Mingliang Li

Generative artificial intelligence (GenAI) can accelerate Building Information Modeling (BIM) and digital-twin workflows, but probabilistic outputs and manipulated inputs create data-integrity risks that conventional construction-software liability rules do not fully explain. This study develops a reproducible doctrinal and comparative-law method that maps the GenAI value chain onto the Architecture, Engineering, and Construction (AEC) lifecycle. Its principal advantage over single-actor product or professional-negligence analyses is a qualitative proportional-liability assessment framework that evaluates technical control, foreseeability, verification capacity, causal contribution, and evidence preservation across developers, deployers, and professional users. The analysis identifies the following two recurrent pathways of harm: autonomous hallucination entering safety-relevant design information and adversarial or erroneous data entering digital-twin feedback loops. It also specifies syntax, semantic, cybersecurity, and human-verification controls linked to AEC information-management and quality standards. The framework is not an empirically calibrated formula and does not determine legally binding percentages. Instead, it provides a transparent decision aid for applying existing tort doctrines and regulatory duties to distributed GenAI-enabled construction workflows.

Public parks are increasingly expected to function not only as recreational green spaces but also as components of urban green stormwater infrastructure. In practice, however, their performance is still commonly judged by stormwater storage capacity alone, a project-level metric that tends to favor larger parks and obscure differences in how parks contribute to broader blue-green stormwater systems. Accordingly, this study aimed to develop and apply an exploratory comparative framework for assessing the differentiated stormwater roles of sponge city parks beyond storage capacity alone. Using 20 sponge city parks in Shanghai as cases, the framework integrates stormwater storage capacity, storage efficiency, flow leverage, and facility configuration. The results show that annual stormwater storage capacity varied substantially across the 20 parks, ranging from 7625.12 m³ to 132,915.24 m³, with a mean value of 47,220.80 m³. However, this capacity-based ranking was strongly associated with park area. Once storage was normalized by area, the ranking changed markedly: line-type parks showed the highest mean storage efficiency of 1.393, followed by node-type parks at 0.921, whereas patch-type parks recorded a much lower mean value of 0.059. Flow leverage scores ranged from 0 to 6, with a mean value of 2.35, indicating that parks with comparable storage performance may occupy different hydrological positions within the broader topographic flow structure. The preliminary capacity-leverage typology further showed that high storage capacity did not always coincide with high hydrological leverage, and facility configuration scores ranged from 1 to 5, suggesting additional variation in internal infrastructural organization. These findings suggest that storage capacity should be complemented by spatial efficiency, hydrological position, and facility configuration when interpreting the differentiated stormwater roles of sponge city parks. The study reframes evaluation from a single project-level storage metric toward a more differentiated understanding of sponge city parks as urban green stormwater infrastructure and supports more sustainable assessment of community-scale stormwater management within broader blue-green systems.

Achieving sustainable urban development and optimizing the urban scale structure are central priorities in global governance. However, the relentless population agglomeration in Chinese megacities, despite astronomical living costs, presents a prominent “China Paradox” that seemingly defies classical spatial equilibrium theories. This study decodes this paradox by endogenizing the strategic land supply behaviors of local governments. Utilizing a comprehensive panel dataset of 287 Chinese prefecture-level cities from 2006 to 2020, we construct a multi-dimensional mediation framework and a panel threshold model to investigate how the structural misallocation of land—specifically, the pro-industrial and anti-residential bias—reshapes urban migration dynamics. Empirical results reveal that this land allocation bias acts as the fundamental institutional engine driving urban polarization. Analysis of the transmission pathways reveals a complex push–pull dynamic at the core of this paradox. The artificial restriction of residential land drives up housing prices, generating a profound centrifugal “push” force. However, this dispersion effect is entirely neutralized by two formidable centripetal “pull” forces: industrial co-agglomeration fueled by subsidized manufacturing land, and premium public service capitalization financed through lucrative land revenues. Furthermore, this demographic pull effect exhibits a pronounced inverted U-shaped dynamic, peaking during the rapid growth phase but diminishing precipitously once cities cross the threshold into highly developed megacities (LnGDP > 11.525). These findings highlight the ultimate unsustainability of the land-driven urbanization model. We propose a paradigm shift towards sustainable urban governance, advocating for stage-specific land supply reforms and the transition from monopolistic land finance to a sustainable property tax system to foster a spatially just and resilient urban hierarchy.

