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.

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.

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.