Human disturbances impact river basins by reducing the quality of, and services provided by, aquatic ecosystems. Conducting quantitative assessments of ecological security at the watershed scale is important for enhancing the water quality of river basins and promoting environmental management. In this study, China’s Songhua River Basin was divided into 204 assessment units by combining watershed and administrative boundaries. Ten human threat factors were identified based on their significant influence on the river ecosystem. A modified ecological threat index was used to synthetically evaluate the ecological security, where frequency was weighted by flow length from the grids to the main rivers, while severity was weighted by the potential hazard of the factors on variables of river ecosystem integrity. The results showed that individual factors related to urbanization, agricultural development and facility construction presented different spatial distribution characteristics. At the center of the plain area, the provincial capital cities posed the highest level of threat, as did the municipal districts of prefecture-level cities. The spatial relationships between hot spot locations of the ecological threat index and water quality, as well as the distribution areas of critically endangered species, were analyzed. The sensitivity analysis illustrated that alteration of agricultural development largely changed the ecological security level of the basin. By offering a reference for assessing ecological security, this study can enhance water environmental planning and management.

The hotel industry in China has experienced rapid growth in the past ten years and made a considerable contribution to the global tourism economy. This paper focuses on the energy performance of hotel buildings in Lijiang, China. Hotel characteristics, daily operational data, and energy use data were collected by carrying out a survey of 24 hotels. The average annual energy use intensity (EUI) of four-, three-, two-, and one-star rated hotels was 180.8 kWh/m², 113.3 kWh/m², 74.2 kWh/m², and 70.2 kWh/m², respectively. Electricity, as the dominant energy source, accounted for 81% of total energy consumption and was used in the operation of air conditioning, lighting, heating, etc. Pearson correlations between EUI showed that hotel star rating, number of guest rooms, room revenue, and number of workers gave a reasonably strong correlation. A regression-based benchmarking model was established to predict EUI, and a standardization process of EUI was illustrated by statistical analysis.