In the context of urban–rural integration, how multidimensional land use conflict (LUC) differentially shapes rural multifunctionality is a key question for sustainable rural development. Existing studies, however, have largely treated the two independently, with limited systematic analysis. This study extends the conflict–coordination perspective from explaining land-use transition itself to analyzing its effects on rural multifunctionality. It combines planning-oriented and machine-learning suitability assessments to construct an integrated analytical framework linking the pattern, mismatch, and competition dimensions of LUC with rural multifunctionality. Drawing on multi-source data for 2000, 2010, and 2020 in the Yangtze River Delta (YRD), we use 3-km rural grid cells as evaluation units and apply generalized additive models and piecewise regression, XGBoost–SHAP, and the geographical pattern causality (GPC) model through a progressive sequence of association identification, impact analysis, and causal inference. The results show that, under the conflict–coordination perspective, the effects of LUC on rural multifunctionality exhibit differentiation, nonlinearity, and bidirectional feedback. During the study period, rural multifunctionality continued to improve, with non-agricultural economic function growing fastest, while composite LUC rose at an accelerating rate. Composite LUC was positively associated with the rural multifunctionality index (RMI) in all three study years, but the association weakened beyond the thresholds. Analysis of changes over 2000–2020, however, showed that intensifying LUC generally coincided with declining rural multifunctionality. Multidimensional LUC produced differentiated nonlinear effects through spatial form, factor allocation, and functional composition. The relative importance of LUC increased. In 2020, spatial pattern conflict showed inverted-U effects on agricultural production and ecological functions. Suitability mismatch conflict showed L-shaped decreasing effects on RMI, agricultural production function, and ecological function, but was associated with stronger non-agricultural economic function. Use-competition conflict had generally positive effects on RMI and agricultural production function, showed an inverted-U effect on living function, and constrained non-agricultural economic function in areas where use-competition conflict was high. These effects were moderated by natural and locational conditions and varied markedly across space. GPC tests revealed bidirectional feedback between composite LUC and RMI, with the LUC-to-RMI direction dominant. The negative causal strength of composite LUC on RMI declined slightly before rising again. Agricultural and non-agricultural economic activities played a greater role in shaping LUC pressure, whereas living and ecological functions more often reflected the consequences of land allocation. Non-agricultural expansion intensified LUC, with gains in living function coexisting with agricultural and ecological losses. Negative effects on agricultural production function operated mainly through spatial pattern conflict and suitability mismatch conflict, while all three LUC dimensions exerted negative causal effects on ecological function in each study year. These findings reveal the coexistence of alignment and dislocation between land allocation and functional demands, providing an analytical perspective for understanding the relationship between land use and rural development in regions where rapid urbanization intersects with rural transformation.
Some urban form characteristics, e.g., dense street network, diverse pedestrian destinations, can promote vitality, because they support walking, social interaction, and public life. Yet these characteristics mainly focus on pedestrians rather than indoor occupants, although people spend around 90% of their time indoors. Using 1,352,550 simulated window views across Hong Kong, this study examines whether vitality-oriented urban form indicators, including floor area ratio, road network density, point-of-interest entropy, and building-age entropy, are associated with model-predicted window-view quality. Multilevel mixed-effects models were used, complemented by height-interaction models (height refers to the vertical elevation), contextual heterogeneity tests, and multi-scale comparisons. The results show that road network density, point-of-interest entropy, and floor area ratio are negatively associated with window-view quality (β = −0.111, −0.082, and −0.054, respectively; all p < 0.001), whereas the building-age association is very small (β = −0.004). Moreover, window height is positively associated with window-view quality (β = 0.396), and all four height-interaction terms are positive (β = 0.026–0.055). Window-view quality was lowest in high-development-intensity Tertiary Planning Units, and associations with road network density and point-of-interest entropy were more negative in old urban areas. Across buffers, the road-density and floor area ratio associations remain negative in the predicted-score models. In the human-rated subsample, the height and two entropy associations remain directionally consistent, whereas the other two are sensitive to outcome measurement. The findings call for more vertically and contextually differentiated urban design strategies that consider indoor visual welfare alongside street-level vibrancy.
