Promoting tourism in China using sustainable practices has become a very important issue. In order to analyze temporal characteristics and spatial regularities of green total factor productivity (GTFP), carbon emissions and the consumption of energy related to tourism in China were estimated using a "bottom-up" method. The construction of a measurement framework (including carbon emissions and energy consumption) of GTFP for the tourism industry was also undertaken. The data envelopment analysis (DEA) model and the Malmquist–Luenberger (ML) index were used to measure and calculate tourism GTFP in China between 2007 and 2018, as well as analyze spatio-temporal differences. Results indicate that: (1) carbon emissions and the consumption of energy are increasing, and they have not yet peaked, with traffic associated with tourism accounting for the largest proportion among tourism sectors; the spatial distribution of carbon emissions and the consumption of energy is not balanced; (2) green development of tourism in China has achieved a good level of performance during the study period, driven by technical efficiency. Since 2014, pure technical efficiency (PE) has been >1, indicating that the tourism industry in China has entered a stage of change and promotion; (3) significant spatial differences exist in tourism GTFP in China. For example, the overall pattern of being strongest in the east and weakest in the west has not changed. Currently, eastern, central, and western regions in China rely on different dynamic mechanisms to promote tourism green development. In addition, some provinces have become the core or secondary growth poles of tourism green development in China.

Tourism economic development is increasingly dependent on resources and environment. Exploring the relationship between tourism economic development and ecological-environmental (eco-environmental) stress is of great significance to promote the high-quality growth of tourism and the sustainable and coordinated development of ecological environment. By constructing a tourism economic development index and an eco-environmental stress index, this study analyzes the temporal and spatial evolution of tourism economic development and eco-environmental stress from 2009 to 2018 in China. It uses a decoupling model to evaluate the relationship between tourism economic development and ecological-environmental stress, and analyzes the reasons for the changes of decoupling relationship. The results show that: (1) During the study period, the development of tourism economy and the eco-environmental stress present a certain time-space effect characteristics. The stress index of China’s tourism economic development and ecological environment showed a fluctuating trend of first decreasing and then increasing, with obvious spatial hierarchical differences and weak agglomeration characteristics, and prominent regional imbalances. The tourism economic development level in the eastern region was higher than that in the central and western regions, while the ecological environment stress in the central region was greater than that in the eastern and western regions. (2) The relationship between tourism economic development and ecological environmental stress of China’s provinces has experienced eight states: Expansive negative decoupling, strong negative decoupling, weak negative decoupling, recessive coupling, expansive coupling, strong decoupling, weak decoupling, and recessive decoupling. During the study period, the state of optimal strong decoupling tends to weaken. Under the constraints of local policy orientation and regional economic development level, the overall decoupling optimization could not be achieved spatially. The decoupling state was always in an unsustainable non-optimal stage. (3) The reasons for the differential changes in the decoupling index between tourism economic development and ecological environmental stress in Chinese provinces come from investment-driven, resource-driven, innovation-driven, and environmental compliance push. This study can provide practical reference for promoting the high-quality development of tourism and the sustainable development of ecological environment.

This paper proposes a network analysis framework for rural tourism flows based on the digital footprint data of Ctrip visitors, and studies network the spatial characteristics and spatial development patterns of rural tourism flows. Research results show the following: (1)rural tourism flow is characterized by large-scale flows between nodes with superior resource endowment conditions and convenient transportation. (2) The overall network structure of rural tourism flow has a low density, and the centrality of rural tourism nodes has an obvious hierarchical structure, and obvious structural hole phenomenon. (3) Rural tourism flow has a significant core-edge structure. (4) The internal space of rural tourism flow exists in several modes, such as core node development, axis development “along traffic arteries” and domain development “clustering groups.” This study addresses the regional imbalance of rural tourism resources and with the intent of boosting the high-quality development of rural tourism destinations.

This paper introduces energy consumption and carbon emission into the analysis framework of the green productivity of tourism. By comparing and analyzing the two main methods used to evaluate the energy consumption and carbon emission estimations of tourism, namely, the “top-down” and “bottom-up” method, and considering the availability of data, the “bottom-up” method was adopted to evaluate the energy consumption and carbon emissions of tourism in the Yangtze River Economic Zone (YREZ). Then, using the Malmquist-Luenberger (ML) index in the super-efficiency data envelopment analysis (DEA) model, the green productivity of the tourism in 11 provinces and cities in the YREZ from 2006 to 2015 was measured. The empirical results show that: (1) The energy consumption and carbon emissions of tourism in the YREZ have increased steadily over the past 10 years, which has caused a certain degree of pollution to the environment, indicating that tourism is no longer a “smoke-free industry”; (2) there are significant provincial differences between the energy consumption and carbon emissions of tourism in the YREZ, with Shanghai always ranking first, while Guizhou and Yunnan ranks last, which represents that the tourism economic development level is positively correlated with the tourism energy consumption and carbon emissions; (3) the green productivity of tourism in the YREZ shows a fluctuating increasing trend in the past 10 years, and technological progress has become the main reason for its growth in green productivity; and (4) the green productivity of tourism in 11 provinces and cities in the YREZ can be divided into three types: Progressive type of tourism green development, stagnant type of tourism green development, and declining type of tourism green development. Consequently, different types of provinces should explore effective dependency paths based on their own conditions.

Widely popular Mehlich-3 phosphorus (P) was not used in the estimation of the intrinsic status of soil P. The objectives of this study were to develop the state-space model to determine the intrinsic soil P pools, a weakly adsorbed P pool, and a tightly adsorbed P pool, from the Mehlich-3 P. A linear relationship between Mehlich-3 P and the intrinsic soil P pools was derived from theoretical analysis of the extraction process of adsorbed P as competitive exchange by aqueous fluoride. Adsorption experiments of soil P were carried out to determine the dynamics of Mehlich-3 P after fertilizer phosphate was applied to soils. The results showed that state-space models of the two soil P pools could describe the dynamics of soil Mehlich-3 P in P adsorption experiments. Mehlich-3 P was a fraction of the weakly adsorbed P, and this fraction showed a linear relationship with soil P-sorption site density.