To clarify the differences in progress and peak values of China's regional dual carbon targets, and to conduct regional dual carbon target pressure tests, this paper selects data from 30 provinces in China over the past 21 years. First, cluster analysis is conducted on the low-carbon economic development status of each province based on the two indicators of per capita carbon emissions and per capita RGDP. Then, semi parametric regression is used to verify the existence of peak per capita carbon emissions, and FGLS empirical method is used to test the robustness of empirical models based on the EKC model, IPAT model, and ImPACT model. Finally, high and low scenarios are established for the four key influencing factors of permanent population, RGDP, carbon intensity, and energy structure. The above empirical models are used to predict peak values for the entire sample, grouped samples, and provincial samples and to conduct comparative analyses. The results showed that: ① The semi parametric regression results of each group sample were close to an "inverted U-shape", indicating the objective existence of peak per capita carbon emissions and providing support for peak analysis using empirical models. ② The peak values calculated for each group of samples under different empirical models are relatively close, but there are significant differences in the peak values of each group under a single model, indicating that there are significant differences in the per capita RGDP levels corresponding to peak values in different types of provinces By comparing the coefficients and peak data of each group, it can be concluded that population and economic growth have the most critical impact on the peak The prediction results show that the 30 provinces exhibit two types of differentiation characteristics, and the energy consumption structure is the key factor leading to differences in regional peak times and peak sizes.