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An empirical study on the measurement and influencing factors of carbon emission efficiency in China's transportation industry

Paper Topic: 
Ecology, environmental change and management

Pages :
110 - 123

Corresponing Author: 
Gang Zeng
Gang Zeng, Yadi Sang, Qizhang Gui, Jiayi Li, Junyao Yuan, Keren Zhu
Paper ID: 
Paper Status: 
Date Paper Accepted: 
Paper online: 
Visual abstract: 

The severe climate problem has forced the Chinese government to put forward the goal of "carbon peak" and "carbon neutrality", and the transportation industry is a key area of carbon emission reduction. Based on this background, this paper introduces the BCC model and the Malmquist-Luenberger index model to measure the carbon emission efficiency of China's provincial transportation industry from the static and dynamic perspectives from 2014 to 2020. At the same time, the Tobit model is used to estimate the influencing factors of carbon emission efficiency. The results show that: (1) The overall transportation carbon emission efficiency level is low. The comprehensive efficiency of 30 provinces is at [0.6634,0.7154], which needs to be improved urgently. (2) The carbon emission efficiency of transportation is in a growing trend, and the ML index is 1.142, which is significantly higher than 1.0. In addition, the mean value of technical efficiency is 1.015, and the mean value of technological progress efficiency is 1.125. The two factors jointly affect comprehensive efficiency. (3) The carbon emission efficiency of different regions is heterogeneous. The carbon emission efficiency of East China and North China is better than that of Northeast China and South China, while the carbon emission efficiency of Northwest China and Southwest China is at the middle level in China. (4) External environmental factors, such as regional GDP, resident population and consumption level, have a significant impact on the carbon emission efficiency of transportation.

Transportation industry; Carbon emission efficiency; BCC model; Malmquist - Luenberger index; Tobit model