This research employed the 2012 national smart city pilot policy as a quasi-natural experiment, and used a sample of Chinese listed firms from 2009 to 2024 to examine the policy's impact on the labor misallocation index. Using a Difference-in-Differences (DID) model, this research evaluated the impact of smart city development on corporate labor misallocation. The findings reveal the following: First, smart city development significantly reduces firm-level labor misallocation. This key finding is proven to be robust under a battery of empirical validation tests, including placebo tests, propensity score matching combined with DID (PSM-DID), the exclusion of specific time periods, substitution elasticity tests, re-estimation of production functions, and controls for concurrent policy interventions. Second, this mitigation effect is more pronounced in firms located in eastern regions, non-resource-dependent cities, and labor-intensive industries. Third, mechanism analysis indicates that smart city construction exerts its impact by accelerating corporate digital transformation, improving human capital quality, and enhancing employees' subjective well-being and job satisfaction. Finally, based on these empirical results, this research proposes policy recommendations to optimize the role of smart city pilots in labor resource allocation.

This paper mainly investigates the causal relationship between the spatial structure of urban agglomerations and carbon emission intensity, and clarifies the underlying mechanisms. We employ the rank-size rule to characterize the polycentric indices of 21 Chinese urban agglomerations from 2006 to 2022, representing their spatial structures, with carbon emission intensity measured as the ratio of total carbon emissions to GDP. Using a two-way fixed-effects model and employing topographic relief as an instrumental variable, our paper examines the impact of urban agglomeration spatial structures on carbon emission intensity and further investigates the underlying transmission mechanisms and heterogeneity effects. The findings reveal that polycentric spatial development in urban agglomerations leads to statistically significant reductions in carbon emission intensity. The robustness and endogeneity tests support the benchmark regression results. Mechanism tests reveal that polycentric spatial structures of urban agglomeration reduce carbon emission intensity by promoting technological progress, guiding industrial relocation, and alleviating traffic congestion. However, they also increase carbon emission intensity by reducing public transportation and promoting urban sprawl, indicating negative environmental externalities. Overall, the positive environmental externalities of polycentric structures outweigh the negative ones. Considering the considerable differences in internal integration across urban agglomerations in China, we perform a heterogeneity analysis. Heterogeneity analysis shows that internal integration moderates the effect of polycentric spatial structure on carbon emission intensity, with greater integration weakening the emission-reduction effect.