Carbon metabolism research has attracted worldwide attention as an important way to cope with climate change, promote carbon emission reduction, increase carbon sequestration, and support low-carbon city construction. Ecological network analysis (ENA) plays an important role in network analysis and simulation of carbon metabolism. However, current studies largely focus on single elements or local processes while rarely analyzing the spatial coupling between land use and carbon metabolism. Therefore, taking Tongzhou District as an example, based on the data of land use change and energy consumption, this study constructed an analysis framework based on ENA to explore the comprehensive impact of land use changes on carbon metabolism. The results show the following: (1) From 2014 to 2020, the total carbon emissions increased year by year. Carbon emissions of other construction land (OCL) were dominant, while the carbon sequestration capacity of forest land (FL) increased by 236%. The positive carbon metabolic density remained relatively stable, while the negative carbon metabolic density decreased year by year. (2) The negative carbon flow was concentrated in the transfer of other land to OCL, accounting for 40.2% of the total negative “carbon flow.” The positive carbon flow was primarily from the transfer of other land to FL. (3) From 2014 to 2016, the spatial ecological relationships of carbon flow were dominated by exploitation and control. From 2016 to 2018, competition relationships intensified due to the expansion of the field; from 2016 to 2018, exploitation and control relationships, competition relationships, and mutualism relationships increased significantly and were evenly distributed. This study provides decision-making guidance for the subsequent formulation of government carbon emission reduction policies.

The effects of global urbanization and climate warming on public health, including the health risk caused by the urban heat environment, have drawn extensive attention. Therefore, methods to appraise the spatiotemporal response mechanism of climate warming and the high temperature exposure dose (HTED) of urban residents need to be investigated to achieve sustainable cities. Based on an environmental health risk appraisal model and using the six ring roads in Beijing of China, this study simulated the spatial distribution of future high temperatures in three shared socioeconomic pathways (SSPs), SSP1-2.6, SSP3-7.0, and SSP5-8.5, by determining the correlation between the future mean maximum temperatures and sunny-day mean temperatures. Additionally, the HTED of commuting on foot or by bicycle was assessed based on the point of interest data of subway stations, architectural composition, and traffic surveys. Results demonstrate that during 2020–2040, 66.57% and 50.07% of areas for commuting on foot and by bicycle within the sixth ring road exposed to high temperatures (including low, medium, and high-risk areas). The exposure risks of both commuting methods were concentrated, and the exposure dose was the highest between the fifth and sixth ring roads, whereas the first to fourth ring roads were dominated by non-risk, minimum-risk, and low-risk areas. Moreover, compared with SSP1-2.6 and SSP5-8.5, SSP3-7.0 exhibited low HTED in both commuting methods. The proposed method provides a scientific basis to aid in identifying urban areas with high temperature exposure risk and a reference in assisting the planning of resilient and sustainable cities. Keywords: Climate warming; High temperature exposure dose; Scenario simulation; Commuting modes; Public health