China’s urban size distribution has increasingly shifted toward concentration in large cities amid global urbanization and the restructuring of urban development patterns. This trend has intensified governance and spatial pressures in major cities while exposing weaker growth momentum in small and medium-sized cities and reducing overall urban system coordination. Existing studies mainly explain this pattern through market forces such as agglomeration economies, housing prices, and migration, while others examine the consequences of local land practices from the perspectives of land finance, local competition, and institutional change. However, there is still no systematic explanation of why centrally imposed aggregate land constraints, operating through heterogeneous local land allocation, generate uneven urban outcomes. Against the background of the 2004 strict land management reform, this paper develops a theory-oriented conceptual framework linking central land constraints, local land allocation, and urban size structure. It clarifies how uniform central constraints may be translated into uneven urban outcomes through differentiated local land-allocation practices. Local land allocation is identified as the key transmission mechanism through which development opportunities are reshaped across cities and, under specific institutional conditions, the upper tiers of the urban hierarchy are reinforced. This paper therefore offers a bounded explanation of how central–local land governance shapes China’s urban size structure, while also underscoring the relevance of land governance to more balanced, resource-efficient, and sustainable urban development.

Since the Brundtland Report (1987), its definition has anchored sustainable development. An EBSCOhost co-mention scan (1987–2025) finds 259,112 records linking “sustainable development” with the Brundtland Report—used only as a descriptive attention proxy, sensitive to coverage, indexing, keywords, and residual duplicates. We then analyze concept-to-implementation barriers in building governance and propose an update pathway: explicit boundaries, minimum disclosures, and assurance logic. Yet in the built environment—characterized by long-lived assets, carbon lock-in, and net-zero commitments—the definition is difficult to operationalize without explicit boundaries, measurable indicators, and auditable trade-offs. We identify two concept-level weaknesses: (1) the definition reflects late-twentieth-century socio-technical conditions and offers limited guidance for practice shaped by digitalized delivery and operations, accelerated climate policy, and whole-life carbon accounting; and (2) its openness around “needs,” “harm,” and trade-offs enables boundary ambiguity (e.g., operational versus embodied emissions), fragmented standards and certifications, and greenwashing risks. We propose a built-environment update pathway that (i) operationalizes “needs” and “harm” through a minimum life-cycle indicator set linking affordability and occupant well-being with operational energy performance and whole-life carbon outcomes; and (ii) strengthens concept-consistent implementation via harmonized boundary declarations and verification principles across existing net-zero and green building tools, supported by targeted AEC capacity building.

The Frontal Area Index (FAI) is a commonly used, cost-effective preliminary screening tool for identifying the Least Cost Path (LCP) of urban ventilated corridors and mitigating the Urban Heat Island (UHI) effect, particularly in situations where data and budget availability are limited. Although its theoretical basis and simulation studies have been extensively examined, empirical validation through field measurements remains limited. This study assesses the FAI method’s applicability in two representative U.S. Midwest cities—St. Louis and Chicago—and proposes key modifications based on field-measurement validation. FAI simulations were conducted to identify optimal ventilation corridors, and the results were subsequently compared with in situ field measurements. Our findings indicated a strong correlation between FAI predictions and field data in St. Louis. In contrast, significant discrepancies were observed in Chicago, where simulated ventilation performance did not align with measured conditions, revealing the standard method’s limitations in complex urban topographies. To address these shortcomings, this study proposes four modifications to enhance the model’s accuracy for U.S. Midwest cities: (1) adjusting the model for varying urban morphologies, (2) limiting the calculation scope, (3) implementing a distinct approach for riverine areas, and (4) adopting a plot-based division for areas with large-scale buildings. This research verifies and refines the FAI method, creating a more reliable tool for diverse urban contexts. The optimized approach provides robust support for wind environment analysis, ventilation corridor planning, and UHI mitigation strategies.

With global warming intensifying, urban public spaces in cold-climate regions are increasingly exposed to heat beyond residents’ adaptive capacity. This study investigates whether smartphone use enhances thermal adaptation in Jingyue Central Park, Northeast China. A seven-day field campaign integrating microclimate monitoring and Passive Activity Observation (PAO) collected synchronized environmental and behavioral data. Results show that smartphone users had higher attendance and longer stays under high temperatures. Their Thermal Neutrality Threshold (NTT) and Critical Thermal Threshold (CTT) increased by about 2 °C and 3 °C, respectively, and up to 4.5 °C during optional activities, suggesting that voluntary media engagement improves heat tolerance and adaptive behavior. The study proposes mediated thermal adaptation to describe how digital media co-regulate environmental perception and adaptation. It extends thermal comfort research to cognitive-behavioral dimensions, links UTCI, NTT/CTT, and PAO data within one framework, and provides practical insights for optimizing thermal environments in cold-climate public spaces. Overall, the findings reveal the growing role of media-mediated behavior in enhancing human resilience to thermal stress.