With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how ...With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.展开更多
Dose–response experiments and data analyses are often carried out according to an optimal design under a model assumption.A two-parameter logistic model is often used because of its nice mathematical properties and p...Dose–response experiments and data analyses are often carried out according to an optimal design under a model assumption.A two-parameter logistic model is often used because of its nice mathematical properties and plausible stochastic response mechanisms.There is an extensive literature on its optimal designs and data analysis strategies.However,a model is at best a good approximation in a real-world application,and researchers must be aware of the risk of model mis-specification.In this paper,we investigate the effectiveness of the sequential EDdesign,the D-optimal design,and the up-and-down design under the three-parameter logistic regression model,and we develop a numerical method for the parameter estimation.Simulations show that the combination of the proposed model and the data analysis strategy performs well.When the logistic model is correct,this more complex model has hardly any efficiency loss.The three-parameter logistic model works better than the two-parameter logistic model in the presence of model mis-specification.展开更多
基金This work was supported by the Qinchuangyuan Project of Shaanxi Province,China(QCYRCXM-2022-145)the Major Project of the Key Research Base of Humanities and Social Sciences of the Ministry of Education,China(22JJD790052)+1 种基金the Chinese Universities Scientific Fund(Z1010422003)the National Natural Science Foundation of China(72373117).
文摘With increasing population and changing demographics,food consumption has experienced a significant transition in quantity and quality.However,a dearth of knowledge remains regarding its environmental impacts and how it responds to demographic dynamics,particularly in emerging economies like China.Using the two-stage Quadratic Almost Demand System(QUAIDS)model,this study empirically examines the impact of demographic dynamics on food consumption and its environmental outcomes based on the provincial data from 2000 to 2020 in China.Under various scenarios,according to changes in demographics,we extend our analysis to project the long-term trend of food consumption and its environmental impacts,including greenhouse gas(GHG)emissions,water footprint(WF),and land appropriation(LA).The results reveal that an increase in the proportion of senior people significantly decreases the consumption of grain and livestock meat and increases the consumption of poultry,egg,and aquatic products,particularly for urban residents.Moreover,an increase in the proportion of males in the population leads to higher consumption of poultry and aquatic products.Correspondingly,in the current scenario of an increased aging population and sex ratio,it is anticipated that GHG emissions,WF,and LA are likely to decrease by 1.37,2.52,and 3.56%,respectively.More importantly,in the scenario adhering to the standards of nutritional intake according to the Dietary Guidelines for Chinese Residents in 2022,GHG emissions,WF,and LA in urban areas would increase by 12.78,20.94,and 18.32%,respectively.Our findings suggest that changing demographics should be considered when designing policies to mitigate the diet-environment-health trilemma and achieve sustainable food consumption.
基金fundings from the National Natural Science foundation of China[Grant Number 11871419]the Natural Science and Engineering Research Council of Canada.
文摘Dose–response experiments and data analyses are often carried out according to an optimal design under a model assumption.A two-parameter logistic model is often used because of its nice mathematical properties and plausible stochastic response mechanisms.There is an extensive literature on its optimal designs and data analysis strategies.However,a model is at best a good approximation in a real-world application,and researchers must be aware of the risk of model mis-specification.In this paper,we investigate the effectiveness of the sequential EDdesign,the D-optimal design,and the up-and-down design under the three-parameter logistic regression model,and we develop a numerical method for the parameter estimation.Simulations show that the combination of the proposed model and the data analysis strategy performs well.When the logistic model is correct,this more complex model has hardly any efficiency loss.The three-parameter logistic model works better than the two-parameter logistic model in the presence of model mis-specification.
