Effective carbon emission control at the provincial level is essential for advancing the high-quality development of the national economy under the “dual carbon” targets. Although Hubei Province is endowed with abundant natural resources and significant potential for sustainable growth, it still faces considerable challenges in industrial and energy restructuring. Therefore, improving carbon emission efficiency (CEE) is imperative. This study thoroughly analyzes the spatial and temporal characteristics of CEE in Hubei Province. Furthermore, the spatial Durbin model (SDM) and geographically and temporally weighted regression (GTWR) were applied to analyze the determinants of changes in CEE. The results indicate that significant disparities in CEE exist across Hubei Province, with the eastern region exhibiting the highest efficiency and the central region the lowest. The year 2016 represented a turning point, as Moran’s I increased from −0.0006 in 2016 to 0.5134 in 2017, indicating a shift in the spatial pattern of CEE from a weak and insignificant spatial autocorrelation to a strong positive spatial autocorrelation. In addition, the CEE in Hubei Province demonstrated a “siphon effect” and exhibited pronounced polarization. Based on these findings, region-specific policies are proposed. The eastern region should optimize its industrial structure and strengthen urban governance. The western region should leverage its clean energy advantage and enhance carbon sink capacity. The central region should advance low-carbon industrial transformation and coordinated governance to prevent core cities from transferring resources and pollution to surrounding areas.

To study the impact of the psychological and behavioral characteristics of people, fire environment, and evacuation routes on fire evacuation efficiency, this study focuses on a university library as the research subject. A fuzzy logic algorithm is employed to analyze how psychological and behavioral traits influence initial evacuation speed during a fire. Also, fire data simulated using PyroSim software is integrated, with gas temperature, CO concentration, and visibility quantified through empirical formulas to adjust the reduction factor of evacuation speed, examining the effects of fire-generated products on evacuation performance. By incorporating fire environment factors into the heuristic function and refining pheromone update rules through iterative strategies, the ant colony algorithm is enhanced to achieve path planning. Results show that the psychological–environmental-route correction method improves evacuation efficiency by 16.2% compared to traditional methods without correction. This demonstrates that the proposed correction method can improve the efficiency of building fire evacuation and provides theoretical support and technical solutions for future library fire safety management.

Taking a reinforced-concrete frame teaching building at a university as a case study, this study established a carbon emission accounting framework based on BIM-derived quantities, identified carbon emission hotspots at the stage, material, and component levels, and comprehensively evaluated the accounting results and emission reduction potential through sensitivity analysis, Monte Carlo uncertainty analysis, and low-carbon scenario assessment. The results showed that the total A1–A5 carbon emissions of the case-study building were 16,812.69 tCO₂e, corresponding to a carbon emission intensity of 442.51 kgCO₂e/m², of which modules A1–A3 accounted for 94.40%. C35 concrete, reinforcing steel, and autoclaved aerated concrete blocks collectively contributed 88.1% of A1–A3 emissions, while reinforcing steel, infill walls, and floor slabs constituted the major component-level hotspots. Monte Carlo simulation showed that the baseline accounting result fell within the main probability distribution range, while the integrated optimization scenario reduced total A1–A5 carbon emissions by 7.72%. This study quantified carbon emissions from the building material production, transportation, and construction stages (A1–A5) of a teaching building and identified priority targets for emission reduction. The results provide a quantitative basis for low-carbon material selection, key component control, and construction-stage optimization of teaching buildings in China.

The authors would like to make the following corrections to the published paper