As an emerging field of eco-efficiency, tourism eco-efficiency is becoming an important way to measure the sustainable development of tourism. This study calculated and decomposed tourism CO₂ emissions in China, explored its evolution and distribution characteristics by using the ratio method of tourism eco-efficiency, and analyzed the influencing factors by adopting a regression model. The results indicate that: (1) tourism CO₂ emissions in China have increased significantly, their largest source is transportation. Its characteristics can be divided into carbon emission intensity factor, income structure factor, consumption level factor, and tourist scale factor through utilizing the Logarithmic Mean Divisia Index (LMDI) method, which also shows that the decisive factor of increased of tourism CO₂ emissions has been the tourist scale factor. (2) Tourism eco-efficiency is marked by spectacular but unstable growth, which can be divided into three stages: inefficient stagnation stage, fluctuating growth stage, and efficient development stage. (3) Tourism eco-efficiency in China has shown that the remarkable high–high cluster and low–low cluster, and the 31 provinces, can be divided into four groups, namely: a high-efficiency high-growth group, a high-efficiency low-growth group, a low-efficiency high-growth group, and a low-efficiency low-growth group. (4) The main influencing factors of tourism eco-efficiency are the scale effect, the structural effect, the technical effect, and the environmental regulation. Since these four factors have different impacts on the tourism eco-efficiency of different groups according to panel data regression, more effective policies should be adopted for the improvement of tourism eco-efficiency in China. Finally, the significances and deficiencies of this study are discussed.

Background: Geographical distribution of endemic species and its multiple scale drivers are an important topic in biodiversity conservation, because these species are especially vulnerable to climate change and habitat degradation, and therefore have high conservation priority. Here, for the first time, we simultaneously linked glacial-interglacial climate change, contemporary climate, plant species richness and altitudinal range with endemic bird distribution in China. Methods: Ordinary least squares models and simultaneous autoregressive models were used to assess the associations between endemic bird richness, endemic bird ratio and each explanatory variable. Structural Equation Models were also performed to test the direct and indirect effects of these variables on endemic bird richness and endemic bird ratio. Results: Higher endemic bird richness and endemic bird ratio occurred in regions with stable glacial-interglacial temperature, i.e., southwest China. Plant species richness and altitudinal range were also positively correlated with endemic bird richness and endemic bird ratio. Glacial-interglacial temperature change, contemporary precipitation, plant species richness and altitudinal range were all included in the best combination of variables for endemic bird richness. Importantly, glacial-interglacial temperature change had strong direct effects on both endemic bird richness and endemic bird ratio, while plant species richness only had a direct effect on endemic bird richness. Conclusions: Our results indicate that endemic birds in China concentrate in southwest regions where there are stable glacial-interglacial temperature, more plant species and larger altitudinal range. Notably, while stable climate has strong direct effects on both endemic bird richness and endemic bird ratio, plant species may affect endemic bird richness through its effect on overall bird species richness. Importantly, the recent anthropogenic activities have also significantly intensified in this region, which would pose huge challenge for biodiversity conservation in China.

This study aimed to determine factors of rural income inequality changes in Aba Tibetan and Qiang Autonomous Prefecture, Sichuan, China, to help formulate measures to reduce regional inequality and alleviate poverty in the southwestern mountainous area of China, which experiences frequent natural disasters and poor living conditions. Changes in rural income inequality are examined, which are then decomposed into different component factors based on the Gini index. Income inequality is comprised of four types of income source: wages, household operations, properties, and transfers. This analysis focuses on the period of 2003 to 2011—a special transitional stage during which the Wenchuan earthquake intervened. The results indicate that income from household operations plays a dominant role in income inequality. Its contribution to the overall inequality fluctuates from 36.40 to 50.39% and had a positive effect on reducing inequality after the earthquake. Wage income contributed the second-most to income inequality and had positive and negative effects on reducing inequality before and after the earthquake, respectively. Transfer and property incomes are important forces in income inequality that have different influencing mechanisms. Transfer income positively helps reduce income inequality. Exclusive favors or preferences should be granted to such areas.