Abstract
Carbon dioxide (CO2) emission significantly affects sustainability efforts, calling for improvements in energy eco-efficiency (EEE). In this study, we use three methods—input–output indicators, projection pursuit regressions, and neural networks—to investigate the CO2 emission characteristics of the logistics industry in China and its provinces from 2010 to 2019. We find influential factors of carbon emissions and their corresponding efficiencies in various provinces of China. Our results reveal excess inputs and deficient outputs and the relationship between economic development levels and the environmental quality. Based on the results, we provide technical measures and managerial implications for effectively reducing CO2 emissions and improving EEE.
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