technical annex: support for the impact …...see appendix 5 for more details about avoidance...

Technical annex: support for the impact assessment of a proposal to address maritime transport greenhouse gas emissions

Ref: CLIMA.B.3/SER/2011/0005

Report for European Commission – DG Climate Action

Ricardo-AEA/R/ED56985 Issue Number 5

Date: 05/03/2013

Technical annex: support for the impact assessment of a proposal to address maritime

transport greenhouse gas emissions

i Ref: Ricardo-AEA/R/ED56985/Issue Number 5

Customer: Contact:

European Commission – DG Climate Action Sujith Kollamthodi

Ricardo-AEA Ltd

Gemini Building, Harwell, Didcot, OX11 0QR

t: 0870 190 6513

e: [email protected]

Ricardo-AEA is certificated to ISO9001 and ISO14001

Customer reference:

CLIMA.B.3/SER/2011/0005

Confidentiality, copyright & reproduction:

This report is the Copyright of the European

Commission and has been prepared by Ricardo-AEA Ltd under a contract with DG

Climate Action dated 12/09/2011. The contents of this report may not be reproduced in whole or in part, nor passed to

any organisation or person without the specific prior written permission of the European Commission. Ricardo-AEA Ltd

accepts no liability whatsoever to any third party for any loss or damage arising from any interpretation or use of the information

contained in this report, or reliance on any views expressed therein.

Authors:

Sujith Kollamthodi, Ana Pueyo, Gena Gibson, Rasa

Narkeviciute, Adam Hawkes, Stephanie Cesbron, Robert Milnes, James Harries, Andriana Stavrakaki (Ricardo-AEA)

Tony Zamparutti, Guillermo Hernandez, Styliani Kaltsouni, Sophie Vancauwenbergh and Gretta Goldenman (Milieu)

Christopher Pålsson. Niklas Bengtsson, Torbjörn Rydbergh, Lennart Nilsson, Andreas Krantz, Kristina Weber (IHS)

Tim Scarbrough, Chris Whall, Chris Green, Jenny Hill, Jin Lee, Richard Noden, Ben Grebot (AMEC)

Haakon Lindstad (Marintek)

Approved By:

Sujith Kollamthodi

Date:

05 March 2013

Signed:

Ricardo-AEA reference:

Ref: ED56985- Issue Number 5



ii Ref: Ricardo-AEA/R/ED56985/Issue Number 5

Table of contents

1 Appendix 1: Description of the TIMES international shipping model and its results ........................................................................................................................................ 4

1.1 Overview................................................................................................................. 4 1.2 Representation of shipping activity ........................................................................ 4 1.3 Representation of vessels...................................................................................... 7 1.4 Cost assumptions................................................................................................... 8

2 Appendix 2: Description of IHS Fairplay model and results ................................... 16 2.1 About IHS forecasts.................................................................................................. 16 2.2 2010 baseline emissions calculations.................................................................. 31 2.3 Baseline emissions forecast to 2050 ................................................................... 34

3 Appendix 3 – Methodology and quantitative assessment of relevant historical emissions ................................................................................................................................ 37

3.1 Introduction........................................................................................................... 37 3.2 Background: trends in the maritime sector 1990 to 2010.................................... 37 3.3 Methodology to estimate historical maritime CO2 emissions .............................. 40 3.4 Results.................................................................................................................. 58 3.5 Comparisons ........................................................................................................ 63 3.6 Uncertainties ........................................................................................................ 66 3.7 References ........................................................................................................... 68

4 Appendix 4 - Administrative burden .......................................................................... 71 4.1 Introduction........................................................................................................... 71 4.2 General MRV costs .............................................................................................. 72 4.3 Option-specific costs ............................................................................................ 80

5 Appendix 5 – Avoidance .............................................................................................. 86 5.1 Introduction........................................................................................................... 86 5.2 Alteration of routes ............................................................................................... 86 5.3 Change in composition of EU shipping fleet........................................................ 88 5.4 Relocation of manufacturing industry .................................................................. 91 5.5 Summary and conclusions ................................................................................... 92

6 Appendix 6– Analysis of fuel tax policy option ........................................................ 94 6.1 Introduction........................................................................................................... 94 6.2 Design of the policy option ................................................................................... 94 6.3 Administrative burdens......................................................................................... 95 6.4 Environmental impacts ......................................................................................... 96 6.5 Economic impacts ................................................................................................ 97 6.6 Social Impacts ...................................................................................................... 97 6.7 References ........................................................................................................... 99

7 Appendix 7 – Use of revenues/rents ........................................................................ 100 7.1 Introduction......................................................................................................... 100 7.2 Potential revenue from market-based measures .............................................. 100 7.3 Potential options for recycling revenues ............................................................ 101 7.4 Design of options for recycling revenue ............................................................ 102 7.5 Impact analysis................................................................................................... 107 7.6 References ......................................................................................................... 115

8 Appendix 8 – Economic Impacts on the Pulp and Paper Industry ....................... 116



iii Ref: Ricardo-AEA/R/ED56985/Issue Number 5

8.1 Introduction......................................................................................................... 116 8.2 Prices.................................................................................................................. 117 8.3 Freight rates ....................................................................................................... 118 8.4 Origin and destination of consumption and production ..................................... 120 8.5 Elasticities of demand ........................................................................................ 123 8.6 Market structure ................................................................................................. 124 8.7 Summary and analysis of impacts ..................................................................... 125 8.8 References ......................................................................................................... 127

9 Appendix 9: Case studies exploring the potential impacts of policy action on specific regions and routes ................................................................................................ 129

9.1 Introduction......................................................................................................... 129 9.2 The Baltic Sea case study ................................................................................. 130 9.3 Motorways of the Sea case study ...................................................................... 140 9.4 Passenger ferries case study............................................................................. 148



4 Ref: Ricardo-AEA/R/ED56985/Issue Number 5

1 Appendix 1: Description of the TIMES international shipping model and its results

1.1 Overview

The European Commission requires a model to assess the impacts of policy instruments for reducing CO2 emissions from EU international shipping. From a modelling perspective, the key points of interest relate to the costs of policy options, the emissions abatement profile over time, and the cost effectiveness (cost per tonne CO2 abated) of taking action in this area. Additional areas of interest include the extent to which shipping routes may change in response to policy action, the potential for modal shift as a policy response, and the extent of in-sector abatement versus out-of-sector abatement.

This background document sets out the assumptions underpinning the TIMES model as follows:

Representation of shipping activity: provides a description of how shipping activity

is represented within the TIMES model; Representation of vessels: provides details of the various ship types and size

categories that are included; Cost assumptions: provides an overview of the references and assumptions used to

define the various costs required to model the maritime sector, including fuel price scenarios, abatement measures and EU ETS credits out to 2050

1.2 Representation of shipping activity

The implemented solution is a TIMES energy system model, which characterises the available routes within/into/out of Europe and available technological and logistical choices out to 2050. Most crucial among these are:

The ability for ships to stop at a port just outside the EU (for avoidance of any carbon

charges);

The ability to divert freight to alternative modes via a port just outside the EU, or for

intra-EU trade;

The possibility for technology change in the shipping fleet (i.e. new ships and/or

efficiency measures); and

The possibility for fuel switching in the shipping fleet.

Therefore, in addition to standard TIMES energy system model functionality, a network model was required depicting the various routes and modes for goods currently shipped into and out of Europe. The model includes the flexibility to switch between these routes and modes.

A simplified example of such a network is shown in Figure 1.1:




Figure 1.1: Hypothetical Network & Technology Model Showing Routes of Fuel Consumption1

1.2.1 Trade forecasts

Trade data for cargo categories, including historical data and forecasts up to 2050 were provided by IHS World Trade Service (WTS). Extra-EU data was available by reporting country, the region traded with and commodity type/trade concept.

1.2.2 Commodities

The traded commodities used in the model were based on the sea cargo categories: Dry Bulk, Liquid Bulk (separating out Crude Oil), General Cargo, and Containers.

Commodities for the TIMES model are defined as specific entities (expressed as codes within the model) that comprise the traded commodity, the origin (where it was produced) and its final destination. There are two types of such commodities required to construct a model of the shipping process:

Supply commodities. The cargo category plus the region in which it was produced. This represents the original resource. For example “Supply of Crude oil in North Africa”. This TIMES variable is not related to actual production data in those regions; the trade is defined from the demand side and it is assumed that the supply will match the projected trade.

Demand commodities. The demand commodities encompass the cargo category,

and the region combination, e.g. “Demand of North African crude oil in EU South”. The numerical values of these demands correspond to trade projections 2010-2050 provided by IHS WTS. Only to/from/within EU demands are defined.

For each origin/destination pair one or two types of movements are defined. One of them is direct movement, e.g. from supply to demand region. Only one movement type is defined for shorter routes, such as Intra-European trade. The other type of movement defined assumes a stopover on the way to/from Europe. In this case, a ship is assumed to stop at an intermediate port (depending on the origin-destination pair) on its way to/from Europe. The CO2 emissions are split to represent the two journey legs. Casablanca and Port Said are assumed to be the intermediate ports (for distance measuring purposes only) for western and eastern routes respectively.

An example movement of these commodities is shown in Figure 1.2. The solid arrows indicate the change in location, i.e. actual movement of goods from one place to another. The dashed arrows show that both routes feed into the same demand, so the model can choose to take one or the other route for all the demand, or move part on one and part on the other.

1 Note: “Slow ” ships require additional capacity in the f leet to serve an equivalent demand

Existing Ship: 1.5PJ

“Slow” Ship: 0.75PJ

Existing Ship: 4PJ

“Slow” Ship: 2PJ

Origin

Non EU Port

EU Port

Alternative Mode: 2PJ

Existing Ship: 5PJ

“Slow” Ship: 2.5PJ




Figure 1.2: Example commodities movement

See Appendix 5 for more details about avoidance (route-shifting).

1.2.3 Regions

The regions within the TIMES model were defined according the definitions used by the IHS World Trade Service, as this was the format in which the trade data was provided. There are two EU regions: EU Northern/Baltic and EU South/Mediterranean, and 13 extra-EU regions. Distances between regions needed to be defined in order to calculate fuel consumption on each route. For this purpose, a representative port was defined in each region, and two ports for EU regions. The distances in nautical miles were calculated between these representative ports using http://www.portworld.com/map/.

Table 1.1: Regions in the model and their representative ports

Region number

Region Key port for measuring average differences

Region 1* EU North Rotterdam, Netherlands

Second port in EU North for intra-region

distance: Klaipeda, Lithuania

Region 2* EU South Gioia Tauro, Italy

Second port in EU South: Cartagena, Spain.

Region 3 Mediterranean - non EU Port of Omisajl, Croatia

Region 4 Northern Europe - non EU Bergen, Norway

Region 5 Middle East Beirut, Lebanon

Region 6 North Africa Port Said, Egypt

Region 7 North America New York, USA

Region 8 Central America/Caribbean

Sea

Colon, Panama

Region 9 South America East Coast Rio de Janeiro (Port of Niteroi), Brazil

Region 10 South America West Coast Valparaiso, Chile

Region 11 Australia/Oceania Melbourne, Australia

Region 12 North East Asia Shanghai, China

Region 13 South East Asia Singapore

Region 14 India Sub Mumbai, India

Region 15 Southern Africa Durban, South Africa

Source: Regions based on IHS WTS data categories; representative ports as assumed by AEA; distances

between ports sourced from www.portworld.com

South Asian Oil Supply

South Asian Oil direct to EU

South Demand of South

Asian oil in EU South

South Asian Oil to EU South via

Port Said

http://www.portworld.com/map/




1.2.4 Modal shift

The TIMES model can allow for modal shift of cargo on intra-EU journeys. The costs are sourced from the DG Environment-funded project entitled COMPetitiveness of EuropeAn Short-sea Shipping (COMPASS) report (2010).

Table 1.2: Assumptions for costs of transport on other modes

Road Rail

2010 2015 2020 2025 2030 All

COST (EUR/t-km) 0.1046 0.0947 0.0968 0.0976 0.0982 0.0063

Of which fixed cost 0.043932 0.039774 0.040656 0.040992 0.04124 0.001638

Source: Short Sea Shipping (COMPASS) report

Notes: Total cost includes: repair, purchase, labour tax, labour, insurance, fuel. Plus Taxes: registration, ownership, network, insurance, fuel. Rail costs assume 32% diesel, 68% electric train (Source: Short Sea Shipping report)

Emission factors for road and rail transport were calculated using the COMPASS report and the UK Department of Environment, Food and Rural Affairs (Defra) GHG conversion factors2.

Table 1.3: Emission factors for transport on other modes (kgCO2-e per tonne km)

Mode of transport Emission factor

(kgCO2e/tonne km)

Road freight 0.0494511

Rail freight 0.03161

Source: Based on information in the Short Sea Shipping (COMPASS) report and Defra conversion factor guidance; emission factor for road assumes 20.13T/vehicle. Notes: Total cost includes: repair, purchase, labour tax, labour, insurance, fuel. Plus Taxes: registration, ownership, network, insurance, fuel. Rail costs assume 32% diesel, 68% electric train (Source: Short Sea Shipping report)

1.3 Representation of vessels

1.3.1 Ship sizes and types

A summary of ship sizes/types is shown here. The full assumptions (including capital cost, CO2 emissions, etc.) are provided in a separate Excel spreadsheet.

Table 1.4: Summary of ship sizes and types

Type Size

Dry Bulk Dry bulk Capesize 120'+

Large Dry Bulk carrier (80' +)

Medium Dry bulk carrier (35' - 85')

Small Dry Bulk carrier (<35')

General Cargo General Cargo 15'++

RoRo 35'-++

GEN long avg of GEN 15'++ and RoRo 35' ++

RoRo 15' - 35'

GEN short avg of GEN 0-15' and Reefer 0-15'

2 http://www.defra.gov.uk/environment/economy/business-efficiency/reporting/




Type Size

Container ships Container 8500 TEU +

Container 5500 - 8500 TEU

Container 2000-5500TEU

Containers 1000-2000TEU

Container 0 - 1000 TEU

Oil (and product) tankers Crude oil tanker 120'++

Crude oil tanker 120' + , Product tanker 75' +

Crude oil tanker 75-120', products 15-75'

Crude oil tanker 0-75'and Products 0-15'

Liquid bulk (Chemical, LNG, LPG tankers) Chemical 40'-++, LNG 60'++

Chemical tanker 40' ++ and LPG 45'++

Chemical tanker and LPG 15-40'

LNG tanker 0'-15' and Chemical 0 - 15'

Passenger vessels Ships carrying up to 1000 passengers

Source: size thresholds based on categories used in data provided by Marintek, IHS and IMO sources

To represent economy of scale, a size larger than usual ships were used on the trade routes.

1.4 Cost assumptions

This section describes the data sources and key assumptions relating to costs out to 2050, including:

Abatement technologies;

Fuel types and associated costs;

EU ETS credits; and Administrative costs.

1.4.1 Abatement technologies

A range of possible emissions abatement options (technological and operational) have been identified and included in the modelling framework. The capital costs, operational costs and CO2 reduction potentials of the abatement technologies were sourced from MEPC 61 INF. 183, an IMO-funded study on the reduction of GHG emissions from ships. These costs are variable depending on the ship size and type. The cost tables are not reproduced here; the reader is referred to the separate Excel sheet provided.

Changes were made to the data sourced from MEPC 61 INF.18 in three areas:

Speed reduction;

Optimisation of hull & superstructure (new ships); and LNG costs (capital cost and operational cost).

1.4.1.1 Speed reduction

Any reductions in vessel speed mean that the capacity of the fleet would be reduced, and more ships would be needed to serve the same demand. The capital cost of speed reduction therefore refers to the cost of purchasing additional capacity (depending on ship

3 http://www.rina.org.uk/hres/mepc%2061_inf _18.pdf




size/type) and the operational cost refers to the cost of operating additional new ships. The fraction of ships that need to be purchased is calculated using the following formula:

The price of newly built vessels was supplied by Marintek. Speed reduction of 25% was used, which leads to different levels of CO2 saving for different ship types. Estimated savings were provided by Marintek, which varies depending on the ship type.

1.4.1.2 Optimization of hull & superstructure

Estimates of CO2 savings from hull and superstructure optimisation (new ships only) were provided by Marintek based on proprietary data. The abatement potential was set at a maximum of 20% (this is also the upper limit suggested by the IMO Second GHG Study, 2009) and costs were scaled according to the building cost of new ships (an increase in new-building cost of 25% per vessel for the first US$100m, and 5% increase in new-build cost for any costs above US$100 million).

1.4.1.3 LNG

Estimates of the additional building cost of a new LNG ship were provided by Marintek based on their analysis of 2011 prices. The cost was expressed as an additional 10% on the total building cost of an equivalent ship, plus an additional US$2 million.

For the purposes of the analysis, the abatement options were grouped into a defined set of categories. The assumptions are as follows:

Table 1.5: Groups of measures

Group Measures New Retrofit

Alternative energy Towing kites

Wind engines

Solar panels

Friction Optimized hull & superstructure

Air lubrication

Hull coatings

Hull cleaning

Engine Common rail technology

Main Engine Tuning

Speed control of pumps & fans

Operation Autopilot adjustment

Weather routing

Hull cleaning

Propeller polishing

Hull coating

Propeller Propeller & rudder upgrades

Speed reduction Speed reduction

Alternative fuels LNG




Note that although hull coating is not an “operational” measure as such, it was assigned to the operation group because coatings should be renewed periodically (approximately every five years according to IMO, 2009).

1.4.2 Fuel types and costs

A generic maritime fuel was assumed to be used in existing cargo ships, rather than defining ships that run on residual fuel (HFO) and distillate marine fuel (MDO/MGO) separately. This assumption was used in order to keep the model compact and facilitate the interpretation of results. A new alternative technology is included in future years – ships that use liquefied natural gas (LNG) as fuel.

Wholesale fossil fuel price projections were sourced from the PRIMES model crude oil price and natural gas price projections developed for the Commission’s 2011 Energy Roadmap (as obtained from the EC). There are three price scenarios: Reference, Current Policy Initiatives, and Decarbonisation. While the prices under the Reference Scenario and Current Policy Initiatives are similar in the years 2010 and 2015, the Decarbonisation Scenario projects significantly lower fossil fuel prices throughout the time horizon (Figure 1.3).

Figure 1.3: Crude Oil Price Projections to 2050, EUR/boe (NCV) (Source: European Commission 2011 Energy Roadmap)

All three of the PRIMES crude oil price projections were used as the basis for developing price projections for maritime fuels. The estimation process for arriving at final bunker fuel prices is described below.

For existing ships, a mixture of residual and distillate fuels was considered. For price projection purposes, this mixture is defined in model as a single marine bunker fuel. The price of that fuel is a weighted average of residual and distillate fuel prices. A few steps were undertaken to create projections of fuel prices for the model horizon. Assumptions of each of these steps are described below.

1. The correlation of crude oil and HFO prices

The residual fuel price and crude oil price difference has varied over time, and it is uncertain what the exact fuel price will be given a specific crude oil price. For the purposes of this study, the correlation obtained from historical data as described in IMO (2010) (MEPC 61/INF 18) was used. This document approximates the relationship between crude oil and HFO prices as linear:

EUR/boe

Reference scenario

Decarbonisation scenario

Current policy initiatives scenario




(

) (

)

Using this relationship, HFO prices were obtained for the three crude oil price scenarios.

2. The expected impact of MARPOL Annex VI reduced sulphur regulations on

marine fuel prices

The impacts of sulphur regulations on prices were calculated using results from the Purvin & Gertz (2009) report to the Commission on the impacts of IMO fuel specification changes. The report investigates the impacts of reducing maximum allowed sulphur in marine fuels as per amendments of MARPOL Annex VI. These amendments consist of: a) For Sulphur Emission Control Areas (SECAs), reducing the maximum permitted

sulphur content in marine bunker fuels to 0.1% (from current 1.5%) in 2015.

b) For non-SECAs, reducing the maximum permitted sulphur content in marine

bunker fuels to 0.5% (from current 3.5%) in 2020.

Thus in this step, two price projection sets are calculated: one for SECA quality fuels, and one for non-SECA quality fuels. For SECA fuels, according to Purvin & Gertz report, the price of 0.1% S bunker fuels will be in the range of 60-75% higher than current quality (1.5%S) fuel price. For non-SECA fuels, the likely price increment compared to current quality (3.5%S) fuel price will be in the range of 30-50%. These numbers comprise different desulphurisation options, including producing low sulphur HFO and distillate fuels.

The prices obtained in Step 1 are thus inflated to create three price scenarios as follows:

CPI-based price projections (High prices): Current policy initiatives

scenario for crude oil price with high price increase due to sulphur content requirements. The highest % increase was used to account for the sulphur requirements (75% and 50% for SECA starting 2015 and non-SECA fuels starting 2020 respectively).

Decarbonisation scenario price projections (Low prices): Decarbonisation oil price scenario with low price increase due to sulphur content requirements. The lowest % increase was used to account for the sulphur requirements (60% for SECA fuels and 30% for non-SECA fuels)

Reference scenario price projections (Central prices): Reference crude oil

price scenario with average price increase due to sulphur content requirements.

Note that the price increases obtained by Purvin & Gertz do not take into account possible global supply/demand change impacts. For SECA fuel prices, low sulphur HFO (LS 380) was assumed as the prevalent grade, which has on average 10% higher price than grades with higher sulphur content (ECSA, 2010). The resulting prices for the central price scenario are shown in Figure 1.4




Figure 1.4: Bunker Fuel price projections including impacts of sulphur requirements (central scenario)

3. Distillate prices and quantity

It was assumed that all distillate fuel already satisfies sulphur requirements as defined in Step 2 (DMA gas oil quality). The differences between prices of different bunker fuel grades and types fluctuate over time. According to ECSA (2010), the MGO price is 93% higher than that of grade IFO 380 residual fuel (average difference for period 1990-2008). This long-term average difference was used to determine a corresponding marine gas oil price from residual fuel price obtained earlier. It is not distinguished between SECA and non-SECA qualities in this case.

4. Residual and distillate fuel consumption.

As the values cited in Purvin & Gertz (2009) report include a variety of options for reducing sulphur in marine bunker fuel, it was assumed that the change in proportion of demand for fuel satisfied by distillates is already accounted for in the price increase. Therefore, throughout the time horizon, current proportions of residual and distillate fuels were used (see Table 1.6).

Table 1.6: Estimated residual and distillate fuel consumption proportions in SECA and non-SECA (2008). Calculated from IMO (2009) – Second IMO GHG Study 2009.

Residual fuel Distillate fuel

SECA 78% 22%

Non-SECA 77% 23%

These were used to obtain weighted averages of SECA and Non-SECA fuel prices calculated in steps 2 and 3 for low, central and high price scenarios.

5. SECA/Non-SECA fuel consumption and final scenarios.

The fuel consumption in SECA/Non-SECA areas was assumed to maintain the same proportion as is now, i.e., any growth in SECAs was not considered. It is also assumed that ships trading with the EU will use a mixture of SECA and non-SECA ports to bunker, in the same proportion as they are used globally. Fuel consumption values used were the Second IMO GHG Study 2009 estimates as shown in Table 1.7

EUR/tonne

SECA fuels -0.1%S from 2015

Non-SECA fuels -0.5% S from

2020SECA fuels-current quality

NON-SECA fuelscurrent quality




Table 1.7: Estimated fuel consumption proportions in SECA and non-SECA (2008). Source: IMO (2009)

Million tonnes %

SECA fuel consumption 27 10%

Non-SECA fuel consumption 312 90%

Total 339 100%

The overall “global” weighted fuel price scenarios were calculated using these proportions, and then inflated from 2008 Euros to 2010 Euros (factor 1.023, from IMF). Figure 1.5 shows the final projections that are the basis for the prices in the model. Prices as they would be without the influence of stricter sulphur requirements are shown for comparison (reference and decarbonisation oil price scenarios).

Figure 1.5: Bunker fuel price projections, EUR(2010)/tonne

Scenarios for prices that are used in model are: Central price for main modelling, Low price and High price for sensitivity.

Table 1.8: Maritime bunker fuel price projections (EUR/tonne)

2010 2015 2020 2025 2030 2035 2040 2045 2050

Reference (Central prices)

328 375 606 710 755 808 861 909 977

CPI (High prices) 386 418 636 745 791 847 903 954 1024

Decarbonisation (Low prices)

328 373 548 575 539 539 533 520 512

1.4.3 Future – alternative fuel (LNG) price

The Liquefied Natural Gas price projections used in the model were also obtained on the basis of PRIMES fossil fuel price projections from the 2011 Energy Roadmap. The wholesale natural gas prices were used as a basis for estimating the market prices of LNG for the

EUR(2010)/tonne

CENTRAL

HIGH PRICE

LOW PRICE

No Sulphur req -Ref

No Sulphur req -decarb




maritime sector, and two fossil fuel price scenarios were considered: reference and decarbonisation (see Figure 1.6)

Figure 1.6: Natural Gas Price Projections

LNG price projections were calculated based on wholesale natural gas prices, considering equivalent energy content and adding an uplift for the various steps in LNG production as follows:

Table 1.9: LNG price components

Step Cost Comment Source

Upstream Natural gas prices were converted into LNG prices based on energy

content equivalent

(1 tonne LNG ~ 7.8 boe Natural gas).

http://www.natgas.info/html/liquefiednaturalgaschain.html

Liquefaction 0.97-1.09 US$/MMBTU The exact liquefaction costs will depend on a number of factors: infrastructure, capacity, process,

competition (Greaker and Sagen, 2004). EIA estimates were used

http://www.eia.gov/oiaf/analysispaper/global/lngindustry.html

Transportation 0.35-1.85$/MMBTU depending on distance and cargo ship


The full price projections are summarised below.

Table 1.10: Price projections for LNG (EUR/tonne)

2010 2015 2020 2025 2030 2035 2040 2045 2050

Central 305 362 478 522 492 502 477 444 442

High price 400 438 582 682 724 776 827 873 938

Low price 246 299 409 451 419 428 402 367 364

Source: Calculations based on natural gas prices (sourced from PRIMES) with uplift factors to account for processing

EUR/boe

Reference scenario

Decarbonisation scenario










1.4.4 Price of EU ETS credits

The projected price of EU ETS credits from 2020 to 2050 was selected in collaboration with the Commission. The prices were sourced from the European Commission’s Impact Assessment on the Roadmap for Moving to a Competitive Low Carbon Economy in 2050.

Two cases were included in the model to represent a high and low price scenario (sensitivity analysis)

Table 1.11: EU ETS price projections (EUR/tCO2)

Scenario Action Fossil fuel

prices 2020 2025 2030 2035 2040 2045 2050

Reference Fragmented action

Reference 16.5 20 36 50 52 51 50

Effective Technologies

Fragmented action

Reference 25 34.2 50.9 53.5 64.2 91.5 147

Source: EC Impact Assessment on the Roadmap for moving to a competitive low carbon economy in 2050

1.4.5 Administrative costs

Additional administrative costs included in the model assume a minimum of five days investigation time (at €500 per day), with additional costs of 5% of the cost of the measure. A sensitivity analysis was performed using additional administrative costs of 10%, which found no significant impact on the results of the model.




2 Appendix 2: Description of IHS Fairplay model and results

2.1 About IHS forecasts

The macro economic and trade forecasts are produced by IHS Global Insight. The forecasts presented in this report are based on the IHS planning case scenario called Global Redesign. Global Redesign is one out of a total of three scenarios that have been developed by IHS over the past two years. The scenario time perspective is 2030. For the purpose of this report, the forecasts have been extended up to 2050.

The scenario framework has been designed by providing different answers to three big questions:

Will major powers cooperate to enhance global security and prosperity or will cooperation fail?

How quickly and to what degree will the world shift to a low carbon economy?

Will major powers avoid a reoccurrence of economic turbulence and preclude long-term weakness in the global economy?

Behind the big three questions we find issues relating to a global GHG deal, financial imbalances, technology innovations, nuclear proliferation, growth in China and India, the future of the dollar, protectionism, the future role of the US and Europe, the availability of oil, state vs market etc.

2.1.1 Key themes of Global Redesign

The Global Redesign scenario is a story of an anxious and difficult transition from a world of concentrated power to broader distribution of wealth and influence over the scenario period.

The power and resiliency of market forces, the continued expansion of globalization, converging national interests on security matters, and pragmatic deal-making on GHG emission policy characterize a world where cooperation supports global economic growth in line with historical trends. The shift in economic and political power to emerging markets, particularly in Asia, is another key feature.

2.1.2 Key characteristics of Global Redesign

Macroeconomic dialogue among major powers prevents protectionism from taking root.

Sustained and pronounced shift in economic and political power to China, India, as well Brazil and other emerging markets

o In Europe and the United States, governments forced to raise taxes and cut spending.

Threat of hyper-nuclear proliferation creates crisis environment

o Convergence of interest among major powers on proliferation only takes place under a severe crisis.

Development of global agreement on greenhouse gas emissions is muddled and ineffective relative to announced targets.

Innovation is incremental, no technology revolution.




2.1.3 Points of Tension in Global Redesign

Little progress in discouraging nuclear proliferation, until crisis hits. After years of inconclusive negotiations, Iran is thought to be a nuclear power. The Iranian proliferation triggers a “sea-change” in security dynamics. The fear of a difficult to control nuclear landscape grows. As proliferation fears escalate, the world is further jarred by a plot to acquire a highly disruptive weapon and use it to harm global commerce. The major powers align to thwart proliferation.

Divisive political debates over tax and spending matters in the United States, Europe, and Japan.

Multispeed approach on limiting GHG emissions creates rising threat of green protectionism over the next decade.

China’s growing global presence creates new challenges for both China and other major powers.

2.1.4 Global Redesign: Key Economic Trends

Strong, sustainable expansion in emerging markets.

Monetary policy gradually adjusted in line with growth prospects. Asia starts tightening first, followed by the United States and Europe/Japan.

Inflation is kept at bay.

Large developed economies adopt measures to reduce budget deficits.

After shrinking in 2009, US trade deficits widen again.

As consumer demand expands in emerging markets a process of global rebalancing begins.

Trade liberalization continues, but troubled by occasional disagreements and conflicts.

US dollar depreciates mostly against emerging markets currencies, especially the renminbi.

By 2030 China’s economy accounts for a significant share of global trade, including key commodities and manufactured goods.

The relative change in real GDP per capita is much quicker in the emerging markets than in the developed countries.

Error! Reference source not found. illustrates the compound annual growth rate (CAGR)

f the developed world (US, W. Europe and Japan) in the 20 years leading up to the great recession. The CAGR was 2.3%. In the Global Redesign scenario the CAGR for the years following the recession up to 2030 is forecasted to be lower, 2.1%.




Figure 2.1: GDP growth in the developed regions

Figure 2.2 shows how the CAGR for three of the leading emerging market economies is expected to be lower in the forecast years compared to the two decades before the recession.

Figure 2.2: Growth in key emerging markets

As a result the world total CAGR for GDP increases as displayed in Figure 2.3. This is a consequence of the still higher growth in the emerging markets which gain market share each and every year and thereby lifts the world total. Error! Reference source not found.

hows the absolute numbers behind the development, where the share of the world GDP of the Asian emerging markets continuously increases over the period on the expense of the developed regions’ share.

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Copyright IHS, 2011All Rights reserved

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Figure 2.3: Global GDP growth

Figure 2.4: Global GDP, trillion US dollar

In the following graphs world exports and imports in tonnes are shown for the period leading up to 2030 and for the major regions. The gradual shift towards the emerging markets is strong here too.

However the differences between the countries/regions become clearer. China’s export is not as dominant as one first may think, while China’s significance as a leading importer is obvious. This is in part a consequence of the use of tonnes as measure. China imports heavy commodities for it energy needs (oil, coal, gas), construction industry (iron ore, coal, steel, cement etc) and for its manufacturing industry (steel, minerals, other metals). China’s exports consist to a large extent of manufactured goods that are of higher value and lower weight. Much of the Chinese exports are containerized. Thus is the Chinese share of exports high in terms of value and in terms of teu (twenty foot equivalent unit).

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Figure 2.5:World exports by region, Bn tonnes

Figure 2.6: World imports by region, Bn tonnes

2.1.5 Global Redesign: Energy

The long term prices of oil remain high in the beginning of the scenario period, then it turns downward and flattens out, still on a fairly high level. On the back of the proliferation crisis a new institutional structure evolves to secure the access to energy.

Oil demand in the OECD has peaked during the scenario period, while non-OECD energy and oil demand develops strongly.

Refining capacity in the developed world has peaked. All incremental refining capacity is being built closer to source or emerging market consumption areas.

There will be a transition to more supply reliance on heavy oil and light liquids such as biofuels, NGLs and condensates.

Coal demand is led by China and India. Different coal qualities are possible to mix which leads to a more competitive buyers’ market.

Most gas production continues to be consumed in the local market. There is a transition to unconventional gas, particularly in the US where shale gas is a game changer. LNG continues to develop firmly. Key supply areas are Australia, Qatar, Nigeria, Indonesia, Malaysia and Russia.

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Figure 2.7 shows the Global Redesign perspective on total energy consumption by region in the world. The European energy consumption development is flat and the North American is only growing marginally. Almost all of the growth takes place in the Asia Pacific in particular and in the Middle East and Rest of the World (RoW) in general. The impact of this on trade patterns is strong with an immediate knock-on effect on the demand for seaborne trade.

Figure 2.7: Global energy consumption by region, Mtoe

In the following slide it is possible to see the distribution of the forecasted energy consumption on the sourcing of fuels. Throughout the scenario period, oil remains the most important source of energy, but the growth rate is gradually tapering off. The CAGR for oil over the 2011-2030 period is 1.0% with a higher rate in the early years and lower at the end.

Coal comes in second at the end of the period after a 20 year CAGR of 1.5%. The development of natural gas is faster, 2.2%, giving natural gas a 23% share by 2030. Renewables (including hydro) grow even quicker but from a lower base.

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ForecastCopyright © IHS, 2011All rights reserved




Figure 2.8: Global energy consumption by energy source Mtoe

Investments in new oil refining capacity are moving east as well. The impact of this on shipping will be that the demand growth for long haul crude oil shipments will slow down, while the demand for medium and long haul shipments of refined oil will increase. This is reflected in the expected fleet development (Figure 2.10).

Figure 2.9: Global oil refining capacity, M bbl/day

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Copyright © IHS, 2011All rights reserved




Figure 2.10: Global oil tanker fleet, M dwt

The global import demand for LNG (liquefied natural gas), which is transported at sea by LNG tankers, is expected to grow by 4% CAGR over the 2011-2030 period as shown in Figure 2.11. The European imports are expected to grow significantly. Much of that is destined for the UK to replace the fading supply of North Sea oil.

Figure 2.11: Global LNG imports, M tonnes

In order to match the foreseen demand growth for LNG, heavy investments are being and will be done in liquefaction capacity (Figure 2.12). A liquefaction plant is where natural gas is liquefied to allow for seaborne transportation in the purpose built LNG tankers. The current, known and expected investment plans are focused to Qatar, Australia and West Africa.

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Figure 2.12: LNG liquefaction capacity, M tonnes

2.1.6 Global Redesign: General Cargo

The liquid and dry bulk cargoes dominate the total seaborne volumes globally. Commodities relating to energy and construction roughly account for ¾ of the total if measured in tonnes.

General cargo is an umbrella term for tens of thousands of different products. There are no distinct borders between general, dry bulk and liquid bulk commodities since several (but not all) of them can be handled in different ways depending on batch size, length of haul, frequency and local conditions.

Figure 2.13: Total seaborne trade, M tonnes

Containerized cargo has been growing fast over the past 30 years. This has been achieved through a combination of factors such as economies of scale leading to lower costs per unit, expanding transport service networks, increased frequency of service, shorter lead times and increased quality of service.

The container and Ro-Ro fleets represent a minor share of the total world fleet (Figure 2.14), but the growth of the same are significant and almost all of the growth is attributable to the container fleet (Figure 2.15).

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Copyright © IHS, 2011All Rights reserved




Figure 2.14: Total world development, M dwt

Figure 2.15: Total container & Ro-Ro fleet development, M dwt

The attraction of the container has led to cargo shifting from other seaborne modes of transport which is exemplified by refrigerated cargo below in Figure 2.16Error! Reference source not found.. This shift is far from completed. More cargo is believed to be moving to

the containerized alternative in the future, particularly on shorter routes such as between Europe and North Africa/Middle East/Black Sea countries. Further into the future this potential will taper off.

Containerized transports have also contributed to the facilitation of trade. This is further boosted by the rapid expansion of e-trade, which creates a large number of smaller shipments between all corners of the world. It is believed that we have only seen the beginning of this development.

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Feb-12 Tanker Bulker & GC Container & ro-ro Passenger Misc.Copyright © IHS, 2011All rights reserved

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Figure 2.16: Seaborne trade with refrigerated cargo & containerised share, M tonnes, per cent

2.1.7 Global Redesign: Europe

The European activity in the Global Redesign scenario is presented below. The GDP development in Western Europe and Emerging Europe shows a slow recovery from Great Recession in Western Europe and a relatively better development in Emerging Europe.

Figure 2.17: GDP development Europe, 2005 US dollars

The trade forecasts are derived from the GDP forecasts in the Global Redesign scenario. The economic activity for each of the European economies, EU plus non-EU, has been forecasted for every year up to 2030. Below is the GDP development for some key European economies presented.

The intra-European trade forecasts are not built on the IHS World Trade services. The baseline trade is from Eurostat. The forecasts are however still derived from the Global Redesign GDP forecasts. The GDP development for the bilateral country pairs (more than 1,800 country relations have been considered) has been calculated and has thereafter formed input to a weighted forecasted trade development for different commodities.

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Figure 2.18: GDP development for key European economies

The cyclical character of the European energy consumption is evident when the other regions are removed from the graph. It is also clear that Europe has passed a peak in the energy consumption in this scenario.

Figure 2.19: European energy consumption, M toe

The oil refining capacity in Europe is slowly falling.

