Small cities warrant focused attention for robust low-carbon development strategies due to their significant numbers. In these cities, residential buildings emerge as notable contributors to carbon emissions, consuming substantial energy in their operations. This study employs an optimized IPAT equation, utilizing government statistical data, satellite remote sensing images, and panel data models to analyze the impact of the urban environment on carbon emissions from residential building operations (CERBOs) in 36 small Chinese cities. The findings reveal geographical variations in sensitivity to scale, economic, and spatial structure factors. Population size, municipal jurisdiction area, urbanization level, GDP, and per capita disposable income significantly contribute to CERBOs. Particularly, a 1% increase in municipal jurisdiction area leads to a 1.698% increase in total CERBOs, the highest influencing factor. Spatial structure only affects western cities, with compact development being more conducive to reducing CERBOs. Notably, carbon emissions from electricity are more influenced by environmental factors than those from heating and gas. The study proposes region-specific low-carbon planning strategies based on these findings. The theoretical optimization model proposed in the study, as well as the identified impact factors, will provide a theoretical basis and data support for understanding and reducing carbon emissions in small cities.