全球气候治理背景下的船舶营运能效评级方法

doi:10.12006/j.issn.1673-1719.2020.048

摘要/Abstract

摘要：

为共同应对全球气候变化,国际海事组织（IMO）计划制定一套强制性船舶营运能效（碳排放强度）国际机制,与现有强制性船舶设计能效机制互为补充。然而,由于受诸多因素影响,船舶营运能效具有较大的随机性和不确定性,为相关国际机制的构建带来巨大挑战。文中通过构建带有年份虚拟变量的碳强度分位数回归模型,实现了对不同时期海运船队碳排放强度特征的量化,包括总体分布的位置、尺度与偏态。在此基础上,提出了以国际海运船队碳排放强度目标为导向的船舶营运能效评级机制。实证分析表明,该评级机制能够引导国际海运业作为一个整体共同实现碳强度目标,同时对船舶个体营运能效表现的波动具有鲁棒性,为科学评价船舶营运能效水平提供了系统性解决方案。

关键词: 营运能效, 温室气体减排, 碳强度, 评级机制, 国际海事组织（IMO）

Abstract:

In a joint effort to address global climate change, the International Maritime Organization (IMO) has considered imposing a regulatory regime on the operational energy efficiency of ships to complement the existing mandatory mechanism on design efficiency. However, the random and uncertain nature of the operational energy efficiency of ships poses a significant challenge to performance regulation. In this paper, a quantile regression model, with dummy variables representing the observation calendar years, is applied to quantify the level, the scale and the skewness of the distribution of the operational carbon intensity performance of international shipping. A rating mechanism is further constructed on this basis, which is able to guide the performance of international shipping as a whole in line with the levels of ambition, whilst being robust to certain individual deviations. This rating mechanism is demonstrated using the bulk fleet of China as a sample, which shows its competency as a systematic solution for scientific evaluation of the operational energy efficiency of ships.

Key words: Operational energy efficiency, Greenhouse gas emissions reduction, Carbon intensity, Rating mechanism, International Maritime Organization (IMO)

张爽, 赵颖磊, 张琨琨, 李路. 全球气候治理背景下的船舶营运能效评级方法[J]. 气候变化研究进展, 2021, 17(2): 236-244.

ZHANG Shuang, ZHAO Ying-Lei, ZHANG Kun-Kun, LI Lu. Methodology for rating the operational energy efficiency performance of ships in the context of global climate governance[J]. Climate Change Research, 2021, 17(2): 236-244.

图/表 8

图1 船舶营运能效五级评价机制示意图

Fig. 1 A five-grade rating mechanism for operational energy efficiency of ships

图2 dd向量与定义能效级别的边界

Fig. 2 dd vectors and the boundaries for defining rating bands

表1 样本船舶的基本情况

Table 1 Description of the sample ships

表2 散货船队碳排放强度分布中间30%和70%部分的尺度和偏态

Table 2 Scale and skewness of the middle 30% and 70% of the carbon intensity distribution

表3 dd向量估计结果以及等价的百分比

Table 3 Estimates of the dd vectors and the equivalent percentage change

表4 中间位置和各级别划分边界的参数估计

Table 4 Estimates of the median and the parameters for determining rating boundaries (in logarithmic form and original form)

