In the context of global climate governance, the study of land-use carbon emissions in the Yellow River Basin is crucial to China’s “dual-carbon” goal in addition to ecological conservation and the high-quality developments. This paper computed the land-use carbon emissions of 95 cities in the Yellow River Basin from 2000 to 2020 and examined its characteristics with respect to spatio-temporal evolution and driving mechanisms. The findings are as follows: (1) The overall net land-use carbon emissions in the Yellow River Basin rose sharply from 2000 to 2020. (2) From a spatial perspective, the Yellow River Basin’s land-use carbon emissions are high in the middle-east and low in the northwest, which is directly tied to the urban development model and function orientation. (3) A strong spatial link exists in the land-use carbon emissions in the Yellow River Basin. The degree of spatial agglomeration among the comparable cities first rose and then fell. “Low–Low” was largely constant and concentrated in the upper reaches, whereas “High–High” was concentrated in the middle and lower reaches with an east-ward migratory trend. (4) The rates of economic development and technological advancement have a major positive driving effect. Moreover, the other components’ driving effects fluctuate with time, and significant geographical variance exists. Thus, this study not only provides a rationale for reducing carbon emissions in the Yellow River Basin but also serves as a guide for other Chinese cities with comparable climates in improving their climate governance.

The vigorous development of tourism has made rural tourism a highlight of the new era. In order to better realize the classification of rural tourism features, this paper proposes a knowledge recognition algorithm based on hierarchical clustering analysis. Firstly, the rationality of the optimization of the rural tourism feature algorithm is analyzed in this paper; secondly, the rural tourism feature classification index system is constructed based on the hierarchical clustering analysis; finally, the index weights are clearly divided according to the characteristics through the hierarchical clustering analysis of the knowledge identification algorithm. According to the specialties of hierarchical clustering analysis, the criteria of the algorithm are determined, and the characteristics of rural tourism are carefully classified. Rural tourists can visit different scenic spots in high density and improve the mobility of rural tourism. The experimental results show that: through the analysis of the characteristic classification data of the Rural Tourism College in this paper, it can be seen that the average daily income of the scenic spot is higher than that of the traditional rural scenic spot. The average daily income of rural tourism has increased by more than 261,900 yuan, which has largely promoted the development trend of rural tourism in my country. It is proved that the hierarchical clustering analysis method is helpful for rational zoning and serious thinking about the characteristics of rural tourism. This paper provides a reference for promoting the classification of rural tourism.

Measuring industrial eco-efficiency (IEE) is essential to improve environmental quality and industrial restructuring. However, most studies ignore the influence of embeddedness on industrial eco-efficiency and lack analysis of the pathways of influence factors. Therefore, this study assesses industrial eco-efficiency in the Yellow River Basin (YRB) using a super-efficiency model slacks-based measure (Super-SBM) that considers non-desired outputs, outlines the social embeddedness of IEE, and empirically analyzes the driving mechanism of IEE from the perspective of embeddedness by constructing hierarchical linear modeling (HLM) to address the pathways of action of the influencing factors of industrial eco-efficiency. The results showed that 50.79% of the overall differences in IEE in the YRB were caused by social embeddedness. Economic development level, industrial agglomeration, and environmental regulation (ER) are significant direct influencing factors. Increasing cognitive and cultural embeddedness will enhance the positive relationship between economic development level and IEE. Political and relational embeddedness significantly moderates the positive relationship between industrial agglomeration and eco-efficiency. Cultural embeddedness can significantly and directly affect industrial eco-efficiency and weaken the positive relationship between ERs and industrial eco-efficiency. Therefore, improving IEE should consider both fundamental and embedded factors. Our findings are conducive to promoting high-quality development in the YRB and support the government in formulating differentiated policies. In addition, this paper tries to establish an empirical analysis method suitable for social embeddedness theory, and the empirical results help to improve the situation due to the lack of empirical analysis of social embeddedness theory.

With the promotion of high-quality agricultural development and ecological protection awareness, studying the spatiotemporal dynamics and influencing factors of green transformation in arable land use (GTALU) is crucial for breaking resource constraints and achieving sustainable agriculture. However, how to quantitatively analyze the spatiotemporal interactive evolution patterns and the comprehensive effect paths of main factors in GTALU remains challenging. Therefore, this research uses exploratory spatiotemporal data analysis (ESTDA) methods to investigate the spatiotemporal evolution pattern of GTALU in the Yellow River Basin (YRB) from 2005 to 2022. In addition, a ‘individual-interaction-comprehensive’ analytical framework was constructed by combining the optimal parameter geographical detector (OPGD) and geographically weighted regression-improved structural equation model (GWR-SEM) models to identify the main influencing factors of GTALU and to interpret its comprehensive spatial non-stationarity characteristics. The research results show that: (1) from 2005 to 2022, GTALU in the YRB exhibited an ‘N’-shaped characteristic. (2) GTALU in the YRB presented a feature of ‘dynamic fluctuations under stable aggregation’, with strong path dependence and lock-in effects. (3) DEM, SP, ALR, PD, and II significantly influenced GTALU, with II playing a dominant role. (4) The combined effect of main factors showed significant spatial non-stationarity characteristics. Among them, II and SP have significant positive impacts in Shandong province and the southwest of the research area; DEM, ALR, and PD have significant negative impacts in the middle reaches of the YRB, the “Hohhot-Baotou-Ordos-Ulanqab” urban agglomeration, and the three cities of Jiuquan-Jiayuguan-Zhangye, respectively. This research provides scientific support for formulating differentiated GTALU development strategies according to local conditions.