This study examines the distributional heterogeneity of circular economy (CE) energy transitions and their relationship with economic growth across six BRICS economies—Brazil, China, India, South Africa, Egypt, and Indonesia—from 2000 to 2025. Using the Method of Moments Quantile Regression (MMQR) as the primary estimator, with fixed effects as a robustness check, we assess effects across the growth distribution while accounting for cross-sectional dependence and panel integration properties. We also provide a direct estimate of the energy rebound effect for the BRICS panel. The results reveal substantial heterogeneity that mean-based estimators obscure. Renewable energy share and energy intensity are positive and statistically significant mainly at middle-to-high growth quantiles (Q50–Q90). In contrast, gross capital formation and FDI are robust positive drivers across all quantiles. Urbanization is negatively associated with growth at Q25–Q90, while trade openness and domestic credit are largely insignificant in the MMQR specification. The rebound-effect estimate indicates that approximately 25.7% of energy efficiency gains are offset by increased consumption, implying net savings of about 74.3%. These findings suggest that CE energy transitions do not affect all economies uniformly: investment and foreign capital remain broad-based growth engines, whereas energy transition dividends are concentrated in faster-growing economies. The study offers differentiated policy implications for low-, middle-, and high-growth BRICS economies and cautions against one-size-fits-all CE prescriptions.

Economic uncertainty shapes how analysts and managers forecast corporate performance, yet comparative evidence across crises is limited. This study compares the earnings and stock price forecasts of financial analysts and company managers during the 2008 financial crisis and the COVID-19 pandemic. Using annual data for five U.S. firms (Goldman Sachs, AIG, Ford, Wells Fargo and Starbucks) over 2009–2012 and 2020–2022, actual earnings and year-end stock prices are related to analyst and management forecasts through panel regressions with fixed and random effects, supported by descriptive, correlation, normality, robustness and Hausman tests. In the 2008 earnings model, neither forecast is a significant predictor of actual earnings, whereas in the stock price models and the COVID-19 earnings model analyst forecasts are significantly and positively related to actual outcomes and management forecasts are insignificant or negatively related. Both groups tended to underestimate the post-crisis recovery. The findings suggest that analysts and managers should adapt forecasts more quickly to changing economic conditions; recommendations on risk analysis, communication, digital transformation and diversification are derived.