基于改进灰关联分析模型的江苏省道路客运低碳运输水平分析
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摘要
研究道路客运低碳运输水平对江苏省实现低碳交通发展、建设低碳城市具有重要意义。通过分析道路客运低碳运输过程的影响因素,在数据可获性与可比性、兼顾完备性与简明性、动态性3个基本原则的基础上,构建了以道路客运能力、道路客运生产率、道路客运碳排量为一级指标,载客位及其增长率、单位载客位的周转量及其增长率、单位周转量的能源消费碳排放量及其减少率为二级指标的道路客运低碳运输评价指标体系。根据灰关联分析法和分辨系数计算法,改进了灰关联分析模型,构建了道路客运低碳运输评价模型。运用该评价模型分析了2000—2014年间江苏省道路客运低碳运输水平。研究结果表明:(1)低碳运输水平在研究时段变化幅度较大,在2000—2004年间呈现波动下降态势,下降率为23. 45%; 2004—2014年间呈现波动增长趋势,增长率为33. 85%。江苏省低碳道路客运系统处于转型期,低碳运输水平有待进一步提升。(2)通过分析道路客运碳排量与道路客运生产率、道路客运能力等指标的关联度,得出道路客运碳排放水平与这些指标关联度较大。在此基础上,从碳减排、运输效率与运输能力3个层面,提出以节能降耗技术研发、运输组织优化和道路客运经营结构调整等为核心内容的政策建议。
It is of great significance to study the low carbon transport level of road passenger transport for Jiangsu Province to realize the development of low carbon transport and build a low carbon city. Through analysing the factors affecting the low carbon transport process of road passenger transport,based on the basic principles of data availability and comparability,completeness and conciseness,and dynamics,the evaluation index system of low-carbon road passenger transport is constructed. In the evaluation index system,the primary indicators include road passenger transport capacity,road passenger transport productivity,road passenger transport carbon emissions,and the secondary indicators include passenger capacity and growth rate,unit passenger capacity turnover and growth rate,energy consumption carbon emissions of unit turnover andreduction rate. According to the grey correlation analysis method and the resolution coefficient calculation method,the grey relational analysis model is improved,and the evaluation model of low carbon road passenger transport is constructed. The evaluation model is used to analyze the low carbon transport level of road passenger transport in Jiangsu Province from 2000 to 2014. The result shows that(1) The low carbon transport level changes greatly during the study period. It shows a fluctuating downward trend from 2000 to 2004 with a decline rate of 23. 45%. However,it exhibits a fluctuating growth trend from 2004 to 2014 with a growth rate of33. 85%. Low carbon road passenger transport system of Jiangsu Province is in the transition period,and low carbon transport level needs to be further improved.(2) According to correlation degree analysis among carbon emission,transport productivity and transport capacity,the carbon emission level of road passenger transport is more closely associated with these indicators. On this basis,from the aspects of carbon emission reduction,transport efficiency and transport capacity,the policy of road passenger transport,which put forward energy saving technology research and development,transport organization optimization as well as road passenger transport operation structure adjustment as the core content,is recommended.
It is of great significance to study the low carbon transport level of road passenger transport for Jiangsu Province to realize the development of low carbon transport and build a low carbon city. Through analysing the factors affecting the low carbon transport process of road passenger transport,based on the basic principles of data availability and comparability,completeness and conciseness,and dynamics,the evaluation index system of low-carbon road passenger transport is constructed. In the evaluation index system,the primary indicators include road passenger transport capacity,road passenger transport productivity,road passenger transport carbon emissions,and the secondary indicators include passenger capacity and growth rate,unit passenger capacity turnover and growth rate,energy consumption carbon emissions of unit turnover andreduction rate. According to the grey correlation analysis method and the resolution coefficient calculation method,the grey relational analysis model is improved,and the evaluation model of low carbon road passenger transport is constructed. The evaluation model is used to analyze the low carbon transport level of road passenger transport in Jiangsu Province from 2000 to 2014. The result shows that(1) The low carbon transport level changes greatly during the study period. It shows a fluctuating downward trend from 2000 to 2004 with a decline rate of 23. 45%. However,it exhibits a fluctuating growth trend from 2004 to 2014 with a growth rate of33. 85%. Low carbon road passenger transport system of Jiangsu Province is in the transition period,and low carbon transport level needs to be further improved.(2) According to correlation degree analysis among carbon emission,transport productivity and transport capacity,the carbon emission level of road passenger transport is more closely associated with these indicators. On this basis,from the aspects of carbon emission reduction,transport efficiency and transport capacity,the policy of road passenger transport,which put forward energy saving technology research and development,transport organization optimization as well as road passenger transport operation structure adjustment as the core content,is recommended.
引文
[1]卢会芬.大城市低碳客运交通系统评价指标体系研究[D].北京:北京交通大学,2011.LU Hui-fen. Research on Evaluation Index System of Metropolitan Low-carbon Passenger Traffic System[D].Beijing:Beijing Jiaotong University,2011.
