The article aims to study the coupling coordination and spatial correlation effects of green finance (GF) and high-quality economic development (HQED) in 30 Chinese provinces. The index system of GF and HQED is constructed by selecting relevant index data from 2007 to 2017. The index of GF and HQED is measured by the entropy value method. Next, the coupling coordination degree (CCD) and spatial association strength are calculated based on the index using the coupling coordination degree model and the gravity model. Then the driving factors of the CCD between GF and HQED are analyzed by using geographic detectors. Finally, the spatial association network is constructed and its robustness is studied. The research results show that the coupling coordination degree between GF and HQED in each province is generally low, with strong regional heterogeneity, and the coupling coordination degree shows a trend of decay from the eastern region to the western region, but the western region has more room for development. Green credit, green, coordination, and sharing are the strong driving factors of the CCD between GF and HQED. The network of spatial association between GF and HQED in each province is gradually tightened, making the western peripheral provinces more closely connected with the eastern provinces through the intermediate node provinces. The network robustness of GF and HQED is more influenced by provinces with higher node degree values. Accordingly, the article proposes that China should continuously improve relevant GF policies, environmental disclosure systems, enhance green innovation technology and guide private capital to enter the GF market.

A-level scenic spots are a unique evaluation form of tourist attractions in China, which have an important impact on regional tourism development. Guizhou is a key tourist province in China. In recent years, the number of A-level scenic spots in Guizhou Province has been increasing, and the regional tourist economy has improved rapidly. The spatial distribution evolution characteristics and influencing factors of A-level scenic spots in Guizhou Province from 2005 to 2019 were measured using spatial data analysis methods, trend analysis methods, and geographical detector methods. The results elaborated that the number of A-level scenic spots in all counties of Guizhou Province increased, while in the south it developed slowly. From 2005 to 2019, the spatial distribution in A-level scenic spots were characterized by spatial agglomeration. The spatial distribution equilibrium degree of scenic spots in nine cities in Guizhou Province was gradually developed to reach the “relatively average” level. By 2019, the kernel density distribution of A-level scenic spots had formed the “two-axis, multi-core” layout. One axis was located in the north central part of Guizhou Province, and the other axis ran across the central part. The multi-core areas were mainly located in Nanming District, Yunyan District, Honghuagang District, and Xixiu District. From 2005 to 2007, the standard deviation ellipses of the scenic spots distribution changed greatly in direction and size. After 2007, the long-axis direction of the ellipses gradually formed a southwest to northeast direction. We chose elevation, population density, river density, road network density, tourism income, and GDP as factors, to discuss the spatiotemporal evolution of the scenic spots’ distribution with coupling and attribution analysis. It was found that the river, population distribution, road network density, and the A-level scenic spots’ distribution had a relatively high coupling phenomenon. Highway network density and tourist income have a higher influence on A-level tourist resorts distribution. Finally, on account of the spatiotemporal pattern characteristics of A-level scenic spots in Guizhou Province and the detection results of influencing factors, we put forward suggestions to strengthen the development of scenic spots in southern Guizhou Province and upgrade the development model of “point-axis network surface” to the current “two-axis multi-core” pattern of tourism development. This study can explain the current situation of the spatial development of tourist attractions in Guizhou Province, formulate a regulation mechanism of tourism development, and provide a reference for decision-making to boost the high-quality development of the tourist industry.

To investigate the spatial evolution process of economic development in the urban agglomeration and its influencing factors, the network construction method, modified gravity model, geographic detector and Geographically Weighted Regression (GWR) model are used to analyze the intensity of urban association; then, the evolution of economic, spatial pattern and its influencing factors are further discussed, and the Chengdu-Chongqing urban agglomeration of China from 2005 to 2020 is studied as an example. The results show that: (1) the economically developed zones of the Chengdu-Chongqing urban agglomeration mainly concentrated in the core cities of Chengdu and the central city of Chongqing, and the region shows an uneven spatial pattern of economic development distribution. (2) The share of economic linkages with the central city of Chengdu and Chongqing as the twin cities is significant, the intensity of Chengdu and its neighboring cities is gradually decreasing, while the central city of Chongqing has increased, but it still has an insufficient influence on the peripheral areas. (3) The intensities and directions of the factors influencing economic development in the Chengdu-Chongqing urban agglomeration are different. The total output value of the secondary industry, total social fixed asset investment, the number of beds in health institutions, and road freight turnover are significant factors with consistently strong explanatory ability for economic development. The promotion effect of these four significant factors on economic development is mainly concentrated in the eastern and western regions of Chengdu-Chongqing urban agglomeration, while the inhibiting effect is mainly on the cities in the south and north. Based on this study, relevant recommendations are made to promote the coordinated development of the Chengdu-Chongqing urban agglomeration.