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Figure 2.20: European oil refining capacity, M bbl/day

As a consequence of the deteriorating oil fields in the North Sea, imports of LNG are expected to increase, primarily to the UK.

Figure 2.21: European LNG imports, M tonnes

2.1.8 Key elements of the extra EU 27 imports & exports forecast

Total trade grew 2% per year between 1995 and 2008. Between 2011 and 2029 growth is projected to 3%, after that growth is projected to slow down to 2%.

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Table 2.1: Compound annual growth rates

Exports grew by 3% between 1995 and 2008. The forecast between 2011 and 2029 is 3% per year. After that growth will slow down to 2%. Dry bulk and container show the highest average increase. Still, the container growth is a lot lower than in the past.

Imports increased by 1.8% between 1995 and 2008. The forecast for 2011-2029 is a higher growth, 2.3%. After that growth is projected to slow down to 1.7%. Imports of containers show the highest increase.

2.1.9 Dry bulk trade 2011-2029

The WTS forecasts a dry bulk import growth of 171 M tonnes, from 357 M tonnes in 2011 to 528 M tonnes in 2029, which is a compound annual growth rate (CAGR) of 2.3%. The average annual growth in tonnes is 9 M tonnes for the entire European Union.

The dry bulk export growth forecast shows a growth of 92 M tonnes over the same period, from 127 to 219 M tonnes. The CAGR is 3.2% which equals 5 M tonnes per annum.

2.1.9.1 Dry bulk imports

Close to half of the import growth is from ores, scrap and coal. These are commodities that feed into the steel industries in the EU.

Steel is produced in most member states, but the large production countries are Germany, Italy, France, Spain, the UK, Poland, Belgium, Austria, the Netherlands, the Czech Republic, Sweden, Slovakia and Finland.

The WTS forecast of the imports of coal, ores & scrap to the EU points at a growth by 84 M tonnes, from 120 M tonnes in 2011 to 204 M tonnes in 2029. The main providers are exporters in Brazil, Colombia, South Africa, USA and Canada. Most of the imports are destined for Germany, the UK and the Netherlands. Coal imports to Germany are expected to increase by about 16 M tonnes. Much of it will be sourced from Colombia and South Africa.

Export 1995-2008 2011-2029 2030-2050

Dry 1.6% 3.2% 2.2%

Liquid 3.5% 1.9% 1.0%

GC 0.8% 3.7% 1.9%

Container 5.0% 4.4% 3.0%

Total 3.0% 3.0% 2.0%

Import 1995-2008 2011-2029 2030-2050

Dry 1.6% 2.3% 1.3%

Liquid 0.8% 1.4% 0.7%

GC 1.8% 3.2% 1.6%

Container 7.6% 4.1% 3.4%

Total 1.8% 2.3% 1.7%

Total 1995-2008 2011-2029 2030-2050

Dry 2.0% 3.0% 2.0%

Liquid 1.0% 1.0% 1.0%

GC 1.0% 3.0% 2.0%

Container 6.0% 4.0% 3.0%

Total 2.0% 3.0% 2.0%




Table 2.2: EU27 imports of coal, ores and scrap 2011-2029

Sweden is the largest iron ore producer in the EU with a production of 28 M tonnes in 2010. Current investments will increase the production capacity of the mines in Lousavaara and Kiruna to 37 M tonnes by 2015. Further investments are to be expected following the move of the entire city of Kiruna to allow for continued production.

2.1.9.2 Dry bulk exports

Sweden and the Netherlands are the biggest iron ore exporters in the EU. Sweden exports from its own production while the Netherlands rely on imported ore and re-exports. The Dutch exports grew from 0.03 M tonnes in 2001 to 26 M tonnes in 2007 (source World Steel Organization) before falling back due to the recession. Sweden exported close to 23 M tonnes (source Statistics Sweden) in 2010 of which 13.7 M tonnes to the EU. Steel exports from EU member states in 2010 went according to Eurostat to Turkey, India, USA, Algeria, Egypt, China, Morocco, Russia and a large number of other countries, of which many in Asia, Middle East and Africa. The largest export commodities in the WTS forecast are ores & scrap, scrap, iron and steel. These commodities are forecasted to grow with 119% until 2029 from 29.8 M tonnes to 65.2 M tonnes. This increase represents a share of 39% of the total dry bulk export increase over the period. The total Swedish extra-EU exports of ores & scrap is projected to increase with 11.3 M tonnes or 91% (2011-2029) from 12.4 M tonnes to 23.8 M tonnes. A marked share of this growth is for exports to Saudi Arabia.

Import Export Commodity 2011 2029 Change Change %

Germany Brazil Ores and scrap 18,232,332 34,182,615 15,950,283 87%

Germany South Africa Ores and scrap 11,005,781 15,606,302 4,600,521 42%

Germany Canada Ores and scrap 6,671,908 10,482,016 3,810,108 57%

Germany Other Ores and scrap 2,987,502 3,601,183 613,681 21%

Netherlands Brazil Ores and scrap 4,195,102 8,459,732 4,264,630 102%

Netherlands Canada Ores and scrap 1,170,829 1,983,045 812,216 69%

Netherlands Other Ores and scrap 1,308,960 2,006,992 698,032 53%

UK Brazil Ores and scrap 7,398,324 16,703,669 9,305,345 126%

UK Other Ores and scrap 3,222,261 5,361,395 2,139,134 66%

Germany Colombia Coal 6,800,860 16,152,146 9,351,286 138%

Germany South Africa Coal 6,340,709 10,070,704 3,729,995 59%

Germany USA Coal 3,342,922 5,493,759 2,150,837 64%

Germany Other Coal 4,993,182 5,617,434 624,252 13%

Netherlands Colombia Coal 6,470,282 13,619,570 7,149,288 110%

Netherlands USA Coal 10,097,619 14,671,647 4,574,028 45%

Netherlands Other Coal 5,275,525 7,281,216 2,005,691 38%

UK Colombia Coal 5,434,280 11,853,813 6,419,533 118%

UK USA Coal 7,134,921 10,740,317 3,605,396 51%

Netherlands South Africa Coal 3,034,598 4,424,785 1,390,187 46%

Other 5,000,400 5,517,065 516,665 10%

Total 120,118,297 203,829,405 83,711,108 70%

Total Dry bulk 357,144,863 528,050,385 170,905,522 48%

Share of total dry bulk import 49%




Table 2.3: EU27 exports of iron, steel, ores and scrap 2011-2029

2.2 2010 baseline emissions calculations

2.2.1 Methodology

Since December 2004, all ships of more than 300 GT are required to carry an AIS (Automatic Information System) trans-receiver that transmits navigation information en route. The equipment transmits ships information such as identity, type, position, course, speed over ground, port of destination, etc continuously for any other nearby ship to receive and make use of in their own navigation system.

AIS Live (fully owned by IHS) has a network of receivers ashore at more than 2,500 locations in the world. The network covers all major shipping areas in the world. AIS Live also has an agreement with a satellite operator. This agreement gives access to satellite coverage at sea and of areas not covered by land based receivers. This means that AIS Live covers all vessel movements in the world. All vessel movements to, from and within Europe are therefore covered including the total voyage of the vessels’ previous and next destinations.

The picture below shows the track of one single vessel over one year. It is the container carrier MSC Texas with a container capacity of 8,238 teu. The vessel track is shown in yellow. Here it is clear that vessel’s movement is captured across the world, also while the vessel is sailing on the open seas such as the Pacific, the Gulf of Alaska, the North Indian Ocean, the Gulf, the Red Sea and the Mediterranean.

Import Export Commodity 2011 2029 Change Change %

China UK Scrap 1,511,911 6,019,595 4,507,684 298%

India UK Scrap 1,474,701 3,939,718 2,465,017 167%

Other UK Scrap 1,428,998 3,291,948 1,862,950 130%

China Netherlands Scrap 916,267 3,150,729 2,234,462 244%

India Netherlands Scrap 287,507 608,287 320,780 112%

Other Netherlands Scrap 577,713 880,742 303,029 52%

China Germany Scrap 687,803 2,146,488 1,458,685 212%

India Germany Scrap 336,111 682,032 345,921 103%

Other Germany Scrap 499,258 817,074 317,816 64%

Algeria Spain Iron & Steel 1,937,181 3,388,577 1,451,396 75%

India Spain Iron & Steel 207,344 628,216 420,872 203%

Other Spain Iron & Steel 1,481,058 2,706,648 1,225,590 83%

China Germany Iron & Steel 748,351 1,771,375 1,023,024 137%

India Germany Iron & Steel 485,921 1,360,423 874,502 180%

Other Germany Iron & Steel 2,809,911 4,709,647 1,899,736 68%

South Korea UK Iron & Steel 527,369 1,743,709 1,216,340 231%

Thailand UK Iron & Steel 440,672 1,161,179 720,507 164%

Other UK Iron & Steel 952,913 2,385,808 1,432,895 150%

Saudi Arabia Sweden Ores and Scrap 7,065,708 13,202,252 6,136,544 87%

China Sweden Ores and Scrap 2,086,333 5,532,405 3,446,072 165%

Egypt Sweden Ores and Scrap 1,275,687 2,841,250 1,565,563 123%

Other Sweden Ores and Scrap 2,026,814 2,225,692 198,878 10%

Total 29,765,531 65,193,794 35,428,263 119%

Total export, dry bulk 127,263,738 218,816,465 91,552,727 72%

Share of dry bulk export 39%




Figure 2.22: Tracks of a single vessel over a period of one year

For the emissions assessment, each vessel’s identity (MMSI & IMO numbers) and the exact position are extracted for each observation. The speed and status are also drawn from the data.

The received identity of the vessel is linked to the information in the Register of Ships. Relevant information is retrieved such as type of vessel, number and type of engines, cruise speed, installed power and fuel consumption at cruise speed.

The starting point for the calculation of the fuel consumption is the installed power of the main and auxiliary engines. These data are retrieved for each individual vessel. The variance is large even in the more standardized ship segments as illustrated in Figure 2.23. Basing the fuel consumption calculations on the actual installed power rather than on a segment average is one of the strengths of this approach.

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Figure 2.23: Main Engine power vs GT, example for bulk carriers

Once the installed power is retrieved for the vessel then the next step is to calculate the power outtake of the main engine(s). This is done by setting the average speed between sequential observations in relation to the cruise (design) speed. The impact on the power outtake is given by a typical power/speed curve of ships Figure 2.24.

Figure 2.24: Typical consumption-speed curve

The next step is to retrieve the specific fuel consumption (SPC) for the main engine designation of the particular vessel from the emissions factor table of the Emissions Model. The SPC is given in grams fuel per kW hour. The SPC is then put into the equation to calculate the fuel consumption for that particular vessel’s movement between two observation points. The number and type of auxiliary engines installed on the vessel are also retrieved from the Lloyd’s Register of Ships as is the installed AE power.

Actual effect could

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The AE power outtake at different types of operation are assigned by default representative values for maneuvering, at berth, anchored, at sea, in approach, loading and discharging. The SPC procedure then follows the same steps as for the main engine(s). Absent engine characteristics are replaced by representative values. These values are based on the findings of the ARTEMIS Sea Transport Emission Modeling report (2005). Missing information about AE arrangements are replaced by a set of averages for detailed vessel types and size segments.

For the task of calculating the emissions from ships sailing to, within and from Europe more than 79 million records of ship movements have been processed for the year 2010. For each vessel calling a defined port zone in the EU, the previous and next ports of call have been identified.

2.2.2 Risks and uncertainties

We find the main uncertainties to be related to the assumptions in the emissions calculation model about:

Weather delay

Currents

Ice

Non-linear route between two AIS observation points

Weather delay is primarily caused by heavy winds and poor sight. Heavy winds could lead to more or less fuel consumption depending on from which direction the wind blows. The same goes with currents. Icy conditions always lead to more fuel consumption than in normal conditions.

The emissions calculation model always assumes that a ship sails in a straight line between two observation points. This is not always the case, particularly not within or nearby port zones. The standard time between two observation points is one hour. Navigation in or nearby port zones is almost always done with reduced speed. The impact of this factor is believed to be very small.

The estimated impact on fuel consumption of weather delay, currents, ice and non-linear sailing has been accounted for in the model. It is our estimate that the degree of uncertainty of these factors would fall within a range of 3-5% lower than the presented results.

2.3 Baseline emissions forecast to 2050

2.3.1 Methodology

The intra-European trade forecasts are built on trade data from Eurostat. The data has been extracted for the bilateral trade between the individual EU member states and all other European countries in metric tonnes. The commodity detail used is 1-level SITC. Some commodities have been extracted that are of more detailed commodity level. These are; 3.2 Coal, 3.3 Oil, 3.342 LPG, 3.34310 LNG, 3.x other fuels. This has been done in order to facilitate a better match with ship operations.

This data contains no information about mode of transport. The seaborne share of the trade is determined by the options available for the route between two countries and the characteristics of the commodity. For some routes and commodities there are no seaborne options available while there for others are several. These cases have been addressed based on knowledge. To some extent have the results been matched with data on volumes handled in ports.

The forecasts of the intra-European trade data have been addressed by calculating the GDP development for the bilateral country pairs (more than 1,800 country relations have been considered) and using this combined GDP development as a starting point for the growth of the trade.




The combined GDP development has in most cases been reduced by the employment of reduction factors that vary with the type of commodity. The reduction is largely motivated by the difference in a value based economic development and a volume based trade development.

The extra-EU trade forecast is for the period up to 2030 built on the scenario described above. For the trade forecast 2031-2050 has a less sophisticated approach been employed. The growth rates from the 2010-2030 period have generally been reduced. The reasoning behind this is that the demand will taper off for the build up of various types of infrastructure in Eastern Europe, Middle East, parts of Africa and parts of emerging Asia. For Eastern Europe, the demand will fall off gradually as these countries get more integrated into the EU.

2.3.2 Economy of scale factor in the fleet transporting EU27 cargoes

The calculation of the economy of scale factor in the fleet transporting EU27 cargoes have been derived from the forecasted global fleet development in the Global Redesign scenario. The fleet forecast is the net development of new ship deliveries and existing ship removals. Thereafter have the average deadweight sizes been calculated for the five main ship categories.

The average size development has formed input to the estimate of the economy of scale development in the fleet transporting cargo to/from and within EU27.

Figure 2.25: Average size development of the world fleet in the Global Redesign scenario

2.3.3 EEDI factor

The EEDI factor is calculated on the installed power (kW) of the annual new ship deliveries to the world fleet as forecasted by IHS up to 2050. The reduction factors used are from the report “Assessment of IMO Mandatory Energy Efficiency measures for international shipping”.

2.3.4 Fuel switch factor

The fuel switch factor is derived from analyses done by IHS CERA and IHS Fairplay in mid-2011

which were presented in a IHS CERA retainer report called “The Next Bunker Fuel”. The shipping industry faces a costly challenge: upcoming regulations will drastically limit sulfur emissions, first in North America and northern Europe in 2015 and then globally by 2020 or 2025. Ship owners will feel the impact first, and then refiners, marketers, and ports. At stake for the oil industry is a major market: 4 million barrels per day (mbd). Substitution of gasoil (diesel) for high-sulfur fuel oil (fuel oil) is unlikely for a number of reasons: the refining industry would struggle to supply an extra 4 mbd of gasoil, and a wide oil-gas price differential opens the door to the use of natural gas. As a result two alternative solutions, both technologically reasonable and economically justified, exist: dual-fuel engines that use

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

2010 2015 2020 2025 2030

Tankers Dry bulk carriers Container carriers General Cargo Ferries




either oil products or liquefied natural gas (LNG); or smoke scrubbers, which enable the continued burning of fuel oil.

Smoke scrubbers will be the preferred short-term solution, limiting a gasoil bunker demand increase. Gasoil is too expensive for fleets as a universal fuel oil replacement; and scrubbers are technically mature and available. Using scrubbers means that the impact on refiners will be only temporary (more gasoil demand around implementation dates) or deferred into the long term (cheaper fuel oil).

LNG could become the “next” bunker fuel, which would substantially increase its long term demand. Projected penetration of LNG in this market is expected to be slow but could add up to 65 million metric tons per year to global LNG demand by 2030 - around 15 percent of projected global LNG demand and 22 percent of global bunker demand at that time.

Distribution of bunker LNG in ports provides a first-mover advantage for retailers. A new bunker supply chain that is more sophisticated and has higher entrance barriers must be put in place. First movers with good supply conditions should benefit from a lasting competitive advantage.

2.3.5 Risks and uncertainties

All forecasts are uncertain. The largest uncertainty in the forecasts above relate to the financial turmoil in the Euro-zone. Should the efforts to save the most troubled nations fail and the financial challenges spread to more and larger economies then would the forecasts for the period up to 2020 be too optimistic.

It is however believed that some of the lost growth in this current decade would be recovered in the following decade. This would mean that the forecast for 2050 emissions should not be affected all that much. It is more the route leading up to that point that would look different.




3 Appendix 3 – Methodology and quantitative assessment of relevant historical emissions

3.1 Introduction

This appendix describes the estimation of historical maritime CO2 emissions for the EU, derived from the CO2 emissions baseline estimate presented for the EU27 in 2010 (see Chapter 1 in main report). It is important to understand the historical emissions of the EU maritime sector due to policy commitments and goals of the European Commission that are related to specific historical years.

The estimation of shipping emissions to air for the present and recent past have been calculated as bottom-up inventories of emissions, based on shipping activity data (Entec, 2002; Entec, 2010; Faber et al., 2009; IMO, 2009). Additional bottom-up methods of estimating shipping emissions based on databases of vessels and their engines, coupled with geographic distribution of vessels to ‘spread’ emission estimates, have also been reported (Endresen et al., 2003; Eyring et al., 2005; Endresen et al., 2007). Previously, estimates of shipping emissions have been derived in a top-down manner based on records of fuel sales, but IMO (2009) concluded that ‘activity-based estimates provide a more correct representation of the total emissions from shipping than what is obtained from fuel statistics ’. It is however very data intensive to estimate emissions using a bottom-up activity method, such that a top-down approach is more appropriate for the estimation of backcasted shipping emissions from multiple historical years.

To estimate CO2 emissions from international and total global shipping for the years 1990-2006, IMO (2009) assumed that global shipping emissions would be proportional to estimates of global seaborne trade (as published by Fearnleys) expressed in tonne-miles over the period. By taking this assumption, their bottom-up baseline estimate for 2007 was ‘backcasted’ to the years 1990 through 2006 using trend in these trade statistics indexed to the base year of the bottom-up baseline. Entec (2010) has also used an approach of backcasting a bottom-up baseline inventory to previous years using indexed trends in time series of relevant statistics, along with additional considerations for other changes that impact on emissions over time.

The over-arching approach adopted for this task is in line with that from IMO (2009) and Entec (2010), in that indices in trade and other statistics are employed to backcast a baseline year estimate, together with consideration for other aspects that affect emissions estimates over time. This work improves upon the simple methodology presented in IMO (2009) in presenting a detailed methodology for estimating backcasting emissions which uses multiple statistical indices split by vessel type, similarly to that used in Entec (2010).

3.2 Background: trends in the maritime sector 1990 to 2010

An estimation of the historical CO2 emissions arising from EU27 maritime transport over the period 1990 to 2010 necessarily needs to take into consideration the variables that may have influenced emissions over the period arising from the combustion of marine fuel, including:

Growth in seaborne trade;

Trends in fuel sales versus activity;




Containerisation;

Vessel sizes

Annual time spent operating at sea;

Transport of passengers by sea;

Fuel trends;

The NOX Technical Code; and

Slow steaming.

These variables are discussed in turn below.

3.2.1.1 Growth in seaborne trade

The close association between global economic growth, industrial activity, international trade and seaborne trade is well documented. This work will principally rely on data concerning European seaborne trade to estimate historical emissions.

3.2.1.2 Activity based versus fuel based estimates

The 2010 emissions baseline has been calculated based on a bottom-up activity data of hour by hour Automatic Identification System (AIS)-reported vessel movements. If data at this level of detail existed and was available at reasonable cost for previous years, then arguably the most accurate and consistent estimation of historical emissions would repeat the analysis undertaken to estimate 2010 emissions for previous years. Unfortunately, this level of data are not forthcoming for previous years, principally as it is not considered likely that AIS data with sufficiently complete coverage was available before 2008 (CE Delft et al., 2012), but secondly due to the time such computation would demand.

Previous estimates of historical emissions from the maritime sector have used both fuel-based and activity based methods. For example:

Eyring et al. (2005) presented estimates of fuel consumption and emissions from global international shipping for the period 1951 to 2001. Their 2001 bottom-up activity based estimate was derived from fleet statistics of vessels, their installed power and specific fuel consumption, coupled with assumptions around load factors. To estimate emissions from previous years, the authors used data on numbers of vessels (and their gross tonnages) in the worldwide fleet together with assumptions and data on the growth in average engine power per vessel.

Endresen et al. (2007) estimated a time series of emission estimates from 1925 to 2002 based on reported fuel sales as well as a series from 1970 to 2000 based on activity data of numbers and gross tonnage of vessels, and assumptions on average fuel consumption rates.

Although as indicated earlier IMO (2009) concluded that activity based shipping emission estimation is preferable to one which is fuel-based, this conclusion was for the estimation of emissions in a base year. For the estimation of historical emissions, the IMO adopted a top-down method that was based on historical activity data rather than historical fuel sales data.

Typically, activity-based estimates have been higher than estimates based on historical fuel sales data. Furthermore, the variation over time of reported fuel sales do not correspond with variations over time of estimates of fuel consumption (Endresen et al., 2007). Eyring et al. (2005) cast doubt on the validity of historically reported bunker fuel records, leading to the conclusion (shared by IMO, 2009) that a historical emissions inventory derived from fuel sales statistics would systematically underestimate emissions. Although Endresen et al. (2007) dispute the discrediting of emissions estimates based on reported fuel sales, it is suggested that for the purposes of this study’s estimation of historical emissions, an activity basis is adopted rather than fuel sales basis.




3.2.1.3 Containerisation

The rise of the transport of goods within containers rather than in general cargo vessels has influenced the rise in total trade, as its form facilitates trade efficiency including inter-modal shipment. The growth rates of container vessels over the period 1990 to 2010 are therefore expected to differ from growth rates of goods transported in other vessel types. As such, this work should ideally aim to consider container vessels separately where possible, in line with IMO (2009). If this is undertaken, then the additional fuel costs associated with the carriage of the tare weights of the containers, including as empty containers, will be taken into account.

3.2.1.4 Trends in vessel sizes

Increasing vessel sizes have increased the efficiency of seaborne transport over time (IMO, 2009). The trend for larger vessels, as noticed in particular for container vessels in recent years during times of economic expansion, achieves economies of scale for shipping companies. However, the IMO (2009) also notes that:

“While using large ships tends to reduce energy consumption in the shipping leg itself, the total impact on overall door-to-door logistics performance may be negative unless such a move is complemented by smaller ships that can assist in the onward distribution of cargoes. Naturally, larger ships are not efficient if not enough cargo is available and they have to sail only partly loaded. Net energy efficiency may be better for a small ship, with access to more ports and cargo types, being able to fill its cargo hold to capacity.”

Vessels of greater gross tonnage need more powerful (MW) engines to propel them, but the additional cargo capacity is not necessarily correlated linearly with additional power demands. Increased “demand for more and faster global trade” has also led to increased engine power demands – particularly of container vessels – such that some argue the benefits of increasingly fuel-efficient engines have been negated (Corbett & Winebrake, 2008).

Any accounting for the addition or not of feeder vessels in the case of the introduction of larger vessels is too difficult to account for in high level estimation of historical emissions. Also, variation in the average loading of each vessel’s capacity is also difficult to account for due to a lack of data captured on this.

3.2.1.5 Time spent in operation at sea

Endresen et al. (2007) identified that a key difference between previous maritime transport emission estimates has been the assumed number of days in a year spent operating main engines at sea. As shipping companies seek to achieve greater fleet efficiencies they will seek to maximise the number of days a vessel is operating on non-ballast voyages per year. Although this variable is considered to contribute to efficiency improvements (IMO, 2009), fleet average potential savings estimated in 2000 by IMO (2000) were small (<1% of total potential savings), and thus it is assumed that this factor can be neglected.

3.2.1.6 Transport of passengers

Two competing trends have been observed in the maritime passenger transport sector over the period 1990 to 2010. On the one hand, some ferry services have declined or ceased over time on routes where inexpensive commercial flights or high speed rail links have competed directly against the ferries. On the other hand, the cruise sector has seen significant growth in the last decade. These competing trends suggest where possible for passenger ferries to be treated separately from cruise vessels.

3.2.1.7 Fuel trends

The coming into force of MARPOL Annex VI regarding fuel sulphur content limits in the sulphur emission control areas of the North Sea and Baltic Sea, together with the Sulphur Content of Marine Fuels Directive, has led to a partial switch from residual to distillate fuels.




This could affect CO2 emissions due to the different energy and carbon contents of the two fuels. This is discussed in more detail in section 3.3.6.

3.2.1.8 NOX technical code

The NOX technical code adopted as part of the MARPOL Annex VI set NOX emission limits for new marine engines produced from the year 2000. Given that the tuning of a diesel engine can typically be optimised for fuel consumption or NOX emissions but not both, there was a shift from fuel-optimised engines to NOX optimised engines towards the end of the 1990s (Eyring et al., 2005).

3.2.1.9 Slow steaming

In recent years, some shipping companies began reducing the sailing speed of their vessels, a practice known as slow steaming. Slow steaming was implemented as a response to two aspects: (i) a rise of fuel prices and (ii) an oversupply of container fleet capacity arising from the economic recession (Notteboom & Cariou, 2011). The reduction in fuel costs to shipping companies is paramount as fuel bills can be a significant portion of total operating costs. Slow steaming has been shown in the literature (Buhaug et al., 2009; Faber et al., 2009) as a measure that reduces fuel consumption (and costs) and therefore CO2 emissions of ships.

Two of the three methods described in this study are simple approaches involving aggregate trade data. The rationale for the simple approaches are to try to follow the approach taken in IMO (2009) to estimating historical global maritime emissions using trends in both global and EU maritime trade. The last of the three methods is more complex and attempts to take into account in the estimation of historical emissions a number of the above-described trends for the period 1990 to 2010, as discussed in section 3.3.6.

3.3 Methodology to estimate historical maritime CO2 emissions

3.3.1 Overview

The over-arching approach is to use indexed trends in trade figures and other time-series of statistics to backcast the 2010 baseline year estimate to previous years in the period 1990-2010. This top-down approach is in line with the approach used in IMO (2009) and Entec (2010) and therefore adopts an activity- rather than fuel sales-based approach. Three methodologies are presented, all of which adopt this top-down approach at varying levels of disaggregation / complexity:

Method A is the simplest methodology and has been chosen to be strictly in line with the approach used for backcasting historical emissions in IMO (2009). This straightforward method utilises the same index of global seaborne trade as IMO (2009) for the estimation of CO2 emissions for the period 1990 to 2006, and utilises more recent world seaborne trade data to estimate emissions for the period 2007 to 2009;

Method B is also a straightforward method of utilising aggregate trade statistics similar to the approach used in Method A, but which utilises EU specific trade data as the aggregate index; and

Method C goes further than the simple methodology presented in IMO (2009) in presenting a detailed methodology for estimating emissions which uses multiple statistical indices split by vessel type, similarly to that used in Entec (2010), and applied at the most disaggregated level possible. This disaggregated methodology also takes into account aspects other than trade flows, as identified in section 3.2, that affect emissions estimates over time.




The baseline 2010 emissions dataset has been used in full for the backcasted inventories, and explicitly includes:

Emissions estimated from domestic, intra-EU and extra-EU (i.e. international) shipping. Emission estimates for domestic and intra-EU shipping cover the exhaust emissions from fuel combustion for the entire voyage from port to port. For extra-EU shipping, the emission estimates have been set at the scope of vessel movements between the last port of call and the EU port call (inbound extra-EU), and from the EU port call to the next port call (outbound extra-EU) ;

emissions arising from shipping services for the overseas territories of the EU;

3.3.2 Use of trade data

Indexed trends in trade flow statistics are utilised in all three of the methodologies presented.

Trade flows are the flow of goods from an origin (exporter) to a destination (importer). Goods are transferred from origin to destination using at least one method of transport, and typically multiple methods of transport. Transport of goods by sea (seaborne trade) makes up the largest proportion of extra-EU trade flows in terms of tonnage and distances of tonnage (tonne-km or tonne-nm), whilst the majority of freight within the EU (intra-EU and domestic) is transported by road (European Commission, 2011) .

An example of a multi-modal transport of a good may be the land-based transport of the cargo from its inland origin to a seaport from whence it is transferred into a vessel and shipped by sea to another country. Upon arrival the cargo is transferred on to land-based transport to be transported inland to its final destination.

Trade data exist both in the form of representing the total trade from export to import, as well as representing the seaborne portion of that trade. Since the 2010 emissions baseline has been derived from ship movements, not from total trade flows, the use of seaborne trade data is appropriate for the purposes of backcasting emissions.4

The use of a time series of seaborne trade statistics as the primary driver for backcasting base year emission estimates allows the taking into account of variations that have occurred in the modal split of cargo transport over time. In contrast if only statistics on total trade data (seaborne and other modes) were used, then it would be assumed that the modal split for trades is fixed over time, e.g. for a cargo transported by sea in 2010 it would be assumed that the same cargo would be also transported by sea in previous years.

The world seaborne trade data used in IMO (2009) expressed in terms of tonne-miles were from a proprietary dataset published by Fearnleys. Fearnleys (2005) also published world seaborne trade data expressed in tonnes. The figure below presents both the world seaborne trade data expressed in terms of tonne-miles and in tonnes, indexed to year 1990 for the period 1985 to 2003. The data is the total of crude oil, oil products, five major dry bulk commodities and other cargoes. The figure shows close correlation between tonnes and tonne-miles seaborne trade data over the period shown.

4 This is in contrast to the approach f or f orecasting emissions to 2050 in this study . Since t he key driv er of the use of sea serv ices to transport

goods is demand placed by the total trade chain f rom exporter to importer, the use of total trade f lows (not seaborne only ) is appropriate f or f orecasting shipping emissions into the f uture, as it is trends in total trades export to import that will driv e the use of s hipping serv ices (and their

emissions) in the f uture. Market f orces will mostly driv e f uture modal choices.




Figure 3.1: Indexed world seaborne trade (source: Fearnleys, 2005)

The use of a time series of seaborne trade statistics expressed as tonnes lifted as the primary driver for backcasting base year emission estimates assumes that the distances travelled by vessels to transport a given tonnage of cargo is fixed, i.e. that the routes vessels ply is fixed over time. As shown above, at a global level this assumption appears to hold true. In the absence of access to data expressing total EU seaborne trade data in terms of tonne-distance, this close correlation between trade data in tonnes and tonne-distance at the global level is assumed to extend both in time to the most recent decade, and in location to the seaborne trade data for the EU. As the geographic scope for the 2010 baseline emission estimate is the emissions from the last port of call to the EU port and vice versa, the use of indices of trade expressed in tonnes-lifted is expected to be close to indices expressed in tonne-distances.

3.3.3 Data sources considered

A number of data sources have been assessed for suitability for the purposes of developing indexed trends to be utilised for backcasting the 2010 baseline. The choice between data sources often needs to balance a number of trade-offs between data sources:

Geographic extent. The geographic scope of the 2010 emissions baseline being

considered is from the port call of vessels prior to arriving at an EU-27 port, and up to the subsequent port calling following departure from an EU-27 port. The emissions baseline is disaggregated by country pairs. Where possible for Method C, it is appropriate to utilise separate indices at the most disaggregated level possible in order to capture variable maritime growth rates around the EU. The most disaggregated geographic extent is trade data expressed at the country to country level (importer-exporter) level. A more aggregate approach is data expressed at the country level (imports / exports). The least disaggregated approach is for data expressed at EU level which represents data aggregated for all 27 Member States, or for years before the EU had 27 Member States the aggregate totals for the countries which now make up the EU-27.

Commodity detail. The 2010 emissions baseline categorises all vessels (and their

emissions) into 18 vessel types, including 11 cargo-carrying vessel types (82% of 2010 baseline CO2 emissions), 3 passenger carrying vessel types (15% of CO2 emissions), and 4 further minor vessel categories (3% of CO2 emissions). The main focus in this work is on the cargo and passenger carrying vessel types. In order to apply separate indices for commodity types, it is necessary to assume which vessel types carry which commodities.

0.6

0.7

0.8

0.9

1.0

1.1

1.2

1.3

1.4

1.5

1.6

1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Ind

ex

(19

90

=1

.0)

Indexed world seaborne trade in tonnes Indexed world seaborne trade in tonne-miles




Temporal coverage. The approach to estimating historical emissions over the entire

period 1990-2010 relies upon time series of trade statistics and other supporting data over the period 1990-2010. The collection of a consistent time series of statistics over long periods such as this can be difficult, and it may be the case that discontinuities exist in the series.

The data sources considered in this study are summarised in the table below.

Table 3.1: Range of data sources assessed

Publisher Data source

Geographical extent

Commodity detail

Temporal coverage

Units Comments Used

Cargo

IMO (2009) Backcasted CO2

emissions

Global seaborne trade

Total seaborne trade not

disaggregated by commodity

1990-2007 Trend represent

s tonne-nautical miles

CO2 emissions w ere

estimated using Fernley’s

global total seaborne trade data. Thus, the

trend in the estimated emissions represents the

trend in global total seaborne trade.

Method A

IHS World Trade Service

Extra-EU trade, country to country

level

Six cargo types (container, dry

bulk, general cargo and three liquid bulk categories)

1995 to 2010

Tonnes cargo

lif ted

Bilateral extra-EU trade.

United

Nations Conference on Trade and

Development (UNCTAD)

Annual

Review of Maritime Transport – multiple

publications

Seaborne trade for

‘Europe’ (see comments). Goods loaded and unloaded are

separated.

Three

categories of crude oil, oil products and all other (dry)

cargo.

1990, 1995-

2010 (note: from multiple publications

)

Tonnes

cargo lif ted

‘Europe’

includes EU-27 MS (and remaining EEA, and MS

overseas territories) for period 2006-10.

See note 1.

Method A, B, C

Organisation for

Economic Co-operation and

Development (OECD)

OECD Stats Containers

Transport

Seaborne, country level. Data

available for all but one of the maritime EU-27 Member States.

Containers only 1990-2009, w ith

reduced coverage for some countries

Tonnes of container

cargo lif ted

Sea containers

transported at country level for OECD member

countries and Non-OECD Member Economies.

Method C

United

Nations

COMTRAD

E

Country level trade

data

Full breakdow n

of commodities

1988-2010

w ith gaps for some countries in some years

Tonnes

lif ted are only available at the

most disaggregated commodit

y level

Total trade

data not seaborne. Most data only available in

value terms.




Publisher Data source

Geographical extent

Commodity detail

Temporal coverage

Units Comments Used

Eurostat External

trade data

As per

COMTRADE

As per

COMTRADE

As per

COMTRADE

As per

COMTRADE

See

COMTRADE above

Eurostat Maritime transport –

goods: [mar_go_aa]:

Seaborne transported goods

imported / exported from all ports at a country level.

No disaggregation

1997-2010, but missing

some MS data for some years

Tonnes lif ted

Full dataset available for

years 2004-2010.

Eurostat Maritime

transport – goods: [mar_go_am_detl]:

Seaborne

transported goods imported / exported from main ports at a

country to country level.

Splits into six

cargo types (dry bulk, container, liquid bulk, other cargo,

roro self propelled, roro non-self propelled)

1997-2010,

but missing some MS data for some years

Tonnes

lif ted

Full dataset

available for years 2004-2010.

Method C

Passenger and cruise vessel categories

Eurostat Maritime

transport – Passengers: [mar_pa_aa]

Inbound and

outbound passenger movements from

all ports at the country level

Ferry and

cruise passengers considered

separately

1997-2010,

but missing some MS data for

some years

Numbers

of passengers

Full dataset

available for years 2004-2010.

Method

C

Eurostat Maritime transport – Passengers:

[mar_pa_qm_detl]

Inbound and outbound passenger

movements from main ports at the country to country level

Ferry passengers only

1997-2010, but missing some MS

data for some years

Numbers of passenge

rs

Full dataset available for years 2004-

2010.

Method C

European

Commission

Passenger

transport, in EU Transport in Figures

(various issues)

EU level All passengers 1990,1994-

2009

Passenge

r-kilometres

Method C

Other cateogires

IHS Ships visiting

European Ports

EU level Three vessel categories:

Yacht, Offshore and service

1990-2010 Offshore and

service: number of vessels

Yacht: gross tonnage

Method C

Eurostat Maritime

transport – vessel traffic [mar_tf_qm]

Main ports,

country level

12 vessel

categories

1997-2010,

but missing some MS data for some years

Number

and gross tonnage of vessels

Considered

use f leet capacity data, but rejected in favour of

activity data in line w ith IMO (2009)

Eurostat Landings of

f ishery products

[f ish_ld]

Country level Fishing 1992-2010 Tonnes of

f ish landed

Method C




Note 1: For earlier periods, the countries included in ‘Europe’ include EU -15 among other countries. Excluded countries (Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Lithuania, Latvia, Malta, Poland, Romania, Slovenia and Slovakia) are listed under multiple geographic areas (with other countries) making it difficult to aggregate an ‘EU-27 equivalent’.

The choices of which datasets to use from the above list were made by prioritising:

those data sources that contain seaborne trade data rather than total trade data;

those data sources which have disaggregation by commodity or cargo type, such that they could be applied to vessel types separately; and

Those data sources with the greatest levels of geographical disaggregation, such as country to country level seaborne trade flows.

