A Study on the Correlation Between Residential Patterns and Carbon Emissions from Residents' Transportation:A Case Study of Caoyang Xincun in Shanghai

As residential patterns and types are closely related to population densities, carbon emissions from residents' transportation are logically correlated with the population density, as well as the diversity of facilities and the accessibility of transportation stations. This paper takes Shanghai Caoyang Xincun as a case, and studies the correlation between the residential patterns and carbon emissions through questionnaire data analysis on eight types of residential patterns. The results show that the relationship between the population density of Caoyang Xincun and the per capita traffic carbon emissions is a nonlinear fl uctuation, that the relationship between the diversity of facilities and carbon emissions is positively correlated, and that the relationship between the accessibility of transportation stations and carbon emissions is negatively correlated. It also finds that the per capita carbon emissions in residential areas with multi-storey enclosed buildings and small high-rise row buildings are different from other patterns. With a further study on occupational structure and family income, it concludes that the carbon emission difference is caused by consumer segregation based on social structure. Therefore, when judging whether a residential pattern is low carbon or not, the spatial and social implications of density should be clarified first. Then the national and regional development conditions need to be considered to guide the future development of residential patterns.

Poisson rectangular pulse(PRP)model establishment based on uncertainty analysis of urban residential water consumption patterns

The commonly used Poisson rectangular pulse(PRP)model,employed for simulating high-resolution residential water consumption patterns(RWCPs),relies on calibration via medium-resolution RWCPs obtained from practical measurements.This introduces inevitable uncertainty stemming from the measured RWCPs,which consequently impacts the precision of model simulations.Here we enhance the accuracy of the PRP model by addressing the uncertainty of RWCPs.We established a critical sampling size of 2000 household water consumption patterns(HWCPs)with a data logging interval(DLI)of 15 min to attain dependable RWCPs.Through Genetic Algorithm calibration,the optimal values of the PRP model's parameters were determined:pulse frequency lλ=91 d^(-1),mean of pulse intensity E(I)=0.346 m^(3) h^(-1),standard deviation of pulse intensity STD(I)=0.292 m^(3) h^(-1),mean of pulse duration E(D)=40 s,and standard deviation of pulse duration STD(D)=55 s.Furthermore,validation was conducted at both HWCP and RWCP levels.We recommend a sampling size of2000 HWCPs and a DLI of30 min for PRP model calibration to balance simulation precision and practical implementation.This study significantly advances the theoretical foundation and real-world application of the PRP model,enhancing its role in urban water supply system management.