Navigating uncertainty in carbon efficiency: A global assessment across income groups

Abstract

This study evaluates the carbon efficiency of 163 countries between 1992 and 2019, focusing on the relationship between economic growth and emission reductions. By using a novel approach that integrates Stochastic Metafrontier Analysis with Bayesian inference, the study robustly analyzes data variability and uncertainty. The results highlight significant differences in carbon efficiency across income groups. High-income countries (G1) show a technology gap uncertainty of 0.118, while low-income countries (G4) have a slightly higher uncertainty at 0.133, indicating challenges in technology transfer for both groups. Middle-income countries (G2), with the lowest uncertainty at 0.045, demonstrate a strong capacity to adopt advanced technologies and improve carbon efficiency. The study also identifies critical factors influencing carbon efficiency uncertainty, such as urbanization, forest area, and foreign direct investment. Urbanization affects these groups differently: it raises uncertainty in G4 by 0.0107 but reduces it in G1 by −0.0069, reflecting varying stages of urban development. These findings suggest the need for targeted policies to improve technology transfer, optimize urbanization, and enhance sustainable resource use, thereby facilitating a more effective shift to a low-carbon economy and reducing carbon efficiency uncertainties.

Keywords

• Carbon efficiency
• Uncertainty
• Stochastic metafrontier
• Bayesian inference
• Cluster analysis
• Heterogeneity