Intergenerational housing inequality is commonly framed in terms of delayed homeownership and prolonged renting; however, this framing derives largely from high-income, mortgage-based systems. This paper examines changes in young adults' household position, tenure and housing conditions in Ghana between 2000 and 2021, using harmonised ten per cent microdata from the 2000, 2010 and 2021 Population and Housing Censuses. Binary and multinomial logistic models indicate three shifts. First, the probability of occupying a household-leading role rose across all age groups, most sharply among adults aged 25 to 34, by nearly 19 percentage points between 2000 and 2021. Second, the tenure basis of housing access changed substantially. Owner occupation weakened among younger heads, non-market or rent-free occupation contracted, and market renting became the majority tenure among young household heads by 2021. Third, tenure-related housing-quality differences changed unevenly: renter-owner gaps in exclusive kitchen and bathroom access widened, while overcrowding gaps narrowed. Ghana's emerging generational housing inequality therefore concerns changing terms of housing access: household-leading roles became more common among younger adults, alongside a more rental-based tenure profile and persistent inequalities in some material conditions. The findings show why generational housing analysis must be grounded in the institutional and political-economic conditions of specific housing systems.
Shade is a fundamental environmental resource in summer, yet how trees reshape its spatial distribution for elderly populations remains insufficiently understood. Existing studies predominantly rely on micro-scale observations and rarely isolate the specific contribution of trees to shade distribution. This study addresses these gaps by employing a high-resolution ray-tracing framework across ten cities in Zhejiang Province, China, to simulate shade scenarios both with and without trees, and by integrating mobile signalling data to evaluate residential shade availability for the elderly. Results demonstrate that trees generally improve shade equity by reducing the Gini coefficient and shifting shade distribution towards neighbourhoods with higher proportions of elderly residents. These patterns remain robust when the analysis is restricted to built-up areas. Nevertheless, persistent Low shade-High elderly (L-H) clusters indicate that many ageing neighbourhoods remain underserved by tree shade. These priority intervention areas are generally characterised by limited tree shade contribution. The identified L-H clusters provide practical guidance for age-friendly precision greening by helping prioritise neighbourhoods where additional tree planting may yield greater improvements in shade equity.
Housing markets in cities experiencing large immigration inflows often display contrasting rental patterns: migrants tend to concentrate in more affordable neighbourhoods while paying higher rents for comparable housing than local households. This paper examines these patterns using data from Santiago, Chile. Combining nationally representative microdata with spatial econometric analysis, we find that immigrant status is associated with higher household rents—rent differentials are larger among tenants without a written contract—whereas neighbourhoods with larger immigrant shares tend to exhibit lower average rental prices and greater rental supply. These findings suggest that the relationship between immigration and rental prices differs across scales of analysis. Rather than reflecting a single homogeneous process, the coexistence of these patterns is consistent with differentiated rental outcomes within urban housing markets and may reflect forms of segmentation documented in the housing literature. By linking household-level rent outcomes with neighbourhood-level rental patterns, the study highlights how scale helps explain contrasting rental outcomes and how residential concentration and rent differentials may coexist within the same urban rental market.
Enhancing the sustainability of old neighbourhood renewal projects is pivotal for achieving the strategic goal of sustainable development across urban areas. However, limited attention is placed on the mechanism driving the sustainability of neighbourhood renewal. Acknowledging this void, a conceptual framework is developed using systems thinking for assessing and understanding the sustainability of old neighbourhood renewal. Drawing upon the ‘production–living–ecological spaces (PLES)’ and stakeholder perspectives, we identify a total of six driving factors covering the capabilities and inputs of the public sector, private entity and general public. Anchored on them, a system dynamics model is constructed to conduct a scenario-based analysis and empirically examine the developed framework through a case study of Baotou, China. The simulation results reveal that the stakeholder decisions and actions aimed at optimising inputs and sustainable capacity-building can enhance the long-term sustainability of old neighbourhood renewal throughout its lifecycle. This study engenders a novel paradigm to (1) clarify the mechanism driving the sustainability of neighbourhood renewal projects from a multidimensional, systematic and dynamic perspective and (2) have a systematic approach in place for enabling project sustainability. It also facilitates the practices in relation to pertinent decision-making processes and sustainable management.
