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Objective: The aim of this study was to investigate the impact of illiteracy on physical health and mental health. Design: Matching methods (nearest neighbour matching, Mahalanobis metric matching, and propensity score matching). Setting: Elderly people at least 65 years old in 22 provinces of China. Methods: The analysis used data from Chinese Longitudinal Healthy Longevity Survey (CLHLS). The independent variable was a dummy variable, which was coded as 1 for illiterate or semiliterate, and 0 for literate. Dependent variables were indicators of physical and mental health derived from the survey results. Matching methodologies controlled for confounding variables including age, sex, living sites, access to tap water and financial support. Results: Illiteracy was found to have a significant impact on physical health, exercise habits, anxiety, loneliness and happiness. On average, illiteracy decreased physical health by 19.9%, decreased exercise habits by 7%, increased anxiety by 11.56%, increased loneliness by 17.6% and decreased happiness by 11.3%. Conclusion: Findings confirm the past literature in which illiteracy has been found to be adversely associated with physical and mental health. The analysis uniquely found that illiteracy had a higher cost on mental health as compared to physical health for elderly people in China.

Urbanization and industrialization have resulted in an excessive intensity of land use, structural imbalances, and regional ecological deterioration. To achieve sustainable development, it is essential to optimize different land use types and enhance the ecosystem service value (ESV). However, previous studies rarely combined the optimization of land use quantitative structure with the simulation of land use spatial layout under the background of maximizing economic and ecological benefits at the same time, and this kind of research was rarely applied to state-level new areas. On the premise of maximizing economic and ecological benefits, this study develops an improved optimization method that integrates the non-dominated sorting genetic algorithm II (NSGA-II) with the patch generating land use simulation (PLUS) model. The approach optimizes land use while predicting ESV for a state-level new area-Liangjiang New Area (LJNA). The model operation takes into full account ecological spatial constraints, including ecological protection red line, prime farmland, and urban development land use suitability. Results show that LJNA encountered the most substantial increase in built-up land (392.31 km2) due to farmland occupation, leading to a continuous decrease in ESV (443.6 million yuan) from 2000 to 2020. Optimization results for 2030 indicate that the main difference between different scenarios is the area of forest and built-up land. Under ecological protection priority scenario, ESV will increase in 2030 but built-up land should be confined within 620 km2. We present multiple land use scenarios based on decision makers' preferences, offering important implications for the balance and optimization of land resources under different policy priorities using coupled spatiotemporal simulation-quantitative allocation model. This study provides valuable references for future land use optimization research. • An improved multi-objective land use optimization method is developed. • The method integrates the non-dominated sorting genetic algorithm II with the patch generating land use simulation model. • The method optimizes land use while predicting ESV for the Liangjiang New Area, China. • The ecological spatial constraints are developed based on ecological protection red line, prime farmland, and urban development land use suitability. • Taking into account both ecological and economic benefit, four development scenarios are set up. [ABSTRACT FROM AUTHOR]