Under rural revitalization and low-carbon development, the sustainable transformation of vernacular architecture has become an important research focus. Taking the Linpan as the research object, this study proposes an integrated design methodology that combines typological translation with ecological material logic for contemporary architectural design. The methodology decodes the Linpan spatial prototype—characterized by the “house–forest–field–water” structure—by abstracting key spatial relationships and translating them into contemporary architectural formal strategies, while incorporating locally grounded ecological materials to coordinate environmental performance and cultural continuity. The proposed approach is validated through the Daoming Zhuli project in Chengdu, where typological translation generates courtyard-centered layouts, semi-open transitional spaces, and bamboo-based envelope systems adapted to a humid subtropical climate. A scenario-based material comparison indicates that the use of local materials can significantly reduce embodied carbon emissions while reinforcing regional identity. In addition, comparative analyses of other vernacular settlements, including Huizhou ancient villages, Fujian Tulou, and Ait Benhaddou, are conducted to examine the methodological transferability across different climatic, spatial, and cultural contexts. This study contributes a design-oriented framework linking spatial typology and material selection, providing guidance for the sustainable renewal of Linpan and references for the contemporary adaptation of vernacular architecture in international contexts.

With the evolution of social structure and the intensification of population aging, traditional community service centers struggle to meet residents’ complex needs due to their functional singularity and spatial rigidity. In response to the continuously evolving social structure and functional requirements, this research proposes a strategy based on the “Separation of Support and Infill,” distinguishing between the building’s permanent Support Structure and its replaceable Infill Components. These two parts are combined with modularization to achieve long-term spatial adaptability and sustainability throughout the entire life cycle. In terms of functional space, through the combination of vertical stratification, horizontal staggering and spatial permeability, a three-dimensional composite space system is constructed, which not only enhances the functional flexibility but also improves the environmental performance. Taking a design case in Yicheng District, Zhumadian City as an example, through a comparative analysis with the traditional building model, the comparative analysis demonstrates that this framework increases the Floor Area Ratio (FAR) by approximately 0.15 compared to traditional models. Furthermore, the modular characteristics significantly enhance demountability and reusability, reducing construction and demolition waste while lowering life-cycle costs by an estimated 15% to 25%. These studies show that the support structure and the composite functional space system can not only promote social interaction and community cohesion but also reduce the life-cycle cost and carbon emissions. The framework proposed in this paper constructs a theoretical and practical system for sustainable community buildings from the perspectives of functional compounding and low-carbon community development. Its innovation lies in its flexible spatial organization mode and the enhancement of the sustainability of community buildings.

Rapid urbanization and climate change pose severe challenges to the sustainable development of the built environment in developing regions

To address the challenges of ambiguous regional feature identification, high digital modeling costs, and limited regional adaptability of general-purpose AIGC tools in the micro-renewal of Western Sichuan Linpan, this study proposes a typology-constrained small-sample learning framework. A structured dataset of 80 high-quality, semantically annotated images was constructed to extract typological features, including spatial layouts, architectural forms, and environmental relationships. Based on Stable Diffusion, the framework integrates LoRA fine-tuning for style adaptation and ControlNet for structural control. To constrain the number of trainable parameters under the limited-data setting, LoRA rank control, dropout regularization, and early stopping were incorporated into the training procedure. Under the fixed 80-image dataset and experimental configuration, the complete framework achieved lower FID values and higher CLIP and expert-evaluation scores than the prompt-only baseline. Compared with Scheme A, the FID decreased by 22.7% (from 284.52 to 220.00), while the CLIP Score increased by 18.3% (from 0.224 to 0.265). Under the evaluated configuration, the complete framework showed closer correspondence with the specified Linpan architectural characteristics and spatial conditions than the prompt-only baseline. The results support the feasibility of translating architectural typological knowledge into semantic and geometric conditioning signals under the evaluated limited-data setting. However, the influence of training-set size on model stability and generation performance was not examined and requires further investigation.

Envelope-parameter optimization for residential buildings is strongly climate-dependent, yet previous studies have mainly focused on single parameters or specific regions, limiting cross-climate comparison of parameter priorities. This study establishes a unified DeST-based residential building model to compare the effects of external wall insulation thickness and external surface solar absorptivity on heating load, cooling load, annual total load, and operational carbon emissions across five representative climate zones in China. Yinchuan, Beijing, Chengdu, Guangzhou, and Kunming were selected as case cities. Single-factor analysis, two-parameter coupling analysis, supplementary sensitivity analysis, and load-to-carbon translation were conducted under consistent building prototypes, operating boundaries, and evaluation indices. The results show that the effects of the two parameters differ markedly across climate zones, reflecting a transition in parameter dominance under different heating–cooling load structures. Over the tested ranges, insulation thickness changes annual total load by up to approximately 10%, whereas solar absorptivity changes it by only 0.17–1.91%. Mechanistically, insulation reduces winter heat loss but may suppress summer heat dissipation, while solar absorptivity directly affects exterior-surface solar heat gains. Further carbon analysis shows that load-optimal configurations do not necessarily correspond to carbon-optimal configurations. These findings provide quantitative support for climate-adaptive and low-carbon envelope design.