Quantitative simulation of urban waterlogging using computer models is an effective technical means for urban storm water management, especially for predicting and preventing waterlogging. In this study, a city in the northern Anhui Plain, China, was selected as the study site. The Storm Water Management Model was applied to simulate the dynamic changes in the pipeline overload, node overflow, and discharge port runoff characteristics from three perspectives: surface runoff, pipe network transmission, and flow control of low-impact development. The operation of the rainwater pipe network under different return periods and the real-time operation of the rainwater pipe network were simulated to seek solutions to urban waterlogging problems caused by flat terrain and slow drainage. The results revealed that surface runoff is the primary source of rainfall in the study area, with a runoff coefficient of 0.599. The drainage pipe network was optimized by expanding the diameter of the pipe from ≤1.5 mm to ≥2 mm. The water reduction rate was more than 50%, and overload did not occur after optimization. Therefore, sinking green space technology and optimization methods for expanding a pipe diameter can reduce urban waterlogging.

Cities play an important role in achieving carbon emission peaking and neutrality targets, yet identifying feasible mitigation pathways remains challenging for prefecture-level cities with limited historical data. This study develops a context-specific extended STIRPAT framework to examine the relationships between urban carbon emissions and nine socioeconomic and sector-related indicators in Bozhou, China. Based on annual data from 2010 to 2022, grey relational analysis was used to support indicator selection, and ridge regression was applied to address severe multicollinearity. Ten scenarios, comprising one baseline and nine alternative development pathways, were constructed to assess carbon emission trajectories during 2023–2035. Population exhibited the largest standardized ridge regression coefficient among the selected explanatory variables. Scenario analysis indicates that carbon emissions under the baseline scenario (S1) reach a projected maximum of 16.78 Mt CO₂ in 2030. The energy consumption adjustment scenario (S6) and the coordinated development scenario (S8) reach their projected maxima in 2025, at 15.60 and 15.69 Mt CO₂, respectively. Under the integrated scenarios S9 and S10, emissions decline continuously throughout 2023–2035 without reaching a new peak during the projection period. Compared with S1, projected emissions in 2030 are 10.65%, 9.06%, 24.74%, and 37.99% lower under S6, S8, S9, and S10, respectively. A supplementary Monte Carlo analysis further illustrates the sensitivity of these projections to uncertainty in the assumed explanatory-variable growth rates. The findings should be interpreted as conditional scenario outcomes rather than precise forecasts or causal estimates of policy effectiveness.