Ending poverty in all its forms is the first of the 17 sustainable development goals (SDGs) of the 2030 Agenda for Sustainable Development. Therefore, it is of great significance to study poverty in the context of sustainable development. At present, the effect of income growth on poverty reduction is becoming less evident, whereas the effect of inhabitant heterogeneity on poverty reduction is becoming increasingly significant in China. Based on the original two-dimensional poverty decomposition of income growth and redistribution, this study introduces the heterogeneity effect to decompose rural poverty in China from three dimensions. It first decomposes the change in income distribution into mean, variance, and residual effects using counterfactual analysis. Then, it introduces the Foster–Greer–Thorbecke index decomposition to decompose China’s rural poverty under the different poverty line. In addition, this paper employs mathematical statistics to analyze the effects of poverty’s growth, dispersion, and heterogeneity. This study finds that the three-dimensional poverty decomposition method can measure the trajectory and trend of poverty more precisely and comprehensively. Moreover, it found that the contradiction between economic growth and poverty regression is due to the fact that the poverty reduction effect of the growth effect and the poverty alleviation effect of the discrete effect have asymmetrical characteristics, whereas the discrete effect and the heterogeneous effect have symmetrical characteristics; that is, the poverty reduction effect of income growth is insufficient to compensate for the poverty deepening effect brought about by the widening income gap, and that the heterogeneous poverty reduction effect plays an increasingly important role. Therefore, to prevent residents from falling back into poverty after being lifted out of it, we must reduce the widening income gap. Moreover, residents’ ability to reduce poverty on their own must be strengthened.

Clarifying the relationship between tourism and green development is conducive to promoting the harmonious coexistence of tourism industry benefits and economic and environmental systems. The externalities of tourism on economies and the environment have sparked numerous fascinating academic research debates; however, few studies have considered the impact of tourism on green development that balances economic growth and environmental protection. This study selects the green development efficiency measured by the super-efficient SBM model with undesired output as a proxy indicator of green development and adopts the panel data regression model and dynamic panel threshold regression model to investigate the linear impact and non-linear characteristics of tourism on the green development efficiency for 284 cities in mainland China at the prefecture level and above. The main findings are as follows: (1) Although China’s green development efficiency showed an upward trend during the study period, the overall level was not high. (2) Tourism has significantly promoted the improvement of China’s green development efficiency, indicating that tourism has become an effective driver of China’s economic green transformation. (3) This type of positive promotion of green development by tourism has a non-linear threshold characteristic, which means that, with the continuous improvement of the development level of the tourism industry, after crossing a specific threshold value and entering a higher level of development, the tourism industry will have an increasing marginal impact on the green development efficiency.

This paper takes 31 provinces in China as the research object and constructs an evaluation index system for the well-being of the elderly in four aspects (health well-being, income well-being, social well-being and educational well-being) and uses a set-pair analysis model to spatially measure the well-being of the elderly. Then, barrier analysis is used to identify the main factors that lead to the differences in the well-being levels of the elderly in different regions. The results show that: (1) The provinces with higher levels of well-being of the elderly are mainly concentrated in the Beijing–Tianjin–Hebei region, Pearl River Delta region, Yangtze River Delta region and Bohai Sea Rim region. (2) The differences in income well-being levels are the largest among provinces, and the differences in health levels are the smallest among provinces. (3) Analysis of the barriers to elderly well-being shows that the number of beds per 1000 population in health care facilities, elderly dependency ratio, number of higher education schools for adults, number of nursing homes and urban road area per capita are the main factors affecting the differences in the well-being levels of elderly people across provinces. Finally, policy recommendations are made to introduce localized policies for the elderly in China to continuously promote solutions to the problems of the elderly.