China’s rapid industrialization and urbanization have led to significant and imbalanced CO₂ emissions, putting pressure on achieving sustainable development goals. This study analyzed the CO₂ emissions of 31 major cities in China from different sectors (total, power, industry, and transport) from 2019 to 2022. This study constructs a city-scale CO₂ emission correlation model to achieve nationwide and urban fine-scale research on CO₂ emission spatial networks from different sectors. This study revealed the following: (i) there is an increasing correlation among regions in China, and collaborative governance is crucial; (ii) there are differences in the structure, characteristics, and roles of CO₂ emission networks from different sectors; (iii) China’s CO₂ emission network is mainly concentrated in the northern and eastern regions, which play an important role in emission reduction; and (iv) the impact factors have different effects on CO₂ emissions from different sectors, and we should actively contribute to promoting emission reduction. Correctly understanding the spatial characteristics and influencing factors of CO₂ emissions can help us formulate targeted and efficient emission reduction policies.

Urban fire prevention is shifting from reactive response to proactive risk governance, yet current approaches often overlook risk-type heterogeneity, spatial dependencies, and underlying behavioral mechanisms, especially equitable risk distribution among vulnerable groups. To address this, this study integrates the Pressure–State–Response (PSR) model with environmental criminology theories (Routine Activity Theory (RAT) and Crime Pattern Theory (CPT)) to couple macro social causal chains with micro behavioral–spatial mechanisms. Using data from the digital urban management system of Shenzhen’s Guangming District in 2019, four fire risk event types are examined: electric bike charging violations (EB), unauthorized power wiring (PW), water heater misuse (WH), and aging gas pipelines (GP). Spatial error models explain 82–89% of the variance across fire risk event types, and spatial 5-fold cross-validation shows minimal performance decline (ΔR² = 0.03–0.08), confirming robust prediction without overfitting. Key findings include: (1) elderly proportion is significantly positively associated with WH and PW (coefficients = 2.64 and 3.06, p < 0.01); (2) restaurant density has a consistently positive association with all four risk types (coefficients = 0.24–0.60, p < 0.01); (3) functional diversity and connectivity exhibit dual patterns, showing negative associations with more visible, easily detectable violations (PW, GP) but positive relationships with relatively concealed behaviors (EB); (4) reported safety deficiencies display strong positive associations with all fire risk event types and can therefore serve as an effective early-warning indicator for broader fire risk. These results support risk-specific, equity-oriented prevention strategies that prioritize vulnerable groups and high-risk environments. The validated PSR–RAT/CPT framework provides a novel theoretical basis for targeted fire risk governance and advances safe, resilient, inclusive cities aligned with Sustainable Development Goal 11.