Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic devel...Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.展开更多
Background:Depression represents a significant global mental health burden,particularly among middle-aged and older Chinese with chronic diseases in high-altitude regions,where harsh environmental conditions and limit...Background:Depression represents a significant global mental health burden,particularly among middle-aged and older Chinese with chronic diseases in high-altitude regions,where harsh environmental conditions and limited social support exacerbate mental health disparities.This paper aims to develop an interpretable machine learning prediction framework to identify the key factors of depression in this vulnerable population,thereby proposing targeted intervention measures.Methods:Utilizing data from the China Health and Retirement Longitudinal Study in 2020,this paper screened out and analyzed 2431 samples.Subsequently,Recursive Feature Elimination and Least Absolute Shrinkage and Selection Operator were applied to screen predictors from 32 alternative variables.Furthermore,through hyperparameter tuning and 5-fold cross-validation,8 machine learning modelswere constructed,namely,Random Forest,Extreme Gradient Boosting,Light Gradient Boosting Machine,Gradient Boosting Machine,K-Nearest Neighbor,Naive Bayes Classifier,Support Vector Machine,and Logistic Regression.Finally,the SHAP algorithm was applied to analyze the interpretability of the best-performing model,quantifying nonlinear relationships and threshold effects.Results:Among the respondents,the prevalence of depression was approximately 46.89%.After feature engineering screening,8 variables were retained for inclusion in the prediction model.Furthermore,the Gradient Boosting Machine performed optimally in terms of comprehensive performance,with an Area Under Receiver Operating Characteristic Curve(AUC)of 0.845,an Accuracy of 0.714,a Sensitivity of 0.655,a Precision of 0.711,a Specificity of 0.766,and an F1 of 0.682.In addition,Life satisfaction,PM2.5,Self-rated health,and Education were identified as the top 4 key factors.Meanwhile,the influence of these variables on depression showed nonlinear and threshold effects.Conclusion:This research highlights the value of machine learning in mental health.Based on the identified key factors,this paper proposed a series of policy measures to improve the health pattern of the middle-aged and elderly populations facing the dual challenges of chronic disease and environmental adversity.展开更多
基金Under the auspices of National Natural Science Foundation of China(No.42571300)。
文摘Transforming urban spatial structures to promote green and low-carbon development is an effective strategy.Although prior studies have examined the impact of urban polycentricity on carbon emissions and economic development,research on its role in the synergistic relationship between these factors regarding carbon emission efficiency is limited.Furthermore,existing literature often overlooks nonlinear effects and interactions with other urban variables.This paper analyzed data from 295 Chinese cities in 2020,calculating urban population polycentricity,population dispersion indices,and carbon emission efficiency.Utilizing local spatial autocorrelation tools,we reveal interactions among urban population polycentricity,dispersion,carbon emissions,and carbon emission efficiency.We then employ a gradient boosting decision tree model(GBDT)to explore nonlinear and synergistic effects of polycentric urbanization.Key findings include:1)polycentric urbanization in Chinese cities exhibits significant spatial differentiation characteristics.The Polycentricity index is relatively high in economically developed eastern coastal regions with an overall low level,carbon emissions are concentrated in industrialized north-central cities and some Yangtze River Delta hubs,and carbon emission efficiency is the highest in the Yangtze River Delta while relatively low in Northeast China;there are significant spatially heterogeneous interaction characteristics among population polycentricity,population dispersion,carbon emissions,and carbon emission efficiency.2)Urban population polycentricity contributes 9.42%to total carbon emissions and 6.24%to carbon emission efficiency.3)The polycentricity index has a nonlinear impact on carbon emissions and carbon emission efficiency:no significant effect when below 0.50 or above 0.55,increased carbon emissions in 0.50-0.53,and reduced carbon emissions with improved efficiency in 0.53-0.55.4)The polycentricity index has an interaction effect with other variables;specifically,when the polycentricity index is between 0.53 and 0.55,its interaction with urban gross domestic product(GDP),urban population,urban built-up area,green coverage rate in built-up areas,urban technological expenditure,and the proportion of the output value of the secondary industry will reduce carbon emissions and improve carbon emission efficiency.These findings enhance the understanding of urban spatial structures and carbon emissions,providing valuable insights for policymakers in developing green and low-carbon strategies.
基金supported by the Project of Zhongyuan Medical Innovation Foundation Hospital Management Research(25YCG1004).
文摘Background:Depression represents a significant global mental health burden,particularly among middle-aged and older Chinese with chronic diseases in high-altitude regions,where harsh environmental conditions and limited social support exacerbate mental health disparities.This paper aims to develop an interpretable machine learning prediction framework to identify the key factors of depression in this vulnerable population,thereby proposing targeted intervention measures.Methods:Utilizing data from the China Health and Retirement Longitudinal Study in 2020,this paper screened out and analyzed 2431 samples.Subsequently,Recursive Feature Elimination and Least Absolute Shrinkage and Selection Operator were applied to screen predictors from 32 alternative variables.Furthermore,through hyperparameter tuning and 5-fold cross-validation,8 machine learning modelswere constructed,namely,Random Forest,Extreme Gradient Boosting,Light Gradient Boosting Machine,Gradient Boosting Machine,K-Nearest Neighbor,Naive Bayes Classifier,Support Vector Machine,and Logistic Regression.Finally,the SHAP algorithm was applied to analyze the interpretability of the best-performing model,quantifying nonlinear relationships and threshold effects.Results:Among the respondents,the prevalence of depression was approximately 46.89%.After feature engineering screening,8 variables were retained for inclusion in the prediction model.Furthermore,the Gradient Boosting Machine performed optimally in terms of comprehensive performance,with an Area Under Receiver Operating Characteristic Curve(AUC)of 0.845,an Accuracy of 0.714,a Sensitivity of 0.655,a Precision of 0.711,a Specificity of 0.766,and an F1 of 0.682.In addition,Life satisfaction,PM2.5,Self-rated health,and Education were identified as the top 4 key factors.Meanwhile,the influence of these variables on depression showed nonlinear and threshold effects.Conclusion:This research highlights the value of machine learning in mental health.Based on the identified key factors,this paper proposed a series of policy measures to improve the health pattern of the middle-aged and elderly populations facing the dual challenges of chronic disease and environmental adversity.
