Abstract: Utilizing national statistical data and remote sensing nighttime light data, this study performs a contribution rate analysis of the influencing factors on carbon emissions in 15 typical cities through the Logarithmic Mean Divisia Index (LMDI), and constructs three sets of variables for predictive analysis using machine learning Ridge and Lasso regression models. The results indicate that seven factors including urban Gross Domestic Product (GDP), Energy Consumption (ES), Population (P), Real Estate Construction Area (RECA), Nighttime Light Intensity (NL), Cargo Transportation Volume (CT), and Passenger Transportation Volume (PT) play a promoting role in CO₂ emissions, whereas Energy Consumption Structure (EI) and the Proportion of Tertiary Industry (TIR) have an inhibiting effect on CO₂ emissions. The more mature a city, the richer the industry, the more diverse the impacting factors of carbon emissions. The correlation coefficient exceeds 0.8 between simulated results from predictive models of ridge and Lasso regression across variables set 1 to set 3 and the results from the test datasets. Among them, the result from set 1 is the best, followed by set 2, and finally, set 3.