Rural construction land provides an essential spatial basis for rural industrial development, yet it remains unclear whether larger land supply is consistently associated with stronger industrial development across sectors and places. Using panel data for 2142 Chinese counties from 2019 to 2023, this study develops an interpretable machine-learning framework combining XGBoost, SHAP analysis, and land-supply sensitivity analysis to examine nonlinear and heterogeneous associations between rural construction land supply and rural industrial development. The results show that the predicted contribution of rural construction land is limited at low stock levels, turns positive after reaching approximately 8715 ha for agriculture-related entry and 9124 ha for non-agricultural, and gradually flattens at higher levels. Under a common 1000-ha land-stock adjustment, the predicted increase in non-agricultural entry is approximately six times that in agriculture-related entry. For non-agricultural entry, the interaction results show that the predicted contribution of land stock is stronger where population density and fiscal self-sufficiency are higher, showing that land responsiveness varies with county-level capacities. Weak responsiveness occurs in both mature counties with diminishing land constraints and constrained counties where existing land remains weakly connected with industrial entry. The findings suggest that rural land governance should therefore move beyond indiscriminate land expansion toward differentiated strategies tailored to sectoral demand, local responsiveness, and the efficient allocation of scarce construction land quotas.
The global industrial structure is undergoing a profound transformation from tangible, production-based industries toward intangible, service-oriented sectors. As a central manifestation of this transformation, de-industrialization has important implications for income distribution and constitutes a key issue in development economics. However, existing research has paid insufficient attention to its effects on the urban-rural income gap, and systematic city-level evidence remains scarce. This study identifies de-industrializing Chinese cities based on changes in the manufacturing share and uses the system generalized method of moments (SGMM) to assess the association between de-industrialization and the urban-rural income gap. The results show that city-level de-industrialization in China is widespread, regionally heterogeneous, and often premature. De-industrialization is significantly and positively associated with the urban-rural income gap, particularly in central and western China, where premature de-industrialization is more prevalent. By contrast, in normally de-industrialized cities with higher levels of economic development, de-industrialization is negatively associated with the urban-rural income gap. Furthermore, a slowdown in the transfer of rural surplus labor and the increasing service orientation of the employment structure are identified as potential mechanisms underlying this association.
Ecosystem services (ESs) exhibit spatially and temporally varying tradeoffs or synergies. Ecosystem service bundles (ESBs), representing the combined supply structures and dominant functions of multiple ESs, translate these interrelationships into spatially explicit ecosystem functional types and thereby provide a direct basis for ecological management zoning. However, the historical and multi-scenario evolution of ESBs has rarely been incorporated into ecological management zoning. Based on land use data from 2000 to 2020 and China's policy context, the PLUS model was employed to simulate land use patterns in 2030 under multiple scenarios. Further, five ESs, including crop provision (CP), carbon storage (CS), water yield (WY), soil conservation (SC), and habitat quality (HQ), were quantified at the county level. Spearman's rank correlation and K-means clustering algorithm were applied to identify pairwise ESs tradeoffs/synergies and ESBs, respectively. Historical and multi-scenario ESBs were subsequently integrated to delineate ecological management zones. Results demonstrated significant spatial heterogeneity in ESs supply, with higher CS, WY, SC, and HQ concentrated in eastern and southern regions, while higher CP predominantly coincided with major plains and basins. Multi-scenario simulations showed that ecological protection scenario enhanced ecosystem quality but compromised cropland security, cultivated land protection scenario increased CP at the expense of ecological functions, and urban development scenario threatened both. Synergies dominated ESs interactions, with tradeoffs only between CP and other ESs. Four ESBs types were identified, representing ecological dominant, multifunctional transitional, agricultural dominant, and low ESs types. Integrating ESBs across historical and multi-scenario evolution, this study derived six distinct ecological management zones using a majority and history priority rule, with the 2020 classification as a tie breaker. Systematic framework can provide scientific reference for spatially differentiated protection and restoration strategies, ultimately facilitating comprehensive ecosystem sustainability.
