Facing the severe challenge of global warming, the construction of photovoltaic (PV) power stations has been increasing annually both in China and worldwide, with mountainous areas gradually becoming preferred sites for such projects. Mountain landscapes are ecologically sensitive, and the large-scale installation of PV panels may lead to destruction of the mountain landscape ecological environment. In this study, soil physicochemical properties were measured in 160 soil test plots, and vegetation community conditions were assessed in 26 vegetation test plots at a mountain PV power station in Damiao Town, Chaoyang County, Liaoning Province, China, using a combination of field sampling and laboratory testing. Based on mean values of 15 soil and vegetation indicators under different PV panel coverage rates, calculated via ANOVA in SPSS 27.0 software with Bonferroni-corrected p-values, the effects of various coverage rates on the mountain landscape ecological environment were investigated through multiple comparisons of the mean values. Using the Euclidean distance principle, the similarity ranking between the ecological environment under different PV coverage intervals and the control point was determined as follows: 0% > 0–5% > 15–20% > 5–10% > 10–15% > over 20%. Ultimately, considering the power generation requirements of the PV power station, the 15–20% PV panel coverage rate was identified as the optimal range that minimizes impact on the mountain landscape ecological environment while meeting electricity production demands. Therefore, construction stakeholders should fully consider the influence of PV panel coverage rate on the mountain landscape ecological environment and control the coverage within the 15–20% range according to the power generation needs of mountain PV power stations, so as to mitigate the environmental impact of PV panel installation.

Amidst the global energy structure transition and intensification of climate warming, the temperature control targets of the Paris Agreement and China’s “dual carbon” goals have driven the rapid development of building-integrated photovoltaics (BIPVs). However, solar cells in BIPV systems may produce exhaust gases that affect local urban air quality if exposed to extreme environmental conditions such as high temperatures during operation. In this study, eight air quality monitoring points were established around the BIPV system at Shenyang Jianzhu University as the experimental group, along with one additional air quality monitoring point serving as a control group. The concentrations of four air pollutant indicators (PM_2.5, PM₁₀, SO₂, and NO₂) were monitored continuously for 14 days. The weight of each indicator was calculated using the principle of information entropy, and the air quality evaluation grades were determined by combining the homomorphic inverse correlation function. The Entropy-Weighted Set Pair Analysis model was applied to evaluate the air quality of the BIPV system at Shenyang Jianzhu University. The results indicated that due to the high concentrations of SO₂ and NO₂, the Air Quality Index (AQI) grade at Shenyang Jianzhu University was classified as “light pollution.” Corresponding recommendations were proposed to promote the sustainable development of urban BIPV. Simultaneously, the evaluation results of the Entropy-Weighted Set Pair Analysis model were similar to those obtained using other methods, demonstrating the feasibility of this evaluation model for assessing the impact on air quality.