考虑碳排放的港口全要素生产率及影响因素分析

Total Factor Productivity and Influencing Factors Analysis for Ports Considering Carbon Emissions

基于我国沿海19个港口2009-2018年面板数据,结合DEA(Data Envelopment Analysis)方法和方向距离函数,同时考虑非期望产出构建港口全要素生产率模型,并采用面板数据模型厘清影响港口全要素生产率的内外因素。模型运行结果表明:中国主要沿海港口的全要素生产率都有不同程度的提高,且货物吞吐量大的港口GML(Global Malmquist-Luenberger)指数不一定高;中国主要沿海港口的技术效率有所降低,但技术进步都有所提高;五大港口群中东南沿海地区、长江三角洲地区和西南沿海地区GML均值较稳定,环渤海港口群和珠江三角洲地区不稳定,时高时低;中国主要沿海港口GML指数除受内部因素影响,还与货物进出口总额有关,货物进出口总额增长率每增长1个百分点,港口GML指数提高0.049242个百分点。

Based on the panel data of 19 coastal ports in China from 2009 to 2018,this paper combines DEA method and direction distance function to develop the total factor productivity model for ports and considers the unexpected output in the modeling process.The panel data model is used to identify the internal and external factors that affect the total factor productivity of the ports.The results indicate that the total factor productivity of main coastal ports in China has been improved in different degrees;the ports with large cargo throughput is not necessarily with high GML index.The technical efficiency of the ports has been reduced,but the technical progress has been improved.The mean values of GML in the southeast coastal area,the Yangtze River Delta and the southwest coastal area of the five major port groups are relatively stable,while the mean values of GML in the Port Group and the Pearl River Delta Economic Zone around the Bohai Sea are relatively unstable and with fluctuations.The GML index of port is not only affected by internal factors,but related to the total amount of goods imports and exports,for example,the total amount of goods imports and exports grow 1 percentage would cause the GML index of port increase by 0.049242 percentage.