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[9]符开业,郭钰,卢毅.区域道路客运一体化物元分析评价模型研究[J].湖南交通科技,2010,36(3):144-148.FU Kai-ye,GUO Yu,LU Yi. Matter-element Analysis Evalantion Model of Integration of Regional Road Passenger Transport[J]. Journal of Railway Science and Engineering,2010,36(3):144-148.
[10]张航,丁文霞.基于DEA的城乡道路客运一体化发展能力评价[J].公路交通科技,2013,30(1):115-120.ZHANG Hang,DING Wen-xia. Evaluation of Development Capability of Urban and Rural Highway Passenger Transport Integration Based on DEA Model[J]. Journal of Highway and Transportation Research and Development,2013,30(1):115-120.
[11]赵胜男.深圳市城市客运交通低碳发展模式与评价研究[D].重庆:重庆交通大学,2012.ZHAO Sheng-nan. Study on Low-carbon Development Mode and Evaluation of Urban Passenger Traffic in Shenzhen[J]. Chongqing:Chongqing Jiaotong University,2012.
[12]张岐山.基于遗传算法的分辨系数的优化方法[J].中国管理科学,2003,11(5):76-79.ZHANG Qi-shan. Genetic Algorithm-based Optimization of the Distinguishing Coefficient in Relational Analysis[J].Chinese Journal of Management Science,2003,11(5):76-79.
[13]吕锋.灰色系统关联度之分辨系数的研究[J].系统工程理论与实践,1997,17(6):49-54.LFeng. Research on the Identification Coefficient of Relational Grade for Grey System[J]. System Engineering-Theory and Practice,1997,17(6):49-54.
[14]范凯,吴皓莹.灰色系统关联度中一种新的分辨系数确定方法[J].武汉理工大学学报,2002,24(7):87-89.FAN Kai,WU Hao-ying. A New Method on Identification Coefficient of Relational Grade for Gray System[J].Journal of Wuhan University of Technology,2002,24(7):87-89.
[15]陈静,程东祥,诸大建.基于灰理想关联分析的中国城市低碳竞争力评价[J].资源科学,2012,34(9):1726-1733.CHEN Jing, CHENG Dong-xiang, ZHU Da-jian.Evaluation of Urban Low-carbon Competitiveness in China Using Grey Relational Analysis[J]. Resource Science,2012,34(9):1726-1733.
[16]李伟,王超,周绍松,等.最优分辨系数和主成分分析在烤烟品种综合评价中的应用[J].中国烟草科学,2017,38(2):45-50.LI Wei, WANG Chao, ZHOU Shao-song, et al.Application of Optimal Resolution Coefficient and Principal Component Analysis in Comprehensive Evaluation of Fluecured Tobacco Varieties[J]. Chinese Tobacco Science,2017,38(2):45-50.
[17]郑华伟,夏梦蕾,张锐,等.基于熵值法和灰色预测模型的耕地生态安全诊断[J].水土保持通报,2016,36(3):284-289,296.ZHENG Hua-wei,XIA Meng-lei,ZHANG Rui,et al.Diagnosis on Ecological Security of Cultivated Land Based on Entropy Method and Grey Prediction Model[J].Bulletin of Soil and Water Conservation 2016,36(3):284-289,296.
[18]董彦非,吴欣蓬,屈高敏.战斗机综合作战效能评估的改进灰色关联模型[J].飞行力学,2017,35(2):92-96.DONG Yan-fei, WU xin-peng, QU Gao-min. The Improved Grey Relation Model for Combined Operational Effectiveness Evaluation of Fighters[J]. Flight Dynamics,2017,35(2):92-96.
[19]杜强,陈乔,杨锐.基于Logistic模型的中国各省碳排放预测[J].长江流域资源与环境,2013,22(2):143-151.DU Qiang,CHEN Qiao,YANG Rui. Forecast Carbon Emissions of Provinces in China Based on Logistic Model[J]. Resources and Environment in the Yangtze Basin,2013,22(2):143-151.
[2]交通运输部.公路水路交通节能中长期规划纲要[R].北京:交通运输部,2008.Ministry of Transport. Medium-term and Long-term Planning Outline of Highway and Waterway Transport Energy Conservation[R]. Beijing:Ministry of Transport,2008.
[3]陈秀波.浙江省道路运输客运业节能减排评价指标体系研究[D].西安:长安大学,2008.CHEN Xiu-bo. Study on Evaluation Index System of Energy-saving and Emission Reduction of Road Passenger Transport Industry in Zhejiang[D]. Xi’ an:Chang’ an University,2008.
[4] HENSHER D A,STOPHER P,BULLOCK P. Service Quality:Developing a Service Quality Index in the Provision of Commercial Bus Contracts[J].Transportation Research Part A:Policy and Practice,2003,37(6):499-517.
[5] AZADEH A, GHADERI S F, IZADBAKHSH H.Integration of DEA and AHP with Computer Simulation for Railway System Improvement and Optimization[J].Applied Mathematics and Computation,2008,195(2):775-785.
