Commuting carbon emissions are an essential component of urban carbon emissions, and determining how to reduce them is an area of great debate among researchers. The current research lacks a tool and instrument that can extensively account for residents’ commuting. Traditional methods are mainly based on questionnaire surveys, which have low accuracy at spatial and temporal aspects. High accuracy carbon emission accounting methods can effectively assist urban planning and achieve precise urban emissions reductions. This study applies a taxi commuting carbon emissions accounting method divided into two main steps. Firstly, the carbon emissions of taxi trajectories are calculated using taxi trajectory data and a carbon emission calculation method developed based on VSP. Secondly, the taxi trajectory and POI data are used to filter the commuter trajectory with the help of a two-step moving search method. In this way, the taxi commuting carbon emissions were obtained. Then, the spatial distribution characteristics of residential taxi commuting carbon emissions are analysed by spatial autocorrelation tools, which could facilitate low carbon zoning management. A typical working day in Hangzhou was selected as the research object of this study. The results show that (1) morning peak commuting carbon emissions in the main urban area of Hangzhou reached 2065.14 kg per hour, accounting for 13.73% of all taxi travel carbon emissions; and evening peak commuting carbon emissions reached 732.2 kg per hour, accounting for 4% of all taxi travel carbon emissions; (2) At the grid level, the spatial distribution of commuting carbon emissions in Hangzhou shows a single central peak that decays in all directions; and (3) The results at the resident community scale show that urban public transport facilities influence resident community commuting carbon emissions. In areas such as at the urban-rural border, resident community commuting carbon emissions show high levels of aggregation, and in the main urban area, resident community commuting carbon emissions show low levels of aggregation. This study not only provides a new method of commuting investigation but also offers constructive suggestions for future carbon emission reduction under Hangzhou’s urban planning.

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.

Community green spaces (CGSs) constitute a crucial element of urban land use, playing a pivotal role in maintaining the stability of urban ecosystems and enhancing the overall quality of the urban environment. Through the post-occupancy evaluation (POE) of green spaces, we can gain insights into residents’ actual needs and usage habits, providing scientific evidence for the planning, design, and management of green spaces. This ensures that CGSs better meet residents’ needs and improve their quality of life. The POE of CGSs relies heavily on high-precision data support. However, the current POE system for CGSs faces challenges, such as limited data collection methods, incomplete indicator systems, and excessive manual involvement. To address these limitations in data collection, this study proposes a comprehensive, dynamically monitored, objective, and sustainable POE system for CGSs. This system incorporates a multi-dimensional perception system that integrates the Internet of Things (IoT) and sensors to collect data from various sources. It establishes an evaluation framework from the perspectives of policy guidance and usage needs for CGSs, utilizing neural network systems and artificial intelligence techniques to compute the evaluation results. Using the Jiayuan Sanli Community in Beijing as a case study, this paper demonstrates the feasibility of the proposed system. A comparison between the POE results obtained using the multi-dimensional perception technique and those obtained manually reveals an 87% improvement in the accuracy of the evaluation results based on the multi-dimensional perception system. This system bridges the gap between planning perspectives and user experiences, contributing significantly to future urban land planning and land policy formulation.

Land transition in China has led to the emergence of highly heterogeneous neighborhoods. This process challenges the social sustainability of public green spaces. This research investigates the driving mechanisms of place attachment within green space across diverse community typologies in Beijing. This study constructed a structural equation model (SEM) based on 626 valid questionnaires, using the Stimulus–Organism–Response (S-O-R) framework. The overall SEM results indicate that place identity significantly contributes to civic behavior (β = 0.439, p < 0.001). However, a persistent ‘value-action’ gap remains, with 65.81% of residents demonstrating high identity yet low participation. Furthermore, the multi-group analysis (MGA) reveals that place attachment logic diverges significantly across groups. Regarding user identity, public events promote visitors’ place identity, but this effect remains insignificant among residents (β = −0.064, p > 0.05). Regarding generational differences, the macro-spatial environment is significantly associated with place dependence for young people (β = 0.330, p < 0.001) but is insignificant for the elderly. Community heterogeneity reveals distinct failure modes. In commodity housing communities, a disconnect exists where daily usage fails to foster dependence (β = 0.026, p > 0.05). Conversely, urban–rural resettlement communities display an identity deficit where public events fail to translate into place identity (β = 0.131, p > 0.05). The study proposes differentiated renewal pathways tailored to three community types. For commercial housing communities, it advocates precise interventions that prioritize social engagement. Meanwhile, for urban–rural resettlement communities, the focus shifts to accessibility and culturally rooted activities to help reconnect displaced populations.

This study investigates the nonlinear, interactive, and temporally dynamic effects of urban form on street vitality within community-oriented metro station areas (MSAs) in Beijing. It offers potential reference value for other cities facing comparable challenges in MSA implementation and increasing motorization. This research addresses gaps in prior studies concerning the integration of multi-source data, nonlinearity, and diurnal variation. Utilizing an extended node-place-design framework, urban form is conceptualized through network, interface, and functional dimensions. The empirical analysis employs multi-source datasets, including 128,199 mobile device trips recorded in April 2024, OpenStreetMap for network data, Baidu points of interest for functional data, and Grasshopper for interface metrics, covering 183 street samples within a 1000 m radius of metro stations. Traditional regression models—such as ordinary least squares and spatial autocorrelation and cross-correlation—are used as baselines, while a novel gradient-boosting decision tree with latitude and longitude features is applied to enhance predictive performance. The results indicate that key contributors include road network density (16.89%), road intersections (10.56%), and point-of-interest density (9.74%), with Shapley Additive Explanations dependence plots demonstrating nonlinear thresholds. The analyses reveal synergistic or antagonistic interactions among features. Temporal fluctuations in feature importance further support the presence of diurnal dynamics. The study provides insights for time-sensitive urban planning aimed at enhancing MSA vitality, sustainability, and resident quality of life, while acknowledging that the conclusions are context-specific to Beijing and require additional validation in other urban environments.