Xingmeng Ma

The Huizhou region is an important area of traditional Chinese culture, and currently, the state of the village’s surroundings in this area is still not perfect. In this study, seven districts (counties) in the Huizhou region were selected for research. The Rural Habitat Environment (RHES) Indicator Program is based on the concept of Socio-Economic-Natural Complex Ecosystems (SENCE) and constructs 18 metrics in three dimensions. Trends and influencing factors were analyzed using entropy weight TOPSIS and a Grey Relational Analysis (GRA) for the years 2013–2022, and spatial and temporal evolution was measured using Geographic Information Systems (GISs). The findings show that the composite index for the Huizhou region grew from 2013 (0.3197) to 2022 (0.6806). Second, the Tunxi District belongs to the high index–high economy category. The Shexian, Xiuning, and Qimen counties belong to the high index–low economy category. Huizhou District and Huangshan District belong to the low index–high economy category. Yixian County belongs to the low index–low economy category. Third, all districts (counties) show an upward trend, and Huangshan District has the best RHES condition. Shexian County ranks relatively low in the comprehensive index.

The difficulties of offline education caused by the COVID-19 epidemic are attracting increasing public attention. Although international research on online education platforms is gradually deepening, the research on the influencing factors of Chinese users’ participation in these platforms shows different results due to regional differences. Therefore, this study adopts the technology acceptance model (TAM) as the basis to build the index system of influencing factors for the Tencent conference online education platform. The questionnaire design covered five dimensions, including external environment, platform satisfaction, and continuous use intention. An online questionnaire survey was conducted on participants in some regions of China, 320 questionnaires were distributed, and 297 valid questionnaires were collected. The data were analyzed by SPSS 26.0, and the results of Cronbach’s coefficient of 0.939 and a KMO value of 0.941 were obtained. The weight value, entropy value, and importance ranking of the factors were determined by combining hierarchical analysis and the entropy weighting method. First, the perceived ease of use is the most critical factor influencing the users’ use of online delivery platforms. Second, freedom from geographical time difference, the ease of operation, system perfection, and proficiency in use greatly influenced the participants’ use of the online lecture platform. Third, the study group suggested that the platform should be optimized in terms of convenience, stability, and freedom from geographical time difference, and provide reasonable references and lessons for future educational efforts.

Habitat is the basic condition for human beings to survive and live. Taking Huizhou Cultural and Ecological Reserve as the research direction, the evaluation system is constructed by selecting three indicators of social layer, economic layer, and natural ecological layer. The research team used the entropy weights-TOPSIS assessment methodology to analyze the Habitat Environment Indices (HEIs) in different regions and at different times of the year. Gray correlation methodology was used to identify key drivers of Habitat quality. Findings: First, rural human settlements show a trend of recovery after a decline in 2013. Second, Jixi County (0.550) was ranked first in the overall evaluation. It is also consistently in first place in year-to-year changes. Third, the spatial pattern shows that the southeastern region is of higher quality, while the northern region is relatively backward. Fourth, the social (S) and natural ecological (NCE) layers are the dominant influences on the quality of village settlements. The secondary factors are mainly the proportion of good days in a year (0.977), the number of rural households (0.974) and the area of green space in gardens (0.974). The study achieves the purpose of enhancing the adaptability of the rural environment in the Huizhou Cultural and Ecological Reserve and provides a reference for enhancing the adaptability of related protected areas.

The Yangtze River Basin (YRB) is an essential location for the development of eco-civilization in China, and the state of water environment in the watershed is still unsatisfactory at this stage. Therefore, 11 provinces (cities) of YRB were selected for the present study. The Water Resources Carrying Capacity (WRCC) indicator scheme is founded on the conceptual structure of Social-Economic-Natural Complex Ecosystem (SENCE), and 21 indicators in 3 aspects are con-structed. The entropy-weighted- TOPSIS method and grey relational analysis (GRA) were used to measure the WRCC of YRB from 2012 to 2021 and determine the driving factors. The results show that, first, there are large differences in WRCC levels in different regions, with an overall up-stream>midstream>downstream. The top three provinces (cities) in order are Szechwan (0.4721), Qinghai (0.4682) and Tibet (0.4655). Second, the trend of WRCC change is divided into three phases, fluctuating decline (2012-2015), recovery (2016-2018), and growth (2019-2021). Third, the subsystems of WRCC show stability, equilibrium, and variance, respectively. (4) The main drivers of WRCC are total fish production (0.8780), per capita daily domestic water consumption (0.8640), and total agricultural water consumption (0.8630).

China’s forest ecological problems are becoming increasingly serious, especially in the Yangtze River Basin (YRB) area. The basin has rich species resources and a well-developed natural forest management and conservation policy. Taking the YRB as the object, we combine the DPSIRM model to build a forest evaluation system containing 6 criterion layers and 24 indicator layers. The entropy weight method-TOPSIS and ArcGIS were combined to assess the forest state and the distribution characteristics of the 11 regions. Furthermore, grey relational analysis (GRA) was used to study the influencing factors of forest status. The results are as follows: (1) the comprehensive index of the YRB forests increased by 192.66% during the study period. The forest status showed the stage characteristics of small climb, basic flatness, and significant improvement. (2) The forest status varied significantly among provinces (cities), with Tibet (0.483) in the best condition and Qinghai (0.103) in a worse condition. (3) Except for Tibet, the rest of the regions are more influenced by the extent of development of the economy. (4) The factor most strongly correlated with the YRB is the forest response (R) indicator.

Forests are associated with countrywide ecological security, and there are significant differences in the forests of different regions. Based on the DPSIR model, 25 indicators were selected from five dimensions to determine the index system, and the entropy-weighted TOPSIS method and gray correlation were applied to determine the index of western China’s forests. The spatial distribution map was used to observe the spatial changes of forests. The results show that first, Inner Mongolia (0.466) has the best forest ecological security status and Ningxia (0.124) has the worst forest resource status. Second, the first and most frequent correlation is the area of planted forests (I1). The last and most frequent correlation is sulfur dioxide emission (P2). Thirdly, Inner Mongolia and Szechwan belong to the high ecological safety–high economic level, Yunnan, Guangxi, and Tibet belong to the high ecological safety–low economic level, and Gansu and Guizhou belong to the low ecological safety–low economic level. The rest of the regions are classified in the low ecological security–high economic level. Fourth, the forest ecological security in western China has gradually become better, with the security index increasing from 0.417 to 0.469, with an average annual increase of 12.47%.