This study analyzes 71 manifest variables and 10 latent variables affecting São Paulo’s economic growth using the PLS-PM technique. Results indicate that credit, industry, and trade significantly impact GDP, while civil construction has an inverse relationship. The model explains approximately 85% of GDP variance, highlighting key economic drivers. The study also provides a freely accessible database to facilitate further research. The study confirmed that credit, civil construction, and industry significantly affect São Paulo’s economic growth. Civil construction has a negative structural coefficient, indicating that more construction activity correlates with lower economic growth. Furthermore, industrial growth negatively affects Trade and Services, while Trade and Services positively influence Agriculture. Industry accounts for 9.2% of the tertiary sector and contributes 22% to Agriculture. The validated hypotheses explain about 85% of São Paulo’s GDP variation, highlighting key economic drivers shaping regional growth. The 2024 Rio Grande do Sul floods, which impacted millions and caused severe economic losses, underscore the importance of predictive economic models. By integrating environmental and economic variables, models like the one proposed here could help policymakers anticipate and mitigate the financial consequences of such disasters.