3.3.4 Methodology A

Of the three methods, method A is the most aggregated. The method is analogous to the method adopted by IMO (2009) and assumes the global seaborne trade growth rates presented in IMO (2009) for the period 1990 to 2007. The growth rates assumed in IMO (2009) for the period 2007 to 2010 (annual percentage increases) have not been adopted as a suitable trend over the period 2007 to 2010 due to the known impacts of the economic recession on seaborne trade (and thus emissions). Instead, for the period 2007 to 2010 the trend in total world seaborne trade reported in UNCTAD (2011) in the World Trade Services dataset are used. In summary, the method follows:

The 2010 emissions dataset are initially backcasted to 2007 using total world seaborne trade as reported by UNCTAD (2011) for the period 2007-2010 (expressed in tonnes of goods loaded). This in effect mimics the approach used in IMO (2009) except that the 2007-2010 estimate is derived from seaborne trade data in tonnes lifted rather than on a tonne-nautical miles basis.

The backcasted 2007 dataset are further backcasted to 1990 using indices of the IMO 2009 study inherent in their tabulated annual CO2 outputs.

No further assumptions are necessary in this straightforward method. The results from this method are shown in section 3.4.1.

3.3.5 Methodology B

Method B applies an approach similar to that of method A – which replicates the methods of historical estimation used in IMO (2009) – but uses trade figures specific to the EU and Europe rather than global trade.

This method therefore relies upon a time series of EU activity data for the period 1990 to 2010. Following a detailed literature review, a single seaborne trade data source has not been identified as suitable for purpose. Therefore two datasets – Eurostat and UNCTAD – have been spliced together.

The ‘mar_go_aa’ Eurostat database of maritime freight transport (as tonnes of cargo), as a total for all reporting EU-27 countries and import and export, were used for the period 2002 to 2010. Although the dataset includes data before this date, it is not complete for the countries that make up the EU-27. This dataset, indexed to 2010 activity levels are displayed in Figure 3.2 below.

For an activity index prior to this date, seaborne trade data from UNCTAD have been utilised, as tonnes of cargo total loaded and unloaded. The UNCTAD data have been gathered from multiple issues of Review of Maritime Transport (RMT), as published between 1997 and




2011.5 The data appear to have series discontinuities between 1997/8 and 2005/6, the latter presumably due to regional re-classification of countries, such that there is no internally consistent time series over the period 1990-2010. Prior to 2007, four of the geographic regions of the world used by UNCTAD included countries that make up the EU-27:

Europe: Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden and the United Kingdom (and five other countries and three other regions)

Countries of Central and Eastern Europe and Republics of the former Soviet Union: Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania and Slovakia (and 13 other countries)

Western Asia: Cyprus (and 12 other countries)

Developing countries and territories in Europe: Malta and Slovenia (and 5 other countries)

For the purposes of this task, only the data for ‘Europe’ have been utilised, as the Members States categorised in the other three regions make up a minority of the countries. The data series also has a gap for the years 1991 to 1994 inclusive. Furthermore, more recent issues of RMT have in some instances amended data for years that were previously published. As such, in order to produce a complete and internally consistent series for the period 1990-2002, the following assumptions were made:

The most recent publication possible is used as the primary data source. Although the 2011 publication is the most recent RMT, as this report does not include figures before the series discontinuity at 2005/6 it cannot be used. The 2006 RMT is the most recent publication containing data for 1990; this RMT also provides data for the years 2000 and 2003.

A single publication only includes internally consistent figures.

To fill in gaps between years in the 2006 RMT for which there are data, but split across multiple RMT publications other than the primary (2006) source, the percentage growth rates implied in the publications’ datasets have been applied to the data in the primary source.

To fill in gaps of years for which no published data appear (years 1991 to 1994, inclusive), a linear interpolation has been undertaken.

Taking into account these assumptions, and combined with the Eurostat trend, Figure 3.2 plots the trends separately indexed to 2005 and combined into a single index which is used in Method B.

5 http://www.unctad.org/Templates/Page.asp?intItemID=2618&lang=1

http://www.unctad.org/Templates/Page.asp?intItemID=2618⟨=1




Figure 3.2: Trends in European seaborne trade (Eurostat and UNCTAD) and combined index 1990-2010, indexed to 2005 levels

Other datasets were considered for Method B but were discarded for various reasons which include:

UN COMTRADE database trade data (and Eurostat COMEXT data) were considered to be unsuitable as seaborne trade data are not separately provided (although this is separately provided in Eurostat from year 2000. Furthermore the COMTRADE dataset appeared to be incomplete for the years 1993 and 1994.

The World Trade Service (WTS) dataset is considered to not hold sufficiently robust data on intra-EU seaborne trade, and this dataset was only available for the period 1995 to 2010.

Furthermore, although the UNCTAD dataset is provided separately divided into goods loaded and unloaded, separate indices have not been extracted with this disaggregation for two reasons: (a) the 2010 emissions baseline is not available as a total split between incoming and outgoing (although this is available for the extra-EU portion only) for intra-EU and domestic maritime traffic. (b) Method C provides for the more complex approach utilising multiple indices. Similarly, the UNCTAD dataset provides for three separate cargo types (crude oil, oil products and dry cargo). Method B does not involve separately indices for each cargo type; this greater disaggregation is provided for in Method C.

3.3.6 Methodology C

3.3.6.1 Introduction

Method C is the most detailed of the three backcasting methodologies, and it builds on the methods used in Entec (2010). It adopts the same principle as the approaches of methods A and B, of using indices of historical trends to backcast the 2010 emissions dataset, but goes further than methods A and B by:

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Combined index (Eurostat 2010-2002; UNCTAD 2002-1990)

UNCTAD (total tonnes of goods loaded and unloaded)

Eurostat (total tonnes of goods loaded and unloaded)




Using activity indices at a more disaggregated level:

- for different vessel / cargo types (where available)

- for country to country pair trade and passenger flows (where available, country level and EU level flows are used where country to country level data are not available)

Taking into account historical trends in fuel types, engine efficiencies and changes in specific fuel consumption.

Taking into account the recent impacts of slow steaming.

An overview of the approach for Method C is presented in Figure 3.3. In terms of the application of activity indices, the approach matches each geographically disaggregated component of the 2010 baseline for each vessel type with an equivalently geographically disaggregated activity index for that vessel type (where available). For example, the emissions estimated in 2010 as arising from container vessels travelling from the Netherlands to the United Kingdom are backcasted using an index of historical activity of container goods transported by sea from the Netherlands to the United Kingdom.

Figure 3.3: Overview of Method C

3.3.6.2 2010 emissions baseline

Method C relies upon a more disaggregated emissions baseline than methods A and B. The 2010 emissions baseline has been aggregated in the following manner for the purposes of the estimation of historical emissions.

Activity indices

- by vessel category - by geographic level, with highest

detail for 2004-2010

Emission factor adjustments

- by vessel category

- varies by year

Slow steaming adjustments

- by vessel category

- varies by year

2010 baseline

1990-2009 emissions




Table 3.2: Aggregation of the 2010 emission baseline for the purposes of historical emissions estimation

Movement Category Vessel Type Origin of vessel movement

Destination of vessel movement

Emission (t)

Split into:

domestic, intra-EU, outbound extra-EU

inbound extra-EU

Split into 18

vessel categories

For both the origin and destination of vessel

movements, the baseline emissions are aggregated as follow s:

Summed according to

aggregation described left

- Member States are retained as Member States

- Individual overseas territories of Member States are

aggregated into a category ‘Overseas territories’

- Other regions of the w orld are aggregated into a category ‘Rest of World’

3.3.6.3 Activity trends

The approach for backcasting the 2010 emission baseline using activity indices is as follows:

gyvgvgvy ,,,,,

indexactivity baseline emissions 2010activityfor backcastedEmissions

where

v is the vessel type

y is the year

g is the geographic disaggregation (e.g. country to country)

The methodology utilises more detailed activity indices for years 2004 to 2010, and a more aggregated approach for years 1990 to 2004; these are discussed separately in the sections below.

Activity indices (2004 to 2010)

In recognition of the need to provide a more robust estimate of 2005 emissions, detailed data on maritime transport of cargo and passengers were gathered from Eurostat.6 Table 3.1 above lists the data that were obtained from Eurostat, and identifies that the most detailed data are only fully complete for the period 2004 to 2010. As such activity indices for the period 1990 to 2004 are considered separately and at a more aggregate level.

The 2010 emissions baseline is disaggregated into 18 vessel types. Method C seeks to apply where possible separate indices for each vessel type in order to capture relative trends in activity among different vessel types. For those vessel categories with the highest baseline emissions in 2010, separate indices have been sought at the most geographically disaggregated level. In order to apply trends separately for each vessel type, there is a need to match the vessel types to the cargo types. The table below summarises which activity indices (and cargo types) were applied to each vessel category.

6 Eurostat datasets of [mar_go_am_detl], [mar_pa_aa] and [mar_pa_qm_detl] prov ided by Personal Communication, Eurostat, 14 February 2012.




Table 3.3: List of activity indices applied in method C for estimating emissions from 2004 to 2010

Vessel category (baseline) Activity index applied Geographic level of activity index application (Note 1)

01 Oil tanker, 02 Chemical tanker,

03 LPG, 04 LNG, 05 Other tanker

[mar_go_am_detl]: liquid bulk goods Country to country, country, EU

06 Bulker [mar_go_am_detl]: Dry bulk goods Country to country, country, EU

07 General cargo, 08 Other dry [mar_go_am_detl]: other cargo not elsew here

specif ied

Country to country, country, EU

09 Container [mar_go_am_detl]: large containers Country to country, country, EU

10 Vehicle [mar_go_am_detl]: Ro Ro, mobile self -propelled

units


11 Roro [mar_go_am_detl]: sum of ‘Other Ro Ro, mobile

non-self-propelled units’ and ‘Ro Ro, mobile self -propelled units’


12 Ferry [mar_pa_qm_detl]: passengers Country to country, country, EU

13 Cruise [mar_pa_aa]: cruise passengers Country, EU

14 Yacht Yacht gross tonnage EU

15 Offshore Offshore vessel f leet numbers EU

17 Fishing [f ish_ld]: total tonnes of f ish landed Country, EU

16 Service, 18 Miscellaneous Service vessel f leet numbers EU

Note 1: The geographic level of application of activity indices are given in the order with which they are preferentially used in the backcasting.

It is established that trade flows reported by exporting countries as exports do not necessarily match the reported imports of the importing countries (LMIU, 2009). For the detailed Eurostat data sources for cargo and passenger transport, a reverse reporter algorithm was implemented to compare the two duplicate reported trades and (i) where both trades were reported and were not equal, to select the maximum of the two, and (ii) where data were missing from the importer or exporter, to select the single reported figure.

As well as listing the activity indices that have been applied for each vessel type,




Table 3.3 also indicated the geographic level at which each index has been (preferentially) applied. The three levels of application are:

Country to country level (most detailed, and preferentially adopted) represents

activity indices and emissions associated with a country pair. For example, emissions arising from vessels travelling from Netherlands to the United Kingdom will have applied to it (where known) an activity index for a particular vessel type of movements from the Netherlands to the United Kingdom. Where the extra-EU components of the 2010 baseline have been aggregated as ‘Rest of World’ (as described above), a corresponding activity index for aggregate extra-EU destinations is applied. For example, the emissions associated with container vessels arriving at the Netherlands from all origins outside of the EU (‘Rest of World’) are backcasted using an activity index of all container vessels arriving at the Netherlands from all countries outside the EU.

Country level represents activity indices associated with a Member State. For example, index trends of cruise vessel passengers reported by Spain are associated with emissions in the 2010 baseline of cruise vessels departing Spain.

EU level (least detailed, and fallback approach) represents indices of activity

aggregated from all Member States (where possible) at EU level.

Where possible the most disaggregated activity indices have been sought and applied. This is manifested in an algorithm in the backcasting calculations that applies all activity indices at all available levels, and selects the backcasted emissions preferentially from one estimated using a country to country level index. Where this is not available (for example, if insufficient data on a particular country to country cargo type flow are available, or as the default for vessel types with more aggregate approaches such as cruise vessels and offshore vessels), the algorithm selects an emission backcasted using country level indices preferentially over EU level indices. In the results section, there is a discussion on the proportion of emissions that have been estimated with each level of activity index

The country to country level data are highly disaggregated. Consequently, those country pairs with insignificant absolute activity levels among years can, when indexed, have significant (and unlikely) relative variation between years. Therefore, in order to reduce the uncertainty in the overall results, a removal process to prevent the application of unlikely country to country level trends was implemented. This was considered to be justifiable given that the emissions in the baseline to which these activity indices would have been applied (a) were small, and (b) would be backcasted using more aggregate (country level or EU level) indices according to the preferential algorithm (described above). The country to country level trends were removed according to the specifications detailed in Table 3.4.

Table 3.4: Approach for removing activity indices at country to country level

Dataset Specification for removal of index at country to country level

[mar_go_am_detl] Sum of total tonnes of cargo transported from country to country over period 2004 to 2010 exceeds

0.00005% of the EU total (domestic, intra and extra-EU) tonnes of cargo transported

[mar_pa_qm_detl]: passengers

Sum of number of passengers transported from country to country over the period 2004 to 2010 exceeds 0.001% of the EU total (domestic, intra and extra-EU) number of passengers transported

Activity indices (1990 to 2004)

As discussed earlier in this chapter in section 3.3.3, literature review has identified that there is not a continuous time series of EU seaborne trade data over the period 1990-2010. Method C has developed robust estimates for the period 2004 to 2010 based on bilateral seaborne trade data. For the period 1990 to 2004, more aggregate data are necessary to backcast the 2004 estimates. The approach for activity indices over the period 1990 to 2004




instead adopts one of the two data sources utilised in Method B to provide activity indices for three cargo types (crude oil, oil products and dry cargo other than containers). Given that the container sector is known to have grown at a faster rate than other dry cargo sectors over the period being considered, a separate index from OECD data (OECD, 2012) for container vessels is used. For ferries, trends in passenger-kilometres from the EU Transport in Figures publication are used, whilst for cruise, yacht, offshore, service and miscellaneous vessels fleet data from IHS (2011) have been used in absence of other published data. The table below summarises the activity indices that have been applied to each vessel type for the period 1990 to 2004.




Table 3.5: List of activity indices applied in method C for estimating emissions from 1990 to 2004

Vessel category (baseline) Activity index applied Geographic level of activity index application

01 Oil tanker, 02 Chemical tanker,

03 LPG, 04 LNG, 05 Other tanker

UNCTAD Review of Maritime Transport (various

issues) seaborne trade - total liquid bulk loaded and unloaded

EU

06 Bulker, 07 General cargo, 08

Other dry, 10 Vehicle, 11 Roro

UNCTAD Review of Maritime Transport (various

issues) seaborne trade - total dry cargo loaded and unloaded

EU

09 Container OECD (2012) Containers Transport EU (note 1)

12 Ferry European Commission (2011) and European Commission (2000) Passenger Transport in

passenger-kilometres

EU

13 Cruise Cruise f leet number of low er berths (IHS, 2011) EU

14 Yacht Yacht f leet gross tonnage (IHS, 2011) EU

15 Offshore Offshore vessel f leet numbers (IHS, 2011) EU

17 Fishing Eurostat dataset [f ish_ld]: total tonnes of f ish landed Country, EU

16 Service, 18 Miscellaneous Service vessel f leet numbers (IHS, 2011) EU

Note 1: OECD Stats available for all non-landlocked EU-27 Member States except Cyprus. Therefore it is assumed that the trend in container vessel activity for Cyprus follows the rest of the EU.

3.3.6.4 Trends in CO2 emission factors

In order to use a 2010 emission inventory baseline to calculate annual emissions back to 1990 it is necessary to develop a change index, i.e. a relative factor for each year that can be multiplied by the 2010 emissions to estimate the emissions for that year. There are two principle components of such a change index; the change in emission factor and the change in volumes of trade. At the highest level, the emission factor change index provides a scaling factor for the change per year in specific emissions (emissions per tonne of freight or per passenger per kilometre) and the annual growth index provides a scaling factor for the change in amount of freight (t-km) or passengers (p-km) moved. Combined these provide a factor for the change in emissions per year.

The approach for adjusting the emissions that have been backcasted using activity indices to take account of trends in CO2 emission factors is as follows:

yvyvyv ,,,index EFactivityfor backcasted EmissionsEF andactivity for backcastedEmissions

where


y is the year

In this section the index to represent how CO2 emission factors have changed over the period 1990 to 2010 is discussed. There are many parameters which influence the specific emissions, namely:

Specific fuel consumption (SFC) of the engine (tonnes of fuel per kWh), an indication of the engine efficiency, which is influenced by:




Engine type (2-/4-stroke, high/medium/slow speed compression ignition, gas/steam turbine etc)

Age of engine (influencing both the maximum potential efficiency of the engine on its commissioning date due to technology developments at that time, and the degradation of efficiency over time due to engine wear, maintenance etc).

Operating load factor (percentage of maximum continuous rating (MCR) for different operating cycles (at sea, manoeuvring, at berth, idle/lay-up) and ratio of time spent in each cycle). As well as a direct impact on emissions, this also affects SFC.

Fuel type (HFO, MDO, LNG etc): Chemical composition and calorific value (energy density) and therefore emission factor (kg CO2 / kg fuel) differs between fuel types and can vary over time. As well as a direct impact on emissions, this also affects SFC.

Installed engine power capacity per vessel freight/passenger capacity (MW / dwt), and ratio between main engine (ME) and auxiliary engine (AE) capacity (as SFC, load factor and fuel type for ME and AE are often different).

The preferred approach would be to establish detailed, annual data for each of these parameters for the European fleet in order to identify the respective trends, which could then be used to calculate the compound emission factor change index. However, the limited availability of much of this data has required a less comprehensive approach to be taken. Consequently there is a degree of uncertainty attached to the emission factor change index which has been developed. The approach taken and the associated uncertainty is set out for each parameter in turn, below.

Specific fuel consumption

A number of sources of data have been identified for engine SFC:

IHS (2011): Fuel consumption and kWh data has been provided for each vessel sub-category for the European fleet for 2010. These data have been used to calculate SFC.

Eyring et al. (2005): SFC averages and ranges are presented for high level aggregated vessel categories for the world fleet for 2001.

Entec (2002): SFC per operating cycle, and time manoeuvring verses time spent not at sea, are presented for medium level aggregated vessel categories for the European fleet for 2000. Endresen et al. (2003) provides indication of percentage time spent at sea for worldwide fleet; this single value has been applied to all categories and therefore does not reflect the differences that are likely to be found for different vessel types. Operating cycle weighted average SFC have been calculated from these data: IMO (2009): SFC by engine type and capacity for world fleet for periods 1970-1983, 1984-2000 and 2001-2007; Endresen et al. (2007): Fleet average SFC for ME for world fleet for 1990 and 2000.

There is inconsistency between the data sets in terms of the fleet covered, categorisation and basis for the SFC presented. Furthermore data are available for too few years in the 1990-2010 period to be able to identify robust trends in SFC.

Alternative data has therefore been identified which presents trends in SFC. Whilst Endresen et al. (2007) does not present suitable data for comparison against other data sets, it does provide a comparison between equivalent data for 1990 and 2000. This information is used to establish a 0.6% per annum reduction in SFC between 1990 and 2000. For 2000 to 2010 this assessment assumes that there is no change in SFC. USEPA (2008) suggests “Conversations with engine manufacturers indicate that it is reasonable to assume SFC will remain constant for the 15-year [2005-2020] time horizon of this study, particularly as they focus on meeting more stringent NOX emissions requirements, such as those imposed by MARPOL Annex VI”.




Figure 3.4: Specific fuel oil consumption over time

Source: USEPA (2008)

Similarly, NERI (2008), a study of fuel consumption and emissions from shipping in Denmark from 1990-2005, also indicates that SFC remains constant from 2000, as presented in the following graph.

Figure 3.5: Specific fuel consumption for marine engines related to the engine production year

Source: NERI (2008)

Operating load factor and cycle

It has been assumed (as in Eyring et al. (2005) and Endresen et al. (2007)) that the average operating load factor of engines within each operating cycle has remained constant over the period of 1990 and 2010. This is assumed as whilst at sea engines will for the most part be operated at optimum efficiency as much as possible, and factors for deviation for this (for example changes in weather) are difficult to model and likely to remain constant when averaged across the fleet and 20 year period. The only exception to this is the recent increase in slow steaming. Slow steaming can be achieved in two ways; either, for vessels with multiple main engines, by reducing the number of main engines which are running, or, if the vessel has only one main engine by reducing the load factor. The effect of slow steaming on emissions is not considered within this emission factor change index as there are many influencing considerations for slow steaming which are considered together in section 3.2.




The ratio of time the vessel spends at sea, manoeuvring and at berth has been assumed to have remained constant over the period of 1990 and 2010. The ratio of time the vessels spends operating verses the time spent idle or laid-up is addressed within the growth index.

Therefore this parameter is taken to be constant for the calculation of the emission factor change index.

Fuel type

A weighted fuel emission factor of 3.15 kg CO2 / kg fuel has been applied for the calculation of the 2010 baseline emissions. IPCC (2006) indicates that the emission factor for MDO is 3.19 kg CO2 / kg fuel and for HFO is 3.13 kg CO2 / kg fuel. Therefore from this it can be estimated that in 2010 MDO accounted for 34.5% of fuel consumed and HFO for 65.5%. IMO (2009) indicates that in 2007 the ratio was 23:77 (MDO:HFO) for the world fleet, and also presents data for 2000-2004 from IEA (2007) and EIA (various). Calculation of a weighted emission factor for these years and comparison against the 2010 value used indicates that the difference between the years is less than 0.5% (calculations shown in Table 3.6 below).

Table 3.6: Ratio of fuel use for different years

Year 2010a 2007

b 2004

c 2003

c 2002

c 2001

c 2000

c

MDO 34.5% 23% 31% 32% 29% 30% 28%

HFO 65.5% 77% 69% 69% 71% 70% 72%

Weighted EF 3.15 3.14 3.15 3.16 3.15 3.14 3.15

Index 1.000 0.998 0.999 1.002 0.999 0.996 0.999

a: IHS (2010) b: IMO (2009) c: average of IEA (2007) and EIA (various)

It has therefore been assumed that the impact of fuel switching between residual and distillate fuels on CO2 emissions – due to the slightly higher carbon content of distillate fuels – is insignificant. The impact of fuel switching between residual and distillate fuels on CO2 emissions due to the slightly higher energy density (calorific value) of distillate fuels is also considered insignificant (although if considered this would act to oppose the impact on CO2 of differences in carbon content). Variation in the CO2 emission factor over time for the same fuel type, due to changes in calorific value of the fuel, is even less significant and therefore has not been investigated.

Engine capacity

It is widely acknowledged that since the 1980s there has been an increasing trend for the average installed engine capacity per vessel as well as an increasing trend for the size of vessel. The main consideration for the effect on emissions is whether or not there has been a change in the installed engine capacity (MW) per size of ship (GT or dwt). An assessment based on data from Eyring et al. (2005) and Endresen et al. (2003) suggests that since 1995 there has been little to no change in MW per GT. There is however insufficient comparable data available to draw a robust conclusion, and therefore this factor has not been incorporated into the emission factor index.

3.3.6.5 Slow steaming trends

The methodology used to estimate the 2010 baseline emissions for this study implicitly takes account of the slow steaming practices that were already implemented on vessels in 2010. This is because the methodology utilises consecutive time-stamped locations of vessels to calculate the actual speeds of those vessels between consecutive locations. As such, for the




purposes of the estimation of historical emissions in this study, it is necessary to consider how implementation of slow steaming has changed over time with respect to 2010.

The approach for adjusting the emissions that have been backcasted using activity indices, and which

take account of changes to the CO2 emission factor, to consider the impacts of trends in slow

steaming as follows:

yvyvyv

,,,

index steamingslow EF andactiv ity f or

backcasted Emissions

steamingslow and EF

activ ity , f or backcasted Emissions

where


y is the year

CO2 emission reduction from slow steaming

Slow steaming of a vessel reduces the fuel consumption rate (expressed for example in tonnes per day) of the individual vessel, as well as the total fuel consumed by a vessel when completing a certain voyage. It is also important to consider that in order for a shipping company to continue offering the same frequency of service, as is expected in (cargo) liner shipping, additional vessels need to be deployed to take account of the fact that the slower vessels take longer to complete a voyage.

For the vessel level impacts, the principle impact is on the main engine’s fuel consumption whilst at sea. The fuel consumption rate reduction occurs because form drag of the vessel is reduced; vessel speed is a cubic function of engine power to a first order approximation such that a 10% speed reduction is commensurate with a 27% reduction in engine power (CE Delft et al, 2012). When taking into account the additional journey time a slower speeding vessel incurs on a fixed voyage length, the relationship between speed and engine power can be approximated as a quadratic function, such that a 10% speed reduction can be achieved with a 19% reduction in engine power.

The specific fuel consumption (SFC, expressed in g/kWh) of a ship’s engine does not vary linearly with engine power or load, as it is typically optimised (i.e. minimised) for the design speed of the vessel, associated with typical engine loads of around 70% to 90% of maximum continuous rate (MCR) (Notteboom & Cariou, 2011). As such, there is normally a small SFC penalty for vessels travelling at speeds slower than their design speed. The penalty is of the order of a 10 to 15% SFC increase at reduced speeds associated with a 75% reduction in engine power (CE Delft et al., 2012).

However, the additional fuel consumed by the need for a liner company to add capacity to maintain an equivalent service with slow steaming vessels is less easy to quantify. It is clear from the literature that additional container capacity can be and has been utilised in this manner for liner routes (Notteboom & Cariou, 2011; Cariou, 2010; Seas At Risk 2010), but it is unclear this also holds true for the dry and liquid bulk carrying sector. In terms of quantifying the service level fuel consumption impacts of slow steaming, a leading container shipping company known for its slow steaming practices (CE Delft et al., 2012) has produced estimates of CO2 emission reductions at a service level (Maersk, 2009). In the absence of further data on the CO2 emission saving at a service level for container vessels, the Maersk estimates have been adopted for container vessels and are included in Table 3.7 below.

The additional vessel types that have been considered to have implemented slow steaming up to 2010 (see section below) are dry and wet bulkers. Lindstad et al. (2011) identified that the cost-optimum speed reduction for (dry and wet) bulkers is 10%. The bulk of literature on the potential fuel consumption savings from slow steaming has focussed on container vessels, which have typically high design speeds of around 24 to 25 knots. The vessel-level relationship between CO2 emissions and speed of vessels described in Smith et al. (2011) suggests that for both crude carriers of design speed 15kn and for dry bulkers of design




speed 13.5kn, a 10% speed reduction would yield an approximate 20% CO2 saving for the vessel. As indicated earlier it is unclear whether in the wet and dry bulk sector additional vessels have been deployed to maintain existing service frequencies. As such, the vessel-level CO2 savings have been adopted as being the overall CO2 impacts of slow steaming for bulkers.

Table 3.7: Speed and CO2 reductions for container and bulk vessels assumed in this study

Vessel type Speed reduction CO2 saving (at a service level) Note 1

CO2 saving per vessel

Container 17% 10% -

25% 30% -

Wet and dry bulkers 10% - 20%

Note 1: where ‘service level’ indicates that additional vessels have been deployed on a service to retain an existing service frequency whilst slow steaming.

Prevalence of slow steaming

The implementation of slow steaming has mostly been documented anecdotally, given the absence of data on actual vessel speeds. The following points have been gathered from a literature review to identify the market saturation of slow steaming:

CE Delft et al. (2012) has summarised multiple literature sources and concluded that “container ships may have slowed down in line with the Maersk Line reduction, or somewhat less, and that other ship types have slowed down less. A fleet average speed reduction would be lower than 15%”.

The latest draft results of a survey of approximately 80 shipping companies (randomly selected from those with fleets of more than 10 ships as listed in Clarksons SIN database) on their implementation in 2011 of general speed reduction found that, of the responders to the survey, 100% of container companies, around 75% of dry bulk companies and around 70% of tanker companies had implemented general slow steaming (Rahmatulla and Smith, 20127). It is important to note that this survey is ongoing at the time of writing in order to reach statistically significant response levels per sector, in particular for the container sector.

Notteboom & Cariou (2011) analysed a number of European container liner services operating internationally; a TEU-weighted average (by the present authors) of their results is that around 48% and 50% of vessels and services respectively were slow steaming in January 2010. Finally, Notteboom & Cariou (2011) conclude that “Slow steaming practices were initiated in the summer of 2008, particularly on the Europe-Far East trade, as a response of shipping lines to fast rising bunker costs. However, the full impact became visible in late 2009 and 2010 as more and more shipping lines decided to opt for slow steaming”.

Maersk (2011) report that “In 2010, 73 percent of the Maersk Line fleet was slow steaming at engine loads below 40 percent.”

A Content Analysis of shipping media reports by Dinwoodie et al. (2010) identified that the earliest media reports reviewed suggest some container vessels began slow steaming in June 2008, tanker brokers advised slow steaming in April 2009, and Maersk introduced super slow steaming in November 2009.

Based on the above sources, the following uptake rates of slow steaming across the fleets have been assumed in this analysis:

7 With updated response data f rom N. Rahmatulla, personal communication 26

th March 2012.




Table 3.8: Slow steaming uptake rates (%) assumed in this study

Vessel type Speed reduction Year 2007 Year 2008 Year 2009 Year 2010

Container 17% 0% 10% 25% 40%

25% 0% 0% 10% 30%

Bulkers (dry and w et) 10% 0% 0% 20% 40%

Methodology and resulting indexed slow steaming factor

The assumptions presented above for the uptake of slow steaming since 2007 and the CO2 reductions for each vessel type have been utilised to calculate a multiplying index that can be applied to the activity-based historical emissions estimates. For container vessels, it was assumed that part of the fleet had adopted slow steaming, and an additional fraction of the fleet had implemented ‘super slow steaming’. The multiplying indices that have been assumed are presented below (year 2010 = 1.00).

Table 3.9: Slow steaming multiplying indices assumed in this study

Vessel type Years prior to 2007 Year 2007 Year 2008 Year 2009 Year 2010

Container as per 2007 1.15 1.14 1.09 1.00

Bulker (dry and w et) as per 2007 1.09 1.09 1.04 1.00

The assumptions are equivalent to stating that the CO2 intensity (i.e. CO2 emissions per tonne of cargo transported) of container (bulk) cargo transport reduced by 13% (8%) between 2007 and 2010 due to the impact of slow steaming.

3.4 Results

This section presents the estimated historical maritime CO2 emissions as estimated through the three methods described in the previous section.

3.4.1 Methodology A

Figure 3.6 below plots historical CO2 emissions from EU maritime shipping as estimated using method A of this study. In summary, method A utilised an index of global seaborne trade to backcast 2010 emission levels. This result shows a year-on-year rise of CO2 emissions, except for a drop for years 2009 and 1998. The year with highest emissions according to this method is 2010. With method A, emissions in 2010 are nearly twice the level of 1990 emissions and 15% higher than emissions in 2005.

Method A has been developed as an analogy to that used in IMO (2009). It is considered however unlikely that the growth in European maritime CO2 emissions was as high a rate as the growth in global maritime CO2 emissions over the period 1990 to 2010. For this reason, the historical emission estimates for method A are considered to be underestimates.




Figure 3.6: Historical CO2 emissions 1990 to 2010 estimated from method A

3.4.2 Methodology B

Figure 3.7 below plots historical CO2 emissions from EU maritime shipping as estimated using method B of this study. In summary, method B utilised an index of EU and European seaborne trade to backcast 2010 emission levels. This result shows a year-on-year rise of CO2 emissions, except for a drop for years 2000, 2001, 2008 and 2009. The year with highest emissions according to this method is 2007. With method B, emissions in 2010 are 60% higher than 1990 emissions, and 2% lower than emissions in 2005.

0

20

40

60

80

100

120

140

160

180

CO

2 e

mis

sio

ns

(Mt)

CO2 emissions (Mt) 92 96 98 102 105 108 111 117 116 118 127 128 130 139 148 156 165 172 176 168 180

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010




Figure 3.7: Historical CO2 emissions 1990 to 2010 estimated from method B

3.4.3 Methodology C

Figure 3.8 below plots historical CO2 emissions from EU maritime shipping as estimated using method C of this study. In summary, method C utilised primarily a complex set of indices of seaborne trade (for different cargo types) and passenger flows among country pairs, and more aggregate indices for other vessel types, to backcast 2010 emission levels. This result shows a year-on-year rise of CO2 emissions, except for a drop for years 2000, 2009 and 2010. The year with highest emissions according to this method is 2008. With method C, emissions in 2010 are 24% higher than 1990 emissions, and 8% lower than emissions in 2005.

This method C attempts to take into account the impacts of slow steaming since 2008, as well as taking into account impacts on CO2 emission factors due to fuel, engine and vessel developments. The emission estimates show a starker drop in emissions between 2008 and 2009 than the method B estimates (as slow steaming is assumed to be implemented more widely, and approximately static emissions between 2009 and 2010 as the growth in activity following the 2007-9 recession and associated emissions growth is countered by greater implementation of slow steaming (which assumes goods are transported with lower CO2 intensity).

Table 3.10 sets out the emissions estimated by method C split by vessel category, and Table 3.11 provides the results disaggregated by vessel movement type.

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CO2 emissions (Mt) 112 123 133 144 154 165 166 166 168 169 168 163 165 170 176 183 189 194 193 170 180

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010




Figure 3.8: Historical CO2 emissions 1990 to 2010 estimated from method C

The estimate for 2005 from Method C has been utilised elsewhere in this study in the TIMES model (Appendix 1)

Table 3.10: Historical CO2 emissions (in Mt) 1990 to 2010 estimated from method C, split by vessel category

Vessel Type 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

01 Oil tanker 25 24 22 21 20 19 19 17 17 16 16 16 16 17 17 17 18 18 18 17 16

02 Chemical tanker 25 24 23 22 21 19 19 18 17 17 17 17 17 17 17 18 19 19 19 17 16

03 LPG 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 2 2

04 LNG 9 8 8 7 7 7 7 6 6 6 6 6 6 6 6 6 6 6 6 6 5

05 Other tanker 0.6 0.6 0.6 0.5 0.5 0.5 0.5 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.4

06 Bulker 13 15 17 19 21 23 23 25 25 26 25 24 25 25 25 26 26 26 28 22 22

07 General cargo 9 10 12 13 15 16 16 17 18 18 17 17 17 17 17 18 18 19 18 12 14

08 Other dry 3 3 4 4 5 5 5 5 6 6 6 5 5 5 5 6 6 7 7 4 5

09 Container 20 21 22 24 27 28 31 35 37 39 40 42 45 49 55 55 60 64 65 56 55

10 Vehicle 3 4 4 5 5 6 6 6 6 6 6 6 6 6 6 7 7 8 8 5 6

11 Roro 3 4 4 5 5 6 6 6 6 7 6 6 6 6 6 7 8 9 8 6 6

12 Ferry 26 26 26 26 26 26 26 26 25 25 24 24 24 24 23 23 21 21 21 20 20

13 Cruise 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 4 4 6 6 7 7

14 Yacht 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.5 0.5 0.6 0.6 0.7 0.8 0.9

15 Offshore 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

CO2 emissions (Mt) 145 149 152 156 161 165 168 170 172 174 173 173 177 183 189 195 200 212 213 181 180

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Vessel Type 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

16 Service 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

17 Fishing 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.4 0.4 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4

18 Miscellaneous 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2

Total 145 149 152 156 161 165 168 170 172 174 173 173 177 183 189 195 200 212 213 181 180

Table 3.11: Historical CO2 emissions (in Mt) 1990 to 2010 estimated from method C, split by movement type

Movement Type 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

Domestic 23 23 23 23 23 23 23 23 23 23 22 22 23 23 23 24 24 26 26 23 22

Intra EU 43 44 45 46 48 49 50 50 51 51 51 50 51 53 54 55 56 59 58 47 45

Extra EU

inbound 42 44 45 47 49 51 52 53 54 55 55 55 56 58 61 63 67 72 71 57 58

Extra EU

outbound 37 38 39 40 41 42 43 44 45 45 45 45 47 49 51 54 53 55 57 54 54

Total 145 149 152 156 161 165 168 170 172 174 173 173 177 183 189 195 200 212 213 181 180

Method C relies on the estimation of emissions at multiple levels of geographical disaggregation. In order to demonstrate the proportion of emissions estimated at each of the three levels of geographical disaggregation (country to country level, country level and EU level), the figure below plots the estimate for 2005 from method C split across the 18 vessel categories and indicating for each vessel category the proportion of CO2 emissions estimated through each of the three levels of geographical disaggregation.

Figure 3.9: Proportion of 2005 emissions estimated from each level of geographic disaggregation

The emissions estimates for each year according to each of the three methodologies are compared alongside each other in Figure 3.10 below. This plot shows more clearly the differences in results of each method. The most complex method (method C) has the highest




emission estimates for all years except for year 1995 when method B yields a slightly higher estimate.

Figure 3.10: Historical CO2 emissions 1990 to 2010 estimated from methods A, B and C

The choice of which method to adopt for the purposes of defining historical EU maritime CO2 emissions will have implications for future EU GHG emissions policy commitments due to the benchmarking of reduction targets against historical years. The Commission’s targets to reduce emissions from international shipping by 2050 are currently benchmarked against 2005 levels. The most complex method, and that which is considered to be most robust due to it taking into account a number of additional factors, yields the highest CO2 emission estimates for year 2005 (195 Mt) compared to an aggregate approach involving EU level trade data (183 Mt). Given a fixed more recent emission baseline (180 Mt in 2010), a higher 2005 emission estimate will mean the target to reduce emissions from international shipping by 2050 will require a smaller absolute reduction in emissions compared to a 2010 baseline.

Table 3.12: Comparisons of 1990 and 2005 emission estimates from each of the three methods

Method for estimating historical CO2 emissions Year 1990 Year 2005

Method A 92 Mt CO2 156 Mt CO2

Method B 112 Mt CO2 183 Mt CO2

Method C 145 Mt CO2 195 Mt CO2

3.5 Comparisons

3.5.1 Comparisons against other historical emission estimates

The authors are aware of only two published sources that estimate historical European maritime CO2 emissions: Entec (2002) and European Commission (2011).