Climate migration research is increasingly analyzing migration's implications for adaptation. However, despite broad recognition that climate change is already exacerbating rural-to-urban migration in many countries in the Global South, especially into vulnerable communities in informal settlements, migration's effects on these communities' vulnerability and adaptive capacity have received limited attention. To begin addressing this gap, we conduct semi-structured interviews with official community leaders in Dar es Salaam's Ilala flood-prone district to elicit their views of migration's effects on flooding and the potential responses to these effects. To this end, we focus on how migration influences three critical determinants of informal communities' vulnerability and adaptive capacity: innovation, collective action, and economic development.The findings show that community leaders perceive rural migrants as mainly enhancing adaptive capacity, yet also identify various effects aggravating vulnerability across the studied dimensions. Although only some leaders view migrants as innovation agents, they highlighted innovation's significant adaptive effects. However, migrants are widely perceived as contributing to collective action vis-à-vis flooding, and, particularly, to economic development that enhances communities' and households' flood preparedness and recovery, as well as boosting collective action and innovation. We also find that community leaders' preferred responses to migration are based on harnessing their communities' social capital, albeit in collaboration with higher-tier urban authorities. We conclude by highlighting communities' economies as potentially crucial spheres for formulating interventions to enhance migration's adaptive effects, and by underscoring the need to adopt community-centered approaches that maximize migration's contribution to adaptation.
This paper has developed a temperature innovation theory model, which elucidates the mechanism by which extreme temperatures suppress urban innovation activities, both by directly affecting the innovating entities and inducing population migration. Empirical analysis based on panel data from 275 prefecture-level cities in China from 2004 to 2021 reveals that both extreme high and low temperatures significantly inhibit urban innovation activities. The study also found that extreme temperatures lead to population outflow, reducing innovative human capital, but government fiscal science and technology and environmental expenditures can alleviate this adverse impact. In addition, the study further reveals significant regional heterogeneity and non-linear relationships in the impact of extreme temperatures on urban innovation activities. Extreme high temperatures have a greater inhibitory effect on innovation activities in the central and western regions and smaller cities. There is an inverted U-shaped relationship between temperature and innovation activities, with around 19 degrees Celsius being the optimal temperature range for urban innovation activities. These research findings deepen our understanding of how extreme temperatures affect urban economic activities, offering a new perspective for dealing with climate change and promoting urban innovation development, and have certain policy implications.
As cities shift from growth-oriented expansion to regeneration, old residential community renovation (ORCR) has become a major policy concern, yet many indicator systems remain top-down and do not fully reflect residents' actual lived needs. Research has concentrated on physical renovation, policy implementation, and infrastructural upgrading, while giving less attention to residents' value perceptions and their role in indicator design. Using Guangzhou as an empirical case, this study reframes ORCR through public perceived value (PPV) by integrating grounded theory with the analytical Kano (A-KANO) model to elicit value dimensions, optimize indicators, and derive implementation guidance. The findings indicate that: (1) PPV comprises functional, emotional, and social value, capturing residents' value perceptions; (2) optimizing Guangzhou's 60-indicator baseline through 16 additions, 1 removal, and the refinement of 2 indicators into 5 yields a 78-indicator framework oriented toward perceived functional value, classified into must-be, one-dimensional, attractive, and indifferent attributes to support budget-conscious resource allocation; and (3) emotional and social value are higher-order pursuits realized through indicator implementation and everyday experience, and can be cultivated by embedding local cultural memory in everyday settings, activating shared public spaces for neighborly interaction, and engaging residents in community governance. By linking renovation indicators directly to residents' value perceptions, this study shifts ORCR from a passive, supply-led practice toward an active, value-driven approach, offering planners a replicable path from formal indicator lists to implementable action. It further advocates continuous post-renovation evaluation of PPV, repositioning ORCR as a sustained feedback process for people-centered improvements in residential quality.
