Xingchen Lai

Industrial robots are increasingly embedded in urban production systems, yet their association with city-level low-carbon transition efficiency remains insufficiently understood. Using panel data for 278 Chinese cities from 2010 to 2023, this study measures urban low-carbon transition efficiency (LCTE), the relative efficiency of urban resource conversion under environmental constraints, with a super-efficiency slack-based measure (Super-SBM) model. We estimate its association with industrial robot exposure using a two-way fixed-effects model, followed by robustness checks, a lagged-exposure instrumental-variable analysis, innovation-pathway analysis, city-size heterogeneity tests and spatial econometric modeling. Industrial robot exposure is positively associated with LCTE; the direction of this association is preserved across sensitivity analyses, although its magnitude falls to about one quarter of the baseline estimate under constant returns to scale. The coefficient patterns for green technological innovation and science and technology investment intensity are consistent with potential innovation-related pathways. The positive association is more pronounced in small and medium-sized cities than in major cities. Spatial Durbin model estimates confirm spatial dependence in LCTE and a robust positive direct association with local LCTE; the indirect association with spatially connected cities is negative in point estimates but not statistically significant once standard errors are clustered by city. These conditional associations suggest that the relationship between production automation and LCTE varies across innovation conditions, city sizes and spatial contexts.

Urban sustainable development increasingly depends on interactions among multiple urban subsystems, yet existing studies often overlook cross-regional linkages and nonlinear development processes. This study investigates the coordinated development of urbanization, smart development, resilience, and low-carbon transition (USRL) from an efficiency perspective. Using panel data from 278 Chinese cities during 2010–2023, this work integrates the Super-SBM model, the Local–Tele Coupling Coordination Degree (LTCCD) framework, Dagum Gini decomposition, and machine learning techniques to examine the spatiotemporal evolution, spatial disparities, and driving mechanisms of coordinated development. The results show that coordinated development improved steadily over time, although subsystem evolution remained uneven, with resilience lagging behind other dimensions. Regional disparities gradually narrowed, but inter-regional differences remained the dominant source of spatial inequality. Innovation intensity, industrial upgrading, and high-quality foreign investment positively contributed to coordinated development, whereas fiscal and financial factors exhibited nonlinear effects. Interaction analysis further revealed that coordinated development is shaped by the combined influence of multiple drivers rather than by individual factors alone. Our findings suggest that urban sustainable development is jointly influenced by subsystem coordination, cross-regional interactions, and nonlinear development dynamics, highlighting the importance of integrating local and tele-coupling processes in urban sustainability research.

Advancing the low-carbon and resilient transformation of urban systems has become a crucial strategy for addressing the climate crisis. Current research predominantly focuses on either the effect of governance or the efficient utilization of urban systems, overlooking the potential structural mismatches and synergistic development governance logic between the two. This paper systematically proposes a theoretical research framework integrating the synergistic development of urban system effect and efficiency, and constructs a multi-level analytical methodology. Through an in-depth examination of 278 prefecture-level cities in China from 2010 to 2023, the following key conclusions emerge: (1) The overall level of synergistic development within urban systems has steadily increased. Specifically, the proportion of cities at low levels of synergistic development decreased from 65.11% to 17.63%, while the proportion at medium and high levels rose from 18.70% to 46.40%. (2) Spatial disparities in urban system coordination have progressively narrowed, as evidenced by the overall Gini coefficient decreasing from 0.195 to 0.153. (3) Key influencing factors for urban system coordination include foreign enterprise attraction, urban infrastructure development, and green technological innovation. Overall, this study reveals the long-standing structural mismatch between effect and efficiency in China’s urban system’s low-carbon resilience transformation, emphasizing the importance of their coordinated development. It provides theoretical foundations and empirical references for the sustainable development of urban low-carbon resilience transformation.

The adaptive reuse of industrial heritage is increasingly recognized as an effective low-carbon strategy that reduces resource consumption, lowers embodied carbon emissions, and supports sustainable urban transitions. Developing appropriate reuse strategies, however, requires a robust understanding of heritage value. As material evidence of China’s modern industrialization, railway-associated industrial heritage possesses the characteristics of linear cultural heritage. Yet systematic and multi-scalar value assessments from a linear heritage perspective remain limited. Focusing on the Guanzhong Section of the Longhai Railway—one of the most representative industrial development axes in Northwest China—this study establishes a two-level value assessment framework and conducts a comprehensive evaluation of fourteen textile industrial heritage units. At the individual level, five dimensions—historical significance, architectural features, structural integrity, authenticity, and rarity—were assessed through field investigation, and type-specific weights were introduced to correct structural imbalances between quantity and value across building categories. At the unit level, the Analytic Hierarchy Process (AHP) was employed to determine the weights of spatial–functional integrity, process completeness, railway connectivity, industrial landscape characteristics, and the integrated individual-level value. The results show that factory workshops and warehouses consistently exhibit the highest value, whereas structures and residential buildings, despite their numerical dominance, contribute relatively little. Spatially, a clear west–east gradient emerges: high-value units cluster in Baoji and Xi’an, medium-value units in Xianyang, and low-value units mainly in Weinan and surrounding counties. The findings indicate that textile industrial heritage along the Guanzhong Section forms a railway-linked linear cultural heritage system rather than isolated sites. The proposed evaluation framework not only supports heritage identification and conservation planning but also provides a theoretical basis for promoting low-carbon adaptive reuse of existing industrial buildings.