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Entec (2002) produced a bottom-up activity-based estimate of CO2 emissions for the year 2000. The geographic scope of the Entec (2002) study was different to that of this study in that it included emissions from vessels whilst they were travelling within the EMEP domain. Thus the Entec (2002) study scope included vessels passing through the EMEP domain but which did not call at EU ports, and excluded the emissions from any journey segments between the EMEP grid perimeter and the last/next port of call. This study considers the emissions from vessels from the last port of call before the EU port call, The Entec (2002) study estimated total CO2 emissions from all vessels except fishing vessels to be 153 Mt in year 2000, and emissions from fishing vessels to be 4 Mt in year 2000. Part of the study also produced a high-level estimate for 1990 CO2 emissions from EU maritime sources of 122 Mt CO2. This estimate for year 1990 is between the estimates of this study’s methods 1 and 2. The growth in CO2 emissions between 1990 and 2000 estimated by Entec (2002) was 29%. For comparison, the overall growth estimated in this study between 1990 and 2000 is similar at 20% in method C (50% in method B and 38% in method A). The Entec (2002) study also made forecasts for the years 2006, 2008 and 2010 of 165 Mt, 169 Mt and 173 Mt respectively using simple annual growth rate assumptions, which did not anticipate recession impacts.

The European Commission’s statistical booklet from 2011 on transport reports CO2 emissions from each transport mode, including domestic navigation and international maritime bunkers. The reported CO2 emissions originate from the Annual European Union greenhouse gas inventory to the UNFCCC and are considered to have been estimated from fuel sales of residual and diesel oil with high uncertainty.8 Figure 3.11 plots the data published in that booklet (adapted from European Commission, 2011) as a total of domestic navigation and international maritime bunkers. The growth in CO2 emissions between 1990 and 2008 estimated by European Commission (2011) was 49%. For comparison, the overall growth estimated in this study between 1990 and 2008 is very similar, at 47% in method C (72% in method B and 91% in method A).

The historical CO2 emissions estimated in this study are plotted against the estimates of Entec (2002) and European Commission (2011) in the figure below.

8 Personal communication with European Env ironment Agency , 27

th March 2012. The annual European Union greenhouse gas inv entory is

av ailable online at http://www.eea.europa.eu/publications/european-union-greenhouse-gas-inv entory -2011.




Figure 3.11: Comparison of historical European maritime CO2 emission estimates from this study (methods A, B and C), Entec (2002) and the European Commission (2011)

Notes: the Entec (2002) figures for 2006, 2008 and 2010 were forecasts of that study, with an error margin for year 2010 and where based on simple annual growth percentages made before the economic recession. The European Commission (2011) cites the European Environment Agency (‘EEA, August 2010’) as its source.

3.5.2 Proportion of historical maritime EU emissions of global shipping emissions

The Method C estimates of EU maritime emissions have been compared to the estimates of global shipping emissions as presented in IMO (2009) to show the trend in the proportion of shipping emissions for the EU. This comparison is shown below in Figure 3.12. This shows, with the overlaid percentages, that using the results of Method C would mean that the proportion of EU maritime CO2 emissions of the global total decreased from 26% in 1990 to 20% in 2007.

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EEA (European Commission, 2011)




Figure 3.12: Comparison of Method C estimates of EU maritime CO2 emissions with total global shipping CO2 emissions in IMO (2009)

3.5.3 Fall in maritime activity and CO2 emissions due to the economic recession

The Method C emission estimates for 2008 to 2009 suggest that the primary reason for the significant drop in EU maritime emissions between 2008 and 2009 was a significant (20%) reduction in extra-EU imported goods (see Table 3.11). To support this suggestion, a second Eurostat trade data source is identified which represents the EU’s trade exchanges in goods with the rest of the world and for which imports are expressed in value (EURO) terms. Figure 3.13 below plots these data, which does indeed support a significant (23%) drop in EU imports from 2008 to 2009.

Figure 3.13: EU imports of goods expressed in value (€bn EUR)

Source: Eurostat code tet00018 9

3.6 Uncertainties

There are a number of uncertainties in the estimated historical emissions presented in this section. Table 3.13 identifies and discusses these uncertainties.

9 http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tet00018

26% 25% 25% 25% 25% 25% 25% 24% 24% 24% 22% 22% 22% 22% 21% 20% 20% 20%

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IMO (2009) Total global shipping CO2 emissions 1990-2007

Data label percentages show estimated EU emissions (this study) as proportion of global emissions (IMO, 2009)

This study, method C estimate: EU shipping CO2 emissions 1990-2010

1000

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EU im

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http://epp.eurostat.ec.europa.eu/tgm/table.do?tab=table&init=1&plugin=1&language=en&pcode=tet00018




Table 3.13: Summary of uncertainties

Aspect of uncertainty

Discussion

Top-dow n approach This study has estimated historical emissions in a top-dow n manner from a 2010 baseline year rather

than estimating each year’s emissions in its ow n right (e.g. from annual fuel sales). This means that any uncertainties present in the estimated 2010 baseline emissions are also present in the historical emission estimates.

Activity: discontinuities in

statistical series

The approach of using a time series of statistics relies upon the time series being internally consistent, i.e. devoid of discontinuities. The approach has tried to mitigate against this by for example the

removal of uncertain highly varying activity trends based on low data volumes.

The approach has also tried to appreciate in the data sources where and w hy these discontinuities

exist. For example, it has been identif ied and described in the chapter that tw o series discontinuities appear to exist in the UNCTAD RMT data utilised by methods B and C. To get around this problem, indices have been developed that rely primarily on a single report (i.e. assuming a single publication is internally consistent) and tracking forward and back from that using other data.

Despite these mitigative actions, an uncertainty on this topic remains.

Activity: geographical scope

The availability of data for countries that w ere not part of the EU earlier in the 1990-2010 period is more limited. As such, alternatives to EU-27 f igures were adopted for some of the back-casting to 1990. This relied on for example data that corresponded to the then EU-15 (UNCTAD, various issues).

The adoption of data that represented the EU-15 and applied to the EU-27 for these earlier years in the assessment (i.e. prior to 2004) means that it has been assumed that the grow th in seaborne trade experienced by the EU-15 w as applied to the EU-27. In practice the remaining 12 countries (of w hich 8 are maritime nations) may have experienced different growth rates over the period and so there is an

uncertainty associated with this approach.

Activity: Method C

pre-2004 estimates

The emission estimates for method C prior to 2004 rely on much more aggregate activity indices. As

such, much greater uncertainty is attached to these estimates.

Activity: trade data Literature has established that major differences can exist between what is reported by exporting countries as exports and w hat is reported as imports by import countries for the same data set (LMIU, 2009). This study adopts a reverse reporter in-filling algorithm w hich selects the maximum of that

reported by either the importer or export in such a scenario. A maximum has been adopted due it being considered more likely that cargoes or passengers are undercounted by one particular trade partner. There is potentially considerable uncertainty for data for individual country -pairs in this approach, but at a broad scale this is considered reasonable.

Trends in vessel

routings over time

This study uses a geographic scope of last port of call to next port of call as the basis of defining the

2010 emissions baseline. Over time, the particular last ports of call that vessels make prior to calling at the EU may w ell change over time. The uncertainty associated with a historical emission estimate that assumes the last ports of call are as per 2010 if in actual fact in 1990 the last ports of call w ere elsew here may be considerable for individual routings. How ever, at an aggregate level, this could be

considered reasonable, since changes to last ports of call could both increase and decrease the distances sailed before calling at an EU port. Also, it is considered that trans-North Atlantic routes w ould not be impacted signif icantly by such changes.

Emission factor: engine capacity

In order to best take into account how emissions per unit of good transported or per passenger transported have changed over time, it is important to understand the trend in installed engine pow er

capacity per vessel freight/passenger capacity (MW / dw t). However, insufficient comparable data has been identif ied to draw a robust conclusion in this study.

Emission factor: sfc There is inconsistency between the datasets investigated for SFC over the period 1990-2010 in order to be able to identify robust trends in SFC. As such, a less robust approach of assuming annual improvements identif ied in the literature for the global f leet have been assumed. A more robust

approach w ould involve relying on datasets of f leet-average (in terms of emissions) SFC going back annually to 1990.

Emission factor: operating load factor and cycle

Endresen et al. (2007) identif ies that the number of operational days at sea is a key sensitivity parameter for historical emission estimates. This w ork assumes no change in the number of operational days per year at sea compared to 2010. This may lead to an overestimate of historical

emissions if vessels operate for a greater number of days per year in more recent years (if it is assumed that f leet utilisation eff iciency increases with time).

Slow steaming: uptake assumptions

The uptake assumptions for slow steaming for container vessels, dry bulk and liquid bulk vessels have been developed based on literature. There is uncertainty around both the proportion of the f leet that have implemented slow steaming (not only in terms of number, but also in terms of emissions), and

the speeds at w hich the vessels are travelling. To really gain the best possible picture of this, it w ould be necessary to gain considerable amounts of data at a vessel level on the speeds sailed in one year to the next, not to mention the specif ic fuel consumption curves for each vessel. Given that there are other reasons for steaming at a particular speed (charter basis, weather, perishable status of goods), it

w ould be impossible to fully attribute speed differences to a particular reason.




Aspect of uncertainty

Discussion

Slow steaming: CO2

reduction

The emission reductions associated with slow steaming are relatively straightforward to estimate at a

vessel level. How ever, as described in this chapter, the slow steaming of container vessels requires additional vessels to be deployed in order to retain a previous service schedule. It is the emissions associated w ith the additionally deployed vessels that are diff icult to estimate. A single source of data

on service level CO2 savings has been used for the purposes of this estimation. This is regarded as uncertain. Furthermore, due to a lack of literature on the bulk sector to suggest otherw ise, it has been assumed that dry and liquid bulk carriers do not need to deploy additional vessels, and so vessel-level CO2 savings have been assumed for these vessel categories.

3.7 References

Cariou, P., (2010) Is slow steaming a sustainable mean for reducing liner shipping CO2 emissions? Euromed Management Mare Forum, 14 September 2010, Marseilles.

CE Delft, The ICCT, Mikis Tsimplis (2012) Regulated Slow Steaming in Maritime Transport. An Assessment of Options, Costs and Benefits. http://www.seas-at-risk.org/1mages/STUDY_RegulatedSlowSteaming.pdf

Corbett, J. and Winebrake, J. (2008) The Impacts of Globalisation on International Maritime Transport Activity. Past trends and future perspectives. Paper presented at Global Forum on Transport and Environment in a Globalising World 10-12 November 2008, Guadalajara, Mexico. http://www.oecd.org/dataoecd/10/61/41380820.pdf

Dinwoodie, J. Tuck, S, Landamore, M. Mangan, J (2010) Slow steaming and low carbon shipping: revolution or recession?" Logistics Research Network 2010 Conference Proceedings: Volatile and Fragile Supply Chains, Harrogate, September; CILT, Corby, Northants, UK, pp. 171-179, ISBN 978 1 904564 31 7.

Endresen, Ø., E. Sørgård, J. K. Sundet, S. B. Dalsøren, I. S. A. Isaksen, T. F. Berglen, and G. Gravir (2003) Emission from international sea transportation and environmental impact, J. Geophys. Res., 108(D17), 4560, doi:10.1029/2002JD002898.

Endresen, Ø., E. Sørgård, H. L. Behrens, P. O. Brett, and I. S. A. Isaksen (2007) A historical reconstruction of ships’ fuel consumption and emissions, J. Geophys. Res., 112, D12301, doi:10.1029/2006JD007630.

Entec (2002) Quantification of emissions from ships associated with ship movements between ports in the European Community. Final report to the European Commission, July 2002. Entec UK Ltd.

Entec (2010) UK ship emissions inventory. Final report to Defra, November 2010. Entec UK Ltd. Available from http://uk-air.defra.gov.uk/library/reports?report_id=636

European Commission (2011) EU transport in figures. Statistical Pocketbook 2011. Available from http://ec.europa.eu/transport/publications/statistics/pocketbook-2011_en.htm

European Commission (2000) EU transport in figures. Statistical Pocketbook 2000. Available from http://www.uni-mannheim.de/edz/pdf/2000/transstat.pdf

Eyring, V., Köhler, H. W., Aardenne, J. van, and Lauer, A. (2005) Emissions from international shipping: 1. The last 50 years, J. Geophys. Res., 110, D17305, doi:10.1029/2004JD005619.

Faber, J., Markowska, A., Nelissen, D, Davidson, M., Eyring, V., Cionni, I., Selstad, E., Kågeson, P., Lee, D., Buhaug, Ø., Lindtsad, H., Roche, P., Humpries, E., Graichen, J., Cames, M., Schwarz, W. with assistance from DNV on some issues (2009) Technical support for European action to reducing Greenhouse Gas Emissions from international maritime transport. Tender DG ENV.C3/ATA/2008/0016. Delft, CE Delft, December 2009.

http://www.seas-at-risk.org/1mages/STUDY_RegulatedSlowSteaming.pdf

http://www.seas-at-risk.org/1mages/STUDY_RegulatedSlowSteaming.pdf

http://www.oecd.org/dataoecd/10/61/41380820.pdf

http://uk-air.defra.gov.uk/library/reports?report_id=636

http://ec.europa.eu/transport/publications/statistics/pocketbook-2011_en.htm

http://www.uni-mannheim.de/edz/pdf/2000/transstat.pdf




Fearnleys (2005) Review 2004. Available from www.astrupfearnley.com/asset/68/1/68_1.pdf [accessed 20 January 2012]

IEA Data Services (2007) Energy Balances and Energy Statistics for OECD and non-OECD Countries.

EIA (various) Energy Information Administration International Energy Annual, Table 31 – various years: http://www.eia.doe.gov/

IHS (2011) Ships visiting European Ports. IHS Fairplay. Report for the European Commission.

International Maritime Organization (IMO) (2000) Study of Greenhouse Gas Emissions From Ships. MEPC 45/8.

International Maritime Organization (IMO): Buhaug, Ø., Corbett, J.J., Endresen, Ø., Eyring, V., Faber, J., Hanayama, S., Lee, D.S., Lee, D., Lindstad, H., Markowska, A.Z., Mjelde, A., Nelissen, D., Nilsen, J., Pålsson, C., Winebrake, J.J., Wu, W., Yoshida, K.O (2009) Second IMO GHG Study 2009. London, UK, April 2009.

IPCC (2006) Guidelines for National Greenhouse Gas Inventories. Intergovernmental panel on Climate Change.

Lindstad, H., Asbjørnslett, B.E., and Strømman, A.H. (2011) Reductions in greenhouse gas emissions and cost by shipping at lower speeds. Energy Policy 39 (2011) 3456–3464.

LMIU (2009) Measuring global seaborne trade. Presentation given at International Maritime Statistics Forum, New Orleans, 4-6 May 2009. Available at http://www.imsf.info/papers/NewOrleans2009/Wally_Mandryk_LMIU_IMSF09.pdf

Maersk (2009) Super Slow Steaming Customer Presentation. Accessed from http://shippersassociation.org/ihsa/NewsLetterItems/Maersk_Slow_Steaming.pdf

Maersk (2011) Slow Steaming. The Full Story by Rasmus Jorgensen. A.P. Moller - Maersk Group. Accessed from http://www.maersk.com/Innovation/WorkingWithInnovation/Documents/Slow%20Steaming%20-%20the%20full%20story.pdf

NERI (2008) Fuel consumption and emissions from navigation in Denmark from 1990-2005 – and projections from 2006-2030 http://www2.dmu.dk/pub/fr650.pdf

Notteboom, T. and Cariou, P., (2011). “Bunker costs in container liner shipping: Are slow steaming practices reflected in Maritime fuel surcharges?”. Paper presented at European Conference on Shipping & Ports- ECONSHIP 2011 “Maritime Transport: Opportunities and Threats in the post-crises world”, Chios, Greece, June 2011.

OECD (2012) Containers transport (Sea containers Tons). Statistics extracted from OECD.Stat (http://stats.oecd.org/ViewHTML.aspx?Theme=CONTAINERS_TRANSPORT&DatasetCode=CONTAINERS_TRANSPORT)

Rehmatulla, N. and Smith, T.W.P (2012) Draft discussion Paper: Follow up on TB7 paper: Implementation barriers to low carbon shipping? UCL Energy Institute.

Seas At Risk (2010) Going Slow to Reduce Emissions. Can the current surplus of maritime transport capacity be turned into an opportunity to reduce GHG emissions? Authorship: Jasper Faber (CE Delft), Malte Freund (GL Environmental Research), Martin Köpke (GL Environmental Research), Dagmar Nelissen (CE Delft)

Smith, T., Parker, S. & Rehmatulla, N. (2011) On the speed of ships. Paper to 1st Low Carbon Shipping Conference, Strathclyde, June 2011.

UNCTAD (2011) Review of Maritime Transport 2011. UNCTAD/RMT/2011. United Nations Conference On Trade And Development.

http://www.astrupfearnley.com/asset/68/1/68_1.pdf

http://www.eia.doe.gov/

http://www.imsf.info/papers/NewOrleans2009/Wally_Mandryk_LMIU_IMSF09.pdf

http://shippersassociation.org/ihsa/NewsLetterItems/Maersk_Slow_Steaming.pdf

http://www.maersk.com/Innovation/WorkingWithInnovation/Documents/Slow%20Steaming%20-%20the%20full%20story.pdf

http://www.maersk.com/Innovation/WorkingWithInnovation/Documents/Slow%20Steaming%20-%20the%20full%20story.pdf

http://www2.dmu.dk/pub/fr650.pdf

http://stats.oecd.org/ViewHTML.aspx?Theme=CONTAINERS_TRANSPORT&DatasetCode=CONTAINERS_TRANSPORT

http://stats.oecd.org/ViewHTML.aspx?Theme=CONTAINERS_TRANSPORT&DatasetCode=CONTAINERS_TRANSPORT




UNCTAD (various) Review of Maritime Transport series. Available online at http://www.unctad.org/Templates/Page.asp?intItemID=2618&lang=1

USEPA (2008) Global Trade and Fuels Assessment - Future Trends and Effects of Requiring Clean Fuels in the Marine Sector http://www.epa.gov/nonroad/marine/ci/420r08021.pdf

http://www.unctad.org/Templates/Page.asp?intItemID=2618⟨=1




4 Appendix 4 - Administrative burden

4.1 Introduction

4.1.1 Definition and scope

This section explores the potential impact on administrative costs of policy options to address GHG emissions from the maritime transport sector. It presents a qualitative overview of the potential administrative costs stemming from each of the options under consideration, and it provides quantitative estimates for administrative costs potentially applying to all options as well as a more detailed assessment of two of them: ETS, and industry-managed compensation fund.

Quantitative estimates are made for the two groups of actors that have been identified as bearing the main costs: the maritime industry; i.e. the owners/operators/charterers of ships that would be subject to the policy options, and the EU and Member State public authorities responsible for administering and enforcing the associated information requirements. Based on the analysis in the main report, the main source of costs will arise from monitoring, reporting and verification (MRV) tasks.

For industry actors, administrative costs relate to the additional resources (manpower, capital) associated with the information obligations imposed by the policy options. The maritime transport industry has a complex structure of ownership and operating arrangements. For the purposes of estimating administrative burdens borne by the maritime industry, ship operators have been used as a basis. These estimates are also presented in terms of average administrative burdens per vessel.

For public authorities, administrative costs include those associated with familiarisation with the different information obligations, preparation of guidelines and information material, oversight of compliance entities’ MRV processes, compliance inspections at ports, as well as a broad range of information communication tasks (including interactions with compliance entities). Estimates presented in this annex distinguish, where applicable, between administrative costs at the EU and Member State level. They also distinguish between compliance- and enforcement-related tasks.

The results here stem from an extensive literature review as well as consultation with stakeholders, including at a workshop held in London on 9 March 2012. Stakeholders included experts in the maritime transport sector as well as independent consultants and public officials in the European Commission and in a number of Member States. Recent experience from the inclusion of the aviation sector in the EU ETS, was used as a basis for some of the estimates provided. All calculations have been performed according to the methodology of the Commission’s standard cost model. Amounts are expressed as net additional administrative costs (i.e. on top of business-as-usual costs) on an annualised basis. A ten-year period has been used for amortisation purposes. Estimates of administrative costs are based on average values calculated from the different streams of data that have been compiled for this study. Following instructions from the European Commission, an hourly wage rate of €41.5 (€67 per hour in some cases explicitly mentioned) has been used in the calculations. This rate includes overhead costs.

Two main scenarios are explored in this analysis. Under the first scenario, only ships above 5,000 GT would be affected by the policy option. This threshold is brought to 400GT under the second scenario. These scenarios would affect, respectively, about 11,400 and 18,400 ships. In terms of the numbers of ship operators, the scenarios would affect, respectively, 2,623 and 5,139 operators. The table below summarises the key figures in this regard:




Table 4.1: Numbers of vessels and numbers of operators split by gross tonnage

Coverage No. of vessels No. of operators No. of operator groups

All ships >400 GT 18,406 5,139 4,255

All ships > 5000 GT 11,391 2,623 2,064

An overarching assumption used in these calculations is that both the scope and the level of stringency of MRV requirements imposed on compliance entities will be comparable to those currently in place under the EU ETS for fixed installations and aviation. As will be discussed later in this annex, some of the policy options considered here (the industry-managed compensation fund in particular) may lend themselves to more relaxed MRV requirements. Should less stringent MRV rules be chosen, administrative costs may be significantly lower. Environmental effectiveness may however be jeopardised by the same token.

All estimates presented here correspond to average values and thus do not account for differential impacts of administrative burdens on compliance entities of different sizes. The information gathering, and in particular the interviews with stakeholders and officials, highlighted the fact that per ship costs are likely to be higher for small owners. The existence of fixed costs means that smaller compliance entities are likely bear a comparatively heavier brunt as a result of administrative burdens. This is similar to the situation in other sectors; for example, small air carriers incur higher costs per aircraft to meet information obligations under the EU Emissions Trading Scheme. Some administrative costs borne by ship operators entities (e.g. obligations to obtain a certificate from the verifier) will depend on the size of their fleet; other costs, however, will be of a similar order of magnitude for all owners, both large and small. It might therefore be worthwhile to investigate simplified procedures for MRV and reporting for small owners and owners affected by the requirements on a limited basis (e.g. owners with only one ship call per year or less at EU ports). Such simplified procedures could be analogous to the provisions for small emitters under the aviation ETS.

In addition, it is worth stressing that the effective administrative costs of the policy options will be determined by the specific policy design elements. In particular, administrative costs are likely to be strongly determined by the level of aggregation allowed within the scheme, as this affects the number of compliance entities.

4.2 General MRV costs

4.2.1 Costs for industry actors

Administrative MRV-related costs for shipping companies are likely to come primarily from the additional manpower and resources required to establish practices to meet the information obligations, increase accuracy and ensure consistency in reporting. Verification is also a fundamental cost parameter. For indicative purposes, estimates presented here can be compared with daily operational costs in the maritime transport industry, which typically range between €2,000 and €7,000 a day.

4.2.1.1 Set-up and preparation: familiarisation with the information obligation

Recent experience suggests that a substantial amount of time is required for understanding the scheme’s rules, definitions and implications, consulting and checking documentation, and interacting with competent authorities are all time-consuming activities.

Data provided by fixed installations as well as airlines and representatives from the maritime industry suggest that approximately 84 man-days are needed per operator per compliance period. On average, this amounts to between 19 and 24 man-days per vessel per compliance period, depending on the scenario.




Table 4.2: Total administrative burden for set-up and preparation costs (understanding schemes and definitions, consulting and checking documentation, interacting with competent authorities), calculated per year over 10 years10:

Coverage Additional administrative burden

(€, 2010 prices)11

Add. Administrative burden per

vessel (€, 2010 prices)

All ships >400 GT 14.7m 800

All ships > 5000 GT 7.6m 660

4.2.1.2 Monitoring plan

The consultation included stakeholders and experts on that concern both the fixed installation sector (NOx/SOx MRV procedures) and the aviation and (monitoring plans required under the EU ETS) and maritime transport sectors. These initial estimates have now been reviewed. These sources suggest that on average, monitoring plans will require between 5 and 6 man-days per vessel per 10-year period, depending on the scenario.

Table 4.3: Total annual administrative costs related to the preparation of the monitoring plan and associated procedures are estimated as follows:

Coverage Additional administrative burden

(€, 2010 prices)

Add. Administrative burden per vessel (€, 2010 prices)


All ships > 5000 GT 1.9m 160

4.2.1.3 MRV obligations: yearly emission reports

Annual emissions reporting obligations include retrieving relevant information from existing data, adjusting existing data and filling in forms and tables (including record keeping).

Monitoring is already technically possible since fuel consumption is systematically recorded by most commercial vessels. Furthermore, independent consultants contacted during the course of this research indicated that most shipping companies keep detailed track of their bunker consumption and have good quality data of deliveries to individual ships. Costs related to gathering documentation are therefore likely to be minor. The same consultants estimated the associated annual administrative costs load at between €7,500 and €15,00012 per operator. This includes data collection, consolidation and quality assurance. Since a larger average Compliance Entity than that identified in the present study seems to have been used for these estimates (the authors mention 20 to 50 vessels as typical fleet size elsewhere in their report), we have retained the lower bound of their range.

We have therefore assumed that ship operators will spend, on average, € 7,500 p.a. on these tasks. Large operators are however likely to spend substantially more. By converting this amount to man-days and averaging the resulting value on a per-vessel basis, an estimate range of between five and seven man-days p.a. has been obtained. The associated estimated administrative burdens that are presented in the table below take account of the fact that about 80% of the time allocated to retrieving information from existing data is assumed to belong to the business-as-usual scenario.

10

In this and subsequent tables, amounts may not add up due to the use of round f igures.

12

These estimates were prov ided by the consulted experts in dollar terms. They hav e been conv erted to euros by using a 0.75 EUR/USD conv ersion rate, which corresponds to the av erage 2010 rate. The same applies to other estimates presented in this annex that were originally

expressed in dollar terms. The latter are indicated by noting the corresponding dollar amounts in brackets.




Table 4.4: Annual administrative costs associated to the preparation of yearly emissions reports

Coverage Additional annual administrative burden

(€, 2010 prices) Add. Administrative burden per


All ships >400 GT 28m 1,500

All ships > 5000 GT 13m 1,100

4.2.1.4 Measurement accuracy

Administrative costs will strongly depend on the level of accuracy required. Sources from the maritime industry suggested that current systems commonly used on ships provide accuracy within ±5% error on each of the elements that are used to determine a vessel’s CO2 emissions (primarily fuel consumption and distance travelled). Consequently, an MRV system that accepts this margin of error would not substantially increase measurement costs for the industry. The same sources pointed out, however, that these measurement errors may be additional.

Industry sources reported that most vessels above 400 GT are likely to be equipped with fuel flow meters, although estimates of the expected accuracy level offered by these meters were not provided. Independent experts interviewed for this study stressed that reliability varies considerably across flow meters depending on their type and maintenance status (they rapidly deteriorate). The same experts indicated that periodic checks and maintenance would probably be required should onboard measurement equipment be used for regulatory MRV purposes. Maintenance would need to take place once every 18 to 36 months. This will be particularly important for traditional flow meters, as oil can clog them, reducing their accuracy. In this sense, consulted stakeholders indicated that choosing the >5000GT threshold would help ensure a more homogeneous level of measurement accuracy while reducing MRV costs.

An increasing number of owners and operators are installing more accurate types of meters, such as Coriolis fuel meters, which do not have moving parts. Maersk, for example, is reported to be installing these across its entire fleet. With these meters, ship owners and operators have a more accurate picture of fuel used: thus, the meters are valuable for containing fuel costs. These meters have a reported accuracy of +/- 0.5% or better.13 Such meters, however, have a cost of about €5000 and more, which apparently does not include installation costs.14 There is expected to be much less need for maintenance. Other owners and operators are installing torque meters on their engine shafts. These are also reported to have accuracies of +/- 0.5% or better. Here too, owners and operators are reported to be installing these devices to better control engine performance and fuel consumption. These meters are also costly, above 10,000 Euros.

In the same vein, should monitoring requirements be particularly stringent in terms of measurement accuracy, part of the fleet covered by the scheme may need to upgrade its measurement equipment and technology. Torque meters have been identified as a cost-efficient solution for reliably measuring fuel consumption.

Further resources would also be required if MRV requirements were to mandate detailed measurement and reporting of fuel density and emission factors of fuel; particularly if this involved more frequent meter proofing than usual. Information on fuel density is crucial if the amount of fuel consumed (in tonnes instead of volume units) needs to be known very accurately. During interviews, representatives from a large shipping company identified fuel density measurements as a potential source of very high administrative costs due to the variety of fuel types used by vessels (and the effects of factors like temperature on flow measurements). Economic actors in the aviation sector also described fuel density

13

Meters go mainstream, World Bunkering, 11 February 2010 14

Taken f rom http://www.instrumart.com/products/33948/krohne-optimass-7000-coriolis-mass-f low-meter

http://www.instrumart.com/products/33948/krohne-optimass-7000-coriolis-mass-flow-meter




information requirements in the EU ETS guidelines for aviation as being highly resource-consuming.

The estimates used in this study also assume that average values would be the preferred option for emission factors of fuel. Should ad hoc calculation of emission factors be mandatory, administrative costs might be substantially increased. Estimates for fixed installations suggest that having an emission factor established by an accredited laboratory can cost, on average, €2,000 to €3,000 p.a. per pollutant per vessel. Measurement equipment on board vessels has a lower level of complexity than that considered in these estimates so costs per calculation may also be lower.

For the purposes of this analysis, it is assumed that no substantial equipment upgrades or substantially increased maintenance or proofing would be required as a direct result of MRV obligations. The reason underpinning this assumption is that, with fuel prices projected to remain at high levels for a protracted period of time, industry actors are expected to keep increasing measurement accuracy of fuel consumption from their vessels.

4.2.1.5 Verification

Verification costs may vary substantially across Member States as well as across compliance entities. For companies, this is true both in absolute terms and as share of their total costs. This variability is acknowledged in the 2009 CE Delft study, which assesses existing practices in other sectors. Experts consulted for this study suggested that verification could be carried out at the fleet level, with the only vessel-specific task being the reporting of fuel quantities remaining on board at designated points in time (presumably year-end), to allow calculation of the annual consumption. Verification would be therefore be carried out onshore by checking consolidated bunker data and supporting documentation.

On-board verification of individual ships would only be required in cases where the company obtains fuel consumption data from on-board metering, in which case a sampling approach could be adopted based on ship type/class. The same experts suggested that, for a fleet size of 20 to 50 vessels, on-board verification would typically be conducted on three to eight vessels. They estimated total annual costs of verification as being within the €3,750-7,500 range p.a. per operator. The same experts also pointed out that these costs rise more slowly than fleet size, and that significant economies of scale could therefore be obtained by larger companies. An exception would however be obtaining certificates for each ship.

By comparison, in the case of the EU aviation ETS, estimates for verification costs for large operators range from €1000 p.a. to more than €26,000 p.a. One reason for this divergence in reported amounts is the difficulty to single out the cost of having CO2 emissions verified, as this may be done as part of the overall external auditing procedures. Another reason has to do with the fact that some companies have access to price deals when negotiating contracts with auditing companies. Experts consulted for this study suggested that some large companies tend to benefit from this kind of arrangements.

As in the case of the emission reports, the lower bound of the estimate provided by the experts consulted has been used, as this source appears to refer to a larger average company fleet size than that identified for the current study. It is assumed here that each vessel would need to spend, on average, €3,750 p.a. to have their emission reports verified. Compliance entities are also expected to spend time to prepare and accompany the work of the verifier. It must be noted, however, that operators with strong internal control mechanisms in place may need only a fraction of this. Based on these assumptions, each vessel is expected to spend two man-days p.a. in verification related tasks and about two man-hours p.a. to obtain the corresponding certificate.




Table 4.5: Verification of annual emissions reports (including preparation for verification and accompanying of verifier’s work)

Coverage Additional annual administrative

burden (€, 2010 prices) Add. Administrative burden per


All ships >400 GT 81m 4,400

All ships > 5000 GT 50m 4,400

Table 4.6: Verification certificates: annual administrative burden


burden (€, 2010 prices)

Additional annual administrative

burden per vessel (€, 2010 prices)


All ships > 5000 GT 1m

4.2.1.6 Submission of information

On average, each vessel is expected to require approximately one man-day p.a. to submit all the necessary information to the Competent Authority. The associated administrative burdens are presented below.

Table 4.7: Submission of information to competent authorities – yearly administrative burden


burden (€, 2010 prices) Add. Administrative burden per


All ships >400 GT 6m 330

All ships > 5000 GT 3.75m 330

4.2.1.7 Other administrative costs for industry actors

Requirements in national regulations may also add further charges on to these costs. For example, the UK national Competent Authority charges aviation operators for submitting their emissions reports. Each operator must open an account and pay a one-off fee. In the same vein, time may be required to adjust internal reporting format to that required by the Competent Authority. For fixed installations in the UK, estimates suggest that these adjustments alone can take one man-day p.a. for small companies and up to five or six man-days p.a. for large companies. The potential impact of Member State-level requirements has not been assessed in the calculations presented here due to the unavailability of comprehensive evidence.

In addition to manpower and measurement equipment maintenance and upgrading, other outlays may be required as a result of MRV requirements, particularly regarding IT solutions for monitoring and reporting. In the case of aviation, some companies reported purchases of ad hoc software, whereas others developed solutions internally. The largest carriers reported expenditure as high as € 1 million for IT developments. These costs, however, remain uncertain and have therefore not been modelled in this study.

Finally, the extent to which increases in inspection time incurred due to the introduction of policy action to control GHG emissions would represent substantial costs for industry actors will depend on both the magnitude of such increases and the level of disruption they cause to shipping companies’ planning. The calculations presented here assume minimal disruptions in efficient ports, but it must be noted that complications may arise for ships calling at smaller or less efficient regional ports.




4.2.2 Costs for public authorities

4.2.2.1 Compliance-related administrative burdens

For all of the options under consideration, administrative set up costs are to be expected for public authorities. Becoming familiar with information obligations arising from new policy measures requires significant resources. Based on sources consulted for this study, we have retained 50 man-days per Competent Authority (either in Member States or EU-level) p.a. as working assumption for this set of tasks.

In the case of the inclusion of aviation in the EU ETS, identifying, tracking down and deciding which companies fall within the scheme has been identified by officials from public authorities in Member States as a large cost item, particularly during the initial stages of the scheme’s functioning. Tasks such as coordination and administration, including answering questions, and liaising with other agents are also likely to entail significant administrative costs for public authorities. We have grouped these tasks to produce estimates (informing compliance entities). Depending on the scenario, we have assumed between 420 and 680 man-days per Competent Authority p.a. for Member State authorities and between 8,500 and 13,800 man-days p.a. if this were to be done at EU level (thus accounting for efficiency gains from centralisation).

Establishing an industry-managed compensation fund may shift some of these tasks from public authorities to industry actors. Accurately determining the extent to which this will occur will only be possible upon agreement on the final design elements for each specific option.

Administrative burdens are also expected to arise from the design and production of information material for compliance entities. This is assumed to be done at EU level, at a cost of 200 man-days p.a. per Competent Authority. In addition, public authorities would be responsible for validating companies’ MRV procedures. In the case of the industry-managed compensation fund, validation could take place at the fund level. The actual costs in this regard will depend on the number of compliance entities under the jurisdiction of a given Member State, but also on the Member State’s experience in the management of similar procedures and on its initial resource endowments.

Validation costs for public authorities will have two main components: validation of monitoring plan (typically once per compliance period) and validation of annual emission reports. For Member State authorities, we assume an average of between 210 and 340 man-days p.a. per Competent Authority depending on the scenario. For an EU Competent Authority, between 2850 and 4600 man-days p.a. Delivering the corresponding certificates is assumed to require, on average, between 100 and 170 man-days per Member State Competent Authority p.a. depending on the scenario, and between 1400 and 2300 man-days p.a. if done at EU-level.

On the basis of these data and assumptions, the tables below present the estimated set-up costs for public authorities (Ranges provided for cost parameters dependent on the number of vessels affected by the scheme; i.e. on the scenario).

Table 4.8: Estimated average annual compliance-related administrative burdens for public authorities that are common to all policy options

If carried out by MS competent authorities If carried out by EU

Competent Authority

Action Additional annual

administrative burden (€, 2010 prices) per CA

Total additional

annual administrative burden (€, 2010

prices)

Additional annual

administrative burden (€, 2010

prices)

Familiarising with information obligation

16k 450k 3k

Information material Done at EU level 210k





Competent Authority

Informing compliance entities

14k - 22k 380k – 610k 460k - 740k

Approval of plans and reports

70k – 115k 1.8m – 3m 1.5m – 2.5m

Certificate issuance 33k – 56k 900k – 1.5k 760k – 1.2m

4.2.2.2 Enforcement-related administrative burdens

Inspections are likely to be required under all of the policy options to ensure compliance. It is assumed here that these inspections would be carried out as part of existing inspection regimes and would therefore not generate additional inspections but just increases in the overall inspection time per vessel. It is also assumed that inspections would replicate current best practices, which include the inspection of relevant documentation but also checks on the qualifications and ability of relevant crew members to comply with information obligations.

Estimates provided by port authorities on inspections of incoming vessels suggest that average inspection time increases resulting from requirements under policy options would be approximately 60 minutes per vessel if they concerned only documentation. An additional 30 to 60 minutes would be required for physical inspections but these have not been considered for calculation purposes. The same authorities pointed out that approximately one in eight seagoing ships calling at their ports were inspected, and that the assessment protocol to decide which vessels to inspect would not need to be substantially modified. The use of remote communication systems between vessels planning to call at an EU port and competent port authorities was recommended to minimise time requirements, as these could help ensure that any single vessel is not inspected more often than necessary. Recent studies suggest that this technology is already widely available at present. Port authorities also suggested that linking inspections to those carried out under the MARPOL Annex 1 regime could help minimise administrative costs for both public authorities and industry actors.

