考虑碳排放的港口全要素生产率及影响因素分析

摘要/Abstract

摘要：

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。

关键词: 水路运输, 全要素生产率, 方向距离函数, GML指数, 面板数据

Abstract:

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.

Key words: waterway transportation, total factor productivity, directional distance function, GML index, panel data

中图分类号:

戈艳艳，王姗姗. 考虑碳排放的港口全要素生产率及影响因素分析[J]. 交通运输系统工程与信息, 2021, 21(2): 22-29.

GE Yan-yan, WANG Shan-shan. Total Factor Productivity and Influencing Factors Analysis for Ports Considering Carbon Emissions[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 22-29.

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http://www.tseit.org.cn/CN/Y2021/V21/I2/22

参考文献

[1] ACCIARO M, GHIARA H, CUSANO M I. Energy management in seaports: A new role for port authorities [J]. Energy Policy, 2014, 71(C): 4-12.

[2] 许冬兰, 张敏. 中国工业低碳全要素生产率的测算及分解: 基于动态EBM-MI指数模型[J]. 青岛科技大学学报(社会科学版), 2018, 34(4): 19-24. [XU D L, ZHANG M. The measurement and decomposition of China's industrial low- carbon total factor productivity: based on the dynamic EBM- MI index model[J]. Journal of Qingdao University of Science and Technology (Social Sciences), 2018, 34(4): 19-24.]

[3] NWANOSIKE, TIPI SMITH W. Productivity change in Nigerian seaports after reform: a Malmquist productivity index decomposition approach[J]. Maritime Policy & Management, 2016, 43(7): 3-22.

[4] ESTACHE A, FÉ B T D L, TRUJILLO L. Sources of efficiency gains in port reform: A DEA decomposition of a Malmquist TFP index for Mexico[J]. Utilities Policy, 2004, 12(4): 221-230.

[5] 李电生, 张圣泽, 员丽芬. 港口物流综合效率测度研究 [J]. 交通运输系统工程与信息, 2013, 13(5): 107-113. [LI D S, ZHANG S Z, WU L F. Research on measurement efficiency for port of comprehensive[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(5): 107-113.]

[6] 宋京妮, 吴群琪, 孙启鹏, 等. 基于 SBM-undesirable 模型的综合运输效率评价[J]. 交通运输系统工程与信息, 2015, 15(5): 32-38. [SONG J N, WU Q Q, SUN Q P, et al. SBM-undesirable model-based efficiency evaluation for integrated transportation[J]. Journal of Transportation Systems Engineering and Information Technology, 2015, 15(5): 32-38.]

[7] 曹玮, 于清波. 基于DEA和Malmquist指数的福建沿海港口效率分析[J]. 华东交通大学学报, 2013, 30(4): 89- 96. [CAO W, YU Q B. Efficiency analysis of fujian coastal ports based on DEA and malmquist index[J]. Journal of East China Jiaotong University, 2013, 30(4): 89-96.]

[8] 艾亚钊, 周坤晓. 基于 SFA 法的集装箱港口效率比较研究 [J]. 物流技术, 2015, 34(3): 141- 144. [AI Y Z, ZHOU K X. Study on scale efficiency of container ports based on SFA[J]. Logistics Technology, 2015, 34(3): 141- 144.]

[9] 崔文田, 高宇, 张博. Malmquist 指数与 MalmquistLuenberger 指数的比较研究[J]. 西安工程科技学院学报, 2005(2): 152-156, 161. [CUI W T, GAO Y, ZHANG B. Comparison between malmquist productivity index and malmquist-luenberger productivity index[J]. Journal of Xi'an University of Engineering Science andTechnology, 2005(2): 152-156, 161.]

[10] 吕晓剑, 邓秋玮. 山东省大气环境效率时空分异与影响因素分析: 基于超效率SBM模型和GML指数[J]. 生态经济, 2020, 36(12): 193-199. [LV X J, DENG Q W. Analysis on spatial- temporal difference and influence factors of atmospheric environment efficiency in shandong province based on super-SBM model and GML index[J]. Ecological Economy, 2020, 36(12): 193-199.]

[11] 郭莉, 南开辉, 王静怡, 等. 基于SBM-GML的电力行业环境效率区域差异分析[J]. 生态经济, 2020, 36(4): 44- 49. [GUO L, NAN K H, WANG J Y, et al. Analysis on regional difference of environmental efficiency in power industry based on SBM-GML model[J]. Ecological Economy, 2020, 36(4): 44-49.]

[12] 门联欢, 甘爱平, 陈可桢. 绿色港口趋势下我国主要港口碳排放量的预测及减排对策[J]. 水运管理, 2014, 36 (8): 6- 8, 11. [MEN L H, GAN A P, CHEN K Z. Prediction of carbon emission from main ports in China under the trend of green ports and countermeasures for reducing carbon emission[J]. Shipping Management, 2014, 36(8): 6-8, 11.]

[13] CHUNG Y H, FÄRE R, GROSSKOPF S. Productivity and undesirable outputs: A directional distance function approach[J]. Journal Environ Manage, 1997, 51(3): 229- 240.

[14] OH D. A global malmquist-luenberger productivity index[J]. Journal of Productivity Analysis, 2010, 34(3): 183-197.

[15] 许利枝, 方述诚, 汪寿阳. 中国运输成本和效率对出口贸易影响的实证研究[J]. 系统工程理论与实践, 2012, 32(5): 1057- 1067. [XU L Z, FANG S C, WANG S Y. Analysis on transport costs and China's exports[J]. Systems Engineering-Theory ＆ Practice, 2012, 32(5): 1057-1067.]