Study on the spatiotemporal evolution characteristics and simulation prediction of urban metabolic efficiency in...

Urban metabolic efficiency (ME) links resource-environmental constraints with high-quality urbanization, yet systematic evidence at the urban agglomeration scale remains limited. This study examines 201 prefecture-level cities in 19 national urban agglomerations in China over 2006-2022. A material-flow-based index system is constructed, and a dynamic meta-frontier SBM-DEA model with undesirable outputs is used to measure ME. meta-frontier inefficiency is decomposed into an inter-agglomeration technology gap component and a within-agglomeration residual component. K-means clustering and conventional as well as spatial Markov chains are employed to characterize temporal dynamics and spatial club patterns, while a panel Tobit model is estimated to identify the determinants of ME. Spatial kernel density analysis and several robustness checks complement the baseline results. The findings show that ME in Chinese urban agglomerations is at a medium level overall, with pronounced stratification across agglomerations and a polarized distribution within them, where high- and low-efficiency cities coexist and medium-efficiency cities are relatively scarce. In several cases, core cities exhibit lower ME than their surrounding cities. The meta-frontier decomposition indicates that inter-agglomeration technology and regime gaps account for a larger share of overall inefficiency than within-agglomeration dispersion, although some developed agglomerations display notable internal residual inefficiency. Markov and spatial Markov analysis reveal strong path dependence and club convergence: low- and high-efficiency clubs are relatively stable, while medium-efficiency cities are easily squeezed between them. Spatial dependence is stratified and more pronounced within specific efficiency ranges than at the global level. The Tobit estimates further identify a U-shaped "metabolic efficiency Kuznets curve" with respect to per capita GDP, and show that digitalization and education expenditure significantly improve ME, whereas openness, fiscal decentralization, industrial upgrading and financial development exert less robust effects. These results highlight the need for differentiated policies across agglomeration types that simultaneously narrow cross-agglomeration technology and structural gaps and strengthen within-agglomeration coordination between economic and environmental objectives.