Whereas this increase in average inspection times can be expected to be easily absorbed by large ports with sophisticated information infrastructure (advanced and well-integrated port community systems), it may be problematic for their smaller, less well-endowed counterparts. Increased inspection times may lead to disruptions in the latter case.

For the purpose of these calculations, it is assumed that, in both scenarios, one-eighth of the vessels would undergo one inspection per year. The estimated average duration of this inspection (in addition to existing inspections) would be one hour. These are rather conservative assumptions and may need to be reviewed if more (or fewer) inspections are foreseen, or if a large number of regular physical inspections were deemed necessary. 5 man-days per 10-year period have likewise been assumed as being necessary for port inspection services to become acquainted with information obligations.

The tables below present the estimates for inspection-related administrative costs.

Table 4.9: Yearly administrative burdens for port inspections: familiarisation with the information obligation

Action Entity

Additional annual administrative burden

(€, 2010 prices) per CA

Add. Administrative burden (€, 2010

prices)

All ships >400 GT Port inspection services 170 4,500

All ships >5000 GT Port inspection services 170 4,500




Table 4.10: Total yearly administrative burdens for port inspections

Action Entity

Additional annual administrative burden

(€, 2010 prices) per CA

Add. Administrative (€, 2010 prices)

All ships >400 GT Port inspection services 3,600 100,000

All ships >5000 GT Port inspection services 2,200 60,000

4.2.3 Conclusions

Although most results are presented on a per ship basis, estimates suggest that MRV-related administrative burdens tend to be proportionately much higher for smaller compliance entities than for their larger counterparts.15 It would therefore be worthwhile to investigate simplified procedures for MRV and reporting for small owners and owners affected by the requirements on a limited basis (e.g. owners with only one ship call per year or less at EU ports). Such simplified procedures could be analogous to the provisions for small emitters under the aviation ETS.

For general MRV requirements, the maritime transport industry as a whole would incur estimated annual administrative costs worth approximately €77m if only vessels above 5000 GT were to be affected by the policy option. This figure would increase by about 75% (to €135m) if the 400 GT threshold were used. On a per vessel basis, this amounts to between €6,800 and €7,400 p.a. Nonetheless, if the maritime industry could report on the basis of larger-sized entities, then administrative burdens may be lowered.

With regard to the costs to public authorities, these will vary according to the burden sharing arrangements between EU and Member State authorities. In all events, set-up arrangements, contacts with compliance entities and the review/approval of monitoring plans and review/approval of emissions reports are likely to be among the major cost parameters. In contrast, it appears that inspection and enforcement costs will be moderate. This is because these activities are expected to be carried out together with existing ship inspections; as a result, the additional time and costs will be relatively contained. Estimates suggest a relatively low elasticity of the administrative costs for public authorities with regard to the number of compliance entities affected, for some of the cost parameters as some of these would be fixed costs (IT development, familiarisation with regulations, information material, etc.). Summary figures are presented in the table below:

15

One of the experts consulted f or this study prov ided usef ul estimates in this regard based on total administrativ e costs incurred by a sample of airlines as a result of the inclusion of the av iation sector in the EU ETS. According to this expert, total annual administrativ e costs per tonne of

CO2 reported amounted to EUR 0.532 f or smaller airlines (i.e. those that emit below 10 kilotonnes of CO2 per y ear) compared t o EUR 0.006 f or

the larger ones. Similarly , a public of f icial from an EU Member State suggested that, at current carbon prices under the EU ETS, administrativ e costs f or v ery small emitters (who theref ore purchased f ew allowances) may ev en exceed compliance costs (i.e. the total amount paid f or carbon

allowances) in a number of cases.




Table 4.11: Average yearly administrative burdens for competent authorities (common to all options)


Competent Authority

Additional annual administrative burden (€,

2010 prices) per CA

Total additional annual administrative


Additional annual administrative


All ships >400 GT

Compliance-related 210,000 5.8m 4.65m

Enforcement-related 3,600 100,000 Done at national level

All ships >5000 GT

Compliance-related 135,000 3.8m 3m

Enforcement-related 2,400 64,000 Done at national level

4.3 Option-specific costs

This section discusses in detail, administrative cost estimates that are specific to policy options 1 and 3b (respectively, emission trading and industry-managed compensation fund). As in the previous section, potential administrative costs for, respectively, industry actors and public authorities are discussed separately. Moreover, it should be underlined that the administrative costs developed here for the options 1 and 3b are in addition to the MRV costs outlined in Part 1.

4.3.1 Option 1: Emission-trading scheme

Recent experience with the inclusion of the aviation sector in the EU ETS offers a good benchmark to estimate potential administrative costs from option 1. These costs will, however, depend on the final design of the option. Estimates presented here assume therefore a design partly replicating that chosen for aviation. From an administrative cost perspective, this firstly means a lack of obligation for shipping companies to apply for emission permits (unlike fixed installations under the EU ETS). It also means that shipping companies would be able to use out-of-sector allowances but other sectors would not be eligible to purchase maritime sector allowances. This is likely to minimise the administrative costs of credit recognition mechanisms. Finally, it is assumed that the scheme would build as much as possible on existing institutional and technical infrastructure.

4.3.1.1 Costs for industry actors

4.3.1.1.1 Administrative burdens common to both full-auctioning and free allocation

As far as industry actors are concerned, research conducted under this study suggests that the main administrative cost parameters specifically related to this option would come from becoming acquainted with the information obligation and purchasing and surrendering allowances. Estimates provided in this section draw on data from the aviation sector as well as fixed installations.

Each vessel is expected to require ten man-days per ten-year period to become acquainted with information obligations. Each vessel will also require one man-day p.a. to purchase allowances and one man-day p.a. to surrender allowances. Corresponding estimates are presented in the table below:




Table 4.12: Estimates of administrative burdens of an emissions trading scheme for industry actors

Coverage Additional annual administrative burden

per vessel (€, 2010 prices) Total add. administrative burden (€, 2010 prices)

All ships >400 GT 1,000 18m

All ships > 5000 GT 1,000 11m

4.3.1.1.2 Administrative burdens specific to the free allocation sub-option (benchmarking)

If the free allocation of allowances were foreseen under this option, compliance entities would face additional administrative costs stemming from reporting. This would probably need to be done on the basis of fuel consumption. There may need to be a benchmarking period in order to assess annual fuel consumption/emission levels before the scheme enters into force. Estimates for administrative burdens associated with the benchmarking process have been produced by using assumptions that are analogous to those applied elsewhere in this study for the calculation of administrative burdens from MRV procedures. Corresponding estimates are summarised in the table below:

Table 4.13: Estimates of the administrative burdens of an emissions trading scheme on industry actors specific to the free allocation of allowances

Coverage

Additional annual

administrative burden per vessel (€, 2010 prices)

Total add.

administrative burden (€, 2010 prices)

All ships >400 GT

Familiarizing with the information obligation

700 13m

Information material 200 3.7m

Retrieving relevant information from existing data

20 366.000

Adjusting existing data 20 366.000

Filling in forms and tables, including recordkeeping

33 €610,000

Verification 450 8.3m

Submitting information to Compliance Entity

33 €610,000

TOTAL 1450 26.9m

All ships > 5000 GT


700 8m

Information material 170 1.9m

Retrieving relevant information from existing data

13 150,000

Adjusting existing data 13 150,000

Filling in forms and tables, including recordkeeping

33 380,000

Verification 450 5.1m

Submitting information to Compliance Entity

33 380,000

TOTAL 1,450 16.5m




4.3.1.2 Costs for public authorities

4.3.1.2.1 Administrative burdens common to both full-auctioning and free allocation

Regardless of whether this option is administered by Member States of EU authorities, the main administrative burdens are expected to stem from becoming acquainted with the information obligation (5 man-days per entity per period), auctioning allowances (60 man-days per entity p.a. + €100,000 in outsourcing costs) and overseeing their surrender (10 man-days per entity p.a.), managing auctioning proceeds (30 man-days per entity p.a.) and delivering certificates (0.5 man-days per entity p.a.). Estimates are presented in the table below. They apply to both the >400 and the >5000 scenarios.

Table 4.14: Estimates of the administrative burdens of an emissions trading scheme on public authorities common to full auctioning and free allowances

If carried out by MS competent

authorities

If carried out by EU

Competent Authority

Additional annual

administrative burden (€, 2010 prices) per CA

Total additional

annual administrative burden (€, 2010

prices)

Additional annual

administrative burden (€, 2010

prices)

Auctioning allowances 60,000 1.6m 1m

Oversight of the surrendering of allowances

1,700 45,000 1,700

Manage proceeds 5,000 135,000 5,000

Delivering certificates 170 4,500 170

4.3.1.2.2 Administrative burdens specific to the free allocation sub-option (benchmarking)

As for industry actors, if the free allocation of allowances was foreseen under this option, public authorities would face additional administrative burdens due to additional reporting and oversight. These tasks are assumed to be carried out by the European Commission. Estimates for benchmarking-related administrative burdens are presented in the table below. They correspond to annual administrative burdens and are based on assumptions that are analogous to those used to calculate general MRV administrative burdens.

Table 4.15: Estimates of the administrative burdens of an emissions trading scheme on public authorities specific to free allocation of allowances

Action

> 400GT scenario > 5000GT scenario

EU Competent Authority EU Competent Authority


2010 prices)

Additional annual administrative burden (€, 2010

prices)

Familiarising with information

obligation

1.700 1.700

Information material 6.600 6.600

Informing compliance entities 458,000 280,000

Approval of plans and

reports/verification

200,000 145,000




Action

> 400GT scenario > 5000GT scenario

EU Competent Authority EU Competent Authority


2010 prices)

Additional annual administrative burden (€, 2010

prices)

Issuance of allowances 76,000 48,000

TOTAL 745,000 485,000

4.3.1.3 Conclusions

For industry actors, annual administrative burdens under this policy option are expected to range between approximately €35m and €55m, depending on design elements and scenario chosen. These burdens would mainly stem from the need to become acquainted with rules and processes and use the market-based mechanism. If grouping were allowed, administrative costs under this option could be lowered.

As for public authorities they could benefit from past experiences with the EU ETS and this would limit the additional administrative burdens they would incur. These burdens are estimated at around 2m between €1.5m and €2m p.a., depending on design elements and scenario, if Member State authorities were to be designated as competent authorities. These figures would be slightly lower (between €1m and €1.5m) if the scheme were to be administered at EU level. Burdens would to some likewise depend on the threshold used for the inclusion of vessels in the scheme as this would affect the number of compliance entities.

4.3.2 Option 3b: Industry-managed compensation fund

Estimates for this option are based on data and information from Norway’s Business Sector NOx-Fund, the purpose and functioning of which are discussed in section 3 of the main report. Although this is by all means a useful reference, it is important to note that resulting estimates must be considered as indicative as no arrangement that can be directly assimilated to the industry-managed compensation fund envisioned under this option presently exists. The estimates also incorporate inputs from Commission officials.

4.3.2.1 Costs for industry actors

The main administrative cost parameters identified for this option are: familiarisation with the information obligations (10 man-days per vessel per period), setting up (€70 per vessel per period + 9 man-hours per vessel p.a.) and running the fund (9 man-hours per vessel p.a. + €375 per vessel p.a. in outsourcing costs) and paying the membership contribution (one hour per vessel p.a.). Corresponding estimates are presented in the table below.




Table 4.16: Estimates of the administrative burdens of the industry-managed compensation fund on industry actors


burden per vessel (€, 2010 prices)

Total add. administrative burden

(€, 2010 prices)

All ships >400 GT

Familiarising with the information obligation

330 6m

Paying contribution 330 6m

Setting up the fund (central) 70 1.3m

Setting up the fund (work by affiliated members)

37,5 690,000

Fund administration 750 14m

TOTAL 1,900 28m

All ships > 5000 GT


330 3.8m

Paying contribution 330 3.8m

Setting up the fund (central) 70 800,000

Setting up the fund (work by affiliated members)

37,5 425,000

Fund administration 750 8.5m

TOTAL 1,900 17.3m

Some of the administrative costs for industry actors that have been discussed in the context of option 1 are likely to apply to option 3b. This is the case of efforts required to carry out benchmarking work, which would probably be required under the target-based sub-option to define and allocate emission reduction targets. Estimates for these can be consulted in previous sections of this annex.

Administrative costs may also be generated if emission reduction plans had to be prepared by shipping companies/vessels that are members to the fund, and then validated by the fund administrator. These costs would depend on the requirements set by the fund administrator, as well as on the extent to which there can be a trade-off between these requirements and some of the MRV costs that have been described as potentially applying to all options in part 2 of this annex.

4.3.2.2 Costs for public authorities

As indicated in the main report, administrative burdens for public authorities specifically stemming from this option are likely to be relatively low, as the fund would be managed by the industry.

4.3.2.3 Conclusions

For industry actors, annual administrative costs burdens under this policy option (are expected to range between approximately between €21m and € 34m, depending on the threshold chosen for the inclusion of vessels in the scheme. These burdens would mainly stem from the need to become acquainted with the different information obligations as well as with administering and managing the fund. Additional administrative burdens may be




expected in the case of the target-based compensation fund, but this sub-option may prove more effective in terms of emission reduction.

Public authorities are not expected to incur any major additional administrative burden specifically stemming from this option but only those already identified as applying to all policy options under consideration.




5 Appendix 5 – Avoidance

5.1 Introduction

Legislation implies that a certain amount of cost will be imposed on the industry; whether through paying taxes, purchasing credits, paying Compensation Fund membership fees or investing in technology. Avoidance of the costs imposed by legislation is problematic as it may reduce the environmental effectiveness of an EU measure.

The first question, therefore, is whether implementation of the policy leads to substantial increases in cost for the regulated entities, either directly or indirectly. The impact of legislation depends on whether the costs incurred are significant, whether they can be passed on through the prices of products and services, the extent to which the shipping sector could reduce its costs by reducing emissions, compensatory mechanisms, offsetting cost increases by fuel savings and the feasible scope for avoidance of the scheme.

Avoidance is a rather nebulous term that includes several mechanisms by which avoidance of the scheme could be achieved as follows:

1. Alteration of routes either through addition of port calls, ship-to-ship transfers or

modal shift;

2. Change in composition of the EU shipping fleet by a transfer of less efficient ships

on routes non related to the EU; and

3. Relocation of manufacturing industry at the border of the EU which leads to a

decrease of the trade activity of the EU and the increase of importation of high value goods.

These mechanisms are discussed in turn. The potential for evasion of the scheme by fraud is not considered to be part of leakage; it is assessed in the design of each policy option.

In all cases, the incentives for evasion are highest when:

The voyage distance is long;

The carbon price is high; and

The benefits (e.g. fuel cost savings) from participating the measure are low

The freight rates are low.

5.2 Alteration of routes

The scope of emissions included would be determined on the basis of port calls. For inbound voyages to an EU port, the starting point for the emissions calculation would be the last port of call outside the EU and the end point would be the first port of call within the EU. For outbound voyages leaving the EU, the starting point for the emissions calculation would be the port or departure within the EU and the end point would be the first port of call outside the EU. Additionally, the emission from all journeys between two EU ports (i.e. intra-EU voyages) would also be included in the scope of coverage.

With respect to the potential for avoiding the scheme, several options are available to reduce the proportion of a given voyage that would be subject to policy action:

a. Addition of port calls or ship-to-ship transfers: the addition of a port call to the

route or a ship-to-ship transfer for the sole purpose of minimizing the distance from the last port of call before arriving at an in-scope port or minimizing the distance to the next port of call after leaving an in-scope port and therefore reducing the emissions covered by an EU measure. Alternatively, the cargo could then be transported by smaller vessels to EU ports. This would reduce the




emissions covered by an EU measure. The total emissions could increase due to the lower efficiency of smaller ships. Theoretically, a similar approach could be applied for cargo exported.

b. Modal shift: the addition of a call at an out-scope port situated close to an in-

scope port, and discharge of cargo there. The cargo could then be transported by another mode of transport. The whole journey would then fall outside of the scope of the policy action. Theoretically, a similar approach could be applied for cargo exported.

The impact of these mechanisms were assessed within the TIMES model. The cost of additional port calls incurs additional costs due to operational costs over the additional time (e.g. employment), fuel for the extra distance covered, and the port dues. The TIMES model analyses the potential for route shifting at a high level, and may not capture the effects on very specific routes that are vulnerable to evasion. In order to evaluate the risk on specific regions, a number of case studies were also carried out.

A summary of the results is shown below.

Table 5.1: Assessment of CO2 emissions (Mt CO2) taking into account route shifting and modal shift in 2030

Scenario No route shifting

or modal shift

With route shifting &

modal shift

Baseline Total net emissions 223.41 0.00

Closed ETS free allocation

Emissions due to route shifting 0.00 18.47

Emissions due to modal shift 0.00 0.00

Emissions covered by policy 175.74 169.10

Total net emissions 175.74 187.57

Open ETS free allocation




Out-of-sector permit purchases 10.99 0.00


Open ETS full auctioning




Out-of-sector permit purchases 11.03 0.00


Emission tax low





Emission tax high





The analysis in this section assesses the risk of avoidance of the regulation by modal shift to other transport modes. However, as the economic impact assessment finds, it is possible that the costs of maritime transport could be reduced, if fuel savings are greater than the investment required for increased energy efficiency. Therefore a shift from land-based modes to sea transportation could also be possible. An assessment of this shift is outside the scope of the current study.




5.3 Change in composition of EU shipping fleet

Changing the ships used to perform maritime transport services in the EU as a response to the legislation is possible. There are several ways in which this could occur:

1. Switching to ship types outside of the scope of the legislation;

2. Switching to ship sizes outside of the scope of the legislation; and

3. Substituting inefficient ships that operate within the scope of the legislation for efficient ships outside of the scope of the legislation.

5.3.1 Switching ship types

In general the potential for avoidance by switching ship types is considered to be low, as there is limited overlap in function between the different ship types. The ETS, tax and compensation fund (Options 1-3) include all ship types except offshore vessels, service vessels, yachts or fishing vessels. These are highly specialised vessels that cannot be expected to substitute for other ship types. The mandatory emission reduction applies to ships covered by the EEDI formula, which are essentially all cargo ships. There is little scope for using non-cargo ships to carry out the functions of cargo ships (IMO, 2009).

5.3.2 Switching ship sizes

The proposed policy options apply to ships larger than 5,000 GT. At the moment, AIS data suggests that this covers 91% of emissions, but only 57% of ships. The large number of ships excluded shows that there are a large number of potential ships that exist below the size threshold, and potentially they could be used to substitute for larger ships.

Figure 5.1 shows the number and percentage of ships of each type that are smaller than 5,000 GT. The potential for avoidance is considered to be relatively high if there are a large number of ships smaller than 5,000 GT (as this indicates an ample supply of substitutes) and if the percentage of ships is high.

Figure 5.1: Ships of each type <5,000 GT

Source: AIS data provided by IHS

According to this analysis, the potential for switching ship sizes in most sectors is low. The sectors most at risk are general cargo ships, ferries, other tankers and LPG ships. Since

0%

10%

20%

30%

40%

50%

60%

70%

80%

0

500

1000

1500

2000

2500

3000

3500

Number Percentage




other tankers and LPG ships account respectively for only 0.2% and 1.3% of total EU-27 emissions, the impacts of avoidance are expected to be small in these sectors and are not considered further. General cargo ships and ferries are discussed below.

General cargo ships are the most common vessel type in the world and one of the smallest cargo vessels, with an average size of around 3,500 GT (IHS, 2011). The general cargo fleet accounts for 8% of total EU-27 emissions. Around 4% of total shipping emissions in the EU-27 are from ships larger than 5,000 GT, so this is the potential CO2 that could become out of scope through switching to smaller general cargo ships. Some intra-EU voyages carried out by large bulkers or container ships could be also carried out by small general cargo ships. Intra-EU voyages by large bulkers and containers account respectively for 6% and 17% of total EU-27 emissions. The option to use a smaller ship is already available to these ships and since they have not chosen to do so it can be assumed that the cargo sizes are too large for it to be economical; however, avoiding a carbon charge could make it more attractive to use several smaller ships in some cases. There are several factors that could limit avoidance using smaller ships including: the capacity of the fleet of smaller ships; the additional cost of fuel (smaller ships could use more fuel per t-km) and losing out on other economies of scale (e.g. staff).

Ferries below 10,000 GT are dominated by passenger-only ships (IHS, 2011). The passenger segment competes with other modes, which are also subject to carbon charges of various types (aviation ETS, road vehicle CO2 standards etc) so cost pass-through is considered to be possible (IHS, 2011). The size of the ferry is determined by market demand; smaller ships would suggest that extra services would be needed. Already ferries account for 50% of port calls in the EEA (IHS, 2011) and the scope to increase this is not certain. In total, ferries contribute 11% to total EU-27 emissions, of which 9% is from vessels larger than 5,000 GT. Other types of ship cannot be substituted by ferries so this is considered to be the maximum potential avoidance.

On the basis of this analysis, it appears that avoidance of ship size scope will not be significant for most ship types. The sectors at highest risk are general cargo ships and ferries. Direct avoidance in the general cargo sector could lead to an additional 4% of total EU-27 emissions falling outside of the scope of the legislation; avoidance in the ferry sector could increase the emissions outside of the scope by up to 9% in addition (13% in total). In practice, the avoidance will be limited initially by the capacity of existing ships that are smaller than 5,000 GT, but in future the legislation may influence decisions over the size of ships that are purchased. Indirect avoidance from switching cargo from large bulkers and containers to several smaller feeder vessels is also possible if carbon charges and administrative costs are significant.

The size limit of 5,000 GT was recommended because of the significant savings in administrative burden. If avoidance becomes a concern after implementation, the scope of the legislation could be extended to the second recommended size threshold of 400 GT, which would eliminate most of this type of avoidance; there are almost no general cargo ships below this size, and the CO2 emissions from ferries below 400 GT are only 0.3% of total EU-27 emissions. However doing so could increase the administrative burden of the scheme with little environmental benefit.

5.3.3 Substituting inefficient ships that operate within the scope with efficient

ships

Ship owners may avoid some of the carbon charge by taking inefficient ships outside the scope of the legislation and redeploying more efficient ships within the scope of the legislation. Thus, the same transport work can be carried out but with fewer emissions subject to a carbon charge. The ship owner does not incur any costs to apply improvements to his fleet. Since no improvements are made, avoidance is said to occur because CO2 emissions are in effect moved out of the scope of the legislation and not reduced overall.




The potential for this type of avoidance depends on the availability of more efficient ships outside the scope of the legislation that are able to substitute for inefficient ships within the scope. Vessel age is taken as a proxy for efficiency, as newer ships tend to be more efficient compared to older ships, as described in the IMO 2009 GHG Study.

Figure 5.2: Baseline improvements in fleet efficiency

Source: IMO 2009 GHG study

In addition, newer ships have more options for energy efficiency because their remaining commercial lifetimes are longer (i.e. there is a lower chance that the lifetime of the new technology will exceed the remaining lifetime of the ship). Figure 5.3 shows the average age of ships in the European Economic Area (EEA) (including Russia) compared to the world fleet. The EEA fleet of bulkers and LNG carriers is older compared to the world average, whereas all other ship types are younger; therefore the potential for changing the composition of the EU shipping fleet is considered to be greatest for bulkers and LNG ships. These two ship types account for 15% of total shipping emissions in Europe.




Figure 5.3: Average age of ships in EEA and world fleet

Source: IHS (2011)

The LNG fleet accounts for only 2% of shipping emissions in Europe, so the effect of avoidance in this market is considered to be low. The bulker fleet accounts for 12% of shipping emissions in Europe so the impact of avoidance is higher, but complete avoidance is not possible, as ships are replaced (rather than being removed). If, say, CO2 emissions could be reduced by 20% on average for the bulker and LNG fleets by substituting inefficient ships with more efficient ships, total avoidance would be 3% of shipping emissions in Europe.

Overall, the impact of avoidance by redeploying ships is considered to be small.

5.4 Relocation of manufacturing industry

Relocation of manufacturing occurs when production facilities move, or those in certain locations gain greater market share. The second mechanism is considered more feasible due to the substantial cost of shutting down and rebuilding facilities. These impacts are

0 5 10 15 20 25 30

Ferry

General cargo

RoRo

Oil tanker

Cruise

LPG

Other dry

Chemical tanker

Other tanker

Yacht

Container

Vehicle

LNG

Bulker

Average ship age

EEA fleet World fleet




expected to be strongest for goods with a low value per volume or weight, and thus whose shipping costs are proportionately high compared to product value (e.g. iron ore). In these cases, firms which are located within the EU or closer to the EU could be expected to gain greater market share for imported goods, as a shorter sea route will reduce the impact of a shipping carbon charge. For European industries, which tend to export high value goods, the impact of legislation on the cost of exports is much less important (see assessment of economic impacts). For instance, commodities such as motor vehicles, chemicals or office and IT equipment are expected to only suffer marginal impacts under the policy options.

From an environmental perspective, relocation of manufacturing industry is a problem when it leads to the same goods being manufactured with higher emissions (leading to net increases).

Relocation of manufacturing industry to reduce shipping carbon charges could lead to decreasing emissions overall, if the same goods are produced with the same CO2 intensity, but shipped from a closer location (to reduce the distance subject to a CO2 charge). Of course, many other factors are important, including infrastructure, the availability of raw materials, access to skilled labour, labour costs, political stability etc. If production is relocated inside of Europe to avoid shipping costs, and transported by road instead, this could increase emissions from freight on a t-km basis, but the distance is likely to be reduced. Furthermore, technologies within Europe are generally more efficient so that the same goods are produced with lower emissions, leading to lower emissions overall (DEHST, 2008). The economic impact assessment finds that in most cases, the impact on costs of goods in small.

Overall, relocation of manufacturing industry in response to a CO2 legislation for European shipping is not considered to have a significant impact on global CO2 emissions.

5.5 Summary and conclusions

The impacts of avoidance are summarised in Table 5.2. The risk factors are given as low, medium or high. A rating of “low” indicates that the expected avoidance would lead to less than 5% of EU-27 emissions being avoided. A rating of “medium” indicates that between 5% and 15% of EU-27 emissions could be avoided. A rating of “high” indicates that more than 15% of EU-27 emissions could be avoided.

Table 5.2: Impacts of avoidance

Avoidance type Mechanism Risk Mitigation

Change in

composition of the EU shipping fleet

Switching ship types Low N/A

Switching ship size – based

on a 5,000 GT threshold

Medium

Up to 4% additional CO2 from general cargo ships and up to 9% from ferries if this proves

cheaper. Avoidance will be limited initially by the capacity of existing ships, but may

encourage purchase of smaller ships.

The scope of the legislation could be extended to

400 GT, which would eliminate most of this type

of avoidance.

Substituting inefficient ships (from inside scope) for

more efficient ships (from outside scope)

Low N/A




Relocation of production.

When production facilities move, or those in certain locations gain greater

market share.

Low N/A

Notes : “Low” indicates that the expected avoidance would lead to less than 5% of EU -27 emissions being avoided; “Medium” indicates that between 5% and 15% of EU-27 emissions could be avoided; “High” indicates that more than 15% of EU-27 emissions could be avoided.




6 Appendix 6– Analysis of fuel tax policy option

6.1 Introduction

In September 2011, the European Commission contracted a team led by AEA Technology to provide technical support in carrying out an impact assessment of a proposal to address maritime GHG emissions.

During the original support contract, a tax on fuel sales was identified early on as a possible policy option for controlling maritime sector GHG emissions. There is significant interest in understanding what the possible environmental, social and economic impacts of a tax on European sales of maritime sector fuels would be if it were to be introduced as a policy measure within the EU27.

This annex analyses the environmental, economic and social impacts of a tax on fuel sales within the EU27. The approach used is consistent with that applied for the other policy options assessed as part of the main report.

6.2 Design of the policy option

The fuel tax policy option was modelled using the TIMES International Shipping Model that AEA Technology developed as part of the support contract.

A tax on fuel sales would only be applied to maritime fuels sold in EU Member States (or within EU waters). This means that vessel operators that have the opportunity to purchase fuels outside of the European Union could avoid a fuel tax by bunkering elsewhere. Large ships in particular are able to go for long periods (up to several months) between refuelling stops. In practice then, it is likely that a fuel tax could only apply to ships that operate in a limited area within Europe, where opportunities for bunkering outside the scheme are few. Thus, in the TIMES International Shipping Model only vessels that operate on fixed, intra-EU journeys are subject to the fuel tax, as all other vessels would be able to purchase untaxed fuel outside of the European Union.

The level of the tax was set in line with the European Commission’s proposal of 13 April 2011 to revise the Energy Taxation Directive (ETD)16. The new proposal sets the minimum rate for taxation of the CO2 component at €20 per ton of CO2 for all uses of the energy products. The rate of the energy component for motor fuel is set at €9.6 per GJ, to be reached gradually by 2018. This equates to a tax of €145.9 for bunker fuels (HFO and MDO)

and €189.2 for LNG per tCO2.

Although the taxation of maritime fuels would not be considered as part of the ETD itself, a tax on bunker fuels should be modelled on these rates in order to provide for equal treatment of transport modes. Therefore, the tax rate on bunker fuels and LNG was set at the level for motor fuels, including the tax for energy content and CO2. Biofuels are assumed to be exempt from taxation, as they would not otherwise be an attractive abatement option17.

The impact assessment of the proposal to revise the ETD considers the issues of removing the exemption for fuel used in shipping (Article 14(1), (b) and (c) of the ETD). However, this issue was however covered in the impact assessment as the current treatment is based on international conventions and agreements and extends to customs duty and VAT as well. The impact assessment study also notes that applying excise duty on fuel supplied within the EU would lead to taxation of fuel consumed outside the EU and put fuel suppliers within the

16

Note that giv en the time and budget constraints on this study , it was possible to carry out an assessment of the env ironmental, social and

economic impacts of a tax on f uel f or a single f uel tax scenario. 17

Although the treatment of biof uels under the ETD assumes that they will be taxed at a rate equal to the f ossil f uel they replace, a tax on maritime

f uel would not be considered under the ETD; theref ore the assumptions are based on, but not restricted by , the prov isions f or motor f uels.




EU at a competitive disadvantage (particularly in the case of international shipping). Hence, it was concluded that excise legislation would currently not seem to be the most appropriate way to address emissions in these sectors.

6.3 Administrative burdens

This section assesses the potential administrative burden stemming from policy option 2, a levy on bunker fuel sales. It is assumed that the levy would be paid by bunker fuel suppliers.

This section presents expected administrative burdens for public authorities (both at EU and Member State level) and industry actors (owners/operators/charterers of ships) separately. As in administrative burden assessments elsewhere in this study, estimates correspond to average administrative burdens per vessel but significant variations may be expected. Enforcement-related administrative burdens are assumed to be equivalent to those presented for the rest of policy options. They are therefore not discussed here.

All calculations have been performed according to the methodology of the Commission’s standard cost model. Amounts are expressed as net additional administrative costs (i.e. on top of business-as-usual costs) on an annualised basis.

The period used for amortisation purposes as well as tariff rates are also consistent with those used elsewhere in this study. The same applies to the overarching assumptions outlined in the Structure and Methodology section of the administrative burden assessment.

Estimates for the main administrative cost parameters as well as underlying assumptions are discussed next. As can be seen, administrative burdens under this option are expected to be relatively low.

6.3.1 Costs for industry actors

It is assumed here that a total 1,200 bunker fuel suppliers would be subject to this policy option: one supplier in each of the 1,200 commercial ports that have been accounted for. This is a conservative estimate as the actual number of suppliers affected by the option may be larger.

Administrative MRV-related costs specifically stemming from this option are likely to come primarily from the additional manpower and resources required to get acquainted with the information obligations associated to the application of the levy. It is assumed that each compliance entity will need an average ten man-days per ten-year period (about one man-day p.a.) for this. An additional man-day p.a. is assumed to be required for administrative work associated with the payment of the levy itself.

Table 6.1: Total administrative burden for industry actors under option 2

Additional administrative burden

(EUR, 2010 prices)18

Add. Administrative burden per compliance entity (EUR, 2010

prices)

800,000 660

6.3.2 Costs for public authorities

The impacts on public authorities remain very limited for this policy option as for monitoring and reporting of emission, internalization of costs of emissions and enforcement, existing structure could in principle be used. The total administrative burden for public authorities for are estimated around €100,000 per year.




6.4 Environmental impacts

6.4.1 Identification of impacts

This section considers the environmental impacts as follows:

A quantitative assessment to the extent possible of major impacts, including:

Impacts on global warming;

Impacts of air pollutants on human health and crops;

Changes to the use of energy in maritime transport;

Changes in resource consumption;

A qualitative assessment is provided of other more minor impacts, including:

Impacts of air pollutants on ecosystems and materials;

Land use change; and

Waste production.

6.4.2 Impacts on emissions

6.4.2.1 Changes in emissions of CO2

Under the baseline scenario CO2 emissions are projected to increase from 180 MtCO2 in 2010 to 223 MtCO2 in 2030. With the fuel tax policy option, CO2 emissions from the maritime sector increase to 216 MtCO2 in 2030, with cumulative emission reductions of 40 MtCO2 over the period from 2010-2030.

Table 6.2: Maritime sector CO2 emissions and savings in 2030 compared to the 2030

baseline scenario

Scenario Maritime sector

emissions (annual MtCO2)

Maritime sector emissions

reductions compared to

baseline (MtCO2)

Percentage change

compared to 2005

emissions

Cumulative emission

reductions 2018-2030

Baseline 223.41 - +14.6% -

Fuel tax 216.97 6.43 (3%) +11.3% -40.1 (1%)

6.4.2.2 Changes in emissions of air pollutants

In terms of the percentage reduction compared to the baseline in 2030, the impact of the

fuel tax scenario very small. The percentage change in NOx and SO2 emissions compared to the baseline in 2030 is less than 0.1%.

Reductions in BC emissions are assumed to correlate with reductions in bunker fuel consumption (heavy fuel oil and marine diesel oil).

6.4.3 Other impacts

In 2030, the fuel tax policy leads to reductions in total fuel consumption of 1.7 Mtoe (0.1% reduction compared to the baseline in 2030).




Table 6.3: Fuel consumption compared to the baseline in 2030

Reduction in total fuel consumption in 2030 (single year) (Mtoe) 1.7

Cumulative reduction in total fuel consumption from 2010-2030 (Mtoe) 12.7

Cumulative consumption of biofuels from 2010-2030 (Mtoe) 1.75

Share of alternative propulsion in 2030 (single year) 1.6%

Impacts on waste production are expected to be negligible.

6.5 Economic impacts

The costs related to the baseline scenario are given in the table below.

Table 6.4: Costs in the maritime sector by 2030, €bn, 2010 prices

Additional costs compared to the baseline up to 2030

Capital costs Operational costs

(excluding

fuel costs)

Fuel costs Carbon costs

Total costs

Value (€bn) +2.5 +1.6 -4.8 +66.7 +65.7

Percentage +0.41% +0.54% -0.82% - +4.53%

A tax on bunker fuel would likely be passed on by suppliers to their customers i.e. ship operators, in turn creating an incentive for them to improve fuel efficiency. As a result, this policy option would incur additional capital costs as ship owners and ship operators operating on intra-EU routes would invest in new vessels and / or abatement technologies to retrofit existing ships. A small rise in overall operational cost (excluding fuel cost) may also occur as a result of implementing these abatement measures. However, both these impacts would be small amounting to an increase of 0.41% in capital costs and 0.54% in operation costs compared to the baseline.

The largest change in cost levels and structure under this policy option would result from the tax itself, which would place an additional cost of approximately €67bn on the industry, compared to the baseline.

Increasing freight rates in the shipping sector could in principle lead to modal shift from shipping to other modes of transport (such as rail or road). However, the expected increase in fuel price would also affect the other transport modes and therefore not undermine the competitiveness of shipping, in particular as in most cases, transport by ship is more energy efficient than by other modes.

As a result, the additional total costs of a fuel tax are expected to be high, at €65.7bn compared with the baseline.

6.6 Social Impacts

6.6.1 Screening likely social impacts

For the assessment of the social impacts of levying a tax on marine fuel sales in the EU, potential social impacts were first assessed on the basis of their significance. Likely social impacts of increased fuel price include changes in employment in the refinery and bunker fuel suppliers, changes in employment in sea-traded goods industries, impacts on price inflation on different socio-economic groups. These similar social impacts are analysed in detail in section 12.5 of the main report. For this additional policy option of a tax on fuel




sales, the most significant impact is likely to be employment change in fuel suppliers and refineries in the EU.

6.6.2 Employment change in marine fuel suppliers

6.6.2.1 Baseline Scenario

The bunker fuel supply chain includes traders, suppliers, brokers, bunker-service providers or facility operators and bunkering ports. Rotterdam is the largest bunker port in Europe with 12.2 million tonnes of bunker fuel volume, 95% of which was bunker fuel oil and the rest marine gas oil (Port of Rotterdam Authority, 2011). Rotterdam does not yet supply LNG bunkers.

Eurostat suggests that the total number or persons employed in the sector, ‘agents involved in the sale of fuels, ores, metals and industrial chemicals’ in the EU27 in 2007 is approximately 60,000. The number of persons employed in this sector is not a significant proportion of the total number of persons employed (<0.01%) in any MS.

6.6.2.2 Policy Option Analysis

The fuel costs estimated by the TIMES Shipping model were used to estimate the change in the expenditure in fuel. As would be expected, the fuel tax policy option estimates a reduced expenditure on fossil bunker fuels relative to the baseline and lead to an increased uptake of biofuels between 2018 and 2030. Table 6.5 shows the estimated change in fuel costs relative to the baseline over the period 2018-2030 and the change as a proportion of baseline costs in brackets.

