High-temperature risk disaster, a common meteorological disaster, seriously affects people’s productivity, life, and health. However, insufficient attention has been paid to this disaster in urban communities. To assess the risk of high-temperature disasters, this study, using remote sensing data and geographic information data, analyzes 973 communities in downtown Wuhan with the geography-weighted regression method. First, the study evaluates the distribution characteristics of high temperatures in communities and explores the spatial differences of risks. Second, a metrics and weight system is constructed, from which the main factors are determined. Third, a risk assessment model of high-temperature disasters is established from disaster-causing danger, disaster-generating sensitivity, and disaster-bearing vulnerability. The results show that: (a) the significance of the impact of the built environment on high-temperature disasters is obviously different from its coefficient space differentiation; (b) the risk in the old city is high, whereas that in the area around the river is low; and (c) different risk areas should design built environment optimization strategies aimed specifically at the area. The significance of this study is that it develops a high-temperature disaster assessment framework for risk identification, impact differentiation, and difference optimization, and provides theoretical support for urban high-temperature disaster prevention and mitigation.

This study examines the governance challenges posed by new forms of employment in the context of China’s new urbanization, focusing particularly on the widespread phenomenon of “residing without settling” among on-demand platform workers in megacities. Based on a survey of 1627 respondents in Guangzhou, the findings reveal a pronounced gradient stratification in on-demand platform workers’ residence intention. Specifically, 57.3% of respondents demonstrate positive short-term residence intention, whereas long-term residence intention is predominantly characterized by neutral attitudes (73.3%), and settlement intention exhibits a clearly negative tendency (67.8%). Structural Equation Modeling indicates that residential environment exerts the strongest positive effect on sense of place (β = 0.47), followed by economic foundation (β = 0.24), while social capital demonstrates the weakest promoting effect (β = 0.22). Scenario analysis using Bayesian Network further reveals that when place attachment reaches a high level, the probability of positive long-term residence intention among on-demand platform workers increases by 22.1%, whereas improvements in residential environment reduce the probability of negative settlement intention by 5.2 percentage points. The results demonstrate that the residence intention of on-demand platform workers arises from the interplay of socioeconomic conditions, material space and emotional embedding. Notably, the sense of place forms a critical emotional attachment mechanism through a mediating effect. Economic deprivation acts as the primary constraint, while environmental quality serves as a fundamental limiting factor. Conversely, social capital accumulation partially mitigates these barriers. The findings contribute to a nuanced understanding of urban inclusiveness and labor force stability in the digital economy. Moreover, this study provides policy insights for optimizing urban population structures and fostering socially sustainable development.