铁道科学与工程学报2025，Vol.22Issue(5)：2303-2316,14.DOI:10.19713/j.cnki.43-1423/u.T20241229
基于超效率SBM-GML指数模型的铁路运营碳排放效率测度
Carbon emission efficiency measurement of railway operation based on Super-SBM-GML index model
摘要
Abstract
Achieving low-carbon transformation in the railway sector is imperative.Addressing the current issue where railway operational efficiency evaluations do not adequately consider carbon emissions,this study focused on 18 railway bureaus in China,calculating the carbon emissions from railway operations and evaluating their carbon emission efficiency from both static and dynamic perspectives.Initially,based on a literature review,four input indicators,one output indicator,and one undesirable output indicator were determined for assessing railway operational carbon emission efficiency.Subsequently,the Super-efficiency Slack Based Measure(Super-SBM)model was employed to calculate the static super-efficiency values of railway carbon emissions,while the Global Malmquist Luenberger(GML)index was used to observe the dynamic evolution of carbon emission efficiency.This approach quantified the rational allocation of railway operational resources and their evolving trends.Ultimately,based on super-efficiency values and total factor productivity,the railway bureaus were categorized into four types,which were"leaders","followers","steady performers"and"potential performers".Additionally,an in-depth analysis of the factors influencing carbon emission efficiency was conducted,and synergistic carbon emission reduction proposals were made based on both static and dynamic performance.The results indicate significant regional differences in the carbon emission efficiency of China's railway operations,with a spatial distribution pattern of high efficiency in the central and coastal regions,followed by the northwest and southwest,and lower efficiency in the northeast.The carbon emission efficiency changes exhibit an"M"-shaped four-stage upward trend,characterized by gradual improvement,brief decline,rapid enhancement,and the impact of the pandemic.Importantly,the overall carbon emission efficiency level remains low,with technological advancements identified as the primary driver for improvement.Additionally,the railway bureaus categorized as"leaders"are mainly located in North and East China,where carbon emission efficiency is at the forefront.Furthermore,the proportion of secondary industry,electrification mileage rate,energy structure,and GDP per capita are the key factors influencing differences in carbon emission efficiency,with all showing positive effects.This study can provide the body of research on low-carbon development in the railway sector and offers theoretical support for reducing carbon emissions in railway operations.关键词
铁路运输/碳排放效率/非期望产出/超效率SBM模型/GML指数Key words
railway transportation/carbon emission efficiency/undesirable output/super-SBM model/GML index分类
资源环境引用本文复制引用
李艳鸽,伍生,王天宇,韩征..基于超效率SBM-GML指数模型的铁路运营碳排放效率测度[J].铁道科学与工程学报,2025,22(5):2303-2316,14.基金项目
湖南省自然科学基金面上项目(2022JJ30700) （2022JJ30700）