Table 6.5: Sum of fuel costs from 2018 to 2030 in the baseline scenario and fuel tax policy option per fuel type

Scenario All fuels (€bn) HFO (€bn) Biofuels (€bn) LNG (€bn)

Baseline 646.2 614.5 0.0 31.7

Fuel tax 638.0

(-1.3%)

612.0

(-0.4%)

1.9

(++)

24.1

(-24.0%)

Although the reduction in HFO and LNG sales will adversely impact on bunker fuel sales globally and within Europe, the negative impact of this will largely be restricted to fuel sales at ports, as:

EU extraction of crude oil and natural gas is small when compared to the level of imports and is likely to continue to decline in the future; reduced demand from shipping is unlikely to impact this sector in Europe significantly;

Refineries supply products to a global market and transport costs are a small proportion of the price (0.4-1.7%). Therefore European refineries should be able to export any surpluses caused by a reduction in demand from shipping, although they may experience a reduction in margins due to higher transport costs.

The expected drop of HFO and LNG sales under this policy option may lead to the loss of jobs in the bunker facilities in ports. Other job losses may be expected in refineries in the EU. However, as this job loss is highly dependent on the strategies of the petroleum companies (producing bunker fuels in the EU and then exporting or producing directly outside the EU), it is not possible to accurately estimate these job losses.

Increased uptake in biofuels for shipping will increase global demand for biofuels and feedstocks used in biofuel production, leading to increased business for the EU biofuel processors and suppliers of biofuel inputs (e.g. farmers). Biofuels for shipping are an




emerging technology and there are a wide variety of methods for production and location of feedstocks. Given the uncertainty over the source of biofuels from 2030 onwards, it is not feasible to estimate the exact impact of additional demand from shipping would be on EU biofuel processors and input suppliers.

6.6.3 Employment change due to energy efficiency measures

6.6.3.1 Technical energy efficiency measures

Table 6.6 below presents the total and additional costs and savings generated by the fuel tax option, between 2018 and 2030 compared to the baseline in terms of capital expenditures.

Compared to the baseline, the fuel tax scenario will lead to an increased capital expenditure of €3.7bn for all technical energy efficiency measures. However, the overall level of capital expenditure for these technologies is smaller compared to the other policy scenarios, although engine efficiency measures under the fuel tax policy option are estimated to receive a high level of capital expenditure compared to some other policy options. The increased capital expenditure in these energy efficiency measures is estimated to add approximately 3,300 employees in the sector relative to the baseline.

Table 6.6: Additional impacts relative to the baseline in 2030

Fuel tax

Alter-native

pro-pulsion

measures

Engine

efficiency measures

Friction

reduction measures

O&M measures

Propeller measures

Slow Down

Abatement

Measures TOTAL

Additional capital

expenditure (€ millions)

- 1,200 7,400 - 2,600 - 11,200

Additional employees in

2030 (unit)

200 2,500 8,100 1,300 2,600 300 15,000

6.6.4 Impacts on human health due to changes in air pollutant emissions

The impacts of the fuel tax option on emissions of SO2, NOX and PM2.5 emissions are estimated to be small. There is an estimated small change in emissions from the baseline (of <0.1% for SO2 and <0.01% for NOX and PM). This translates to a small (but not negligible) disbenefit for human health and crop damage impacts, i.e. in contrast to the other policy options for which benefits in health and crop damage were estimated. The total estimated cost to human health and crops due to increases in SO2, NOX and PM emissions following

the methodology set out for the other policy options is €0.1bn to €0.4bn.

6.7 References

European Commission, 2011. Impact Assessment for the proposal for a Council Directive amending Directive 2003/96/EC restructuring the Community framework on the taxation of energy products and electricity. COM(2011) 169 http://ec.europa.eu/taxation_customs/resources/documents/taxation/sec_2011_409_impact_assesment_part1_en.pdf

http://ec.europa.eu/taxation_customs/resources/documents/taxation/sec_2011_409_impact_assesment_part1_en.pdf

http://ec.europa.eu/taxation_customs/resources/documents/taxation/sec_2011_409_impact_assesment_part1_en.pdf




7 Appendix 7 – Use of revenues/rents

7.1 Introduction

Certain policy measures for controlling GHG emissions from the international maritime sector could generate substantial revenues. This section explores how revenues could be used to increase the effectiveness of policy options.

7.2 Potential revenue from market-based measures

The level of potential revenue available varies greatly with the policy option. The level of revenue from the relevant policy options has been estimated using the TIMES International Shipping model and is presented below. Key variables that determine the revenues for each policy option are listed in the final column. In addition, there are several exogenous factors that could reduce the amount of revenue available for recycling, including:

Issues in terms of national sovereignty;

Compensating for impacts on developing countries;

Significant unforeseen reductions in vessel activity; and

Deductions for administrative fees.

Therefore the figures in Table 7-1 provide indicative revenues only.

Table 7-1: Potential revenue from policy options

Policy option Total discounted revenue 2010-2030 (€bn)

Key variables

Open ETS – full auction 30.4 Level of cap

Linked schemes

Carbon price

Tax on emissions 26.1 (low) - 203.5 (high) Tax rate

Emissions from ships

Target-based compensation fund 30.4 Levy charge


Contribution-based compensation fund

26.1 Levy charge


Revenues from the ETS options are only significant if auctioning is used to distribute allowances. Other policy options beyond those listed in the table above are not included because they will not generate revenues that can be recycled:

A closed ETS with full free allocation of permits will not generate revenues to competent authorities

Options involving mandatory emissions reductions will not raise significant and systematic revenues, beyond possible fines for non-compliance.




7.3 Potential options for recycling revenues

There are a number of options for revenue recycling, which are outlined in Table 7-2. While many social, environmental and economic goals could benefit from the revenues generated, the primary objective of revenue recycling in this instance is to increase policy effectiveness in terms of CO2 abatement. Therefore, only two types of mechanism are considered further:

1. Options that focus on GHG abatement in the shipping sector; and 2. Use of revenues for international climate finance.

The use of revenues for international climate finance is included for two reasons: first, there has been significant support within the IMO for using revenues from market-based measures to support mitigation and adaptation activities in developing countries. For example, at MEPC 59, the Committee noted that a general preference prevailed within the Committee that a greater part of the revenues generated by a MBM under the auspices of IMO should be used for climate change purposes in developing countries through existing or new funding mechanisms under the UNFCCC or other international organization such as the Green Climate Fund. Secondly, analysis of this option will provide a benchmark of the level of CO2 reductions that would have been possible outside of the sector.

Table 7-2: Possible options to recycle revenues

Use of revenue Comments Screening

Outcome

Refunding

participants

Whilst in theory revenues that are refunded to the shipping

sector could be used in a number of different ways, it is assumed that this option for revenue entails the revenues going back to industry to be used to incentivise further mitigation

efforts. The key feature of this option is that revenue would be refunded only to those who made payments to the Competent Authority of the scheme (e.g. through returning a portion of

payments made), which would directly compensate for the costs imposed by the policy on the actors paying any charges (through taxes, levies or purchasing credits).

Taken forward

Funding R&D Revenues could be disbursed to industry for funding R&D to

develop additional abatement options in the long term. Management of these funds could also be carried out more centrally, with (for example) the Commission managing the pot

of money and deciding how to allocate to appropriate R&D projects.

Taken forward

Support uptake of abatement options

Support could be provided for uptake of available abatement options in a variety of ways, including grants or loans. The key feature of this option is that support would not necessarily be

provided to those who made the payments in the first place (as in the refunding option above). Therefore it could indirectly compensate for the costs imposed by the policy

Taken forward

Investment in

international mechanisms, e.g. CDM, JI

This option provides a benchmark for GHG reductions that

could be possible from buying international carbon credits, as these are generally believed to be cheaper mitigation options compared to those available in developed countries.

Taken forward

Use the revenue as a source of

Government income

It is assumed that this is not a revenue neutral measure and would involve Member State Governments’ using the proceeds

from the policy action as a source of additional Government revenue.

Not taken forward




Use of revenue Comments Screening Outcome

Reductions in existing

Government taxes, duties or charges

This could include, for example, a reduction in a consumption tax, which may stimulate consumer spending and increase

economic activity. This could be a form of “revenue recycling” where funds raised from undesirable activities (in this case, producing CO2) are used to lower particular distortionary taxes

Not taken forward

Addressing social

impacts of price increases in the maritime sector

If prices rise, there could be impacts on vulnerable members of

society. There may be a desire to use revenue raised through a shipping market-based mechanism to mitigate these impacts.

Not taken

forward

Compensating countries affected

by price increases

This could entail compensating the developing countries most affected by increased shipping costs, e.g. countries with more

than X% of GDP coming from shipping trade. It is expected that this compensation would not represent a substantial proportion of overall revenue.

Not taken forward

Sequestration Funding could be provided for sequestration projects such as

carbon capture and storage demonstration or biological sequestration.

Not taken

forward

7.4 Design of options for recycling revenue

In the following sections, each mechanism is explored in more detail. The options have two main parameters:

1. Funding mechanism (the form of support and eligibility to receive funds) 2. Allocation mechanism (determining the quantity of funding received and priority

areas)

Each of these parameters is discussed and recommendations are made on the most efficient and effective choices.

7.4.1 Mechanism design: refunding revenue to participants

7.4.1.1 Funding mechanism

The form of support would be a full or partial refund of payments made under the legislation, which provides an economic incentive to improve performance.

Only participants of the scheme who had made payments would be eligible for refunds. The aim would be to increase uptake of abatement options compared to what could be achieved by the policy alone, by providing greater financial incentives for uptake. This has the effect of offsetting the charges paid under the policy, so that the direct costs to the participants eligible for refunds is reduced but the fuel savings remain the same, thereby improving the cost/benefit ratio for industry.

7.4.1.2 Allocation mechanism

The methodology should aim to encourage uptake of abatement measures by providing stronger incentives – this means that refunds should be allocated based on a performance metric, where higher refunds are awarded to participants who demonstrate higher performance. If refunds are not linked to a performance metric, the effect of the refund would be to reduce the cost to industry without specifically incentivising GHG abatement.

Options for measures of performance include:




Allocation mechanism Advantages Disadvantages

Improvement in carbon intensity of operations

Accurate reflection of performance improvements, taking

into account all possible actions

Higher administrative burden to measure, report and verify t-km

Does not reward early action

Requires a benchmarking year to

measure initial performance

Requires comparison between different years of operation – therefore will be

affected by ships entering and leaving the scope of the legislation

Absolute carbon intensity of operations (e.g. reaching a threshold or through a

“league table”)

Accurate reflection of performance achieved

Rewards early action

Could inherently favour larger ships (provided they are fully loaded)

Difficult to find an equitable threshold to

suit all ship types and operations

Improvement in absolute

EU emissions reductions Lower administrative

burden compared to measuring carbon intensity


Rewards ships that reduce activity within the EU (e.g. by moving elsewhere)

Requires comparison between different years of operation – therefore will be affected by ships entering and leaving the scope of the legislation

Absolute reductions in EU

emissions (e.g. reaching a threshold or through a “league table”)

Lower administrative

burden compared to measuring carbon intensity

Rewards early action

Difficult to find an equitable threshold to

suit all ship types and operations

Requires comparison between different years of operation – therefore will be affected by ships entering and leaving

the scope of the legislation

Equal amount to each operator

Easy to administer Does not effectively encourage efficiency

Penalises ships that are highly active in the EU while disproportionately

rewarding those that make small payments under the scheme, even if they are not efficient.

Proportion of payments

based on current or historic absolute EU emissions, size of

operations, activity in EU (tkm) etc


Ships with different efficiencies are provided with the same revenues (e.g. if they have the same activity)

Requires reliable data for the basis of

distribution

The design of this option must consider many of the same issues that were discussed in Section 5.2 of the main report, which looked at options to allocate ETS credits. It was noted that in general, the problems related to free allowances are more pronounced for the shipping sector than for other sectors, because of the mobile nature of ships and the huge variation in activity levels. Allocation based on historical emissions would not penalise early action, but would have high administrative burdens. Allocation based on output benchmarks would also have high administrative costs and it would be difficult to find a metric that could apply to the whole sector.

In order to reduce burdens on the sector, only ships that expect to meet “good performance” standards need to have their performance verified, as suggested in the proposal to the IMO for a Leveraged Incentive Scheme (now combined with the Vessel Efficiency System)




7.4.1.3 Discussion

The refunds should be allocated based on a performance metric in order to encourage more abatement. However, in all cases considered, the disadvantages are significant and it is difficult to ensure that incentives are aligned correctly. Any performance measure that allocates refunds based on comparisons between years (through reductions achieved) will be adversely affected by ships entering and leaving the scheme and would therefore be ineffective for a regional measure.

In addition, the mechanism will only target economic barriers through reducing costs to efficient operators. This does not target non-price barriers.

7.4.2 Funding R&D in the shipping sector


Funding R&D in the shipping sector could be used to develop new abatement options in the long-term, or to accelerate the commercialisation of other options that are closer to market but still require further development. The rationale behind this is that funding R&D projects specifically aimed at improving GHG emissions of ships should stimulate additional innovation in the sector. Many shipping companies are not able to invest large sums in R&D related to their specific operations, so this option would enable significant sums to be invested that would not otherwise have been spent. By improving the development of new abatement options, or by accelerating their commercialization, funding R&D should allow for greater GHG reductions overall compared to the policy option alone.

The funding could be awarded as a stand-alone grant, or based on matched funding, for example funding up to 50% of the cost of the R&D. Funding could be awarded to research units within the shipping industry or institutions outside of the industry, or could require collaboration between different actors.

At the EU level, the main instruments for funding research are the Framework Programmes, which distribute grants to fund collaborative research efforts. The revenues from all of the policy options are of the same order of magnitude as a Framework Programme; for example, the sixth Framework Programme (FP6, 2002-2006) had a budget of almost €18bn, whereas

FP7 (2007-2013) has a budget of over €50 billion over 7 years.


Under this approach, revenues could be allocated in several ways, such as:


Rebates to participants who can demonstrate a

certain level of R&D investment

Technology neutral

Allows shipping industry to

determine its own needs

Private research may not lead to knowledge spillovers

Could lead to duplication of effort

A call for R&D grant applications, without strict specifications on

technologies/topics

Technology neutral Administrative burden to determine which projects receive funding

Submissions may not meet stakeholder needs

A predetermined list of technologies/topics eligible for support

Greater control over spending to ensure that environmental, social and

economic gains are maximised

Topics can be selected in

collaboration with various stakeholders

Risks “picking losers” or limiting the extent of innovation




The priority areas for funding could be identified by looking at the potential GHG reductions and the level of technology maturity.

7.4.2.3 Discussion

Innovation in efficiency measures would help to reduce maritime transport emissions by making new measures available or by improving cost-effectiveness of existing technology. As technologies may have long lead times, it may be important to provide R&D funding early on.

The effectiveness of this option will therefore depend on whether revenues become available soon after policy implementation. R&D funding to develop new abatement technologies would not be expected to contribute to actual maritime sector GHG reductions for a long time, whereas funding to improve existing or near-market measures may deliver results sooner, there will still be a significant time lag of several years.

7.4.3 Support for uptake of abatement options


Support for uptake of abatement options could be provided directly by targeting funds towards abatement options on ships, or indirectly by supporting enabling measures such as shore-side investments. This option differs from the first option of refunding revenues because the funding can be disbursed to actors other than those who made the initial payments. The aim would be to encourage greater uptake of abatement options than would be possible under the policy alone, by using the funding to overcome market barriers.

Funding could take several forms:

Grants (awarded before the abatement option is taken up)

Loans or loan guarantees

Rebates (awarded after the abatement option is taken up)

In order to best address market barriers relating to lack of access to finance, grants and loans would be preferable. It would be necessary to verify that the money was spent on the abatement option proposed, for example, by providing funding through approved installers.

The funding could be designed to cover some or all of the cost of an abatement option. In certain cases, full funding may preferable, particularly where split incentives exist and for measures that will ensure emission reductions within Europe, such as funding port infrastructure. Partial funding may help to ensure that the most effective measures for each ship are taken up, as opposed to indiscriminate uptake of technologies simply because they are paid for by the funding. Furthermore, full funding would not provide as strong an incentive to use/maintain the measure properly once the money has been received, as there are no costs to recoup – although the high cost of fuel would provide some incentive. Finally, full funding of technical measures for ships would compensate some ship owners at the expense of others – e.g. infrequent visitors would benefit disproportionately. Therefore, in most cases a partial grant would be preferable to full funding.

Providing loans would be particularly effective if access to finance is a barrier to uptake, as identified in Maddox (2012). Loans may also be attractive if payback periods are long, as the upfront capital cost must be recovered gradually through fuel cost savings. The provision of loans in these cases has the effect of reducing the risk of the investment. Measures with long payback periods include waste heat recovery and propeller/rudder upgrades.

In a regional scheme, support for uptake of abatement options could be provided to non-EU flagged ships, provided they sail to European ports. This approach is used by Norway under the NOx fund, as grants are allocated to ships regardless of flag or ownership. Support could also be provided to other actors that could demonstrate that GHG reductions in the maritime sector would be achieved, for example, shipyards and ports. This could have additional benefits for the economy in terms of improving the competitiveness of ports,




particularly those that could be at risk of losing trade to evasion ports (although the risk of this was found to be small). Providing funding to ports is attractive because it allows support of infrastructure needed for certain abatement options such as shore-side power and LNG infrastructure.


Under this approach, revenues would be allocated to shipping operators that can demonstrate they have introduced or will introduce GHG abatement measures.


A predetermined list of abatement measures eligible for support

Greater control over spending to ensure that environmental, social

and economic gains are maximised

Risks limiting uptake of innovative measures

Risks funding measures that are not

suitable for the ship’s type/operational profile (could be limited by verification of suitability as

part of approval process)

Administrative burden to determine suitable measures and update the list periodically.

Fund measures that meet a certain expected level of abatement in the sector e.g. % reduction or % reduction per Euro

Allows support to a

variety of measures, including port-side measures

No need to pre-commit

to certain measures

Administrative effort required to

verify calculations of effectiveness

Threshold approach does not prioritise where to spend funding in measures that surpass the threshold

Fund measures that achieve the highest scores relative to certain criteria (ranking options)

Allows support to a variety of measures, including port-side measures

No need to pre-commit to certain measures

Administrative effort required to verify calculations of effectiveness

Could disproportionately benefit certain sectors/ship types

A wide range of criteria could be used to allocate funds, including:

Marginal abatement cost of the measure;

Absolute emission reductions of the measure; Achieving a balance between different measures;

Implementation period;

Monitoring, recordkeeping and reporting elements.

Based on proposer (e.g. prioritize SMEs or short-sea shipping)

These criteria could be weighted to obtain the desired balance. For example, if a desired outcome is to incentivise abatement options that are further from market, then a higher weight could be assigned to innovative technologies.

7.4.3.3 Discussion

Support for uptake of measures allows funding to be distributed to actors that do not necessarily make payments under the legislation, meaning that a wider range of options can be supported compared to the refunding mechanism. This allows the funding of coordination measures such as infrastructure investment and vessel fuel consumption certification, which is not possible under the other options for use of revenues

Funds could be allocated as grants or loans, and could be provided to meet the full cost of the abatement option, or part of the cost. In most cases, partial funding (grants or loans) is preferable, as it would reduce the incentives for free-riding and improve the efficiency of the scheme. However, in some cases – particularly for investments in infrastructure – higher




levels of funding could be offered. Loans would have the effect of reducing barriers related to access to capital and would reduce the risk of investment in new technologies. The approach could play an important role in developing a track record for promising technologies. The abatement measures supported under the scheme could change over time under any of the suggested allocation mechanisms. In addition, funding could provide an opportunity for new suppliers of abatement technologies to enter the market, thereby improving employment and competitiveness within Europe.

7.4.4 International climate finance


This option is included as there has been significant support within the IMO for using revenues from market-based measures to support mitigation and adaptation activities in developing countries. Carbon markets offer important opportunities for directly financing new technologies in developing countries, and for leveraging private investment. The main focus of discussions at the MEPC to date has been on carbon market offsets. The aim of this option would be to increase the net GHG reductions possible compared to the policy option alone, by purchasing low cost out-of-sector GHG credits.


The funding would be used to purchase carbon market offsets at the market price. The precise design of the scheme depends on international negotiations over developments in the CDM scheme. The purpose could be to secure the cheapest GHG reductions, regardless of their origin, or the finance could be directed towards certain developing countries – for example as a means of compensating costs to EU trading partners who may pay additional costs for exports/imports.

7.4.4.3 Discussion

The certainty of out-of-sector reductions would depend on the funds available for this purpose, the split between adaptation and mitigation activities, and the quality of the mitigation activities funded. There could be some restrictions on the type of international project financed, as in the current rules for purchasing out-of-sector credits under the EU ETS, which aims to guarantee a higher level of certainty of GHG reductions.

7.5 Impact analysis

This section provides a qualitative assessment of the impacts. In most cases there are clear benefits to certain mechanisms over others, and a ranking is provided.

However, it is not possible to provide a quantitative assessment, as many key parameters lack enough observations to give a credible estimate of impacts.

7.5.1 Impact of mechanisms on environment

An important impact of the mechanisms will be the potential additional reductions on CO2

emissions that could be achieved. To understand these additional reductions, we must first analyse the impact of the mechanisms on barriers to uptake.

7.5.1.1 Impact on barriers to uptake

One of the key purposes of a revenue recycling scheme will be to target barriers to uptake of GHG abatement options in the maritime sector. These barriers were the focus of a parallel study carried out on behalf of the European Commission on market barriers to cost effective GHG emission reductions in the maritime transport sector (Maddox Consulting, 2012). The study examined twelve cost-effective abatement solutions, both technical and operational. These solutions are shown in Table 7-3. It is important to note that the solutions are not cumulative and that the uptake of one can have an impact on the effectiveness of another.




The final column identifies high priority options for the purposes of revenue recycling, based on the technology having:

High abatement potential

Low levels of current uptake

Table 7-3 – Abatement measures, global CO2 abatement potential and uptake in 2011

Likely 2030

abatement potential (Mt CO2)

Mean

MAC in 2007

($/tCO2)

Mean MAC in 2030

($/tCO2)

Indicative penetration

2007

Indicative penetration

2011

Priority

Speed reduction 383.7 -332.6 -406.7 - High

Propeller / rudder upgrade

63.9 +57.7 -214.7 - - High

Hull coating 39.4 -143.3 -389.0 - High

Waste heat recovery 36.4 +530.1 +0.3 - - High

Autopilot upgrade /

adjustment

20.3 -142.3 -389.3 Low

Hull cleaning 19.7 -71.6 -323.7 Low

Optimisation of trim & ballast

19.7 -1.3 -304.0 - Low

Propeller polishing 19.7 -124.5 -140.2 Low

Main engine tuning & common rail

19.7 -103.3 -348.7 Low

Weather routing 18.3 -155.2 -399.3 Low

Speed control pumps & fans

3.9 +384.7 +89.4 Low

Energy saving utilities

(e.g. lighting)

0.1 +655.5 +345.6 Low

LNG n/a n/a n/a - - High

Source: Maddox Consulting (2012) and author analysis

*A “likely” case is provided rather than a “low” case, because a low case can in most cases be zero . Key:

- Very little penetration (e.g. test installation only) High priority

Less than half of potential penetration achieved

Little to no barriers to implementation – solution either at full penetration or is progressing through product life cycle to full penetration

Low priority

Based on this approach, the measures that are considered to be high priorities are:

Speed reduction;

Propeller / rudder upgrade; Hull coating;

Waste heat recovery (not compatible with speed reduction); and

LNG.

These solutions have the largest abatement potential to 2030. However, in all cases less than half of the potential penetration has been achieved. Note that waste heat is not generated at the same intensity at slow engine speeds, so waste heat recovery (WHR) is no longer possible if speed reduction is implemented. Speed reduction has a much larger




potential and should be the preferred option, but speed reduction may not be possible on some ships (e.g. ferries and cruise ships). We have therefore included WHR in this analysis.

LNG is also included as a priority option, because there are numerous barriers that must be overcome, but in principle all ship types could use LNG.

Some solutions have not faced significant barriers to implementation but rather were already sufficiently viable from an economic perspective that they were undergoing “normal” product introduction. These solutions include weather routing, autopilot upgrade, propeller polishing, hull cleaning, and main engine tuning (Maddox, 2012). As such, they should not be targeted by revenue recycling as the GHG reductions would not be additional. A summary of the main barriers affecting the priority abatement options is provided in Table 7-4 (Maddox, 2012).

Table 7-4: Summary of main barriers to uptake of priority abatement options

Abatement option Main barrier Other barriers

Speed reduction Split incentives

(contracts do not allow for ships to slow steam in some cases)

Lack of investment in port-side and communication infrastructure in some cases

No major technical barriers, as speed reduction is already being implemented

Propeller/rudder upgrades

Lack of certainty over performance

High capital cost

Principal agent barrier

Lack of management expertise required to

evaluate the solution and/or the time available to evaluate and install the solution

Hull coating Economic barriers with the use of more expensive new hull

coatings



Lack of measurement standards

Waste heat recovery

Lack of clear financial incentive due to high

capital cost

Only suitable for some ship types



Lack of management expertise required to evaluate the solution and/or the time available to evaluate and install the solution

LNG Lack of bunkering facilities at ports

Technological barriers including engine development issues (e.g., methane slip) and

safety concerns regarding fuel transfer

Storage requirements for LNG fuel

Lack of management expertise

The lack of measurement, monitoring and independent valuation and the principal agent barrier are repeatedly identified. The initial capital expenditure is an issue for some solutions. However, it is important to bear in mind that:

One size does not fit all i.e. some barriers apply to specific ships types, trade routes etc.

The barriers are inter-connected and tackling one can have an impact on another

The discussion only considers existing technologies available on the market.

The extent to which each mechanism targets the various barriers depends on the design of the option, but the following discussion points apply in general:

Refunding revenue




o Economic barriers: should improve the economic incentives for abatement

options by reducing the cost of uptake to participants. The mechanism could indirectly help market failures relating to lack of information by increasing uptake, leading to knowledge spillovers.

Funding R&D o Technical barriers: can in theory help to resolve technical issues by

improving the effectiveness of technologies. Could also be used to help fund the development of completely new abatement technologies, or to commercialise novel technologies that are not yet in production.

o Operational barriers: through the testing and development of new ways of

operating that increase fuel efficiency o Economic barriers: could improve the economic case by reducing production

costs (either through learning from demonstration projects, or from discovering cheaper methods to achieve abatement).

o Administrative barriers and market failures due to lack of knowledge can be

reduced through dissemination of research results.

Support for abatement options o Technical barriers: could help to reduce technical barriers related to

uncertainty of performance by de-risking investments. o Economic barriers related to capital cost would be reduced by financing

investment. o Market failures due to split incentives could be overcome by directly funding

uptake of measures. This also allows the option of investing in “public goods” such as port infrastructure.

o Administrative barriers: The lack of accurate information could also be

improved if releasing performance data is a condition of the funding. In addition, funding could be directly used to support information schemes such as vessel fuel consumption certification.

International climate finance does not address any barriers to uptake in the sector

When considering barriers, it is important to directly address all of the constraints to uptake. Thus in terms of targeting barriers, it appears that the mechanisms can be ranked in terms of the number of barriers they could target:

1. Support for abatement options 2. Funding R&D 3. Refunding revenue 4. International climate finance

7.5.1.2 Impact on CO2 emissions

Under an ETS or a target-based Compensation Fund, the environmental effectiveness is already guaranteed by the cap. The impact on CO2 emissions under other policy options (tax, compensation fund) is closely linked to the analysis of barriers, although there are some additional factors to consider in terms of the cost-effectiveness.

Funding international climate finance is intended to provide relatively cheap abatement

outside of the shipping sector. Therefore it provides a useful benchmark to understand the potential for additional abatement through use of revenues. The calculations here are for benchmarking purposes only, as the future of CDM credits and the associated price of carbon reductions through climate finance is highly uncertain.

Estimates of carbon credit prices used in the IMO impact assessment (MEPC 61 INF-2) are from $25/tCO2 to $40/tCO2 in 2020, rising to $40/tCO2 to $100/tCO2 in 2030. The table below shows the maximum abatement that could be achieved if 100% of revenues were used to




purchase carbon credits at these prices, over the period from 2010-2030 and allowing for 10% of the revenues to be used to cover administrative costs:

Policy option

Maximum out-of-sector

abatement 2010-2030

(Mt CO2)

In-sector mitigation

under policy option 2010-2030

Ratio of out-of-sector permits to

in-sector CO2

reductions

Open ETS – full auction 689 – 1,422 336 2.0 – 4.2

Low tax on emissions 573 – 1,216 335 1.7 – 3.6

High tax on emissions 4,045 – 9,283 390 10.4 – 23.8

Target-based compensation fund

689 – 1,422 336 2.0 – 4.2

Contribution-based

compensation fund

1,682 – 3,177 335 1.7 – 3.6

Open ETS – free

allocation

0 334 0

Closed ETS – free allocation

0 377 0

In all cases with revenue generation (all policy options except ETS with free allocation and mandatory targets), the potential additional out-of-sector abatement that could be achieved by purchasing credits is several factors larger than the in-sector emission reductions. Total CO2 reductions could be 1.7-4.2 times higher under these assumptions. The high tax on emissions is an extreme option that would place heavy financial burdens on the shipping sector; therefore the out-of-sector abatement is much larger due to the large revenues involved.

Comparing this level of abatement to mechanisms that aim to overcome economic barriers to abatement within the shipping sector depends on the marginal abatement costs of these measures. For propeller/rudder upgrades and waste heat recovery the high capital costs mean that economic barriers are a significant factor preventing uptake at current fuel prices. The abatement achieved by subsidising uptake of these two measures will be higher compared to that achieved by international climate finance if the marginal abatement cost is lower than the carbon credit price. According to Maddox (2012), propeller/rudder upgrades will have a lower marginal abatement cost compared to international carbon credits by 2020, whereas for waste heat recovery the marginal abatement cost will be lower by 2030. Therefore, at least in the short term, it seems likely that funding uptake of WHR will be less cost-effective than international climate finance, but funding propeller/rudder upgrades may be as cost-effective or more cost-effective. However, the maximum abatement potential from the shipping sector for these measures is lower than the out-of-sector abatement that could be achieved – meaning that once the best candidates for the technology have been selected, it is likely that there would be money left over.

Subsidising particular abatement measures on ships could raise the overall social cost if the measure were not the least-cost means of reducing emissions. Indeed, uptake of those measures which are not cost-effective may reduce the incentives to take up other measures (as efficiency is increased and/or cumulative reductions are smaller), and lead to fewer additional CO2 reductions than would be expected.

Additional CO2 reductions could also be achieved by overcoming barriers to abatement measures that are already cost-effective, such as speed reduction and hull coatings. There may also be additional market barriers to solutions that are not cost-effective. The carbon credit price could be set as a threshold for eligibility to receive funds, where the criteria are set in terms of the cost to achieve an expected level of CO2 abatement (€/tCO2). A similar




approach using predicted cost efficiency is used for the New Entrant Reserve (NER) 300 funding, where the selection process chooses proposals with the lowest “ranking cost” per total amount of energy that the proposed project will produce over the first five years of operation.

Lack of information/certainty on the cost and expected fuel efficiency gains is a critical

barrier that has been identified in the Commission’s study on maritime sector market barriers (Maddox 2012). In a survey of the industry, UCL (2012) found that lack of reliable information was the most significant barrier to uptake of abatement measures, being cited by over 18% of respondents. Due to the highly diverse nature of the shipping industry, it is important to understand how different abatement options perform under various operating profiles and on different ship types and sizes. In addition, there are numerous measures that were not explicitly considered in the TIMES model that could improve energy efficiency in the maritime sector. Data in the TIMES model were taken from an IMO report that provided cost and abatement potential of 22 abatement options, differentiated by ship size and type. However, the authors of the IMO report identified a further 28 abatement measures for which sufficient data could not be obtained - even after consultation with the industry. If revenues were to be used as support for abatement technologies, information on the performance of supported

abatement options could be improved, particularly if reporting on fuel efficiency improvements was required as a condition of the funding. Recycling revenues may

improve information, although it would be more difficult to encourage uptake of unknown measures, so it would be expected to have a much smaller effect. R&D investment could also improve information, particularly on the measures that are further from market. Investing in climate finance would not be expected to have an impact on this issue.

The assessment of barriers in the previous section notes that one of the market barriers to speed reduction is lack of port/communication infrastructure (in some cases). Support for uptake of abatement options would be the most effective option to overcome this particular

barrier, as it is the only mechanism that could fund port/communication investments. The other options are not expected to increase uptake of speed reduction. In addition, support for uptake of abatement options is the only recycling mechanism that could directly target the “chicken and egg” problem of LNG uptake by investing in refuelling infrastructure for LNG ships.

Funding R&D could result in additional CO2 reductions in the future, but the outcomes are

highly uncertain in time and magnitude. Furthermore, there is a significant lag between (a) receiving the funds (b) achieving any outputs from the supported R&D programmes and (c) achieving reductions in GHG emissions through implementation of technologies developed via the R&D programmes. Therefore in the short term, R&D is not expected to achieve significant additional CO2 emission reductions. In the longer term the potential is greater, but the impact is still highly uncertain. Maddox (2012) notes that: “The likelihood of success will be driven by the interests of manufacturers (including LNG suppliers), ship yards, governments and research institutions to support ongoing R&D for each of these promising solutions. Because of earlier successes in industry-government collaboration and the ongoing interest of commercial organisations in investing in fuel saving technologies, we rate the likelihood of success as high.”

Estimating the overall effect in order to provide a clear ranking is rather arbitrary and it is not possible to do so with a high degree of confidence.

7.5.1.3 Other environmental impacts

The assessment of the various mechanisms on other important environmental impacts such as air pollution is closely linked to fuel consumption and thus CO2 emissions; therefore the ranking is expected to be the same.




7.5.2 Impact on economic activity and employment

Support for uptake of abatement options will encourage investment in and growth in the

market for efficiency measures above the level expected if revenues were not used within the shipping sector. This will increase supplier revenues and could create professional jobs within Europe. Accelerating early investment in these measures should allow existing market leaders (mostly in Europe) to capitalise on first-mover advantages. Refunding revenues

may also encourage investment in efficiency measures, but to a much lesser extent.

Investing in R&D will create jobs directly, and also have additional economic benefits via

multiplier effects. Multipliers of 1.5 to 2.5 have been observed for shipping industry R&D (Maddox, 2012).

International climate finance will have minimal impacts on economic activity and

employment in Europe.

The economic impact assessment in the main report examined possible impacts on SMEs.

It found that SMEs were more likely to be sensitive to the introduction of policy measures than larger companies. This was mainly due to the potential difficulty in implementing abatement measures if they did not have sufficient access to capital. This is more likely to be an issue in the bulker and tanker sectors, which have a very large number of small owners, many of whom have fleets of only one or two vessels. Conversely, container shipping is highly concentrated due to its capital-intensive nature and economies of scale. If revenues provide support for abatement technologies, this option could be designed to

assist SMEs through loans/targeted grants, thereby overcoming the barrier of access to finance (if it were found to be significant). Other options for use of revenues would not provide targeted support to SMEs.

The World Bank (2010) notes that most "green stimulus" programs that have large short-run benefits on employment and the environment are likely to have less significant positive effects in the long-run, and vice versa. This implies a trade-off in many cases between short-run and long-run impacts. The trade-off between short-run and long-run impacts suggests that a mixture of mechanisms that target near-market and innovative abatement options would be beneficial.

7.5.3 Impact on innovation

The impact on innovation is considered in terms of the extent to which each mechanism could target abatement measures that are not existing or proven.

R&D funding is the only mechanism that would directly encourage development of new

abatement measures.

Supporting uptake of abatement options can be used to increase innovation, depending

on the design of the mechanism, as the competent authority has a lot of freedom over which options could be supported. The potential for funding to be allocated based on applications means that the criteria for selection could include requirement for positive impacts on innovation. Indeed, in order to encourage additional abatement (as opposed to funding the sector for options it would have taken up anyway), it is probably desirable to actively encourage innovation within the scheme design.

Recycling revenue will not provide direct incentives for innovation. Recycling revenue

according to a performance metric may encourage uptake of tried-and-tested abatement options rather than innovative measures.

International climate finance will have no direct impacts on innovation in the shipping

sector.




7.5.4 Conclusions

In this section, only options that focussed on GHG abatement within the shipping industry were considered, along with international climate finance as a benchmark measure. In purely economic terms, limiting the reinvestment of revenues in the shipping sector would reduce cost-efficiency, as the authority cannot choose between the complete set of possible options.

Table 7-5: Summary of impact of mechanisms

Refunding revenue

Funding R&D

Support for

uptake of abatement

options

International climate

finance

Overcoming barriers to

abatement

+ + ++ O

Reducing CO2 emissions + ? ++ ++

Increasing economic activity and

employment

O ++ + O

Increasing innovation O ++ + O

Notes: ++ = strong positive impact relative to other options; + = positive impact; o = neutral impact

A summary of the key points is provided as follows:

Refunding revenues to participants:

o On the basis of the discussions in this section, refunding revenues is not a recommended option.

o This is mainly due to the difficulty of designing an efficient and equitable mechanism, the lack of effectiveness in targeting non-price barriers, and the limited scope of abatement measures that could be incentivised.

Funding R&D:

o The benefits are highly uncertain, but funding R&D could be an option for at least part of the revenues.

o The main benefits are due to the ability to increase innovation in the shipping sector, and an increased supply of abatement options would improve the chances that the 2050 reduction target will be met.

Support for abatement options in the shipping sector: o Support for abatement options in the shipping sector would be a good option

for at least part of the revenues. o Our analysis suggests that this option would be able to target the majority of

market barriers to uptake of cost-effective abatement options, depending on the scheme design.

o This mechanism would also be able to target funds toward other actors in the shipping industry, such as ports and shipyards, to overcome a wide range of market barriers.

International climate finance:

o This provides a low-cost abatement option for out-of-sector carbon credits. o It compares poorly against in-sector funding in terms of overcoming market

barriers. o The main argument for allowing international climate finance would be on the

grounds of investing in the most socially optimum abatement measures.




