While urban green spaces (UGSs) are important places for residents’ leisure activities, studies describing the long-term daily UGS usage of residents (including the total number of activities, the types of activities, and the touring experience) have not been conducted due to difficulties in data collection. Based on social media text data (SMTD), in this study, the total number of daily activities, the intensities of optional and social activities, and the daily touring experience in 100 UGSs in Xiamen, China, were inferred based on the ERNIE 3.0 text pre-training semantic classification model. Based on this, linear regression modeling was applied to analyze the internal environmental factors of the effects of places and external urban form factors regarding daily UGS usage. The research results revealed the following. (1) A descriptive study was conducted on the total numbers, types, and touring experience of activities using SMTD, and the results were verified by line transect surveys, management statistics, and a publicly available dataset. (2) The number of human activities in UGSs was found to be significantly influenced by historical and cultural facilities, nighttime lighting, population density, and the proportion of the floating population. (3) During the daytime, optional activities were found to be significantly influenced by the park type and historical and cultural facilities, and social activities were found to be significantly influenced by historical and cultural facilities and population density. In the evening, optional activities were found to be significantly influenced by the park type, historical and cultural facilities, nighttime lighting, and the proportion of the floating population, and social activities were found to be influenced by the proportion of the floating population. (4) Regarding the touring experience, in the daytime, the park type, green space ratio, and proportion of the floating population had significant effects on the touring experience. In the evening, the park type, historical and cultural facilities, and security factors were found to have significant effects on the touring experience. The methodology and findings of this study aid in the understanding of the differences in daytime and nighttime activities, and in the discovery of planning tools to promote human leisure activities in UGSs.

The implementation of carbon peaking and carbon neutrality is an essential measure to reduce greenhouse gas emissions and actively respond to climate change. The net carbon sink efficiency (NCSE), as an effective tool to measure the carbon budget capacity, is important in guiding the carbon emission reduction among cities and the maintenance of sustainable economic development. In this paper, NCSE values are used as a measure of the carbon budget capacity to measure the spatiotemporal evolution of the carbon neutral capacity of three major urban agglomerations (UAs) in China during 2007–2019. The clustering characteristics of the NCSE of these three major UAs, and various influencing factors such as carbon emissions, are analyzed using a spatiotemporal cube model and spatial and temporal series clustering. The results reveal the following. (1) From the overall perspective, the carbon emissions of the three major UAs mostly exhibited a fluctuating increasing trend and a general deficit during the study period. Moreover, the carbon sequestration showed a slightly decreasing trend, but not much fluctuation in general. (2) From the perspective of UAs, the cities in the Beijing–Tianjin–Hebei UA are dominated by low–low clustering in space and time; this clustering pattern is mainly concentrated in Beijing, Xingtai, Handan, and Langfang. The NCSE values in the Yangtze River Delta UA centered on Shanghai, Nanjing, and the surrounding cities exhibited high–high clustering in 2019, while Changzhou, Ningbo, and the surrounding cities exhibited low–high clustering. The NCSE values of the remaining cities in the Pearl River Delta UA, namely Guangzhou, Shenzhen, and Zhuhai, exhibited multi-cluster patterns that were not spatially and temporally significant, and the spatiotemporal clusters were found to be scattered. (3) In terms of the influencing factors, the NCSE of the Beijing–Tianjin–Hebei UA was found to be significantly influenced by the industrial structure and GDP per capita, that of the Yangtze River Delta UA was found to be significantly influenced by the industrial structure, and that of the Pearl River Delta UA was found to be significantly influenced by the population density and technology level. These findings can provide a reference and suggestions for the governments of different UAs to formulate differentiated carbon-neutral policies.

In the context of global aging, people’s awareness of health is deepening, and the rapid economic development has drawn widespread attention to the health tourism industry. As a way of experiencing health, forest health tourism is becoming increasingly favored, and the site selection and construction of forest health bases (FHBs) have also developed accordingly. To ensure sustainability in the process of the site selection and construction of FHBs, the suitability of regional development and the relative coordination of the market, environment, and resource levels should be considered. Although there have been numerous studies on sustainable forestry management, a comprehensive sustainability assessment framework based on development suitability and coordination in three dimensions is needed to guide the site selection and the construction of FHBs. The following tasks were carried out in this study: (1) based on market sustainability goals, environmental optimization goals, and ecological resource sustainability goals, a comprehensive sustainability evaluation framework for development suitability indicators and coordination indicators in three dimensions was established; (2) via the use of this framework, the construction potential of FHBs in 41,636 towns in China was evaluated; the evaluation results show that the towns in Anhui, Jiangxi, Guangdong, Guangxi, Fujian, Zhejiang, Hunan, Hubei, Guizhou, and other provinces of China generally have superior conditions for the development of FHBs; (3) a multi-dimensional comprehensive analysis of FHB site selection sustainability based on development suitability and coordination was carried out for four batches of approved pilots. The comprehensive analysis results demonstrate the worsening evaluation results of the four batches. The proposed framework can provide a reference for FHB development policies for countries worldwide.

Differences in urban land values affect residents’ living experiences and may contribute to sentiment inequality. Due to the popularity of smart mobile devices and social media platforms, online tweets with location information can be used as objective information to reflect sentiment differences of urban residents in different locations, overcoming the limitations of previous studies with small sample sizes or a lack of spatial information. Sentiment quantification based on deep learning enables the identification of spatial patterns of urban residents’ sentiments. It also provides a new approach for analyzing data from big data platforms using an intelligent computing platform. This paper quantitatively analyzes the sentiment contained in social media tweets using a deep learning sentiment analysis algorithm to reveal inequalities between urban residents’ sentiments and land values. The Baidu Intelligent Cloud sentiment analysis platform is used to identify 460,000 Weibo tweets in Xiamen, China, in 2020. We quantitatively analyze the positive and negative sentiments of residents and create a spatial distribution map. The concentration curve indicates sentiment inequality and the impact of high land values on residents’ sentiments. The positive sentiment concentration index (CI) and correlation analysis show that the CI value is 0.07, and significant sentiment inequality exists due to the high land value. The use of social media tweet data to analyze sentiment inequality provides a reference for future interdisciplinary research in psychology, urban planning, geography, and sociology. The proposed approach of analyzing social media data using an intelligent computing platform provides new insights into multiplatform data interaction in the context of the Internet of Everything.