图3 散货船队的EEOI指标评级结果注：图中EEOI为营运能效指标,DWT为船舶载重吨,虚线代表各级别分布的中间位置。

Fig. 3 Rating results based on EEOI in (a) 2015-2017, (b) 2018

表5 各级别船舶所占比例

Table 5 Proportion of ships rated as each grade %

参考文献 23

[1]	Oberthür S. Institutional interaction to address greenhouse gas emissions from international transport: ICAO, IMO and the Kyoto Protocol[J]. Climate Policy, 2003, 3(3):191-205
[2]	张琨琨, 赵颖磊, 张爽. 国际海运温室气体减排初步战略浅析[M]//谢伏瞻, 刘雅鸣. 气候变化绿皮书: 应对气候变化报告(2018). 北京: 社会科学文献出版社, 2018: 158-168.
	Zhang K K, Zhao Y L, Zhang S. Analysis on the initial strategy of greenhous gases reduction from internatiaonl shipping [M]//Xie F Z, Liu Y M. Green book of climate change: annual regport on actioans to address climate change (2018). Beijing: Social Sciences Academic Press, 2018: 158-168(in Chinese)
[3]	Brooks M R, Faust P. 50 years of review of maritime transport, 1968-2018: reflecting on the past, exploring the future [R/OL]. 2018 [2019-01-11]. https://unctad.org/en/PublicationsLibrary/dtl2018d1_en.pdf
[4]	Smith T, Jalkanen J P, Anderson B A. et al. Third IMO GHG study 2014 [R/OL]. 2015 [2019-01-01]. http://www.imo.org/en/OurWork/Environment/PollutionPrevention/AirPollution/Documents/MEPC%2067-INF.3%20-%20Third%20IMO%20GHG%20Study%202014%20-%20Final%20Report%20%28Secretariat%29.pdf
[5]	张爽, 张硕慧, 李义良. 船舶温室气体减排措施及对我国的影响分析[J]. 中国航海, 2010, 33(3):69-72.
	Zhang S, Zhang S H, Li Y L. Marine GHG emission reduction measures and their effects on China[J]. Navigation of China, 2010, 33(3):69-72 (in Chinese)
[6]	International Maritime Organization (IMO). Resolution MEPC.304 (72), initial IMO strategy on reduction of GHG emissions from ships [S/OL]. 2018 [2019-01-01]. http://www.imo.org/en/KnowledgeCentre/IndexofIMOResolutions/Marine-Environment-Protection-Committee-%28MEPC%29/Pages/default.aspx
[7]	Zhang S, Li Y, Yuan H C, et al. An alternative benchmarking tool for operational energy efficiency of ships and its policy implications[J]. Journal of Cleaner Production, 2019, 240: 118-223
[8]	Deligiannis P. Ship performance indicator[J]. Marine Policy, 2017, 75: 204-209
[9]	Ančić I, Vladimir N, Cho D S. Determining environmental pollution from ships using index of energy efficiency and environmental eligibility (I4E)[J]. Marine Policy, 2018, 95: 1-7
[10]	Right Ship. Calculating and comparing CO₂ emissions from the global maritime fleet [R/OL]. 2013 [2019-01-01]. https://www.rightship.com/sustainability/ghg-rating
[11]	李碧英. 绿色船舶及其评价指标体系研究[J]. 中国造船, 2008 (183):27-35.
	Li B Y. Study on green ship and it’s assessment index system[J]. Shipbuilding of China, 2008 (183):27-35 (in Chinese)
[12]	杜太利, 张鹏, 吴桂涛, 等. 绿色船舶定量评价体系研究[J]. 航海技术, 2011 (2):76-78.
	Du T L, Zhang P, Wu G T, et al. Research on quantitative evaluation system of green ship[J]. Marine Technology, 2011 (2):76-78 (in Chinese)
[13]	Gilbert P. From reductionism to systems thinking: how the shipping sector can address sulphur regulation and tackle climate change[J]. Marine Policy, 2014, 43: 376-378
[14]	高梓博, 杜太利, 张勇, 等. 聚类算法在船舶能效数据挖掘中的应用[J]. 武汉理工大学学报: 交通科学与工程版, 2019, 43(2):286-290.
	Gao Z B, Du T L, Zhang Y, et al. Application of clustering algorithm for data mining in ship energy efficiency[J]. Journal of Wuhan University of Technology: Transportation Science & Engineering, 2019, 43(2):286-290 (in Chinese)
[15]	李伟, 刘清. 西江水系运输船舶能耗评价体系研究[J]. 武汉理工大学学报: 交通科学与工程版, 2011, 35(5):1035-1039.
	Li W, Liu Q. Research on energy consumption evaluation system of transportation ships in Xi Jiang river system[J]. Journal of Wuhan University of Technology: Transportation Science & Engineering, 2011, 35(5):1035-1039 (in Chinese).
[16]	杨国豪, 徐轶群, 林荣模. 基于模糊评判的船舶能耗评估[J]. 中国航海, 2011, 34(4):22-25.
	Yang G H, Xu Y Q, Lin R M. Fuzzy evaluation of ship energy consumption[J]. Navigation of China, 2011, 34(4):22-25 (in Chinese)
[17]	International Maritime Organization (IMO). Resolution MEPC.231 (65), 2013 guidelines for calculation of reference lines for use with the energy efficiency design index (EEDI) [S/OL]. 2013 [2019-01-01]. http://www.imo.org/en/KnowledgeCentre/IndexofIMOResolutions/Marine-Environment-Protection-Committee-%28MEPC%29/Pages/default.aspx
[18]	Koenker R, Bassett G W. Regression quantiles[J]. Econometrica, 1978, 46(1):33-50
[19]	Koenker R, Hallock K F. Quantile regression[J]. Journal of Economic Perspectives, 2001, 15(4):143-156
[20]	郝令昕, Daniel Q N. 分位数回归模型[M]. 上海: 格致出版社, 2017.
	Hao L X, Daniel Q N. Quantile regression models [M]. Shanghai: SAGE Publications, Inc., 2017 (in Chinese)
[21]	International Maritime Organization (IMO). MEPC.1/Circ.684, guidelines for voluntary use of the ship energy efficiency operational indicator (EEOI) [S/OL]. 2009 [2019-01-01]. https://docs.imo.org/
[22]	International Maritime Organization (IMO). Resolution MEPC.203 (62), amendments to MARPOL annex VI (inclusion of regulations on energy efficiency for ship [S/OL]. 2011 [2019-01-01]. http://www.imo.org/en/KnowledgeCentre/IndexofIMOResolutions/Marine-Environment-Protection-Committee-%28MEPC%29/Pages/default.aspx
[23]	International Maritime Organization (IMO). Resolution MEPC.278 (70), amendments to MARPOL annex VI (data collection system for fuel oil consumption of ships) [S/OL]. 2018 [2019-01-01]. http://www.imo.org/en/KnowledgeCentre/IndexofIMOResolutions/Marine-Environment-Protection-Committee-%28MEPC%29/Pages/default.aspx