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Annex 7 257 The volatile situation in the Middle East also led to avoidance of the Suez Canal, and ships also increased dramatically in size such that the Panama Canal became unnavigable for some ships. Therefore, the period 1970 to 1985 is known to have a particular explicable data excursion for tonne-miles of crude oil, so that those data were excluded from the analysis. Historical data on global GDP were obtained and GDP projection data for the five SSP scenarios obtained from the website of the International Institute for Applied Systems Analysis (IIASA). The GDP projection data are already shown in Chapter 3. For liquid fossil fuels (essentially, oil) and coal, relationships with historical global oil and coal consumption were constructed. Previous studies, such as Eyring et al. (2005) and Eide et al. (2007), have based projections on linear regression models. Non-linear statistical models have been used for some time in long-term projections of aviation. Such models are often referred to as “logistic models”, or more simply “non-linear regression models”. A range of these models exists, such as the Verhulst or Gompertz models, and they are commonly used in econometric literature where the requirement is to simulate some form of market saturation (Jarne et al., 2005). The sigmoid curve mimics the historical evolution of many markets with three typical phases: emergence, inflexion (maturation) and saturation, where the period of expansion and contraction are equal with symmetrical emergent and saturation phases. The first phase involves accelerated growth; the second, approximately linear growth; and the third, decelerated growth. Logistic functions are characterized by constantly declining growth rates. The Verhulst function is particularly attractive as it calculates its own asymptote from the data and is described as follows, where x is future demand, t is time in years and a, b and c are model constants: x = a/(1 + b × exp(- c × t)) eq. (6) The constants a, b, and c are estimated from initial guesses of asymptote, intercept and slope, and solved by converged iterative solution. SPSS v19 provided a suitable program for this model. Different ship types are quite different in size, power and market growth rates, so that individual models were derived for each transport type. Results The ratios of TST in tonne-miles to GDP for the four different cargo types (total oil, coal, total (non-coal) bulk dry goods and other dry goods) are shown in Figure 34.
258 Third IMO GHG Study 2014 Figure 34: Ratios of TST (different subtypes) in billion tonne-miles to historical global GDP in US$ (constant 2005 prices) and coal/oil consumption (from BP Statistical Review) Figure 34 shows a set of complex signals. The pattern of growth in oil transported between 1970 and 1985 has already been mentioned, and its reason for exclusion from the model construction. Statistically significant and robust Verhulst models were calculated for the four main cargo types, and the future ratios growth curves are shown, as calculated, in Figure 35. Figure 35: Historical and modelled growth curves to 2050 for ratios of total oil, coal, total (non-coal) bulk dry goods and other dry cargoes
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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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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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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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3 Scenarios for shipping emissions
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160 Third IMO GHG Study 2014 Pre-pr
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168 Third IMO GHG Study 2014 data s
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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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