Abstract
Measuring energy and environmental performance has become a significant public concern, owing to increasingly severe environmental pressures. The transportation industry plays an essential role in modern economic and social development and has caused many environmental problems due to its huge energy consumption and carbon emissions. This study examined the provincial energy and environmental efficiency (EEE) of the transportation industry in China, considering a newly announced dual-carbon policy target with a total fixed-sum (FS) constraint on carbon emissions. The transportation industry is modeled as a parallel production system with passenger transportation and freight transportation. Because both subsystems would inevitably generate carbon dioxides, a parallel transportation system is innovatively considered with a FS constraint of shared carbon emissions under the dual-carbon policy target. This study contributes to the literature by measuring the EEE of the Chinese transportation industry under the FS constraint of shared carbon emissions in a parallel system. To address this problem, we propose a novel parallel data envelopment analysis (DEA) approach that considers FS shared undesirable outputs (FSSUO) based on the generalized equilibrium efficient frontier (GEEF) concept. The GEEF was obtained by minimizing the total adjustment of carbon emissions and used to calculate the overall parallel transportation efficiency. Furthermore, we developed a decomposition framework to estimate the individual passenger transportation performance and freight transportation performance. To the best of our knowledge, this is the first attempt to study the fixed-sum outputs (FSO) in a parallel production system, whereas previous studies addressed FSO in either black-box or two-stage series production systems. Finally, we apply the proposed approach to empirically analyze the EEE of the Chinese transportation industry at the provincial level from 2015 to 2019 and provide valuable empirical findings and policy suggestions.
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