Heterogeneous Interaction Effects of Environmental and Economic Factors on Green Efficiency of Water Resources in China
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Identifying the green efficiency of water resources and its driving factors is paramount for promoting sustainable development in China. The existing research has primarily focused on the spatial heterogeneity of individual factors that impact green efficiency of water resources. However, it has often overlooked the heterogeneity in the interactions between these factors. In this study, we utilized a multiscale geographically weighted regression (MGWR) model to discern the spatial heterogeneity of the individual factors influencing the green efficiency of water resources in China between 2002 and 2016. Subsequently, we demarcated several subregions based on the coefficients derived from the MGWR model. Employing a geographical detector (GD), we quantified the interactive impacts of different factors within these subregions. Our findings unveiled, for the first time, the diverse patterns in the temporal and spatial fluctuations in the factors impacting the eco-friendliness of water resources. The findings underscored that disregarding the spatial heterogeneity of these interactive effects may result in an underestimation of the interactions among factors. Significantly, in 2016, the impact of tertiary industry proportion and completed investment in pollution treatment displayed an enhanced non-linear effect across the entire sample and concurrently demonstrated a bivariate enhanced effect within subregions. These discoveries contribute to a deeper comprehension of the mechanisms influencing these factors, providing valuable insights for policymakers in crafting region-specific water resource policies tailored to the unique developmental requirements of different areas.Keywords:
Spatial heterogeneity
Investment
Driving factors
Sample (material)
Green development
The identification of fine particulate matter (PM2.5) concentrations and its driving factors are crucial for air pollution prevention and control. The factors that influence PM2.5 in different regions exhibit significant spatial heterogeneity. Current research has quantified the spatial heterogeneity of single factors but fails to discuss the interactions between factors. In this study, we first divided the study area into subregions based on the spatial heterogeneity of factors in a multi-scale geographically weighted regression model. We then investigated the interactions between different factors in the subregions using the geographical detector model. The results indicate that there was significant spatial heterogeneity in the interactions between the driving factors of PM2.5. The interactions between natural factors have significant uncertainty, as do those between the normalized difference vegetation index (NDVI) and socioeconomic factors. The interactions between socioeconomic factors in the subregions were consistent with those in the whole region. Our findings are expected to deepen the understanding of the mechanisms at play among the aforementioned drivers and aid policymakers in adopting unique governance strategies across different regions.
Spatial heterogeneity
Driving factors
Geographically Weighted Regression
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This manuscript considers discrepancies between the bivariate correlation and several indices of association estimated from regression results. These indices can be estimated from results typically reported in primary studies. In recent years, many researchers conducting meta‐analyses have used these indices in place of, or together with, the bivariate correlation. I illustrate the differences among these indices and the bivariate correlation. I demonstrate the inaccuracy of these indices as replacements for bivariate effects. Thus, I recommend discontinuing the use of these indices and partial effect sizes as replacement for the bivariate correlation. Copyright © 2014 John Wiley & Sons, Ltd.
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With urban heat challenges increasingly severe, assessing heat health risk has become crucial for human settlements. Unfortunately, previous studies have not accurately identified the driving factors, posing a significant obstacle to translating assessment results into policymaking. Particularly, potential spatial heterogeneity of driving factors at the indicator-level may exist. Therefore, this study developed a systematic method to examine the spatial heterogeneity of driving factors for heat health risk in Chongqing. Based on this heterogeneity, an integrated framework linking heat health risk, driving factors, and response strategies was proposed, supporting specific solutions for different cities. The results indicate that driving factors exhibit strong heterogeneity at the indicator-level. Even within the same prevention zone and urban functional areas, the maximum differences in the number of driving factors and combination categories can reach four and five, respectively. Moreover, relying solely on the driving factors obtained through traditional methods to develop cooling measures is unreasonable. When these driving factors are consistent, there are still, on average, six combinations of driving factors at the indicator-level, and each combination includes an average of 2.9 indicators. The higher the level of the risk prevention zone, the more driving factors it contains. The average number of driving factors above the moderate risk level is 3.9, higher than the 1.1 and 2 found in moderate and below moderate risk levels. Overall, this study provides a reference for understanding spatial heterogeneity of driving factors for heat health risk and offers an approach to assist policymakers in formulating guided cooling strategies.
Spatial heterogeneity
Driving factors
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Urban Heat Island
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