Material Footprint, Institutional Quality, and Carbon Emissions in BRICS: Distributional Evidence Using Dematerialization Proxies

The BRICS economies have expanded rapidly while remaining large contributors to global carbon dioxide emissions, which motivates a closer look at the structural covariates of their carbon trajectories. The growth–emissions nexus is well documented, but fewer studies jointly place material footprint and institutional quality in a distributional panel setting. This study estimates the heterogeneous associations of electoral democracy, political corruption, economic growth, and material footprint with per capita carbon emissions in the five BRICS countries from 1980 to 2022. The baseline estimator is the Method of Moments Quantile Regression (MMQR). The Dynamic Common Correlated Effects (DCCE) estimator is used as a mean-based check for persistence and cross-sectional dependence. Double/Debiased Machine Learning (DML), SHAP, and Quantile Regression Forests (QRF) are used as non-parametric checks of pattern consistency, not as independent causal identification. MMQR estimates show that material footprint is positively associated with emissions across quantiles, with the coefficient rising from 0.3859 at the 10th quantile to 0.6224 at the 90th quantile. Economic growth and electoral democracy are negatively associated with emissions mainly at higher emission quantiles; at the 90th quantile the democracy coefficient is −0.2988. The political corruption index also enters with a negative coefficient at the upper tail (−0.8468 at the 90th quantile). Because higher values of this index denote more, not less, corruption, that sign does not support a simple claim that corruption control reduces emissions, and it is not recovered in DCCE or DML. Dumitrescu–Hurlin tests indicate a unidirectional Granger-predictive link from material footprint to emissions and bidirectional Granger links between the institutional variables and emissions. These results are associations from a small-N panel (N = 5, T = 43). They suggest that resource throughput remains tightly linked to carbon outcomes at the upper tail, while several of the growth and governance associations are specification-dependent.