The identification of the moderate scale of agricultural land was recognized as one of the key measures promoting sustainable agriculture development. However, due to the research gap in mountainous areas, new agricultural business entities (NABE) in these areas usually either refer to the plain area or simply pursue large scale, resulting in low production efficiency and even posing a threat to their sustainable survival. In this study, the Data Envelopment Analysis (DEA) model and Tobit regression model tools were employed to quantitatively reveal the moderate scale and key driving factors of agricultural land under the scale operation modes of greenhouse and open-field types. It was based on 154 NABE questionnaires in the mountainous areas around the Sichuan Basin in China, where NABEs are flourishing. The findings show an approximately "inverted U-shaped" curve relationship between NABE's production efficiency and their planting scale. The primary reason for the failure of NABE to achieve an overall high level of production efficiency is scale inefficiency. The optimal scale intervals for the greenhouse and open-field types of scale operation modes are 3.0–4.3 ha and 3.3–5.0 ha, respectively. Business entities' age, land circulation scale, land rent, and agricultural insurance are common factors that influence the scale efficiencies of both the greenhouse and open-field types. Accordingly, policy interventions regarding the guidance of moderate-scale operation of agricultural land are proposed for achieving the dual goal of cultivating NABE and implementing the Rural Revitalization Strategy in mountainous areas of China. While contributing to the knowledge on scale efficiency of agricultural land, this research also enlightens the practice of policy-making targeted to the sustainable development of agricultural industry led by NABE worldwide.

Economic activities in disaster-prone areas are significantly susceptible to mountain disasters, and enhancing the resilience of new rural collective economies (RRCEs) is a pressing challenge that needs to be overcome in the areas of disaster risk management and sustainable development. The target research area comprises 48 representative villages in Liangshan Yi Autonomous Prefecture (LP). An assessment framework based on the Resilience Index Measurement Analysis (RIMA) model is established to evaluate the RRCEs in the face of mountain disasters, and the influencing factors regarding the RRCEs are examined. The results show that (1) typical villages in the new rural collective economies (NRCE) have a low level of resilience. (2) Transformational capacity is the key to improving RRCEs. (3) Off-farm villages exhibit the highest level of collective economic resilience, followed by diversified villages, while the lowest resilience level is observed in purely agricultural villages. (4) Talent security and institutional security are important for achieving a high level of resilience. Both of these factors significantly influence RRCEs. (5) The combined influence of talent, financical, institutional, technological, and business security contributes to the diverse factors that shape RRCEs. In other words, the path to achieving resilience in the new rural collective economies is characterized by multiple routes that lead to a common goal. Building on this, we propose recommendations in five key areas, namely, encouraging scientific research and innovation, improving disaster insurance coverage, strengthening the emergency protection system, facilitating collective economic development, and selecting suitable strategies to enhance resilience based on local conditions. The aim is to offer valuable insights for disaster-prone areas to enhance RRCEs and realize sustainable development and rural revitalization.

The Trombe wall (T-wall) system is one of the most effective systems for passive solar energy utilization technology, which is of great significance for the alleviation of the energy crisis and the protection of the environment. Taking as an example Tibetan dwellings in the Sichuan–Tibet alpine valley which have installed T-walls for heating, the effects of the length of the room (Factor A), the width of the room (Factor B), the width of the opening on the north wall of the room (Factor C), and the distance from the lower edge of the opening to the floor (Factor D) on the indoor air temperature and room energy consumption are studied by orthogonal experiment and numerical simulation. Results show that the four factors all have a significant effect on the two analysis indicators. The rankings of the factors are consistent for their impact on the two analysis indicators, as, in both cases, Factor A > Factor B > Factor C > Factor D. Therefore, the influence of room configuration cannot be ignored in the optimization of T-wall design. Additionally, the optimal parameter combination for the highest indoor temperature and low energy consumption in winter is also proposed. This research can further improve the study of T-walls, and provide a reference for the thermal environment design of buildings.