Exploring social, economic, and ecological drivers of human well-being in the Qinling Mountains, China

Abstract

Understanding the effects of different factors on human well-being (HWB) is essential for achieving sustainable development. Recent related studies have mainly focused on the effects of socioeconomic or ecological environmental factors on HWB, while less effort has been devoted to quantitatively assessing the long-term effects of multiple variables on HWB. In this study, we applied a spatial regression model to data representing 19 social, economic, and ecological environmental variables to characterize the spatial pattern of the county-level HWB in the Qinling Region. First, we quantified the HWB in 2000, 2010 and 2020, and then, we analyzed its spatial heterogeneity in the Qinling Region. Correlation analysis, multicollinearity test, and ordinary least squares (OLS) analysis were used to identify three and four key factors in 2000 and 2020, respectively. Finally, the performances of the OLS, geographically-weighted regression (GWR), and multi-scale geographically weighted regression (MGWR) methods were compared, and it was found that the MGWR achieved the best overall performance. The model results indicated that the significant factors in 2000 included the disposable income of rural households, the number of health profession technicians, and the average annual temperature; those in 2020 included the disposable income of urban households, the number of beds in medical and health institutions, and the average annual precipitation. Economic factors had the strongest coefficient of influence, and the western Qinling Region was the most vulnerable. Selecting impact factors from multiple dimensions and conducting multi-model comparisons can help improve the reliability of our results. The results of this study provide a scientific reference for improving human well-being and for achieving sustainable development in the Qinlinig Region.