Based on panel data from 285 Chinese cities from 2008 to 2021, this research constructed a “dual pilot” quasi-natural experiment using intellectual property model cities and sci-tech finance pilot cities. Multi-period difference-in-differences (DID) model and double machine learning (DML) model were employed to investigate the synergistic effects of cross-policy tool combinations on promoting urban green and low-carbon development. The findings showed that the “dual pilot” policy significantly promoted green and low-carbon development, with effects markedly stronger than those of single pilot policies, and these results passed a series of robustness tests. The “dual pilot” policy promoted green and low-carbon development indirectly through talent agglomeration effects and green technology innovation. The promotion effect of the strategy that demonstrated intellectual property rights first and then piloted sci-tech finance was better. The “dual pilot” policy demonstrated significant synergistic effects in cities with higher levels of financial development, cities with lower levels of government intervention, and key environmental protection cities. A spatial proximity effect was also observed, whereby the policy boosted local development while creating a siphon effect on neighboring regions. These findings provide important insights for rationally coordinating the spatial synergy and sequential configuration of the “dual pilot” policy to accelerate urban green and low-carbon development.