Informal settlement is the most pervasive form of urbanization in many cities of the global South. Eviction and demolition by the state is a common response. While some are formally redeveloped other sites are re-encroached informally. This paper explores the processes and morphologies of informal re-encroachment through three case studies in the African and Latin American contexts. Re-encroachment is conditioned by the process of eviction and community organization as well as the political, economic and spatial context. Through a mixed-method approach combining archival research, mapping, interviews and observations, the paper analyses the communities, urban form and architecture before and after resettlement, in relation to eviction/demolition processes, land tenure and political economy. Re-encroachment is a multi-faceted process that can produce a return to similar social and spatial conditions, or it can produce new morphologies and populations, with standards that may improve or decline. Of key significance in the re-encroachment are the social networks that were formed in the erstwhile demolished neighbourhoods, which have a ‘reconstitutive’ role in the re-encroachment process. Understanding what happens after demolition can help in understanding how affordable housing might be built better in the first place, how on-site upgrading may be better supported by the state, and how demolition might be avoided.
Digital innovation plays a crucial role in promoting industrial structure upgrading (ISU). Adopting a network-based perspective, this study analyzes interregional digital innovation and investigates how it affects urban ISU. Using panel data from 274 prefecture-level cities in China from 2010 to 2022, we find that spatial connections in digital innovation among cities have been continuously strengthened during the sample period, with intercity digital innovation cooperation steadily advancing. A city's deeper participation in interregional digital innovation network is significantly associated with enhanced ISU. Improvements in a city's network position contribute to ISU by advancing digital industrialization and industrial digitization. Additionally, the elevation of a city's position within the interregional digital innovation network facilitates knowledge diffusion, thereby producing spatial spillover effects on ISU. These results underscore the importance of digital innovation in driving industrial transformation, particularly for cities capitalizing on network-based knowledge spillovers and interconnectivity. This research provides fresh insights into how digital innovation networks influence industrial upgrading and offers policy recommendations for fostering dense interregional digital innovation networks.
The coexistence of the integration of deep artificial intelligence (AI) into government governance and widespread work overload at the grassroots level has emerged as a research theme. Thus, clarifying the impact of these two factors on improving urban public service and the multidimensional effect of this process hold both theoretical value and policy urgency. Using a sample of 284 Chinese cities from 2016 to 2023, this study quantifies government AI applications (GAIA) and government work overload (GWO) using text big data and remote sensing big data, respectively. We examine the multidimensional impact of these two factors on urban public service, including their inherent, interactive, and matching effects, via a threshold regression model. The study finds that both GAIA and GWO significantly promote urban public service. Regarding scale effects, GAIA exhibits increasing returns to scale, while GWO demonstrates an inverted U-shaped effect. Regarding substitution and amplification effects, GAIA exhibits the strongest substitution effect when GWO is in the range of [0.710, 1.020), while GWO shows an amplification effect once GAIA crosses the threshold of 0.625. Regarding synergistic effects, their impact on urban public service increases significantly with increasing coupling and coordination. The research reveals that compared with the “carrying the load” GWO, the “switching lane” GAIA is more long-term and sustainable, providing a theoretical basis and policy inspiration for optimizing urban public service.
The digital transformation of food systems has fundamentally reconfigured the household food environment, yet whether online food purchasing translates into improved dietary quality remains unclear. Grounded in the food environment framework, this study investigates this relationship using survey data from Nanjing, China, collected during the COVID-19 pandemic period. Dietary quality was assessed using two complementary metrics: the Household Dietary Diversity Score (HDDS) to capture dietary diversity, and the Food Consumption Score (FCS) to evaluate nutritional adequacy. The econometric results reveal a positive baseline effect: online food purchasing significantly improves urban households’ dietary quality. However, this improvement is not unconditional; it is impeded by two critical factors. First, reliance on a single online mode significantly undermines dietary gains. Second, inadequate delivery infrastructure in suburban areas attenuates these positive effects. These findings extend the food environment framework into the digital domain, offering critical implications for platform governance, nutritional education policy, and urban delivery infrastructure planning.
