger sites necessitate greater emphasis on peak runoff control and rational catchment zoning. The analysis empirically validated the framework and model, revealing that current policies risk over-incentivizing specific high-performance technologies, potentially compromising strategic diversity and resilience to extreme weather events. Consequently, optimizing policy evaluation metrics and refining design guidelines is recommended to achieve more balanced, efficient, and resilient stormwater management during preliminary urban design phases.