The accelerated aging of China’s population has made the optimization of elderly care facility locations a critical priority. The field of big data presents innovative approaches for determining the optimal site selection for such facilities. This study uses Xi’an City, in Shaanxi Province, China, as a case study to explore how big data and the ID3 decision tree model can enhance the optimization of elderly care service facility locations. The study begins with a comprehensive analysis of Xi’an’s aging demographics, focusing on the current aging trends and the distribution characteristics of existing elderly care facilities. Utilizing the Baidu Map API, the study collected Point of Interest (POI) data for Xi’an, which were spatially analyzed using ArcGIS 10.8 software to identify the distribution patterns of elderly care facilities and their relationships with other public amenities. The ID3 algorithm was then employed to construct a decision tree model to simulate and predict optimal sites for elderly care facilities in Xi’an. By classifying and filtering POI data and dividing Xi’an into 500 m × 500 m grid units, the model was trained and validated, achieving an accuracy of 85.8%. The findings suggest that suitable sites for elderly care facilities in Xi’an should prioritize proximity to government offices and medical institutions, which would better address the healthcare needs of the elderly population. The application of the ID3 algorithm in planning the locations of elderly care facilities helps mitigate human biases and provides valuable insights for the planning of other public amenities.

Global warming caused by carbon emissions is an environmental issue of great concern to all sectors. Dynamic monitoring of the spatiotemporal evolution of urban carbon emissions is an important link to achieve the regional “double carbon” goal. Using 14 cities (prefectures) in Hunan Province as an example, based on the data of carbon emissions generated by land use and human production and life, and on the basis of estimating the carbon emissions in Hunan Province from 2000 to 2020 using the carbon emission coefficient method, this paper uses the Exploratory Spatial–Temporal Data Analysis (ESTDA) framework to analyze the dynamic characteristics of the spatiotemporal pattern of carbon emissions in Hunan Province from 2000 to 2020 through the Local Indicators of Spatial Association (LISA) time path, spatiotemporal transition, and the standard deviation ellipse model. The driving mechanism and spatiotemporal heterogeneity of urban carbon emissions were studied by using the geographically and temporally weighted regression model (GTWR). The results showed that: (1) In the last 20 years, the urban carbon emissions of Hunan Province have had a significant positive spatial correlation, and the spatial convergence shows a trend of first increasing and then decreasing. Therefore, priority should be given to this relevance when formulating carbon emission reduction policies in the future. (2) The center of carbon emission has been distributed between 112°15′57″~112°25′43″ E and 27°43′13″~27°49′21″ N, and the center of gravity has shifted to the southwest. The spatial distribution has changed from the “northwest–southeast” pattern to the “north–south” pattern. Cities in western and southern Hunan are the key areas of carbon emission reduction in the future. (3) Based on LISA analysis results, urban carbon emissions of Hunan from 2000 to 2020 have a strong path dependence in spatial distribution, the local spatial structure has strong stability and integration, and the carbon emissions of each city are affected by the neighborhood space. It is necessary to give full play to the synergistic emission reduction effect among regions and avoid the closure of inter-city emission reduction policies. (4) Economic development level and ecological environment have negative impacts on carbon emissions, and the population, industrial structure, technological progress, per capita energy consumption, and land use have a positive impact on carbon emissions. The regression coefficients are heterogeneous in time and space. The actual situation of each region should be fully considered to formulate differentiated emission reduction policies. The research results can provide reference for the green and low-carbon sustainable development of Hunan Province and the formulation of differentiated emission reduction policies, and provide reference for other similar cities in central China.

Amidst escalating global energy demands, the advancement and utilization of renewable energy sources have emerged as critical strategies for addressing environmental concerns and alleviating energy crises. Among them, wind power, as a renewable and clean energy source, has been widely applied and developed in China. However, the construction of wind farms may have some impact on vegetation cover and soil properties. This study aims to assess the impact of wind farm construction on vegetation cover and soil characteristics, thereby offering a scientific foundation for the sustainable management of wind farm development sites. The present study was carried out in the area of Jingzhushan wind farm in Linxiang City, Hunan Province, to examine the trends of the normalized difference vegetation index (NDVI), the fractional vegetation cover (FVC), and the indexes expressing the physicochemical properties of the soil in this area. The results showed the following: (1) The NDVI of the wind farm for the three periods was 0.742 in 2013, 0.770 in 2016, and 0.758 in 2023, respectively. According to the analysis of the index of FVC, it can be seen that the trend of the FVC of the study area for the three periods was basically the same as that of the NDVI. The average value of FVC was 0.754 in 2013, 0.791 in 2016, and 0.769 in 2023. This indicated that the vegetation cover in the early stage of wind farm construction (2013) was lower than that in the late stage of operation (2016, 2023), and it also suggested that the vegetation cover gradually recovered over time. (2) Compared with natural ecosystems, both altitude and wind farm construction significantly affected the organic carbon, the total nitrogen, the effective phosphorus, and the rapidly available potassium in the soil. At the same altitude, these four soil indicators in the area where the wind turbines were constructed had significantly lower levels compared with the control (CK), which indicated a decrease in soil fertility—the closer to the turbine construction area, the lower the levels of each indicator. In addition, soil pH did not change significantly during the construction of the wind farm. The analysis and comparison of various data showed that the construction and operation of wind farms can have an impact on local vegetation cover, and it had a significant negative impact on soil properties. Reasonable measures are needed to protect vegetation and soil to achieve the sustainable development of the ecological environment.

