Tourism eco-efficiency is an important indicator that has often been applied to measure the quality of green tourism development. This paper takes the 31 provinces of China as examples to analyze regional tourism eco-efficiency. By constructing multiple input and output indicator systems for regional tourism, we estimated the eco-efficiency of 31 provinces in 1997–2016 using an undesirable output model of a slack-based model (undesirable-SBM) for data envelopment analysis (DEA). Then, we analyzed the spatial–temporal evolutionary trends and patterns of the eco-efficiency over 20 years by using the Hot Spot Model and Spatial Center of Gravity Model. Finally, we explored the driving forces internal and external to the tourism eco-economic system using the Panel Tobit Regression Model and Geodetector Model, respectively. The results show that: In the last 20 years, the tourism eco-efficiency of provinces in China declined, though tourism has experienced rapid but extensive development. The western regions of China, which have better eco-environmental conditions, and the southeastern coastal regions, which have higher levels of economic development, have higher tourism eco-efficiency. Regions with lower tourism eco-efficiency show diffusion trends, while regions with higher tourism eco-efficiency are characterized by a lack of obvious space spillover effects. Technology is the core driving force of regional tourism eco-efficiency, while traffic conditions and social civilization levels are key external influence factors leading to improvement of tourism eco-efficiency. The research results reveal the great significance of laws for sustainable green tourism development with different economic levels in the different regions. Our work could provide a reference for similar countries and regions in the world with the rapid growth of tourism or obvious spatial differentiation in socioeconomic development.

Urban agglomeration is an advanced spatial form of integrating cities, resulting from the global urbanization of recent decades. Understanding spatiotemporal patterns and evolution is of great importance for improving urban agglomeration management. This study used continuous time-series NTL data from 2000 to 2018 combined with land-use images to investigate the spatiotemporal patterns of urbanization in the three most developed urban agglomerations in China over the past two decades: the Beijing–Tianjin–Hebei urban agglomeration (BTH), the Yangtze River Delta urban agglomeration (YRD), and the Guangdong–Hong Kong–Macao Greater Bay Area (GBA). The NTL intensity indexes, dynamic thresholds, extracted urban areas, and landscape metrics were synthetically used to facilitate the analysis. This study found that the urbanization process in the study areas could be divided into three stages: rapid urbanization in core cities from 2000 to 2010, a fluctuating urbanization process in both core cities and surrounding cities from 2010 to 2015, and stable urbanization, mainly in surrounding cities with a medium size after 2015. Meanwhile, the urbanization level of GBA was higher than that of YRD and BTH. However, with the acceleration of urban development in YRD, the gap in the urbanization level between GBA and YRD narrowed significantly in the third stage. In addition, this study confirmed that the scattered, medium-sized cities in YRD and GBA were more developed than those in BTH. This study showed that continuous NTL data could be effectively applied to monitor the urbanization patterns of urban agglomerations.

The environmentally responsible behaviour (ERB) facilitates the sustainability of tourism destinations. This study aims to identify the key impact factors to promote visitors’ ERB in urban park and examine the impact mechanism. A total of 567 visitors were surveyed in Beijing’s Yuyuantan Urban Park. Structural equation modelling results indicate that personality traits are the most important factors that affect visitors’ ERB, while the effect of how satisfaction with interpretive services plays upon visitors’ ERB using place attachment as a mediator. Openness traits positively affect satisfaction with experiential services and general behaviour. Additionally, implications in urban park management are provided.

Eco-efficiency analysis can provide useful information about sustainability in the tourism industry, which has an important role in both global economy recovery and Sustainable Development Goals (SDGs), generating considerable indirect carbon emissions with respect to the supply chain due to its significant connections to other industries. This study, from the perspective of tourism sectors, including tourism hotels, travel agencies, and scenic spots, integrated the environmentally extended input–output analysis (EEIO) and data envelopment analysis (DEA) models to develop a research framework, analyzing the indirect carbon emissions of the tourism supply chain, evaluating eco-efficiency with respect to both direct carbon emissions and total carbon emissions (including direct and indirect parts), and exploring the driving factors of eco-efficiency of tourism sectors using Tobit regression models. This study took Gansu as a case, a province in China characterized by higher carbon intensity, an underdeveloped economy, and rapid tourism growth. The results demonstrate that (1) tourism hotels contribute the most carbon emissions in tourism sectors, especially indirectly due to the supply chain, with carbon emissions mainly resulting from the manufacturing of food and tobacco; (2) the eco-efficiency of tourism sectors in Gansu presents a U-shaped curve, which is consistent with Kuznets’ theory; and (3) energy technology is key to improving the eco-efficiency of tourism sectors. The research results provide a clear path for the reduction of carbon emissions and the improvement of eco-efficiency in Gansu tourism sectors. Against the backdrop of global climate change and the post-COVID-19 era, our research framework and findings provide a reference for similar regions and countries who are in urgent need of rapid tourism development to effect economic recovery.

Deterioration of the urban thermal environment, especially in megacities with intensive populations and high densities of impervious surfaces, is a global issue resulting from rapid urbanization. The effects of landscape patterns on the urban thermal environment within a single area or single period have been well documented. Few studies, however, have explored whether the effects can be adapted to various cities at different urbanization stages. This paper investigated the variations of these effects in the five largest and highly urbanized megacities of China from 1990 to 2020 using various geospatial approaches, including concentric buffer analysis, correlation analysis, and hierarchical ridge regression models. The results indicated that the effects of landscape patterns on the urban thermal environment were greatly variable at different urbanization stages. Although landscape composition was more important than landscape configuration in determining the urban thermal environment, the standard coefficients of composition metrics continuously decreased from 1990 to 2020. However, configuration metrics, such as patch density, edge density, and shape complexity, could affect the land surface temperature (LST) to a larger extent at the highly urbanized stage. The urbanization process could also affect the cooling effect of urban green space. At the initial stage of rapid urban expansion in approximately 2000, urban green space explained the most variation in LST, with a value as high as 10%. To maximize the cooling effect, the spatial arrangement of urban green space should be highlighted in the region that was 10–15 km from the city center, where the mean LST experienced a significant decline. These results may provide deeper insights into improving the urban thermal environment by targeted strategies in optimizing landscape patterns for areas at different urbanization stages.