Extreme heat increasingly disrupts urban mobility, yet planning still lacks street-scale evidence regarding which urban green infrastructure (UGI) typologies and park hierarchies are associated with retained cycling activity under heat and within what distance ranges these associations emerge. To address this gap, we introduce Heat Resilience of Street-segment Cycling activity (HRSC), defined as the retention of segment-level bike-sharing volume during hot conditions relative to normal baselines. Utilizing 6,185,753 bike-sharing trips in Shanghai’s central city during summer, we map-match trajectories to the road network to compute segment-specific HRSC. We then relate it to distance-to-nearest measures across a four-level park hierarchy (City, District, Neighbourhood, Pocket), POI-tagged functional green spaces, and major non-park UGI elements. By applying explainable machine learning, we identify nonlinear distance-response ranges, function-related proximity contrasts, and weekday-weekend differences. Results show that UGI-HRSC associations are strongly nonlinear and type-specific. Small embedded parks are more consistently associated with higher HRSC across neighbourhood-relevant ranges. By contrast, proximity to large parks can coincide with lower weekend HRSC in adjacent segments. This pattern is more consistent with baseline demand composition and network connectivity constraints than with simple cooling performance. Furthermore, functional green spaces show context-dependent associations that vary by accessibility, local urban context, and day type. Water bodies offer wider beneficial ranges, and the built-environment context systematically moderates whether nearby blue-green assets translate into retained cycling. This study operationalizes mobility heat resilience at a planning-relevant street scale, providing evidence-based insights to maintain active travel amidst escalating climate risks.
Poverty mapping is increasingly important for monitoring Sustainable Development Goal 1 (SDG 1) of the United Nations 2030 Agenda, which aims to end poverty in all its forms everywhere. Yet timely and fine-resolution poverty estimation remains difficult because conventional census- and survey-based approaches are costly, infrequent, and often sparse precisely where deprivation is most severe. As poverty emerges from complex socioeconomic systems shaped by human mobility, social interactions, infrastructure, and economic activities, emerging computational methods and nontraditional data sources have created new opportunities for poverty estimation and mapping. At the intersection of statistical physics, complex systems science, and data science, these approaches enable poverty estimation at finer spatial and temporal resolutions. This review summarizes the main concepts of poverty and the principal frameworks used to measure it, and examines recent advances in poverty estimation and mapping using satellite imagery, mobile phone data, social media data, and multisource data fusion. The review also discusses persistent challenges related to representativeness, transferability across regions, interpretability, and uncertainty quantification. Finally, the review clarifies both the analytical promise and the practical limits of contemporary poverty mapping.
This study examines the impact of water availability on the urban land market by focusing on a large-scale inter-basin water transfer project in China. Employing a difference-in-differences framework and a unique parcel-level dataset, we find that the project increased average land prices in water-receiving areas by 8.47% while also stimulating urban land market activity, and ultimately promote urban economic growth and expansion. These effects are primarily driven by two channels: population growth and mobility, and accelerated industrial expansion—all induced by the large-scale water transfer. Additional analyses further reveal that the project’s influence on land price is almost entirely concentrated in residential and commercial land parcels, with negligible effects observed for industrial land. And the positive impacts are more pronounced along the project’s middle route and in arid counties. Notably, we find no empirical evidence of adverse effects on land markets in water-supplying areas. A back-of-the-envelope calculation suggests that the appreciation in land values could recoup over one-third of the project’s total investment, underscoring its substantial economic benefits.
Brand activism is a major way companies are getting involved in societal issues, but the conditions on the consumer side that drive engagement are not well understood. This research focuses on the effects of social disconnection, the perception of disruption or weakening of meaningful relationships, on consumer intentions to engage with activist brands. In this paper, we argue that social disconnection also inspires people to actively re-construct and re-anchor their sense of self, which we call identity reconstruction, and that this reconstruction results in consumers' interactions with activist brands as symbolic platforms for the expression of values and self-reconstruction. In four experiments with controlled stimuli and fictitious brands, we found that social disconnection was associated with increased self-reported intentions to engage with activist brand content via digital media, a relationship mediated by identity reconstruction. The relationship between identity reconstruction and engagement intentions was also moderated by the construct of perceived message authenticity, indicating that the credibility of the brand is an important factor in determining whether activist brands can serve as a suitable platform for identity reconstruction. These results add to the research on brand activism and consumer identity by proposing social disconnection as an antecedent factor at the consumer level and by revealing the reconstructive process through which it operates. From a managerial perspective, findings suggest that authentic, value-congruent activism is more effective when consumers are experiencing social instability. Because the studies relied on controlled experimental stimuli and self-reported engagement intentions, future research should examine whether these effects extend to real brands, observed digital behaviors, and diverse cultural contexts.
This study examines how the state and the market shape income divisions among paid care workers in China's domestic service economy. We develop the concept of the state-led reproductive boundary to examine how state intervention, through certification, training, work arrangement, and long-term care insurance, redraws the lines along which reproductive labour is valued, financed, and protected. Drawing on a 2021 survey of 919 domestic workers in Shanghai, the first Chinese city to implement a municipality-wide long-term care insurance scheme, we distinguish maternity-infant care, eldercare, and non-nurturant work, and model both monthly and hourly income. We compare a purely cultural account of care valuation with one that incorporates the state. Cultural valuation alone cannot explain the wage divisions within paid care. The division between nurturant and non-nurturant work in China is organised less through race than through human capital, certification, and market recognition. Within nurturant work, the maternity-infant care advantage holds in monthly but not hourly terms, since live-in arrangements obscure the low hourly returns of feminised, time-intensive care. The culturally anticipated eldercare penalty does not materialise. Eldercare workers absorbed into the hourly-priced, state-financed insurance scheme are no longer disadvantaged relative to non-nurturant workers, showing that state intervention can partially insulate a culturally devalued form of care from market discounting.