7.5.4.1 Final recommendations

In order to fully complement the main policy option, it is recommended that a combination of support for abatement options and funding R&D is used. This would allow both short-run

and long-run GHG reductions to be improved. International climate finance would also be possible in order to ensure socially optimum outcomes. Refunding revenues to participants would have some benefits, but is not as effective compared to support for abatement options through grants and/or loans; therefore it is not recommended here.

7.6 References

CE Delft (2009). Technical support for European action to reduce GHG from international maritime transport. European Commission. http://ec.europa.eu/clima/policies/transport/shipping/docs/ghg_ships_report_en.pdf

IEA (2011) Shipping Infrastructure ETSAP brief. http://iea-etsap.org/web/HIGHLIGHTS%20PDF/T17_Shipping_Infrastructure_v4_final_gs%20HL.pdf

Imperial College London (2010) Unlocking the capacity of CO2 abatement in ships arriving and departing from UK ports

Maddox Consulting (2012) 1st Interim Report: Analysis of market barriers to cost-effective GHG emission reductions in the maritime transport sector. European Commission

World Bank (2010). Green stimulus, economic recovery and long term sustainable development.

UCL (2012). Barriers to Low Carbon Shipping Survey – Draft Report. http://www.lowcarbonshipping.co.uk/attachments/081_Barriers%20to%20Low%20Carbon%20Shipping%20-%20Survey%20report%20(draft).pdf)

http://ec.europa.eu/clima/policies/transport/shipping/docs/ghg_ships_report_en.pdf

http://iea-etsap.org/web/HIGHLIGHTS%20PDF/T17_Shipping_Infrastructure_v4_final_gs%20HL.pdf

http://iea-etsap.org/web/HIGHLIGHTS%20PDF/T17_Shipping_Infrastructure_v4_final_gs%20HL.pdf




8 Appendix 8 – Economic Impacts on the Pulp and Paper Industry

8.1 Introduction

Pulp is the basic ingredient for the manufacture of paper and board and is produced mainly from fresh wood, woodchips from sawmills and recovered paper.

Over the last two decades, the growth of EU paper production has averaged 2.8% per year, whilst consumption has climbed on average by 2.5% per year (European Commission, 2012). The stable but ageing EU population offers little long-term scope for growth in overall demand, especially once the 'catch-up' phase in the new EU Member States has come to an end.

EU pulp production has grown more slowly, averaging +1.6% p.a. since 1991. This is mainly due to increased use of recycled fibre for paper-making, but also because some old pulp mills have closed, thus partially offsetting increased output from the upgrades or re-builds of remaining plants. There have been very few opportunities in the EU for new pulp mills, since EU companies find the sub-tropics more attractive for their "green-field" investments.

The EU’s competitive position in the sector is vulnerable as non-EU competitors do not have to bear the high costs of compliance with strict environmental regulation which prevail in the EU. Since most pulp and paper grades are effectively commodities, prices are set by lowest-cost producers on the global market. In addition, the continuing development of electronic media has meant a reduction in certain paper-based printing and publishing segments, such as newsprint. Distribution patterns are also changing, with for example smaller, local print batches of publications. This is partly to offset increasing road transport costs. Nonetheless, other "smart" applications for technology, such as intelligent paper and packaging, are providing new market opportunities.

The EU market for paper and pulp may be directly affected by the introduction of policies to reduce emissions from shipping through the additional cost placed on:

imports of pulp to converting mills and imports of paper.

EU exports to overseas markets with possible impacts on the competitiveness of EU paper and pulp producers.

It is important to bear in mind that the pulp and paper sector is one link in a longer supply chain, each level of which can be impacted by a change in shipping costs. The wood and recovered paper on which the sector relies may become more or less expensive. This will affect the operation of pulp and paper producers, with possible consequences on printers, retailers and eventually consumers. This is, however, beyond the scope of this analysis.

In order to capture the major impacts from the policy options on the market for pulp and paper, we follow the assessment framework illustrated below.




Figure 8.1. Assessment framework for the analysis of economic impacts of EU policy action on shipping GHG emissions on the pulp and paper sector

In order to understand the impact of possible EU policy action on maritime sector GHG emissions on the EU and overseas pulp and paper markets, we have collected the information listed below. The analysis considers the combined ‘paper and pulp’ sector but where possible data have been provided at the product level.

Table 8.1- Data requirements of an analysis of economic impacts for the pulp and paper market

Impacts for the pulp and paper market

Price of pulp and paper products

Freight rates for container shipping

Split of EU pulp and paper consumption by local production, imported by sea and by other mode

Price elasticity of demand of pulp and paper

Margins of pulp and paper production

Concentration of pulp and paper production market

8.2 Prices

Historical data on prices is not available for free, however the latest weekly prices are available from FOEX, the key reference source for pulp, paper, recovered paper and wood based bioenergy/biomass price indices. FOEX's PIX indices are benchmark price indices for various qualities of pulp and paper based on real trade information from buyers, sellers as well as from agents. The highest 10% and the lowest 10% of the prices are eliminated, and the PIX value is calculated as an average price from the remaining prices. Price indices for 10th July 2012 are summarised below.

EU MARKETOVERSEAS MARKET

SHIPPING

Other modes of transport

Other modes of transport

Carbon cost passed through

Production of Pulp and Paper

Retailers

Final consumers

Converting Mills


Production of Pulp and Paper

Printers

Final consumers

Converting Mills

Printers


Retailers







Table 8.2 Prices of pulp and paper

€ per ton Europe USA China

Pulp 639-664 € 716€ 518-528€

Paper 504-865€ 505-537€ n/a

Source: FOEX Ltd

The range of prices within each country reflects the differences in prices across various products. For instance, with regard to paper, the lower price refers to newsprint and the higher price to Paper A4 B-Copy which is used mainly for photocopying.

As demonstrated above, there are differences across countries. Pulp is significantly cheaper in China than in Europe. Given the growing influence of China in the sector, this is important to bear in mind.

8.3 Freight rates

Shipping is the predominant mode of transport for imports and exports of pulp and paper, representing 74% and 65% respectively. As discussed in the next section the main suppliers of pulp and paper to the EU by sea are the US, Brazil and China and the main export partners are China, the US and Turkey.

Pulp and paper is transported by a number of vessel types, shown in the table below.

Table 8.3. Vessel types used for transport of pulp and paper by sea

Vessel Type Exports Imports

Dry Bulk 5% 28%

General Cargo/Neo Bulk 31% 30%

Container 64% 42%

Total 100% 100%

Source: IHS

For the purpose of this study the impact of possible EU policy action on shipping emissions has been assessed for container vessels (i.e. those types of vessels that are predominantly involved in transporting pulp and paper products).

The freight rates for container ships travelling the main trade routes are provided in the table below, using 2011 data presented in euros per tonne. As would be expected, there are significant economies of scale to be achieved in the transport of pulp and paper.

Table 8.4. Freight rates by weight of good, 2011, (€/tonne)

Container Class Turkey US China Brazil

IHS Data

A 8,000+ teu €7.30 €8.60 €28.40 €13.50

B 5 -7,999 teu €8.80 €10.30 €34.30 €16.30

C 3 -4,999 teu €8.70 €10.10 €33.60 €16.00

D 2 -2,999 teu €8.80 €10.30 €34.20 €16.30

E 1 -1,999 teu €10.50 €12.30 €40.70 €19.40

F -999 teu €15.50 €18.10 €60.10 €28.60

IHS Average €9.90 €11.60 €38.60 €18.40

OECD Data

OECD Imports Average €49.40 €42.20 €42.60

OECD Exports Average €40.40 €137.90




Source: IHS, 2011 and OECD, 2003-2007 average

In order to represent the range of freight rates depending on the trade route travelled, the following low and high estimates have been used:

- On import routes, the IHS average for the US route (€11.60/tonne) was used as the lower estimate and the OECD average for the route to Brazil (€42.60/tonne) was used as the higher

estimate for 2010. - On export routes, the OECD average for the US(€40.40/tonne) is the lower estimate and the

OECD export average for China (€137.90/tonne) is the high estimate for 2010.

8.3.1 Freight rates ad valorem

The ad valorem of freight rates helps in assessing the impacts of a change in freight rates. Combining the wide range of freight rates already collected with the differing prices of pulp and paper gives the range of possible ad valorem values below.

Table 8.5. Freight rates as a proportion of the pulp and paper price

Freight Rate (Euros/tonne)

Ad Valorem

Trade Direction

Trade Route Price

(Euros/tonne) Low High Low High

Imports Pulp €639-664 €11.60 €42.60 1.7% 6.7%

Paper €504-865 €11.60 €42.60 1.3% 8.5%

Exports Pulp €639-664 €40.4 €137.90 6.1% 21.6%

Paper €504-865 €40.40 €137.90 4.7% 27.4%

The ad valorem shows that the shipping costs of seaborne paper imports are small compared to the price of the goods in most cases. Ad valorem costs are higher on export routes. In this assessment both the lower values and the highest value - exporting to China – are assessed in order to gauge impacts where they are most severe.

8.3.2 Freight rate elasticities

The relationship between container freight rates and oil prices is weaker than it is for other types of vessel. Vivid Economics (2010) estimated that the oil price elasticity of container freight rates for the route of Asia to EU is 0.12, meaning for a 10% increase in fuel prices there will be an associated 1.2% increase in container freight rates.

UNCTAD (2009) provided combined estimates for the three main East-West container routes (the transpacific, the transatlantic, Asia-Europe) of 0.137 and 0.291.

For the analysis of imports it is most appropriate to use the Vivid Economics figure of 0.12 which specifically applies to the Asia – EU route. For exports, however, there is no specific route data available, and the UNCTAD range, which includes some of the export routes, is used.

8.3.3 Impacts of the policy options

The TIMES model assesses the impacts of the policy options on freight rates for containers over time and compared to the baseline.

8.3.3.1 Change in freight rates for key import routes

The results are displayed in Table 5 for the lower and higher freight rate estimates on the US and Brazil routes. The first one relate to the route between Europe and the US, and the latter to trade routes with the East Coast of South America (Brazil).




Table 8.6: Differences in freight rates for pulp and paper per policy option compared to the baseline by 2030

Freight rates (€ per tonne)

%

ETS Closed -€16 to -€2 (-34% to -17%)

ETS Open (free allowances) -€16 to -€3 (-34% to -22%)

ETS Open (full auctioning) -€13 to -€2 (-28% to -15%)

Emissions tax (low) -€13 to -€2 (-29% to -15%)

Emissions tax (high) €11 to €23 (50% to 90%)

Target-based compensation fund -€13 to -€2 (-28% to -15%)

Contribution-based compensation funds

-€13 to -€2 (-29% to -15%)

All policy options except the high emissions tax are expected to result in lower freight rates compared to the baseline by 2030, with a drop of 15% to 34% depending on routes and options. Given the low elasticity of demand for pulp and paper, it is assumed that those savings will be retained by shipping operators. This means that there will be no impact on EU producers and consumers under any of these options for the motor vehicle market by 2030.

Under the high emissions tax, freight rates would rise by 50% to 90%, adding €11-€23 per tonne to the baseline by 2030. Given the high level of competition importers face on the European market both from EU producers and other media, it is assumed that only a low proportion of this increase will be passed on to their customers.

8.3.3.2 Change in freight rates for key export routes

The top two destinations for European exports of paper and pulp are China and the US. The table below displays the change in freight rates on these routes under each policy option.

Table 8.7: Differences in freight rates for pulp and paper per policy option compared to the baseline by 2030

Freight rates

(€ per tonne)

%

ETS Closed -€22 to -€7 (-17% to -15%)

ETS Open (free allowances) -€17 to -€10 (-22% to -11%)

ETS Open (full auctioning) -€7 to -€4 (-15% to -2%)

Emissions tax (low) -€7 to -€4 (-15% to -2%)

Emissions tax (high) €40 to €143 (91% to 94%)

Target-based compensation fund -€7 to -€4 (-15% to -2%)

Contribution-based compensation funds

-€7 to -€4 (-15% to -3%)

All policy options are expected to result in a drop in freight rates by 2030 compared to the baseline. As explained in the previous section, these savings would be retained by EU exporters and the policies would therefore not have any impact on the rest of the supply

chain.

A high tax on emissions on the other hand would result in a significant increase in price which would in turn penalise EU exporters on overseas market, in particular in view of the competition to which the industry is subjected.

8.4 Origin and destination of consumption and production

For data collection purposes, the following sub-categories have been used as definitions of paper and pulp:




i) pulp of wood or of other fibrous cellulosic material; recovered (waste and scrap) paper or paperboard; and

ii) paper and paperboard; articles of paper pulp, of paper or of paperboard (Comext categories 47 and 48, Prodcom codes 171 and 172).

The EU is the world’s largest producer of paper and it is a net exporter of paper and pulp, although this hides net imports of pulp and net exports of paper. The diagram shows the scale of the trade balance in relation to production and consumption in the EU.

Figure 8.2. EU trade balance for paper and pulp, all modes

Source: Eurostat 2012

This pattern of production and consumption shows that the EU is relatively self-sustaining with respect to pulp and paper compared to other commodities. It only relies on imports for 9.5% of its consumption and it exports 16% of its production.

8.4.1 The role of shipping

Shipping is the main mode of transport for pulp and paper. Road transport provides the next most popular option, accounting for 21-28% of trade.

Figure 8.3 Extra EU imports of pulp and paper by transport mode (% value)

Total: € 13,033m

Figure 8.4 Extra EU exports of pulp and paper by transport mode (% value)

Total: € 23,097m


€ - € 20 € 40 € 60 € 80 € 100 € 120 € 140 € 160

Billions

Production

Consumption

Exports

Imports

Sea 74%

Road 21%

Rail 3%

Air 2%

Sea 65%

Road 28%

Rail 4%

Air 3%




The EU import market is dominated by a few trade partners with the US, Brazil, China, Canada, Norway and Chile accounting for over half of all imports. On the other hand, the export market for pulp and paper is highly fragmented with the main partner, China, only accounting for 9% of the total.

Figure 8.5: Origin of EU seaborne imports (percentage of total value)

Figure 8.6: Destination of EU seaborne exports (percentage of total value)


As mentioned above, imports of pulp and paper are largely supplied by the US, Brazil, China and Canada. The source of imports has remained relatively constant over time, with Brazil and China having grown their share over the last five years. The yearly fluctuations can be seen in the chart below.

Figure 8.7. Seaborne imports of pulp and paper by origin, 2000 – 2010


China provides the highest value imports, whilst US fuel tax credits allow its supplies to be more competitive with Brazil. The US and Brazil supply the higher amounts of pulp and paper at the lower value per tonne, which has stayed constant over the last decade. Imports from China, however, have been declining in value, possibly due to the upturn in electronic media over the past decade. Nonetheless, imports have responded well since the recession, perhaps due to other "smart" applications for technology, such as intelligent paper and packaging, providing new market opportunities.

Exports from the EU are very dispersed, with China, the US, Turkey and India as the top ranking destinations, although they only capture a small share of the market as a whole. As with imports, China is a growing market for EU exports, with the US reducing its share of

Figure 1 Origin of EU seaborne imports, 2010 (% value

Total: € 9,299m

Figure 2 Destination of EU seaborne exports, 2010 (% value)

Total: € 14,891m

US18%

Brazil15%

China10%

Canada5%

Norway4%

Chile4%

Uruguay3%

Russia2%

Other37%

China9% US

8%

Turkey4%

India3%

Australia2%

Brazil 2%Other72%

Figure 1 Origin of EU seaborne imports, 2010 (% value

Total: € 9,299m

Figure 2 Destination of EU seaborne exports, 2010 (% value)

Total: € 14,891m

US18%

Brazil15%

China10%

Canada5%

Norway4%

Chile4%

Uruguay3%

Russia2%

Other37%

China9% US

8%

Turkey4%

India3%

Australia2%

Brazil 2%Other72%

€ -

€ 2

€ 4

€ 6

€ 8

€ 10

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Bill

ion

s URUGUAY

RUSSIA

NORWAY

INDONESIA

CHILE

CANADA

CHINA

BRAZIL

UNITED STATES

EU Total




consumption of EU pulp and paper slightly between 2006 and 2010. The overall trend is increasing, and like imports, exports bounced back well after the 2009 financial crisis.

Figure 8.8. Seaborne exports of pulp and paper by destination, 2000 – 2010


Overall, Europe exports a lower value of pulp and paper per tonne compared with its imports. The EU exports its highest value pulp and paper products to the US, while its moderate and lower value per tonne exports go to Turkey and China respectively. This pattern has been consistent over the past ten years, with the value of products in general decline. This may be explained by the continuing development of electronic media leading to a reduction in certain paper-based printing and publishing segments as explained above.

8.5 Elasticities of demand

Shipping is the main mode of transport for pulp and paper. Road transport provides the next option accounting for price elasticities of demand.

The literature review has uncovered estimates for the own price and Armington elasticities of paper but not for pulp. The estimates for paper are therefore applied to the whole sector in our analysis.

The own price elasticity of paper is low according to sources found for the EU and China. A range of own price elasticities for Europe of between -0.5 and -0.88 was identified in the literature. For China, one of the EU’s main export markets, the own price elasticity is similarly inelastic at -0.69.

Table 8.8 Own price elasticities

Source Product definition Region Elasticity

Ecorys (2009) Paper EU -0.5

JRC (2005) Paper EU -0.88

Li (2004) Paper China -0.69

Armington elasticities were used to gauge the impact of a change in shipping costs on international trade. An Armington elasticity is a form of cross-price elasticity which shows the rate of substitution towards domestically produced goods in the event of a price change of imports. A range of Armington elasticities from the literature is presented below.

€ -

€ 2

€ 4

€ 6

€ 8

€ 10

€ 12

€ 14

€ 16

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Bill

ion

s

TURKEY

INDIA

BRAZIL

AUSTRALIA

CHINA

UNITED STATES

EU Total




Table 8.9. Armington elasticities

Source Product definition Origin Armington Elasticity

Zhang (2006) EU imports of paper Non-EU 0.700

Ecorys (2009) EU imports of Paper Non-EU 0.900

Welsch (2007) EU imports of Paper

France 0.205

Italy 0.420

UK 0.400

The first two studies, Zhang and Ecorys, treat the EU as a bloc and assume all imports into the EU would suffer from the same rate of substitution with domestic products. Welsch on the other hand looks at different EU countries on the basis that the Armington elasticities are likely to differ depending on the level of domestic production. As this analysis considers Europe as a whole, a range of Armington elasticities between 0.7 and 0.9 is used in this study.

8.6 Market structure

Understanding the market structure is essential in order to assess the ability of EU and overseas firms to pass costs through to their customers by raising retail prices as levels of concentration will determine producers’ market power.

A selection of data has been collected on the pulp and paper sector in the EU in order to assess how vulnerable it may be to carbon costs on shipping, these are presented below.

Table 8.10. Information on EU and overseas pulp and paper market structure (Source: Eurostat 201219)

Manufacture of pulp, paper and

paper products

Manufacture of pulp, paper and

paper products as a percentage

of all EU

manufacturing

2007 2007

Number of enterprises 19,161 1%

Turnover or gross premiums written (M€) 170,000 2%

Production value (M€) 166,000 2%

Value added at factor cost (M€) 42,000 2%

Gross operating surplus (M€) 15,800 2%

Total purchases of goods and services (M€) 137,000 3%

Personnel costs (M€) 26,200 2%

Gross investment in tangible goods (M€) 8,320 3%

Number of persons employed 6,960 2%

Number of employees 6,800 2%

Apparent labour productivity (Gross value added per person employed) 60 114%

Average personnel costs (personnel costs per employee)

(thousand euro) 38.8 113%

Gross operating surplus/turnover (gross operating rate) (%) 9.04 95%

Investment per person employed 12 158%

19

Eurostat, European Business - selected indicators f or all activ ities (NACE div isions) [ebd_all] Extracted 7-2-12




Relative to all manufacturing, the pulp and paper industry has higher than average levels of labour productivity, personnel costs and investment per person employed. This is likely to be influenced by the high capital intensity of the industry and the high capital costs involved in pulp and paper plants. On the other hand, the paper and pulp sector has lower margins (95% of the average) than manufacturing as a whole.

The industry is also characterised by larger firms than average - it employs 2% of employees in 1% of businesses. This is also driven by the large capital investments needed: a new state-of-the-art pulp mill costs around €1 billion, or even more if it is part of a paper mill. Paper mills for "commodity grades" of paper, i.e. those intended for further cutting into sheets or rolls or subsequent conversion into products, are most often also large or very large and also quite capital-intensive, especially if there are several paper machines on one site. Plants producing speciality grades may be smaller and most converting mills, i.e. those producing usable paper products, are SMEs.

Ranked by the CR10 index for concentration (i.e. market share of the ten biggest suppliers), concentration in the paper-making subsector is as follows: high (> 85%) for coated mechanical paper, uncoated mechanical paper, newsprint and coated wood free paper; medium (65% to 85%) for cardboard, market pulp, and tissue paper; low (< 65%) for uncoated wood, free, container board and wrapping papers20.

As mentioned earlier, it is also an industry that is regularly restructuring, as it is facing increasing competition from extra-EU countries and other digital media. The growing supply of low-cost and high-quality imports of commodity-grade papers, especially from China, is a concern. Other trade concerns include increasing supply costs for raw materials from certain sources (notably Russian hardwood, following tariff hikes in 2006), distortion of the competition resulting from subsidies to companies in third countries (e.g. fuel tax credits granted to US paper producers) and access to new markets.

These features of the market limit the ability of importers to pass costs through to their European customers, and of EU exporters to pass costs through to their overseas customers.

8.7 Summary and analysis of impacts

Given the trade profile of the pulp and paper sector in Europe, implementing a policy to reduce GHG emissions from shipping is likely to have impacts on both the internal and export markets. The findings from the analysis of these impact are summarised in this section.

8.7.1 Internal market

The impacts of a policy to reduce GHG emissions from shipping on the European market for pulp and paper will be determined by the following key factors:

The EU meets a large share of its needs for paper and pulp: imports only amount to 9.5% of total consumption.

Shipping is the dominant mode of transport for paper and pulp, accounting for 74% of all imports in value terms.

The weight of transport in the price of paper and pulp is not insignificant. It can even be considerable on long routes, especially to China.

The market for paper and pulp is highly competitive with a significant potential for substitution of imports by European production and a range of trading partners.

20

European Commission website




The European market is dominated by large companies but with tight margins and growing competition from other media limiting producers’ ability to pass cost increases onto their customers.

The respective impacts of the policy options are estimated in Table 8.12 after a reminder of the key assumptions underlying our analysis in Table 8.11.

Table 8.11 – Key figures and assumptions

Variable Assumption

Initial price (€/unit) 505-720

Initial total EU demand (Million tonnes) 141

Market size (€ M per annum)21

137,333

Initial export demand (€m) 23

% seaborne imports 74%

% seaborne exports 65%

Import freight rate per tonne and ad valorem

12-43 (1.3-8.5%)

Export freight rate per tonne 40-138

Own price elasticity -0.5 to -0.88

Armington elasticity 0.7 – 0.9

Cost pass through to EU converter mills and printers

25%

Table 8.12 – Summary of policy impacts on the internal pulp and paper market by 2030 compared to the baseline

Variable Change in freight

rates (€ per tonne and

%)

Resulting change in price

per tonne of pulp & paper (€)

Resulting change in price per tonne

of paper & pulp as % of total price

Change in demand for

EU production (€m and %)

ETS closed

-16€ to -2€ (-34% to -

17%)

0 0% 0

ETS open (free allowances)

-16€ to -3€ (-34% to -

22%)

0 0% 0

ETS open (full auctioning)

-13€ to -2€ (-28% to -

15%)

0 0% 0

Emissions tax (low)

-13€ to -2€ (-29% to -

15%)

0 0% 0

Emissions tax (high)

11.5€ - 23€ (50%-90.5%)

3€ - 6€

0.4% - 0.6% 6-12 (0.3%-0.55%)


-13€ to -2€ (-28% to -

15%)

0 0% 0


-13€ to -2€ (-29% to -

15%)

0 0% 0

21

Estimated as total consumption in 2009 multiplied by oil prices in this y ear




All policy options except the high emissions tax are expected to result in lower freight rates compared to the baseline by 2030. Given the elasticity of demand for paper and pulp, it is assumed that those savings will be retained by shipping operators. This means that there will be no impact on EU producers and consumers under any of the options for the motor vehicle market by 2030.

The high emissions tax would lead to an increase in freight rates, a small proportion of which is assumed to be passed on by extra-EU suppliers to their EU customers. This in turn would make Europe products more attractive, shipping some of the demand from imports to domestic production. Impacts on the export markets cannot be quantified in the same way as for the internal market as each export country would have a different Armington elasticity and different elasticities of demand. Broad estimates of the impact of the policies on freight rates on trade routes with China and the US are presented in Table 8.13.

Table 8.13 – Summary of policy impacts by 2030 on export prices of pulp and paper

Variable Change in freight rates (€ per

tonne and %)

Resulting change in price of paper

and pulp (€ per tonne and

%)

ETS closed

-22€ to -€7 (-17% to -15%)

0

ETS open (free allowances) -17€ to -10€ (-22% to -11%)

0

ETS open (full auctioning) -7€ to -4€ (-15% to -2%)

0

Emissions tax (low) -7€ to -4€ (-15% to -3%)

0

Emissions tax (high) 40€-143€ (91-94%)

10€-36€ (1.4%-6.7%)


-7€ to -4€ (-15% to -2%)

0


-7€ to -4€ (-15% to -3%)

0

As seen above, all options except for the high tax are expected to have no significant impact on the price of Europe’s exports of pulp and paper. They would therefore have no impact on the industry’s international competitive position.

8.8 References

European Commission (2012) Wood, Paper, Printing Pulp and paper: competitiveness. http://ec.europa.eu/enterprise/sectors/wood-paper-printing/paper/competitiveness/index_en.htm (Accessed 25th June 2012).

FOEX (2012), PIX Pulp Indexes Europe, http://www.foex.fi/ (Accessed 25th June, 2012)

Ecorys, 2009, Study on European Energy-Intensive Industries – The Usefulness of Estimating Sectoral Price Elasticities. http://ec.europa.eu/enterprise/policies/sustainable-business/climate-change/energy-intensive-industries/carbon-leakage/files/cl_executive_summary_en.pdf (Accessed 26th June 2012)

Joint Research Council (JRC) (2005), An Econometric Input Output model for EU countries based on supply and use tables: the production side. http://www.iioa.org/pdf/17th%20Conf/Papers/1068331049_090529_135524_SAOPAULOEIOPRODUCTION090529.pdf (Accessed 26th June 2012)

http://ec.europa.eu/enterprise/sectors/wood-paper-printing/paper/competitiveness/index_en.htm

http://ec.europa.eu/enterprise/sectors/wood-paper-printing/paper/competitiveness/index_en.htm

http://www.foex.fi/

http://ec.europa.eu/enterprise/policies/sustainable-business/climate-change/energy-intensive-industries/carbon-leakage/files/cl_executive_summary_en.pdf



http://www.iioa.org/pdf/17th%20Conf/Papers/1068331049_090529_135524_SAOPAULOEIOPRODUCTION090529.pdf

http://www.iioa.org/pdf/17th%20Conf/Papers/1068331049_090529_135524_SAOPAULOEIOPRODUCTION090529.pdf




Li (2004), Economic Transition and Demand Pattern: Evidence from China’s Paper and Paperboard Industry. http://www.cpbis.gatech.edu/files/papers/CPBIS-WP-04-05%20Li_Luo_McCarthy_Economic%20Transition%20and%20Demand%20Pattern.pdf (Accessed 26th June 2012)

Zhang (2006), Armington parameter estimation for a computable general equilibrium model: a database consistent approach. http://www.animals.uwa.edu.au/__data/assets/pdf_file/0005/99257/06_10_Verikios.pdf (Accessed 26th June 2012)

Welsch (2007), Armington elasticities for energy policy modeling: Evidence from four European countries. http://www.sciencedirect.com/science/article/pii/S0140988307000898 (Accessed 26th June 2012)

http://www.cpbis.gatech.edu/files/papers/CPBIS-WP-04-05%20Li_Luo_McCarthy_Economic%20Transition%20and%20Demand%20Pattern.pdf

http://www.cpbis.gatech.edu/files/papers/CPBIS-WP-04-05%20Li_Luo_McCarthy_Economic%20Transition%20and%20Demand%20Pattern.pdf

http://www.animals.uwa.edu.au/__data/assets/pdf_file/0005/99257/06_10_Verikios.pdf

http://www.sciencedirect.com/science/article/pii/S0140988307000898




9 Appendix 9: Case studies exploring the potential impacts of policy action on specific regions and routes

9.1 Introduction

9.1.1 Objectives and scope

Ricardo-AEA has been commissioned by the European Commission to provide support for the Impact Assessment (IA) of a proposal to address maritime transport GHG emissions, hereafter referred to as the IA support study.

This study focused on providing a detailed assessment of the environmental, social and economic impacts of the proposed policy options on the shipping sector and the wider economy, using a dedicated version of the TIMES energy system model designed to focus on international shipping and additional off-model quantitative and qualitative analysis. Whilst a detailed assessment of the generalised impacts was carried out for different types of vessels and for different types of commodities transported by sea, it was not possible to investigate in detail the potential impacts for specific regions or routes.

The following policy options have been considered as part of the IA support study:

A ‘do nothing’ scenario to be used as the baseline for the assessment of the other options.

A cap and trade Emissions Trading Scheme (ETS). This would set a cap on aggregate emissions defining the overall limit of emissions from all of the participants in the scheme and therefore providing certainty over the amount of reduction that will be achieved. Sub-options were considered in order to reflect the remit of the ETS (open or closed) and the way allowances would be allocated (free or auction).

A tax on emissions. Scenarios for high and low taxes on emissions were analysed.

A tax on fuel sales.

A compensation fund. A maritime sector GHG Compensation Fund could be funded either by a levy on all EU maritime sector fuel purchases or via contributions from ship owners/operators based on the emissions from their ships on voyages to and from Europe. Fund members would pay into the Fund monetary amounts in line with their emissions performance on all journeys to and from Europe. The Fund membership cost per tonne of CO2 would need to be set in advance. The contributions collected in the Fund would be used largely to finance emission reduction measures in the maritime sector and to purchase recognised offset credits from the international carbon market to count towards the reduction target, up to a certain limit (designated as a percentage of the yearly reduction target).

Mandatory emission reductions per ship. This option was not taken forward for a detailed assessment, in part because its implementation would likely result in legal challenges.

The purpose of this report is to carry out three detailed case studies in order to explore the possible impacts of the key policy options on selected routes and vessel types. In particular, the case studies aim to explore the potential risks of policy evasion (through route shifting), policy avoidance (through modal shift) and how individuals could be affected by changes in passenger ferry prices.




In order to explore the potential impacts for specific regions or routes, the following case studies have been selected:

A container ship or bulk carrier calling into a Baltic Sea port

A ship using one of the “Motorways of the sea”

Passenger ferries operating in the Mediterranean Sea.

The remainder of this report is structured as follows: Section 9.1.2 briefly describes the methodological assumptions common to all case studies. Chapters Error! Reference source not found., Error! Reference source not found. and Error! Reference source not found. present the detailed assumptions and results for each of the above-mentioned case

studies.

9.1.2 Methodology

The methodology followed for undertaking the cases studies draws from the analysis undertaken in the main IA support study. Data inputs and parameters on costs and vessel performance used are based on Marintek and IHS data as well as Ricardo-AEA calculations, and are consistent with those used in the TIMES international shipping model. Where specific input data were available on the routes considered, such as distances travelled and speed of vessels, these have been integrated into the calculations. Where possible, assumptions have been refined with specific data collected. Data, assumptions and information have been obtained by reviewing readily available published information. In addition to this, relevant stakeholders/experts have been contacted for their input (six ship operators, three port operators, and two additional stakeholders/experts).

9.2 The Baltic Sea case study

This case study focuses on assessing the risk of evasion from the introduction of the key policy options considered to reduce GHG emissions from shipping. Evasion will occur if shipping operators decide to add an additional port call outside the EU to their route in order to limit their exposure to policy and the ensuing compliance costs.

The route selected for this study is a voyage from Rauma, in Finland, to the East coast of the USA, with Kaliningrad as the potential evasion port.

9.2.1 The route

The port of Rauma is situated on the West coast of Finland. It is the fifth largest Finnish port in terms of total exports. It is also the largest container port on the West coast of Finland. It has a total of 20 berths and full-service facilities for import, export and transit traffic.

In 2011, total traffic through the Port of Rauma amounted to 6.14 million tonnes, showing an increase of 9.8% over the previous year. The increase was primarily based on significantly higher pulp and grain export volumes and round wood, liquid and bulk import volumes 22.

Exports in 2011 accounted for 3.95 million tonnes of traffic (+4.7 % compared to 2010), imports for 2.05 million tonnes (+13.6 %) and domestic shipments for 0.14 million tonnes (+727 %). The most important export articles by far were paper and cardboard (68% of the total) and pulp (13%). That year, 2.7m tonnes of paper and cardboard were shipped from Rauma, mainly to Europe and the United States22. The link to the US is the focus of this case study: the Port of Rauma offers 19 weekly departures to European ports and six monthly departures to the USA.

22

Port of Rauma




9.2.2 Ship owners / operators response to GHG policies

Three different options have been identified and analysed with regards to the ship owners ’ / operators’ response to GHG policies for this specific route:

1. Business As Usual (BAU) option: This is the ‘do nothing’ option: as the policy is

introduced, ships continue to operate directly from Rauma to the US. This option assumes that ships do not evade or adopt any abatement measures in order to comply with the policy. Emissions will thus be the same as if the policy was never introduced and any tax/ETS permits will be paid in full. This option has been included as a counterfactual scenario in order to assess whether it is economically attractive to evade the policy by making an additional port call.

2. Evasion option: Ships may introduce an additional port call, outside the EU, in order

to avoid paying in full for the policy, i.e. evade the costs associated with the policy for part of the journey. This assumes that ships do not adopt any efficiency measures (neither technical nor operational), and therefore do not reduce GHG emissions. Under this option, two cases are possible and considered in the analysis: (a) the vessel calls briefly at the evasion port and then continues its journey without loading/unloading any cargo, or (b) the vessel calls at the evasion port where full transhipment of cargo occurs.

3. Abatement option: In this option ships adopt technologies and measures in order to

reduce GHG emissions. Vessels are assumed to reduce emissions in line with reductions estimated by the TIMES model used for the IA support study (i.e. for a specific policy, the TIMES model estimated the percentage reduction in total emissions when compared to the baseline for that year). For simplicity in the case study, this reduction is assumed to be the same for each ship as well. In order to do that vessels implement a number of efficiency measures (not specified) that amount to the required emission reductions. The cost increment (as a proportion of costs) is assumed to be the same as the overall investment cost increase estimated by TIMES. The vessel complies with the policy by paying the appropriate tax/permits for the remaining emissions.

9.2.3 Potential for evasion

The most likely port of evasion on the route between Rauma and the East coast of the US is considered to be Kaliningrad as illustrated in the map below.




Figure 9.1: Location of Rauma and Kaliningrad ports

Kaliningrad port is located in the mouth of the River Pregol, within Kaliningrad city limits. It is accessible from the Baltic Sea via a 24 nautical miles long canal.

9.2.3.1 Practical feasibility of evasion

The main technical obstacle preventing evasion by making an additional port call in Kaliningrad is the draft limitation in order to use the canal.23 This limits the size of ships which can use the harbour. Indeed, it is understood that some ships used on the line from Rauma to the East coast of the US exceed the draft limitations and would therefore not be able to evade to Kaliningrad.24

There are other, non-technical, obstacles to using Kaliningrad as an evasion port. The main obstacle is the fact that it is not possible for a vessel to just stop at Kaliningrad, register and then move on to Rauma in order to avoid the costs associated with a policy on GHG emissions. An operator has to have some business in the port, such as loading or unloading goods. This means that if an operator wanted to evade the policy but did not already have business in Russia, they would have to develop new business activities to enable them to use Kaliningrad. This is not a straightforward matter, both from a business development point of view and because using a new port creates additional administrative procedures24.

Another possible restriction on evasion is the fact that access times to the port of Kaliningrad are limited to short windows of time. These times might not fit in with business operations and could create additional costs if they lead to delays and ships being forced to wait several hours before they can load / unload goods in the port24.

Finally, Kaliningrad is a significantly smaller port than Rauma which means there could be issues of capacity as well, if more ships were to stop there.

23 Kaliningrad port website. 24 Discussions with operator.




9.2.3.2 Financial attractiveness of evasion

Aside from the operational and technical considerations mentioned above, the potential for evasion will depend on the net financial impacts of making an additional stop at Kaliningrad en route to the East coast of the US. This in turn will depend on:

The additional fuel costs due to the longer route, combined with the opportunity costs from taking longer to reach the destination as a result of evasion (resulting in loss of revenue), as well as the additional port costs incurred because of the stop at Kaliningrad. (A)

The carbon cost savings associated with only being liable for policy costs (e.g. ETS allowances, emissions tax) on GHG emissions for the journey from Rauma to Kaliningrad, rather than for the whole journey from Rauma to the US. (B)

The additional costs and savings due to the uptake of abatement measures. (C)

The costs, including opportunity costs for lost revenue, and emissions were calculated for the baseline scenario as well as for each policy scenario analysed, for each of the three response options (BAU, Evasion, and Abatement). The baseline costs associated with the direct voyage and voyage via Kaliningrad are considered below, followed by the costs associated with the policy scenarios and the response options. As in the main Impact Assessment study, impacts are estimated for 2030.

A. Shipping costs with and without evasion

Table 9.1 shows the basic data inputs and assumptions used that are common for all response options and policy scenarios. For this case study it has been assumed that the vessel type is a general cargo ship of medium size that is suitable for journeys of similar length to the journey analysed.

