RESUMO
Global ecosystem services (ESs) are experiencing a significant decline, necessitating the development of robust environmental governance policies. To address the lack of integrated planning with heavy industry as the research object and a lack of knowledge of ES trade-offs and synergies in China's ecological and environmental governance. In this study, the spatial and temporal variations of four ESs (water yield (WY), soil conservation (SC), carbon storage (CS), and habitat quality (HQ)) were determined in the study area of Liaoning Province. Explore the mechanisms that shape ecosystem service trade-offs and synergies and the factors that influence them. Spearman's correlation and difference analyses were proposed to determine the spatial and temporal distributions of trade-offs and synergistic relationships among ESs. In addition, we constructed a multiscale geo-weighted regression (MGWR) model to investigate driver spatial heterogeneity affecting trade-offs and synergies. The results revealed that (1) In the study area, ESs were on the rise in Liaoning Province. (2) Temporally, ESs were overwhelmingly dominated by synergies; at the spatial scale, ESs were dominated by trade-offs of varying degrees, with the area of synergy between WY and SC being the highest. (3) ESs demonstrated spatial heterogeneity in intensity and were more impacted by natural factors such as vegetation cover, elevation, and precipitation than by characteristics related to human activity. This study helps improve understanding of the interactions and dependencies among ESs and can provide a reference for ecological governance and improvements in Liaoning Province.
RESUMO
Understanding the characteristics of PM2.5 and its socioeconomic factors is crucial for managing air pollution. Research on the socioeconomic influences of PM2.5 has yielded several results. However, the spatial heterogeneity of the effect of various socioeconomic factors on PM2.5 at different scales has yet to be studied. This paper collated PM2.5 data for 359 cities in China from 2005 to 2020, as well as socioeconomic data: GDP per capita (GDPP), secondary industry proportion (SIP), number of industrial enterprise units above the scale (NOIE), general public budget revenue as a proportion of GDP (PBR), and population density (PD). The spatial autocorrelation and multiscale geographically weighted regression (MGWR) model was used to analyze the spatiotemporal heterogeneity of PM2.5 and explore the impact of different scales of economic factors. Results show that the overall economic level was developing well, with a spatial distribution trend of high in the east and low in the west. With a large positive spatial correlation and a highly concentrated clustering pattern, the PM2.5 concentration declined in 2020. Secondly, the OLS model's statistical results were skewed and unable to shed light on the association between economic factors and PM2.5. Predictions from the GWR and MGWR models may be more precise than those from the OLS model. The scales of the effect were produced by the MGWR model's variable bandwidth and regression coefficient. In particular, the MGWR model's regression coefficient and variable bandwidth allowed it to account for the scale influence of economic factors; it had the highest adjusted R2 values, smallest AICc values, and residual sums of squares. Lastly, the PBR had a clear negative impact on PM2.5, whereas the negative impact of GDPP was weak and positively correlated in some western regions, such as Gansu and Qinghai provinces. The SIP, NOIE, and PD were positively correlated with PM2.5 in most regions. Our findings can serve as a theoretical foundation for researching the associations between PM2.5 and socioeconomic variables, and for encouraging the coequal growth of the economy and the environment.