Making full use of rooftop resources in rural areas is significant to promoting rural revitalization, achieving carbon neutrality. However, photovoltaic panels affect the heat changes of detached rural houses, thereby having inevitable impacts on indoor and surrounding environment. With the precipitation line has shifted northward, the attention to the problem of high indoor temperatures in summer has been increasing year by year. This study quantified the effects of installation height and area of rooftop photovoltaic panels on indoor air temperature, smoke exhaust efficiency and natural lighting under summer conditions. Based on response surface methodology, the independent and interactive influences of installation height and area were explored. It was found that at a lower installation height of 300 mm, the heating effect caused by photovoltaic panels was stronger than the cooling effect due to shading, and an increase in photovoltaic area caused indoor air temperature to increase by 3.36 ℃. When the installation height was 1000 mm, the heating effect was equivalent to the shading effect. Photovoltaic panels affected local airflow field, creating a jet-like high-velocity airflow layer around the detached house. The vertical height of the jet (D₁) and the vertical distance from the lower boundary layer to chimney (D₂) were proposed to evaluate the promoting effect on smoke exhaust. Overall, the installation of photovoltaic panels had weak influence on natural lighting. The entropy weight-TOPSIS method was used to quantify the optimal installation scheme of photovoltaic panels. According to the climate of Shenyang, the configuration scheme with an installation height of 1.0 m and area of 47.8 m² was recommended. This study provides a theoretical basis for rural photovoltaic installation and promotes the transformation from power-centered type to environmentally friendly design approach.

Urban visual environment plays a critical role in city planning and public health by influencing residents’ emotional responses. However, existing studies rely on subjective assessments of small-scale areas, lacking objective, large-scale emotional maps. This study introduces a novel electroencephalogram (EEG)-based framework to quantify emotional responses to urban visual environments. First, urban visual indicators are extracted from 352,112 street view images (SVI) in Guangzhou and 680,280 in Shenzhen using a deep learning-based semantic segmentation. Then, EEG experiments are conducted with 24 participants exposed to four groups of SVI visual stimuli, employing machine learning models to quantify relationships between visual indicators and emotions. Finally, a series of models are integrated to generate city-wide visual emotional maps across four emotion dimensions and valence-arousal space. Results show that the models established between the visual environment and emotional responses display R² values in the range of 0.39 to 0.69, enabling visual emotional mapping. Correlation analysis further shows a higher green view index and color entropy significantly correlate with positive emotions (p < 0.01). Conversely, elevated building density and openness are linked to negative emotions (p < 0.01). In terms of HAPV (high-arousal, positive-valence) dimensions, Shenzhen had higher emotion scores than Guangzhou, with mean values of 0.237 and 0.226, respectively. These differences correspond to colorful urban center landscapes in Shenzhen and predominantly green landscapes in Guangzhou as a result of their different urban planning strategy. This study contributes to the optimization and transformation of urban landscapes for improved visual comfort.