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Annex 3 207 Table 15 – List of ship types (number of ships) used in this analysis Ship Type 2007 2008 2009 2010 2011 2012 Bulk carrier 3 3 6 9 39 52 Chemical tanker 0 0 3 1 17 1 Container 1 1 0 4 4 10 General cargo 0 0 0 0 0 57 Liquefied gas tanker 1 3 2 7 17 10 Oil tanker 11 11 12 16 54 40 Service – tug 0 0 0 0 5 0 Miscellaneous – fishing 0 0 0 0 2 0 Offshore 0 0 0 0 1 0 The total number of ships for which data has been collected represents approximately 1% of the total number of ships in the fleet, and approximately 2% of the total fuel consumption of the fleet. Noon report data contains inherent uncertainties because measurement on board ships is of variable quality depending on the techniques used. Many noon reports (including many of those used in this study) are populated using tank soundings which can have high measurement error (see Aldous et al., 2013). To address this issue, we have discussed quality procedures with the companies from which the data is collected (many of which have processes in place to assure the quality of the data). Furthermore, we have aggregated the data to quarterly totals (main engine and auxiliary engine fuel consumed, days at sea and in port, and distance travelled) and averages (speed, draught and tonne-per-day fuel consumption). This process of aggregation controls for the uncertainty in daily observations, providing there is no systemic bias in the reporting of any of the data. While systemic bias (e.g. consistent under-reporting of fuel consumed by the crew) cannot be ruled out, the magnitude of the error that this could create is not considered likely to be large relative to the level of assurance that is sought from these comparisons. Method of processing noon report data in preparation for comparison against bottom-up model output The noon report data for each ship was aggregated per quarter, and summary statistics on activity and fuel consumption were output for comparison with the bottom-up method. Only ships for which the noon report data are fully populated for a full quarter (plus or minus five days) is suitable for comparison; incomplete quarters are filtered out. Obvious outliers, usually due to human error in the reporting, are identified manually and removed. There are a small number of observations for which ship speed and distance travelled is logged but fuel consumption is not recorded. In this instance, the fuel consumption is filled in by conditional mean imputation: fuel consumption is predicted based on information from fully observed variables (ship speed, loading condition and weather) through multiple regressions. Filtering for part days precedes the regression in order to avoid skewness arising from manoeuvring/in-port operations. If none of the coefficients from the regression are found to be statistically significant, simply the mean at-sea fuel consumption for that ship is used. Overall, this approach introduces additional uncertainty in the comparison, but since fuel consumption is compared on an aggregate basis, on balance this is an improvement. Only a small number of observations are adjusted in this way (approximately 2.3% of all observations). Where a time and distance travelled is logged but there is no speed recorded, the speed is calculated from these two fields and filled in (0.3% of observations). Generally, the noon report fuel consumption fields cover only days at sea; where End of Sea Passage (EOSP) or Free Away on Passage (FAP) are not explicitly defined in an “activities” field (or similar), port days are therefore calculated when zero monitored fuel consumption coincides with zero speed and distance travelled. Fuel consumption associated with part days – i.e. on a day when the ship is leaving or arriving in port – is included in the per-quarter aggregates, and the hours’ steaming during part days are included in the totals for
208 Third IMO GHG Study 2014 time spent at sea. However, average at-sea ship speed is calculated from full days’ steaming only, to ensure that manoeuvring activities do not skew the results. Results of noon report and bottom-up output quality assurance of activity estimate and fuel consumption (all years) 2011
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Third IMO Greenhouse Gas Study 2014
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Published in 2015 by the internatio
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Contents Page Preface .............
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vii 3.1.2 Outline .............
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ix Fleet productivity projectio
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xii Third IMO GHG Study 2014 The re
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xiv Third IMO GHG Study 2014 MEPC m
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List of figures Page Figure 1: Bott
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List of figures xix Page Figure 46:
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List of figures xxi Annex Figures P
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List of figures xxiii Figure 45: Ca
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xxvi Third IMO GHG Study 2014 Page
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xxviii Third IMO GHG Study 2014 Tab
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Executive Summary Key findings from
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Executive Summary 3 rigorous testin
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Executive Summary 5 emissions than
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Executive Summary 7 Figure 2 shows
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Executive Summary 9 because, in con
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Executive Summary 11 Particular car
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Executive Summary 13 larger ship si
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Executive Summary 15 Summary of Sec
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Executive Summary 17 a. CO 2 b. CH
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Executive Summary 19 Demand for coa
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Executive Summary 21 Figure 15 show
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Executive Summary 23 • ships obse
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Executive Summary 25 b Noon report
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1 Inventories of CO 2 emissions fro
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Inventories of CO2 emissions from i
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96 Third IMO GHG Study 2014 Emissio
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98 Third IMO GHG Study 2014 Method
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100 Third IMO GHG Study 2014 Table
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102 Third IMO GHG Study 2014 Year T
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104 Third IMO GHG Study 2014 2.2.7
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106 Third IMO GHG Study 2014 This s
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108 Third IMO GHG Study 2014 N 2 O
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118 Third IMO GHG Study 2014 shippi
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122 Third IMO GHG Study 2014 a) CO
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3 Scenarios for shipping emissions
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Scenarios for shipping emissions 20
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154 Third IMO GHG Study 2014 IHSF o
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Page 260 and 261: Annex 4 Details for Section 1.5.1:
Page 262 and 263: Annex 4 231 IEA sources of uncertai
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264 Third IMO GHG Study 2014 Regard
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266 Third IMO GHG Study 2014 LNG ma
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270 Third IMO GHG Study 2014 ULCCs
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274 Third IMO GHG Study 2014 potent
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276 Third IMO GHG Study 2014 Size c
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280 Third IMO GHG Study 2014 Year o
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282 Third IMO GHG Study 2014 Johnso
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284 Third IMO GHG Study 2014 The re
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286 Third IMO GHG Study 2014 MGO, t
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