Research On Total Factor Productivity Of Chinese Transportation Industry Considering Carbon Emissions

As one of the major sources of carbon emissions,China’s transport industry faces enormous pressure of energy conservation and emission reduction,which must satisfy social and economic development with the least resource input and the least environmental cost.Total factor productivity is the core sign that reflects the economic growth quality of a country or industry.Improving the total factor productivity of transportation industry is an effective way to reduce carbon emissions,relieve environmental pressure and achieve high-quality development.This study takes China’s transport industry as the research object,investigates the period from 2004 to 2020,systematically studies the total factor productivity of China’s transport industry considering carbon emissions,including the following research contents:(1)Calculation and analysis of carbon emissions of China’s transport industry.The "top-down" method was used to measure carbon emissions,the FMOLS method was used to evaluate the relationship between carbon emissions and economic growth,the Dagum Gini coefficient method was used to describe the spatial differences of carbon emissions,and the Kernel density method was used to describe the temporal changes of carbon emission intensity.(2)Analysis of driving factors of carbon emissions in China’s transport industry.LMDI model was used to decompose the contribution rate of five static factors of carbon emission,and GMM model was used to analyze the dynamic driving factors.(3)Analysis of total factor productivity of China’s transportation industry.The three-stage DEA model was used to measure the total factor productivity without considering carbon emissions,the Malmquist-Luenberger index method was used to measure the total factor productivity with considering carbon emissions,and the convergence theory was used to analyze the convergence or discretization of the total factor productivity with considering carbon emissions.(4)Spatial econometric analysis of influencing factors of total factor productivity of China’s transport industry considering carbon emissions.The spatial measurement method is used to analyze the influencing factors of total factor productivity,and the spatial spillover effect model is applied to analyze the spatial direct effect and spatial indirect effect.The conclusions are as follows:(1)The average annual growth rate of carbon emissions from China’s transport industry is 14.14%,and the Dagum Gini coefficient of carbon emission intensity shows an "M" shaped evolution trend;There is a co-integration relationship between carbon emission and GDP per capita and a short-term and long-term Granger causality relationship.(2)From the static perspective,economic development,energy efficiency and population factors drive the growth of carbon emissions,while transport intensity and energy structure inhibit the growth of carbon emissions.From the dynamic perspective,the driving factors of carbon emission are GDP per capita,total retail sales of consumer goods and turnover of goods,while the inhibiting factors are urbanization rate,technological market turnover and proportion of tertiary industry.(3)Without considering carbon emissions,the technical efficiency of total factor productivity is 0.4748,the pure technical efficiency is0.5751,and the scale efficiency is 0.8337.Considering carbon emissions,the total factor productivity is 0.9585,showing a gradual decline law of "east-west".There is no σ convergence trend of total factor productivity,but absolute β convergence and conditional β convergence exist in each province.(4)The global index of total factor productivity of China’s transport industry considering carbon emissions is positive and passes the 1% level test;The degree of transport structure,transport intensity,energy intensity,energy structure,economic development,foreign trade status,industrial structure and scientific and technological input all significantly affect total factor productivity.There is spatial spillover effect of total factor productivity between regions.