Community is the foundation of modern cities, where urban residents spend most of their lifetime. Effective and healthy community design plays a vital role in improving residents’ living quality. Pedestrian network is an indispensable element in the community. Successful pedestrian network design can help the residents be healthy both physically and mentally, build the awareness of “Go Green” for the society, and finally contribute to low-carbon and green cities. This paper proposes a community pedestrian network design method based on Urban Network Analysis with the help of the Rhino software. A case study of a typical community in Guangzhou, China was implemented, specifying the steps of the proposed method. The findings presented include the features of the citizens and the accessibilities of the neighbors that are obtained from the community pedestrian network simulation. The limitation and scalability of this method was discussed. The proposed method can be essential to designing healthy and sustainable communities.

During the process of rapid urban expansion, there has been a growing interest in understanding the spatial requirements of green spaces. However, limited research has evaluated green space demand specifically in terms of carbon storage and carbon emissions. This study introduces a novel methodological framework that aligns ecosystem service functions with both supply and demand, considering carbon storage and carbon emissions as crucial perspectives. The goal was to develop a comprehensive approach to assess the matching between the supply and demand of green spaces based on their carbon-related ecosystem services. The following research questions were developed to guide this study: (1) What are the spatial and temporal characteristics of carbon storage? (2) What are the spatiotemporal variations in carbon emissions on a city scale? (3) How does a city obtain the demand priority evaluation of green spaces in terms of carbon neutrality? Using Guangzhou as a case study, we employed the integrated valuation of ecosystem services and tradeoffs (InVEST) model to measure the spatial and temporal patterns of carbon storage. Remote sensing data were utilized, along with emission factors, to analyze the spatial and temporal characteristics of carbon emissions. The line of best fit method was employed to predict future carbon storage and carbon emissions, as well as population density and average land GDP. Based on these predictions, we prioritized the demand for green spaces. The results indicate the future demand priority order for green spaces in different districts. We suggest that this green space demand evaluation model can serve as a reference for future policy making and be applied to other cities worldwide.