The permeability (k) and electrical resistivity of carbonate rocks with similar porosity (ϕ) may exhibit multiple orders of magnitude differences due to the diversity of pore types and the multiscale characteristics of pore size. As a result, traditional permeability-porosity and resistivity-porosity models usually have strong limitations when applied to carbonate reservoirs, let alone evaluate permeability from electrical resistivity. In this study, a new rock typing parameter named relative transport quality index (RTQI) is proposed to categorize rock samples using permeability and formation resistivity factor (F). Samples with similar RTQI values tend to have similar relative capacities of hydraulic and electrical transport. Subsequently, the classification performance and microscopic differences between RTQI and other rock typing indices including flow zone indicator (FZI), current zone indicator (CZI), and electrical quality index (EQI) were compared and discussed based on the reported petrophysical data of 316 carbonate rock samples. The results indicate that the RTQI method significantly reduces the scattering of k and F by dividing the samples into distinct groups, and each RTQI group corresponds to a special k−F correlation with a high determination coefficient. However, the number of rock types identified by different rock typing indices is different, meaning that samples belonging to the same rock type in the k−F plot of the RTQI method may be classified into different rock types in the k−ϕ plot of the FZI method and the F−ϕ plots of the CZI and EQI methods, which may be caused by the microscopic differences of rock typing parameters. Different from the strong dependence of FZI on hydraulic tortuosity and CZI/EQI on electrical tortuosity, RTQI is mainly dominated by the ratio of electrical tortuosity to hydraulic tortuosity. Additionally, in view of the good correlation between petrophysical data after rock typing, two workflows are proposed to estimate the hydraulic and electrical parameters of carbonate rocks from log data.