[6]董彬.道路客运现代化评价指标体系研究[D].西安:长安大学硕士论文,2014.DONG Bin. Study on Evaluation Index System of Road Passenger Transport Modernization[D]. Xi’ an:Chang’ an University,2014.
[7]喻伟,方磊,王佳.基于PCA-DEA模型的城市群道路客运一体化协调性评价研究[J].铁道科学与工程学报,2011,8(4):74-79.YU Wei,FANG Lei,WANG Jia. Study on Integrated Road Passenger Transportation Assessment Mediation in Urban Agglomeration Based on PCA-DEA Model[J].Journal of Railway Science and Engineering,2011,8(4):74-79.
[8]彭伟.城市群道路客运一体化评价研究[D].长沙:长沙理工大学,2011.PENG Wei. Study on Urban Agglomeration Road Passenger Transport Comprehensive Evaluation System[D]. Changsha:Changsha University of Science and Technology,2011.
[9]符开业,郭钰,卢毅.区域道路客运一体化物元分析评价模型研究[J].湖南交通科技,2010,36(3):144-148.FU Kai-ye,GUO Yu,LU Yi. Matter-element Analysis Evalantion Model of Integration of Regional Road Passenger Transport[J]. Journal of Railway Science and Engineering,2010,36(3):144-148.
[10]张航,丁文霞.基于DEA的城乡道路客运一体化发展能力评价[J].公路交通科技,2013,30(1):115-120.ZHANG Hang,DING Wen-xia. Evaluation of Development Capability of Urban and Rural Highway Passenger Transport Integration Based on DEA Model[J]. Journal of Highway and Transportation Research and Development,2013,30(1):115-120.
[11]赵胜男.深圳市城市客运交通低碳发展模式与评价研究[D].重庆:重庆交通大学,2012.ZHAO Sheng-nan. Study on Low-carbon Development Mode and Evaluation of Urban Passenger Traffic in Shenzhen[J]. Chongqing:Chongqing Jiaotong University,2012.
[12]张岐山.基于遗传算法的分辨系数的优化方法[J].中国管理科学,2003,11(5):76-79.ZHANG Qi-shan. Genetic Algorithm-based Optimization of the Distinguishing Coefficient in Relational Analysis[J].Chinese Journal of Management Science,2003,11(5):76-79.
[13]吕锋.灰色系统关联度之分辨系数的研究[J].系统工程理论与实践,1997,17(6):49-54.LFeng. Research on the Identification Coefficient of Relational Grade for Grey System[J]. System Engineering-Theory and Practice,1997,17(6):49-54.
[14]范凯,吴皓莹.灰色系统关联度中一种新的分辨系数确定方法[J].武汉理工大学学报,2002,24(7):87-89.FAN Kai,WU Hao-ying. A New Method on Identification Coefficient of Relational Grade for Gray System[J].Journal of Wuhan University of Technology,2002,24(7):87-89.
[15]陈静,程东祥,诸大建.基于灰理想关联分析的中国城市低碳竞争力评价[J].资源科学,2012,34(9):1726-1733.CHEN Jing, CHENG Dong-xiang, ZHU Da-jian.Evaluation of Urban Low-carbon Competitiveness in China Using Grey Relational Analysis[J]. Resource Science,2012,34(9):1726-1733.
[16]李伟,王超,周绍松,等.最优分辨系数和主成分分析在烤烟品种综合评价中的应用[J].中国烟草科学,2017,38(2):45-50.LI Wei, WANG Chao, ZHOU Shao-song, et al.Application of Optimal Resolution Coefficient and Principal Component Analysis in Comprehensive Evaluation of Fluecured Tobacco Varieties[J]. Chinese Tobacco Science,2017,38(2):45-50.
[17]郑华伟,夏梦蕾,张锐,等.基于熵值法和灰色预测模型的耕地生态安全诊断[J].水土保持通报,2016,36(3):284-289,296.ZHENG Hua-wei,XIA Meng-lei,ZHANG Rui,et al.Diagnosis on Ecological Security of Cultivated Land Based on Entropy Method and Grey Prediction Model[J].Bulletin of Soil and Water Conservation 2016,36(3):284-289,296.
[18]董彦非,吴欣蓬,屈高敏.战斗机综合作战效能评估的改进灰色关联模型[J].飞行力学,2017,35(2):92-96.DONG Yan-fei, WU xin-peng, QU Gao-min. The Improved Grey Relation Model for Combined Operational Effectiveness Evaluation of Fighters[J]. Flight Dynamics,2017,35(2):92-96.
[19]杜强,陈乔,杨锐.基于Logistic模型的中国各省碳排放预测[J].长江流域资源与环境,2013,22(2):143-151.DU Qiang,CHEN Qiao,YANG Rui. Forecast Carbon Emissions of Provinces in China Based on Logistic Model[J]. Resources and Environment in the Yangtze Basin,2013,22(2):143-151.