Innovation has obvious spatial characteristics. As the key carrier of technological innovation activities, innovative space (IS) serves as a critical spatial platform and operational environment for the diffusion of innovation elements, including technology transfer. A network-based approach can effectively reveal the regional development patterns of ISs and the rules of knowledge exchange. This study integrates spatial interaction theory with complex network methods to empirically analyze the Guangdong–Hong Kong–Macao Greater Bay Area using large-scale patent transfer data. First, the radiation zones (RZs) of the ISs are identified using Wilson's maximum entropy model, and the radiation effect of the IS is characterized by the radiation radius and intensity. Second, a multi-layer innovation association network is constructed by integrating ISs, RZs and other external micro-innovation entities. The characteristics of regional innovation linkages are analyzed, revealing the interaction patterns of ISs. Finally, considering the interaction patterns and radiation effects of ISs, ISs are classified into four types, namely, Hub-type, Outward-type, Inward-type and Island-type. Based on the identification of the dominant industries of the IS themselves, further determine their industrial sectors, thereby revealing the industrial heterogeneity characteristics of different types of IS, and ultimately proposing differentiated development strategies for each type. These findings provide empirical evidence and policy recommendations for promoting the coordinated development of regional ISs.
The urban built environment (UBE) substantially shapes carbon emissions trajectories, yet existing studies largely rely on fragmented dimension analyses, coarse administrative units, and homogeneous linear assumptions, leaving the comprehensively multidimensional, spatiotemporally heterogeneous, and nonlinear interplay between UBE and carbon emissions largely understudied, particularly from an intra-urban perspective reflecting real-world anthropogenic activities. Taking 286 Chinese prefecture-level cities from 2010 to 2024 as testbeds, this study architected an intra-urban UBE framework encompassing 5D (e.g., density, diversity, design, destination accessibility, and distance to transit) and developed an interpretable ensemble learning model to decouple the intricate association between intra-urban UBE and carbon emissions. The result reveals that intra-urban UBE collectively accounts for 61.51% of the impacts, surpassing the socioeconomic factors. Moreover, the results identify non-linear thresholds and interactive effects: density and diversity exhibit inverted U-shaped relationships, whereas urban compactness and urban green and blue spaces show consistent negative associations. Critically, active transport networks reduce emissions only when combined with public transit proximity; road density expansion cuts emissions only in compact cities; and polycentricity with compactness achieves emission reduction only within a moderate window. Temporally, the relative importance of greenness, active transport, diversity, and polycentricity has increased, while that of density has declined. Spatially, the effects of intra-urban UBE vary markedly across regions and cities. Polycentricity works best in emerging agglomerations, and transit proximity has reached diminishing returns in core megacities. The findings underscore that low-carbon urban policy must be spatiotemporally tailored with context-sensitive thresholds, positioning intra-urban UBE as a foundational climate mitigation instrument.
The networked development of urban agglomerations (UAs) contributes to ecological footprint (EF) expansion and spillovers, yet creates opportunities for cross-regional ecological resource synergy. However, research gaps persist in constructing intercity synergy networks and planning ecological resource allocation and transfer paths within them. This study develops a novel multi-scenario approach using a Network Flow Allocation-based Particle Swarm Optimization (NFA-PSO) model to construct an efficient synergy network for balancing ecological surplus–deficit within UAs. The approach's effectiveness is empirically validated in the Beijing–Tianjin–Hebei urban agglomeration (BTHUA), China. Results show severe ecological resource supply–demand imbalance in the BTHUA, with 84%–86% of units in deficit. From 2010 to 2020, EF continuously shifted from the core metropolitan area to peripheral underdeveloped regions. Regarding the ecological synergy network, underdeveloped transportation infrastructure in ecological surplus areas imposed a high resource flow load, constraining the allocation efficiency of the ecological synergy network. Multi-scenario simulations further show that different development orientations alter ecological resource flow paths and network loads. Among S1–S5, the economy-priority scenario (S2) has the lowest network resource load and the highest efficiency. By contrast, total network load rises by 8.01%–20.57% under scenarios integrating population, vulnerability, and sustainability objectives, demonstrating that more balanced resource allocation requires higher cross-regional network flow costs. Moreover, the resource flow network exhibits structural vulnerabilities and limited spatial coverage due to over-dependence on a small number of trunk paths. This study provides novel insights and solutions for optimizing ecological synergy networks, thereby advancing the sustainable development of UAs.