The differences in CO₂ equivalent emissions and carbon compensation due to land use changes can provide a basis for formulating low-carbon development policies in various regions according to net CO₂ emissions. Based on the land use and energy consumption data of Hunan Province from 2000 to 2020, the calculation model is constructed to calculate the CO₂ equivalent emissions and carbon compensation values for different cities in different periods. The results showed that: (1) From 2000 to 2020, there was a significant growth trend in the area of built-up land in Hunan Province, mainly from the forest and cropland, while the area of forest is relatively stable. (2) The net CO₂ equivalent emissions from land use changes in Hunan Province shows a trend of increasing first and then decreasing with an initially fast and then slowed growth rate. Built-up land is the main carbon source, and its CO₂ equivalent emissions increased by 26.78 million tons, while the forest is the main carbon sink, and its carbon absorption decreased by 4.11 × 10⁴ tons. (3) The carbon sink areas are mainly located in Zhangjiajie and Xiangxi in western Hunan, and the CO₂ equivalent emission intensity of other carbon source areas is gradually increasing from eastern Hunan to southern Hunan. (4) The carbon compensation value is very similar to the spatial distribution of CO₂ equivalent emissions in different cities. The high carbon compensation areas are mainly Yueyang, Loudi, and Xiangtan due to their backward energy structure, un-upgraded industrial structure, and large net CO₂ equivalent emissions, while the high carbon compensation areas are mainly Changsha, due to its high economic development level, great technological progress, and small net CO₂ equivalent emissions. To achieve regional coordination and low-carbon development, it is necessary to continually improve the carbon compensation mechanism and to build on carbon compensation to promote regional low-carbon coordinated development from a low-carbon level. Meanwhile, the government should rank and direct the transformation and development of different types of cities, to build a low-carbon land development model and achieve the goal of developing carbon neutrality.

Refractory organic pollutants pose a great threat to public health in water bodies due to their toxicity and low biodegradability. Developing a method of activating persulfate efficiently and in an environmentally friendly way has become a popular topic of research in current advanced oxidation water treatment technologies. Fe(III)-g-C₃N₄ was prepared by the calcination method. Fe(III) was anchored on the framework of g-C₃N₄. The characterization analysis indicated that Fe(III) was successfully loaded on g-C₃N₄. The best effect for MB degradation was Fe(III)-g-C₃N₄ (0.1 g/L) dosed with 30 µmol/L KMnO₄ for synergistic catalyzed PMS (0.1 g/L), where the degradation rate could reach 95.4%. The optimum temperature for MB degradation was determined to be 10 °C. The optimum pH range of Fe(III)-g-C₃N₄/Mn(VII) synergistic catalyzed PMS for MB degradation was pH 4.4–6.6 under acidic conditions, and the optimum pH range for MB degradation was pH 8–10 under alkaline conditions. The Fe(III)-g-C₃N₄/Mn(VII) synergistic catalyzed PMS system was also tested for the degradation of methyl orange and rhodamine b, and good degradation results were obtained with the degradation rates of 87.37% and 84%, respectively. It facilitates the reduction in pollutant emissions, improves water quality and will have a positive impact on the sustainability of the environment.

Cadmium (Cd) pollution in soil is a serious threat to food security and human health, while, currently, the most widely used detection methods cannot accurately reflect the content of heavy metals in soil. Soil heavy metal detection combined with microelectronic sensors has become an important means of environmental heavy metal pollution prevention and control. X-ray Fluorescence spectrometry (XRF) can capture the excitation spectrum of metal elements, which is often used to detect Cd (II). However, due to the lack of high-performance optoelectronic devices, the analysis accuracy of the system cannot meet the requirements. Therefore, this study proposes a high-detection-efficiency photodiode (HDEPD) which can effectively improve the detection accuracy of the analyzer. The HDEPD is manufactured based on a 0.18 μm standard complementary metal-oxide-semiconductor (CMOS) process. The volt-ampere curve, spectral response and noise characteristics of the device are obtained by constructing a test circuit combined with a spectral detection system. The test results show that the threshold voltage of HDEPD is 12.15 V. When the excess bias voltage increases from 1 V to 3 V, the spectral response peak of the device appears at 500 nm, and the photon detection probability (PDP) increases from 41.7% to 52.8%. The dark count rate (DCR) is 31.9 Hz/μm² at a 3 V excess bias voltage. Since the excitation spectrum peak of Cd (II) is between 500 nm and 600 nm, the wavelength response range of HDEPD fully meets the detection requirements of Cd (II).