The advent and application of biophilic architecture bring numerous environmental, economic, and energy-efficiency benefits, playing a crucial role in advancing low-carbon, energy-saving, healthy, comfortable, and sustainable development within the construction industry. Thanks to its many advantages—such as aesthetic enhancement, improved microclimates, and negative carbon potential—biophilic architecture has been widely adopted in building design, particularly as a response to the escalating environmental crisis. Integrating plants with various architectural forms can optimize building performance, especially by reducing operational energy consumption. This study uses knowledge mapping tools like CiteSpace 6.1.R3 and VOSviewer 1.6.19 to analyze 2309 research papers from the Web of Science (WoS) published over the past decade on the topic of “energy efficiency in biophilic architecture”. It conducts visual analyses of publication trends, collaborative networks, and key themes. The research categorizes plant–architecture integration methods, focusing on three primary areas: green roofs, vertical green systems, and green photovoltaic systems. Additionally, it reviews the ways in which biophilic architecture contributes to energy savings, the research methodologies employed, energy-saving rates, and the factors influencing these outcomes. Finally, a SWOT framework is constructed to assess the strengths, weaknesses, opportunities, and potential threats of biophilic architecture, as well as its future development prospects. The findings indicate that integrating plants with building roofs is an effective energy-saving strategy, achieving energy savings of up to 70%. Furthermore, combining biophilic elements with photovoltaic systems can enhance the efficiency of solar energy generation. This study offers valuable insights for architects and researchers in designing more energy-efficient and sustainable buildings.

With rapid urbanization, rooftop photovoltaic (PV) systems play an important role in mitigating the energy crisis and reducing emissions, yet achieving scientific and cost-effective deployment at the urban block scale remains challenging. This study proposes a transferable framework that integrates spatial morphology quantification, clustering, and numerical simulation to evaluate PV potential in residential blocks of Jinan, China. Six key morphological indicators were extracted through principal component analysis (PCA), and blocks were classified into five typical types, followed by simulations under different PV material scenarios. The main findings are: (1) Block type differences: Cluster 1 achieved the highest annual generation, 61.76% above average, but with a 75.08% cost increase and a 3.54-year payback. Clusters 4 and 5 showed moderate generation and the shortest payback of 2.91–2.97 years, reflecting better energy–economic balance. (2) PV materials: monocrystalline silicon (m-Si) yielded the highest generation, suitable for maximizing output; polycrystalline silicon (p-Si) produced slightly less but reduced costs by 32.43% and shortened payback by 19.58%, favoring cost-sensitive scenarios. (3) Seasonal variation: PV output peaked in February–March and September–December, requiring priority in grid operation and maintenance. The proposed framework can serve as a useful reference for planners in developing PV deployment strategies, with good transferability and potential for wider application, thereby contributing to urban energy transition and low-carbon sustainable development.

Urbanization in China and the proliferation of high-rise office buildings have led to increased demand for daylighting and thermal comfort. These requirements often result in reliance on active systems, including heating, cooling, and artificial lighting, which increase energy consumption. Existing studies have often focused on individual cases or room-scale models, which makes it difficult to generalize findings to the design of various high-rise office building types. Therefore, in this study, parametric prototype building models for high-rise office buildings were developed based on surveys of completed and under-construction projects. These surveys reflected actual design practices and were used to support systematic performance evaluation and typology-level optimization. Building performance was simulated using Grasshopper and Honeybee to generate large-scale datasets, and stacking ensemble learning models were used as surrogate predictors for energy use, daylighting, and thermal comfort. Multi-objective optimization was conducted using the non-dominated sorting genetic algorithm III (NSGA-III), followed by strategy formulation. The results revealed the following: (1) the proposed prototype model establishes clear parameter ranges for geometry, envelope design, and thermal performance, offering reusable models and data; (2) the stacking ensemble model outperforms individual models, improving the coefficient of determination (R²) by 0.5–16.1%, with mean squared error (MSE) reductions of 4.4–70.6%, and mean absolute error (MAE) reductions of 2.8–45.8%; (3) space length, aspect ratio, usable area ratio, window U-value, and solar heat gain coefficient (SHGC) were identified as primary performance drivers; and (4) optimized solutions reduced energy use by 3.79–11.81% and enhanced daylighting comfort by 40.16–50.32% while maintaining thermal comfort. The proposed framework provides localized, data-driven guidance for early-stage performance optimization in high-rise office building design.