Air pollution emissions (PE) and carbon emissions (CE) pose significant challenges to achieving the Sustainable Development Goals (SDGs) globally. The intensity of urban block development and spatial form can influence the relationship between PE and CE. This study analyzed 11,228 neighborhood samples from various climate zones across China using spatial statistics and an optimized random forest model to examine the impact of block spatial form on PE and CE. The findings reveal that: (1) The PE–CE correlation in non-first-tier city blocks in southern China is stronger than in those in northern China. The correlation is strongest in urban neighborhoods located in temperate climates. Additionally, the PE–CE correlation is weakest in Beijing and Shanghai. (2) The variation in explanatory power of different driving factors is more pronounced for CE than for PE, with PR, NDVI, and AH emerging as the most significant factors. (3) The synergy between PE and CE is strongest when BD is in the 20%-30% range. Similarly, the synergy is strongest when PR is in the 2-3 range. (4) BD in the 40%-60% range is most effective in reducing PE and CE, with 40%-50% range favoring CE reduction and 50%-60% range favoring PE reduction.