Adaptive building envelope systems can manage energy and mass transformation between indoor and outdoor environments, which contributes to the achievement of environmental benefits via reducing energy consumption and greenhouse gas emission while maintaining human comfort and well-being. However, the market penetration of adaptive façades (AFs) is far from sufficient, even though their capabilities have been recognized in research. Hence, this paper explores the factors hindering the growth of the market share of AFs, based on an exhaustive examination of designs, evaluation criteria and tools, and control systems. Insufficient commercial technology, inaccurate and incomplete performance data, and inconsistent evaluation criteria are demonstrated to be the factors that have hindered the widespread utilization of AFs thus far. Future research tendencies, including reducing costs, retrofitting existing building façades, developing building performance measurement tools, and building consensus evaluation criteria that favor the wide applicability of such façades in actual practice are identified.

The surging global population and urbanization trends present new challenges to food production systems and energy, especially in resource-limited urban environments. Vertical farming on façades (VFOF) is an innovative strategy to address this challenge by growing crops on building skins, efficiently using urban space, increasing food self-sufficiency, and reducing the environmental impact of carbon emissions. This article is a comprehensive review of VFOF and closely related topics based on 166 journal articles. It covers the latest research advances in design, technology, social impact, and environmental benefits. In addition to enhancing the autonomy of urban food supply and improving residents’ quality of life, VFOF also has the potential to optimize the thermal performance of buildings and promote energy conservation by having some of the qualities of vertical greening systems (VGS). The planting system design and technical support factors for different façade locations are explained in detail. The symbiotic relationship between VFOF and architecture is examined to enhance sustainability. The popularity of VFOF is increasing in terms of social acceptance, and the government, together with the private sector and communities, play a vital role in promoting its development. In addition, this review also collates the cases of VFOF implementation in recent years. Research shows that the implementation of VFOF has many advantages, especially when considering future urban challenges under climate change scenarios and the need to provide solutions to achieve carbon neutral buildings and cities. Still, high initial investment, operating costs, technical complexity, security issue, policy and regulatory constraints, and public acceptance are all challenges to overcome. Further research should be carried out in the above fields.

Population growth and urban expansion have led to increased demand for buildings. Optimizing the building façade design, using integrated photovoltaic (PV) shading and vertical farming (VF) can reduce building energy consumption while ensuring a partial food supply. However, the importance and prevalence of productive façades have not received significant attention. Furthermore, few studies have focused on the impact of productive façades on both indoor and outdoor environmental qualities. Therefore, this study aimed to explore the potential of integrating productive façades with residential façades in high-density cities. A typical community in Guangzhou, China was investigated. Thermal comfort, light comfort, electricity production, and crop yield were considered, and the optimal façade configuration was chosen from the established 146-model library. The integrated module can effectively improve the indoor lighting and thermal comfort of residential buildings. The module also mitigates the outdoor thermal environment to a certain extent, meeting 6.3–10.3% and 7.6–9.6% of the annual electricity and vegetable demands, respectively, in residential communities. This study can guide other densely populated cities with subtropical climates to advance the research and construction of productive façades, improving occupant comfort, reducing energy consumption, and mitigating food security and urban climate change issues.

Productive facades, consisting of photovoltaic shading and vertical farming systems, have been proposed as a means to improve the thermal and visual status of residential buildings while also maintaining energy performance and providing vegetables. However, how to quickly and accurately predict electricity and vegetable output during the numerous influencing architectural and environmental factors is one of the key issues in the early stages of design, and few studies have investigated the impact of such structures on both indoor environmental qualities and production performance. In this paper, we present a novel prediction method that uses experimental data to train and test an artificial neural network (ANN). The results indicated that using the Bipolar Sigmoid activation function to process the experimental data input to the artificial neuron network gives more accurate predicted results both in the yield of photovoltaic shading and vertical farming systems. In addition, this prediction method was applied to a typical high-rise residential building in Singapore to assess the self-sufficiency potential of high-rise residential buildings integrated with productive facades. The results indicated that the upper part of the building can meet 20.0–23.1% of the annual household electricity demand of a family of four in a four-room residential unit in Singapore and almost the entire year’s vegetable demand, while the middle part can meet 18.4–21.2% and 89.1%, respectively. The results demonstrated the importance of a productive facade in reducing energy demand, enhancing food security, and improving indoor visual and thermal comfort.

This study explores the integration of photovoltaic (PV) shading devices and vertical farming (VF) in school buildings to optimize indoor daylight, thermal comfort, and energy performance across three different climate regions in China: Beijing, Shanghai, and Shenzhen. With rapid urbanization and increasing energy consumption in educational buildings, this research investigates the impact of innovative facade design on both energy efficiency and occupant comfort. Through parametric simulations and multi-objective optimization, various PV and VF facade prototypes were evaluated to determine the best configurations for reducing energy consumption while enhancing thermal and visual comfort. This study optimized facade systems integrating photovoltaic and vertical farming for school buildings in Shenzhen, Beijing, and Shanghai. Key findings include: In Shenzhen, Model B’s UDI increased by 5.1% and Model C by 19.02%, with glare areas reduced by 5.4% and 21.40% and stable thermal comfort (PMV 0.52–0.59) throughout the year. In Beijing, Model B’s UDI decreased by 0.2%, while Model C increased by 6.55%. Glare areas reduced by 2.92% and 14.35%, with improved winter comfort (PMV −0.35 to −0.1). In Shanghai, Model C’s UDI increased by 6.7%, but summer thermal discomfort was notable (PMV up to 1.2). The study finds that PV shading systems combined with vertical farming can provide significant energy savings, reduce greenhouse gas emissions, and offer organic vegetable production within school environments. The findings suggest that integrating these systems into the building envelope can optimize the energy performance of school buildings while improving the comfort and well-being of students and staff.

Integrating artificial intelligence (AI) into residential architectural design faces challenges due to fragmented workflows and the lack of localized datasets. This study proposes the CorbuAI framework, hypothesizing that a multimodal AI system integrating Pix2pix-GAN and Stable Diffusion (SD) can streamline the transition from floor plan generation to elevation and interior design within a specific regional context. We developed a custom dataset featuring 2335 manually refined Chinese residential floor plans and 1570 elevation images. The methodology employs a specialized U-Net V2.0 generator for functional layout synthesis and an SD-based model for stylistic transfer and elevation rendering. Evaluation was conducted through both subjective professional scoring and objective metrics, including the Perceptual Hash Algorithm (pHash). Results demonstrate that CorbuAI achieves high accuracy in spatial allocation (scoring 0.88/1.0) and high structural consistency in elevation generation (mean pHash similarity of 0.82). The framework significantly reduces design iteration time while maintaining professional aesthetic standards. This research provides a scalable AI-driven methodology for automated residential design, bridging the gap between schematic layouts and visual representation in the Chinese architectural context.