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The carbon emissions are essential for climate change and 26% of the world's carbon emissions are related to transport. But focusing only on fewer carbon emissions might be biased at times. In order to keep a balance between economic growth and carbon emissions reduction, this paper evaluated the performance of carbon control by considering the input factors and output factors together, which is more comprehensive and reliable. Firstly, this paper has calculated the transport carbon emissions reduction efficiency (TCERE) based on the model of super SBM with undesirable outputs. The input factors include capital stock, labor force and fossil energy consumption. And the output factors include gross domestic product and carbon dioxide emissions. Then the influencing factors of TCERE were analyzed using econometric models. The economic growth, transport structure, technology level and population density were posited as influencing factors. This paper creatively proposed the per capita nighttime lights brightness as a new indicator for economic growth. An empirical study was conducted in East China from 2013 to 2017, and this study has found that the relationship between TCERE and economic growth shows an U-shape. Besides, transport structure and technology level both show a positive impact on TCERE. The implications of our findings are that: (a) The TCERE declines slower in East China, giving us reason to believe that the improvement of TCERE is predictable; (b) When economic growth exceeds the turning point, economic growth is conducive to the improvement of TCERE. We could develop the economy boldly and confidently; (c) Increased investment in railway and waterway transportation infrastructure projects is needed to strengthen the structure of the railway and waterway transportation systems. Furthermore, the general public and businesses should be encouraged to prefer rail or river transportation; (d) Investment in scientific and technological innovation should be enhanced in order to produce more efficient energy-use methods.
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