Table 9.1 Assumptions on ship operation parameters in 2030 (2008 prices)

Parameter Unit Value Source

Discount factor for the

vessel’s capex % 10 As agreed for the IA support study

Fuel price € per tonne 776

As estimated in the IA support study (Ricardo-AEA calculations, based on the EC energy roadmap CPI scenario oil prices, Purvin & Gertz

(2009), and IMO studies). 2010 cost deflated to 2008.

Operating costs per day

€ per day 3,061 Provided by Marintek for the IA support study. 2010 costs deflated to 2008.

Investment/financial

costs – yearly

1,000€ per

year 2,135

Marintek capex estimates; yearly payment calculated with given discount rate, assuming

repayments over the lifetime of the vessel. 2010 costs deflated to 2008.

Max cargo intake tonnes 19,545 Ricardo-AEA estimate based on operator data

Freight rate per tonne (low)

€ per tonne 9 Based on 2010 IHS data25

Freight rate per tonne (high)

€ per tonne 74 Based on 2010 IHS data26

25 IHS estimates for general cargo freight rates betw een New York and North West Europe at €9/t. How ever, this is a shorter route than the one considered in this case study and in the opposite direction of travel. 26 The OECD Maritime Transport Cost Database provides estimates from Norw ay to the US (Finland is unavailable). The freight

rate according to this source is: €75/t





Vessel speed

(service) knots 17 Average speed for operator vessel type

Fuel consumption at

sea tonnes/day 40

Average specific fuel consumption for operator

vessel type

Days in operation days/year 341 Marintek estimate (includes time at sea, at ports, and at slow zones)

Table 9.2 presents the detailed baseline scenario costs that a single ship would be subject

to on its voyages from Rauma to the East Coast of the US in 2030. It reflects a situation where no EU policy action on maritime GHG emissions has been introduced and is later used as the basis for policy scenario calculations.

For simplicity, it is assumed that the vessel will only travel on this route and the return journey will be equivalent in terms of cost and emissions to the outward trip. This means that when estimating yearly costs for 2030, these are equivalent to those of multiple identical trips from Rauma to the East Coast of the US.

Table 9.2 Estimated costs of shipping between Rauma and the East coast of the US with and without evasion in 2030 (2008 prices)

Rauma – the East coast of

the US

Rauma – Kaliningrad – East coast of the

US (no transhipment)

Rauma – Kaliningrad – the East coast of the US

(with transhipment)

Route information

Distance (nm) 4,700 4,966 4,966

Journey time (days) 17.3 18.3 20.5

Fuel consumption (t) 613 645 653

Number of journeys per year

19 18 16

Yearly costs (in thousands of €)

Investment costs 2,135 2,135 2,135

Operating costs 1,004 1,009 1,003

Fuel costs 9,030 9,000 8,108

Additional port costs n/a 541 481

Total costs 12,408 12,923 11,947

Opportunity costs* - 868 2,391

Total costs incl. opportunity cost

12,169 13,552 14,118

Freight rate to break even (€)

32.8 36.1 37.5

Source: operator information and Ricardo-AEA calculations * based on average freight rate (revenue lost due to less journeys in one year)

Overall, adding an additional port call results in fewer trips in a year, and as a result less fuel is consumed. However, this may affect an operator’s competitiveness and may also result in loss of revenue. The key consideration when discussing evasion is thus seeing whether this opportunity cost is higher than the savings of carbon cost that could be achieved by evasion.




B. Cost of carbon for each policy scenario with and without evasion

Policy options will introduce a carbon charge in one way or another. The evasion route would enable the shipping operator to reduce its exposure to carbon costs as only the journey from Rauma to Kaliningrad would be subject to the policy.

Table 9.3 shows the 2030 carbon prices used. The projected ETS prices were based on projections provided in the EC Roadmap for moving to a competitive low carbon economy in 205027 and were used in the open ETS scenarios and the low emission tax scenario. Carbon prices in the extreme high emission tax scenario were based on the marginal abatement cost value obtained from the TIMES model for the Closed ETS policy scenario. The closed ETS carbon price would be determined in-sector, and it is difficult to provide an estimate for this. The final impact on operators will depend on how many additional permits over and above their allocation they would need to buy in order to comply or on the costs of reducing their emissions with abatement measures. In the analysis, it has been assumed that only the abatement response option would ensure compliance with this policy scenario. For the other response options under the closed ETS, the marginal carbon price was used, and should be seen as the worst case scenario where the operator buys all permits at maximum price.

The emission reduction achievable was extracted for each policy option from the TIMES outputs for the main IA support study. This was obtained by setting a target for emission reductions in 2050 of 40% compared to 2005 emissions and an intermediate target in 2030 of 10% compared to 2005. For the closed ETS scenario, the latter target corresponds to a reduction of 21% compared to the baseline scenario emissions in the same year. This and other scenario assumptions for required emissions are shown in Table 9.3.

Table 9.3 Carbon related assumptions for 2030 (2008 prices)

Carbon prices EUR/tCO2

Emission reduction compared to baseline

Closed ETS (free allocation) 516 21%

Open ETS (free allocation) 36 16%

Open ETS (full auctioning) 36 16%

Emission tax – low 36 16%

Emission tax – high 516 21%

Target-based compensation fund 36 16%

Contribution-based compensation

fund 36 16%

Source: TIMES

In order to calculate the effect of policies on costs for a single ship, these emission reduction figures were assumed to be passed down to every single ship as a target reduction against each vessel’s baseline performance. Carbon costs associated with each policy were obtained using the carbon prices shown in the table above and the emissions included within the scope of the policy action. The level of carbon emissions covered within the scope of possible EU policy action under each of the three possible response options is presented in Table 9.4 below, based on fuel consumption assumptions listed in Table 9.2. For the evasion response option, emissions are assumed to stay the same irrespective of policy scenario, as no abatement measures are adopted. For the abatement response option, the resulting emissions for each journey are assumed to follow the general modelling output obtained in the IA study, i.e. be reduced by percentages shown in Table 9.3 above.

27 Reference fossil fuel prices, fragmented action scenario: http://eur-

lex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2011:0288:FIN:EN:PDF

http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2011:0288:FIN:EN:PDF





Table 9.4: Total CO2 emissions in the scope of the proposed EU policy options per

journey (in 2030)

Response

option

Policy scenario

1: Business as usual

2a: Evasion without

transhipment

2b: Evasion with

transhipment

3: Abatement (achievable

emissions with

technical measures)

Actual emissions

for each response option, t

CO2

Closed ETS 1,930 396 396 1,518

Open ETS (free allocation)

1,930 396 396 1,613

Open ETS (full auctioning)

1,930 396 396 1,613

Emission tax – low

1,930 396 396 1,613

Emission tax – high

1,930 396 396 1,521


1,930 396 396 1,613

Contribution-based

compensation fund

1,930 396 396 1,613

Target, t CO2

ETS options 1,518 311 311 1,518

Both evasion options result in equivalent emissions in the emission control area, as only the journey leg from Rauma to Kaliningrad is subject to potential EU policies. In the abatement option, the ship is assumed to continue on the same route as in the business as usual option (no additional non-EU stop-over).

The carbon emissions subject to charge will depend on the combination of policy scenario and the response option. For the open ETS option with free allocation, permits would be bought for each tonne of emissions above the target which corresponds to 21% reduction compared to baseline. For the tax options, a tax would be charged for each tonne of all actual emissions, while for options that include auctioning of permits, sufficient permits would have to be bought for all emissions. The emissions subject to these charges and the resulting carbon costs for each policy scenario are shown in Table 9.5 and Table 9.6.

Table 9.5 Quantity of carbon emissions incurring charges for each policy scenario (per journey in 2030)

1: Business

as usual

2a: Evasion without

transhipment

2b: Evasion with

transhipment

3: Abatement

Charged CO2

emissions

per voyage (t)

Closed ETS 412 84 84 -

Open ETS (free alloc) 412 84 84 95


1,930

396

396

1,613

Emission tax – low 1,930 396 396 1,613

Emission tax – high 1,930 396 396 1,521


1,930 396 396 1,613


1,930 396 396 1,613




As seen above, the quantity of carbon emissions exposed to EU policy and subject to charge is significantly reduced through evasion. For the abatement response option, a carbon charge only applies to the remaining emissions after the implementation of technologies/measures. The cost of carbon for each policy scenario with all three response options is given in Table 9.6.

Table 9.6 Cost of carbon for each policy scenario and response option per journey in 2030 (2008 prices)

Cost of carbon (€) per journey

1: Business as

usual

2a: Evasion without

transhipment

2b: Evasion with

transhipment 3: Abatement

Closed ETS 212,497 43,597 43,597 -


14,825 3,042 3,042 3,417


69,475 14,254 14,254 58,079

Emission tax – low 69,475 14,254 14,254 58,073


995,344 204,209 204,209 784,533


69,475 14,254 14,254 58,079


69,475 14,254 14,254 58,073

C. Additional costs and savings due to the uptake of abatement measures

In the case of the abatement response option, the adoption of abatement measures incurs extra capital costs (in order to implement them), and also saves fuel costs.

The fuel cost savings were calculated using the same percentages as used for emission reduction calculations (Table 9.3), i.e. the fuel cost per journey multiplied by the proportion of fuel savings. For the costs of abatement measures, TIMES model outputs were used. For each policy, the cost of abatement measures as a proportion of baseline investment costs was obtained. The additional investment cost for a single ship was assumed to follow the same trend as the overall model result. The resulting extra costs and savings per trip are shown in Table 9.7. Note that these figures do not represent the full costs of abatement measures, but the proportion of the required financial repayment expressed per voyage. This is so the costs and savings can be compared for a single year.

Table 9.7 Additional costs and savings due to abatement measures per trip in 2030 (2008 prices)

Costs and savings (€) per journey

Costs of abatement measures

Fuel cost savings

Closed ETS (free allocation) 6,391 101,419

Open ETS (free allocation) 1,342 78,041

Open ETS (full auctioning) 1,338 77,959

Emission tax – low 1,346 78,000

Emission tax – high 6,312 100,661

Target-based compensation fund 1,338 77,959


1,346 78,000




The basic costs in Table 9.2 are supplemented by carbon costs in Table 9.6 and additional abatement related costs and savings in Table 9.7 to obtain total yearly costs of shipping on the route from Rauma to the U.S. Table 9.8 below summarises the total costs for each policy scenario and response option. This includes opportunity costs based on average freight rates.

Table 9.8 Total costs in 2030 for each policy scenario with and without evasion and abatement (2008 prices)

Total costs (thousand €) per year (including opportunity costs)

1: Business as

usual

2a: Evasion without

transhipment

2b: Evasion with


Baseline 12,169 13,552 14,118 12,169

Closed ETS (free allocation)

16,207 14,337 14,815 10,364


12,451 13,607 14,166 10,777


13,489 13,809 14,346 11,817

Emission tax – low 13,489 13,809 14,346 11,816


31,081 17,228 17,385 25,283


13,489 13,809 14,346 11,817


13,489 13,809 14,346 11,816

The costs for evasion response options are higher than Business As Usual and abatement responses for all policy scenarios except the extreme high tax scenario, while the total yearly cost of the abatement response option is lower in the same cases. This demonstrates that with the given carbon prices in 2030, evasion is not likely to be the most economical option; only in the case of a high emissions tax would it be worthwhile for operators to consider changing their route. However, the high tax scenario is considered to be an extreme case rather than a realistic policy scenario.

For all other options, with or without transhipment, evasion would cost the operators more than retaining the existing route and complying with the policy. As mentioned in the previous section, it would not be possible to stop at Kaliningrad, if this is not linked to a business activity. This means that the ‘no transhipment’ alternative is less likely (unless the call at Kaliningrad is linked to other activities, such as refuelling) and that the additional cost of evading could be high, ranging between €1.9 million per year under the baseline to €3.4 million per year under the Open ETS (free allocation) compared to the abatement response. Note that the closed ETS scenario was assumed to require abatement to comply.

Table 9.9: Incentive to evade or abate

Incentive to evade or abate compared to BAU option

2a: Evasion without

transhipment 2b: Evasion with


Baseline No No Yes

Closed ETS (free allocation)

Yes Yes Yes


No No Yes




Incentive to evade or abate compared to BAU option

2a: Evasion without

transhipment 2b: Evasion with



No No Yes

Emission tax – low No No Yes

Emission tax – high Yes Yes Yes


No No Yes


No No Yes

Abatement is always more economical than business as usual, and nearly always more financially attractive than evasion (Table 9.9). However, as discussed in the main IA support study, achieving the financial benefits requires initial investment, and there are hidden costs and market barriers preventing this from occurring at present.

The level of the carbon price (i.e. the tax rate or the costs of permits) is one of the essential parameters determining whether evasion is financially attractive. The above analysis was conducted with a single carbon price for each policy. It also tested what carbon price would be needed in 2030 to render evasion desirable for each policy. The lowest carbon prices that make evasion a financially attractive option are shown in Table 9.10.

Table 9.10: Carbon prices at which evasion becomes attractive (€/tCO2)

Carbon price (€/tonne CO2) at which incentive to evade occurs

2a: Evasion without transhipment 2b: Evasion with

transhipment

Open ETS (free allocation) 220 302

Open ETS (full auctioning) 47 65

Emission tax – low 47 65


47 65


47 65

9.2.4 Conclusions

As mentioned in the introduction, this case study focuses on assessing the risk of evasion on a trade route between Rauma and the East Coast of the US as a result of the introduction of policy options designed to reduce GHG emissions from shipping.

Shipping operators can respond to the policies in three ways: they can continue operating as before and pay the cost of carbon on their emissions; they can evade and reduce the scope of emissions subject to the policy; they can introduce abatement technologies in order to reduce emissions and therefore their carbon costs while complying with the policy.

Each response generates specific costs and savings:

Under the Business as Usual option, operators will incur carbon costs related to their emissions.

If they evade, they will save on carbon costs as well as fuel costs (as they will be able to do fewer trips over a year due to longer journeys) but the reduced activity will have an opportunity cost on their revenue. In the case of this particular route and evasion through Kaliningrad, there are also technical constraints which have been mentioned earlier.




If the industry chooses to comply with the policy and implement abatement measures, they will face upfront investment costs in new technologies but long-term benefit from fuel savings.

The choice of response will ultimately depend on what is technically feasible and what is most economical.

The analysis shows that, with the exception of the high tax scenario, the net impact of these responses by 2030 favours compliance with the policy and the uptake of abatement technologies. Choosing this response would result in a drop in freight rates which, if retained by shipping operators, will improve their profit margin. If they choose to pass this saving on to their customers, it will improve their competitiveness.

This is an important conclusion for this trade route in particular. This suggests that the policy options considered would not damage its long-term competitiveness and may in fact provide it with a cost advantage by 2030.

9.3 Motorways of the Sea case study

The Commission has a strategy in place to stimulate modal shift of freight movements from road transport to seaborne transport. The Communication on Freight Transport Logistics in Europe28 highlights the economic importance of the European freight transport logistics sector but also the risks of road-based transport growth for the sustainability and competitiveness of Europe's economy as a whole. Concerns such as congestion and its associated economic costs, problems with traffic safety, energy dependency from imported sources, noise, emissions of air quality pollutants and greenhouse gases called for decisive action.

The Commission’s Transport White Paper29 included a relevant goal that states: 30 % of road freight over 300 km should shift to other modes such as rail or waterborne transport by 2030, and more than 50 % by 2050, facilitated by efficient and green freight corridors .

Motorways of the Sea (MoS) are one of the key measures to help restructure long distance freight transport in Europe, putting it on a more sustainable path. MoS can be defined as follows:

‘MoS are existing or new sea-based transport services that are integrated in door-to-door logistic chains and concentrate flows of freight on viable, regular, frequent, high-quality and reliable Short Sea Shipping links. The deployment of the MoS network should absorb a significant part of the expected increase in road freight traffic, improve the accessibility of peripheral and island regions and states and reduce road

congestion.’ 28

Given the strategic objectives in support of moving freight on to ships, understanding the potential risk of modal shift from sea to road which may result from implementing policies to reduce carbon emissions is important. As there has been much effort and funding put into the development of the Motorways of the Sea, this case study focuses on the potential risk of modal shift from sea to road on such a route. More specifically, this case study considers the Nantes (France) to Gijón (Spain) Motorway of the Sea route.

9.3.1 The route

The first Motorway of the Sea between France and Spain was inaugurated in September 2010 between Nantes and Gijón. Its core aim is to help reduce traffic congestion on the main

28

EC (2007) Commission Staf f Working document: The EU’s f reight transport agenda: boosting the ef f iciency, integration and sust ainability of

f reight transport in Europe 29

EC (2011) White Paper 'Roadmap to a Single European Transport Area - Towards a competitiv e and resource ef f icient transport sy stem' (COM(2011) 144 f inal)




road axes and in sensitive zones, in particular in the Pyrenees, by shifting a significant number of lorries onto ships.

Figure 9.2 Nantes – Gijón Motorway of the Sea

Source: GLD Atlantique (2010) The MoS Gijon-Nantes: from the operator’s perspective

The port of Nantes is located in western France, with port facilities on the 65km long Loire Estuary, between Nantes and Saint Nazaire. Nantes – Saint Nazaire is the leading port on France’s Atlantic Seaboard. The selected MoS route uses the ro-ro terminal at Montoir de Bretagne.

In 2010, total traffic through the Port of Nantes amounted to 31.1 million tonnes, an increase of 4.5% over the previous year, although traffic showed a decrease of 7.5% when compared to 2008. The growth in 2010 is thought to be driven by increased container and ro‐ro traffic

volumes: Ro‐Ro traffic rose by 20.8%, primarily due to the increasing volumes handled by the MoS route between Nantes and Gijón. Significantly higher grain and cereal export volumes and cattle feed import volumes were also reported in 2010. Although several vessels had to be rerouted to Northern European ports on account of the strike action during the year, container traffic increased by 10.3%, partly due to the recent link‐up of the Montoir de Bretagne terminals with major transhipment ports including Rotterdam, Valencia and more recently Tangiers Med30.

The port of Gijón is located in Asturias, in the north of Spain, and in the middle point of the Cantabrian Sea. It is the leading port for moving dry bulk in Spain. Overall port traffic at Gijón increased considerably in 2010 when compared to the previous year (+7.6%). This is partly due to the recovery in production at the Arcelor-Mittal factory that increased its purchases of iron ore and coal. Container traffic and general goods also increased (by 29.5% and 46.1% respectively), whilst the introduction of the MoS route with Nantes-Saint Nazaire in September, led to the transport of 2,194 intermodal transport units in 201031.

The new Motorway of the Sea line Gijón – Nantes/Saint Nazaire is operated by GLD Atlantique, providing services for both freight and passengers. The new line currently operates three times a week in both directions, with a roll on/roll off passenger (Ro-Pax)

30

Port Atlantique Nantes – St Nazaire 31

2010 Annual report, Foreword and reports, Port of Gijon, Puerto de Gijon, Autoridad Portuaria de Gijon https://www.puertogijon.es/recursos/doc/English/Annual_Reports/3920_5454201111308.pdf

Road route avoided

Area targeted in the North

Area targeted in the South




vessel. The journey typically lasts 14-15 hours. Freight transport includes: cars, vans, trucks as well as other rolling equipment such as excavators, earth moving equipment etc. The vessel currently used for this route has a capacity for 518 passengers, 120 freight vehicles and 195 cars.32

Discussions with the operator have identified two key commodities traded via this Motorway of the Sea line: new vehicles and slate. All commodities transported on this route are reported and recorded for security reasons. However this is done manually and summary statistics for other commodities are not currently available. The graph below presents the truck traffic for the first five months of the operation of this line.

Figure 9.3 Number of trucks

Source: Motorways of the Sea in Spain, 2011

According to GLD Atlantique, the line benefits from the strategic position of its ports. Nantes has good links to Paris, the Benelux countries and ports serving the UK, whilst Gijón has good links to Portugal and Madrid.

With this new service, the transport costs and delivery time of goods are expected to be reduced. In addition to this, the service can be used by private vehicles as well, offering an alternative mode of transport for tourists travelling between the south of France and north of Spain.

Currently, road hauliers are the main clients of the Motorway of the Sea line selected, although logistic operators also use the service33. It seems that the uptake of the Motorway of the Sea line has been high: more than 40,000 passengers and over 31,000 vehicles used this service during the first year of operation34 resulting in an estimated 90% occupancy rate.

Goods and vehicles may be transported to different destinations, further from Nantes and/or Gijón. In order to factor this in, the route selected for this study is between Valladolid in Spain and Nantes. However, it should be noted that the origin/destination of goods and vehicles will vary significantly, depending on the product and the market demand. This case study focused on the Valladolid - Nantes route for illustrative purposes and does not consider variations to the route. Valladolid was selected as the origin due to the fact that one of the key commodities traded in the MoS route is new vehicles, and the Renault body assembly

32

LD Lines, Welcome onboard - Norman Asturias, Discov er the LD Lines f erry f leet. 33

Motorway s of the Sea in Spain, Álv aro Rodríguez, Planning and Dev elopment Director, Puertos del Estado, Genov a 2011 34

Port of Gijón Newsletter, Successf ul y ear f or the Gijón-Nantes motorway of the sea, 09/11/2011, http://www.puertogijon.es/newsletter/index.asp?id=763&idioma=es

Nu

mb

er

http://www.puertogijon.es/newsletter/index.asp?id=763&idioma=es




plant is located close to the city of Valladolid. Therefore, this case study will provide an indication of the costs for hauliers using the MoS route as part of their journey, and will not necessarily reflect the actual costs incurred when transporting goods.

9.3.2 Potential for modal shift

In general, rail and shipping have been shown to be more cost-effective than road for transferring goods. However, road has the highest modal share in the EU 27, clearly suggesting that other factors are important.35 These could include technical barriers, such as lack of infrastructure, as well as practical and operational considerations, in particular the time required for transporting goods, the reliability of the service, the commodity transported, delivery times etc.

The potential for modal shift on this specific route will depend largely on how ships and lorries compare in terms of costs and time in order to transport goods and vehicles between Nantes and Valladolid.

9.3.2.1 Practical feasibility of modal shift

There are no technical obstacles identified for shifting from ships back to road for the MoS route selected. However, there are other, non-technical, obstacles to shifting back to road on this route. A potential obstacle is that as a result of the introduction of the MoS line, hauliers (and exporters / importers) may have already taken actions in order to use this service, reduce the environmental impact of their operations and save costs. For example, using the shipping service that is available at fixed days and hours may have added a constraint to the logistics system, and as a result operational adjustments or investments may have been required in order to handle freight volumes (and fluctuations in transport volumes) in an efficient and flexible way, or in order to meet time delivery requirements..

Furthermore, in the EU, there is a 90 km/h speed limit that all commercial vehicles over 3.5 tonnes are required to comply with.

36 There is also a requirement that the daily driving time

for all drivers of road haulage vehicles over 3.5 tonnes shall not exceed nine hours, with an exemption twice a week when it can be extended to ten hours. Thus, depending on the distance travelled, hauliers will need to adjust their operations in order to comply with European legislation, and as a result the time required to transport goods may significantly increase to around 24 hours or over, or two drivers may be used to enable a non-stop road haulage service (with cost implications).

Finally, realised benefits from using the MoS line, such as reduced total freight transport times and reduced risk of accidents, could also act as a barrier to shifting back to road. With regards to freight transport times, for the specific route considered, road may save seven to eight hours, if two drivers are used, or potentially add about nine hours or more to the journey time if one driver is used. Congestion on roads, especially in the Pyrenees area, can considerable increase road transport times. Delivery times may be more important than transport costs for some goods.

9.3.2.2 Financial attractiveness of modal shift

Aside from the operational considerations mentioned above, the potential for modal shift back to road will depend on the financial attractiveness of road transport compared to shipping. The net costs or savings to hauliers and other service users from such as shift will depend on:

The difference between the fuel costs associated with driving and the fares associated with shipping.

The additional road infrastructure charges incurred.

35

DG Env ironment (2010) The Competitiv eness of European Short -Sea f reight shipping compared with road and rail transport. 36

Regulation (EC) No 561/2006 of the European Parliament and of the Council of 15 March 2006 on the harmonisation of certain social legislation

relating to road transport and amending Council Regulations (EEC) No 3821/85 and (EC) No 2135/98 and repealing Council Regulation(EEC) No 3820/85




The potential opportunity costs from taking longer to reach the destination as a result of shifting back to road. Discussions with the operator have confirmed that the selected MoS line is still heavily subsidised. Currently, using the shipping service for this route is thought to bring savings of 5-10% to customers. The operator has raised concerns that if costs for using the MoS line increased, cost savings for hauliers would not be realised, resulting in a modal shift back to road. The operator also highlighted the importance of keeping the MoS line more cost competitive, as the alternative, i.e. using the road network, offers other benefits (e.g. trucks can depart any time of the day to deliver goods) that may affect hauliers’ decisions when selecting transport modes.

An initial appraisal by GLD Atlantique of the comparative advantages of the MoS estimated that it would offer:

Competitive freight rates (€450 per semi-trailer vs. €1,000 by road)

Reduced travel time compared to road transport (14h vs. 24h)37

Table 9.11 shows the basic data inputs and assumptions used in this study to derive the costs associated with the transport of goods on this route by hauliers, using the two different modes i.e. road and sea.

Table 9.11 Basic assumptions on ship operation and road transport parameters in 2030 (2008 prices)


Sea transport

Freight sea fare € per one way trip

439-683 LD Lines freight for 1 adult & 1 rigid truck up to 8m × 4m or a rigid truck up to 12m × 4m / artic truck up to 16.5m × 4m laden. 2012 data deflated to 2008.

Road transport

Fuel costs per kilometre travelled

€/km 0.28 - 0.33 Based on TREMOVE v3.3.2_Pivots on DEMAND, i.e. on costs and vkm projections in 2030 for HDVs (16-32t & >32t) in France and Spain. 2005 data inflated to 2008.

Labour costs per kilometre travelled


Repair costs per kilometre travelled


Toll costs from Nantes to Valladolid

€ 95 - 119

Autoroutes.fr. Price depends on the number of axles (the recommended route was selected). 2012 data deflated to 2008.

Toll costs from Gijon to Valladolid

€ 13 - 18

Autoroutes.fr. Price depends on the number of axles (the recommended route was selected). 2012 data deflated to 2008.

Distance from Nantes to Valladolid km 880 - 925

Autoroutes.fr. Depending on the route selected (e.g.

shortest, quickest, recommended and most economical).

Distance from Gijon to Valladolid km 275 - 315

Autoroutes.fr. Depending on the route selected (e.g. shortest, quickest, recommended and most economical).

Distance from Montoir de Bretagne to Nantes

km 55 Autoroutes.fr. Depending on the route selected (e.g.

shortest, quickest, recommended and most economical).

As shown in the table above, two different truck types have been considered for this analysis, trucks >32t and trucks between 16 and 32t. The main reason for this is that it has been

37

GLD Atlantique (2010) The MoS Gijon-Nantes: f rom the operator’s perspectiv e. Workshop 2.9: Shipping in the Common European Maritime Space.




projected that these two truck categories will be responsible for the majority of tkm of cargo transported in 2030 in Spain and France.38

The estimated costs associated with the transport of goods on this route by hauliers in 2030 for one trip are shown in Table 9.12. Freight can be transported by road by one driver, assuming that departure and destination is less than ten hours; alternatively, rest periods are required, or two drivers need to be used in order to switch shifts. This has been considered by doubling the labour costs (€/km) associated with the route. However, there is an opportunity cost associated with such measures, i.e. lost revenue from the transport of other goods, which has not been quantified. Care is also needed when comparing sea fare prices to road costs, as the former depend on truck dimensions, i.e. the length, width and height of a truck, and the latter depend on the truck’s weight.

Table 9.12 Estimated baseline costs for hauliers of freight transport between Nantes and Valladolid in 2030 for one trip (2008 prices)

Road route that uses the

MoS service Road route only

Route information

Distance (km) 500 (sea)

360 (road)* 900*

Journey time (hours) 15 (sea)

4 (road) 10

Costs for hauliers (€)

Fuel costs 104 - 122 252 - 297

Labour costs 255 - 233 621 - 567

Repair costs 85 - 22 207 - 54

Toll costs 13 - 18 95 - 119

Sea fare costs 439 - 684 N/A

Total cost per route (€) for the baseline (no additional policies in place)

One driver 896 - 1079* 1175 - 1037**

Two drivers N/A 1796 - 1604

* The recommended distance was selected. ** Labour costs associated with an overnight stay are excluded. *** Accommodation costs are excluded as well as any labour costs associated with an overnight stay. These costs will be incurred only if the journey lasts for more than 10 hours (e.g. due to congestion).

From this analysis it can be concluded that without a policy on GHG emissions from shipping, in 2030 it is likely that it will be more economical for hauliers to use the sea service instead of the road network. This is particularly the case for hauliers that use 16-32t trucks (rigid or articulated). However, the introduction of a policy that aims to reduce GHG emissions from shipping may drive a modal shift back to road, if the costs associated with the policy are passed through to hauliers using the service.

ECG noted39 that even without any additional measures targeting CO2 emissions from shipping, future increases in maritime fuel costs due to the provisions of the IMO’s revised MARPOL Annex VI regulation are expected to result in a modal shift back to road from 2015 onwards. Alternative solutions (such as abatement technology) also give rise to significant cost increases. Furthermore in the current economic climate the customers, who make the

38

TREMOVE, Serv ice contract f or the f urther dev elopment and application of the transport and env ironmental TREMOVE model Lot 1

(Improv ement of the data set and model structure) Serv ice Contract 070501/2005/420798/MAR/C1, Final report f or the European C ommission, July 2007 39

Discussion with ECG, the Association of European Vehicle Logistics that represents around 100 leading v ehicle logistics companies f rom 25

countries across Europe. ECG members consider all transport modes when proposing a logistics solution to a customer. In order to choose

between the dif f erent modes of transport (or combinations), the associated costs are considered, as well as other criteria, like f lexibility , quality, lead time, serv ice etc.




ultimate decisions, invariably put cost first. For the particular route considered, shipping competes with road haulage. It is therefore important to know that Member States and the EU will continue to fund projects that promote modal-shift or traffic avoidance, such as the Gijon-Nantes MoS project that can help maximise the use of sea-borne freight routes.

9.3.3 Policy impacts

A policy leading to a carbon cost applicable for this route will impact the shipping operators for this route and may potentially impact on customers and businesses using the shipping service. This section explores these issues.

9.3.3.1 Impacts on ship operators / owners

For this case study it has been assumed that the ships operating on the MoS line will comply with the policy by paying the tax/ETS permits. The adoption of abatement measures to reduce emissions has not been considered as it has been assumed that this will happen only if the associated costs for this are lower in the long term for the operator / owner.

The costs associated with this route were calculated for the baseline scenario as well as for each policy scenario analysed.

Table 9.13 shows the basic data inputs and assumptions used that are common for all policy scenarios. The table portrays the detailed baseline scenario costs that a single ship would

be subjected to on its voyages from Nantes to Gijon, and vice versa, in 2030. It reflects a situation before any policy is introduced and is later used as the basis for policy scenario calculations.

Table 9.13 Assumptions on ship operation parameters and costs on Nantes-Gijón route in 2030 (2008 prices)


Route information

Distance Km 500 MARCO POLO, FRESMOS project, 2009

Journey time Hours 15 LD Lines

Fuel consumption Tonnes/trip 35 Based on Notteboom et al (2010)40

and the distance above

Fuel price € per tonne 777 As estimated in the IA support study (Ricardo-AEA calculations, based on the

EC energy roadmap CPI scenario oil prices, Purvin & Gertz (2009), and IMO studies).

Vessel emissions tCO2/trip 110 Calculated from above

Emission reduction target

% from baseline in 2030

24% IA study output for passenger ships, corresponds to overall (fleet level)

achievement of interim target of 10% off 2005 emissions

Voyage costs for vessel (€)

Operating costs per day

€ per day 11,009 SSS report and Ricardo-AEA calculations

Investment/financial costs

€ per day 9,675 SSS report and Ricardo-AEA calculations

40

Notteboom, T.; Delhay e, E.; Vanherle, K. (2010), Analy sis of the Consequences of Low Sulphur Fuel Requirements.





Fuel costs € per trip 27,179 Calculated from above

Total costs for operating the vessel

in this route

€ per day (assuming one 15-

hour trip per day)

47,862 Calculated from above

The level of carbon emissions covered by EU policy on the Nantes to Gijon route would be 110 tonnes as seen in the table above (except under an ETS with full free allocation).

Table 9.14 shows the 2030 carbon prices used. The projected ETS prices were based on projections provided in the EC Roadmap for moving to a competitive low carbon economy in 205041 and were used in the open ETS scenarios and the low emission tax scenario. Carbon prices in the extreme high emission tax scenario were based on the marginal abatement cost value obtained from the TIMES model for the Closed ETS policy scenario. The closed ETS carbon price would be determined in-sector, and it is difficult to provide any estimate for this. The final impact on operators will depend on how many additional permits over and above their allocation they would need to buy in order to comply or on the costs of reducing their emissions with abatement measures. In the worst case scenario, the operator has to buy all permits at maximum price, which is set here at the marginal abatement cost. However, in practice, complying by reducing emissions would be the most cost-effective option under the closed ETS.

The resulting cost of carbon for each policy scenario for this MoS route and the increase in total costs are also presented in the table below. It is assumed that the ship does not take up any abatement measures, and pays all relevant carbon charges as appropriate.

Table 9.14 Carbon related assumptions and costs to shipping operator in 2030 (2008 prices)

Carbon prices

EUR/tCO2

Cost of carbon (€)

per journey

% Increase in total costs (€) per

journey

Closed ETS (free allocation) 516 Up to 13,801* Up to 29%*

Open ETS (free allocation) 36 963 2%

Open ETS (full auctioning) 36 3,969 8%

Emission tax – low 36 3,969 8%

Emission tax – high 516 56,863 121%


36 3,969 8%

Contribution-based

compensation fund 36 3,969 8%

* Assumes that all permits are bought at a price equivalent to the marginal abatement cost of abating the last tonne of CO2 required to meet the emission reduction target. In practice, the total carbon costs would be much lower than this.

As seen above, the policy options would lead to an increase in the cost of shipping for operators ranging from 2% and 8% under most scenarios. Operators will be faced with two choices: to pass on this increase in cost to their customers, taking the risk that they may shift back to road transport; or to absorb it leading to a reduction in their profit. The impacts of these decisions on road hauliers are examined next.

41

Reference fossil fuel prices, fragmented action scenario: http://eur-

lex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2011:0288:FIN:EN:PDF






9.3.3.2 Impacts on road hauliers

In order to estimate the possible impacts from each policy option on customers, and in particular on hauliers / logistics companies using this sea route, the ability of operators to pass costs through has been considered. As indicated by the operator, the demand for this MoS service is considered price sensitive, as an increase in sea fare prices may lead to modal shift back to road. Therefore, four scenarios have been considered, assuming a 25%, 50%, 75% and 100% pass through of costs to customers and assuming that in 2030 the use of the MoS line by customers will continue to be high, i.e. 90% occupancy and the capacity of the ship will be similar to the one used on this route currently, approximately 120 freight vehicles. The impact of the policy on sea fares is shown in Table 9.15 below. The additional costs can either be absorbed by hauliers or passed through to their customers.

Table 9.15 – Summary of policy impacts on single sea fares for the hauliers in 2030

Variable

Sea fare assuming a 25% pass

through (€)

Sea fare assuming a 50% pass through (€)

Sea fare assuming a 75% pass

through (€)


ETS closed

470 - 733 502 - 782 534 - 832 565 - 881


441 - 687 443 - 691 445 - 694 448 - 697


448 - 698 457 - 712 466 - 726 475 - 740

Emissions tax (low)

448 - 698 457 - 712 466 - 726 475 - 740


569 - 887 699 - 1090 830 - 1293 960 - 1496

Target-based compensation

fund

448 - 698 457 - 712 466 - 726 475 - 740

Contribution-based

compensation fund

448 - 698 457 - 712 466 - 726 475 - 740

9.3.4 Conclusions

This case study has focused on assessing the risk of modal shift away from sea-borne freight transport to road freight as a result of introducing policy action to control GHG emissions from EU international shipping. The Motorway of the Sea between Nantes and Gijón provides an alternative to road freight transport on increasingly congested roads between the France and Spain, and pushing traffic back on roads would be in contradiction with Europe’s strategic objectives for freight transport.

The choice of sea or road as a means of transporting freight depends on a wide range of factors such as flexibility, speed and reliability as well as cost. Assuming the respective strengths and weaknesses of both modes remain broadly the same, the risk of a shift back to road haulage as a result of the policy options will depend on the extent to which they affect the cost of sea transport relative to road transport. The table below summarises the costs associated with the specific route considered between Nantes and Valladolid.

9.4 Passenger ferries case study

The analysis of economic impacts in the main IA support study is very much focused on examining the impacts of possible EU policy action on the trade and prices of commodities,




so it does not examine impacts on passenger vessels. Each of the chosen policy options may alter operating costs for passenger vessel operators and may result in higher / lower passenger fares which in turn would cause a change in the demand for such services. This could create ripple effects amongst businesses and employees relying on tourism. Therefore, this case study focuses on analysing the potential impacts of policy action on a passenger ferry route.

The route selected for this study is between Patras in Greece and Ancona in Italy. The ferries operating on this route stop at Igoumenitsa in Greece, before crossing the Adriatic Sea and vice versa.

Figure 9.4 Case study route

Source: Minoan Lines website, http://www.minoan.gr/en

9.4.1 The route

Ancona is a city and a sea port in the Marches region, in central Italy. The port of Ancona is one of the main ports on the Adriatic Sea, especially for passenger traffic. In 2011, more than 1.5 million passengers passed through the port of Ancona. Passenger traffic has decreased by 6.1% when compared to 2010, primarily due to a decrease in transits from Greece, as shipping operators have reduced the number of connections per week (thought to be linked with the Greek economic crisis). In 2011, the route from Ancona to Greece (i.e. Igoumenitsa and Patra) accounted for 69% of passenger traffic on ferries from this port.42 Figure 9.5 below shows passenger traffic data for the port of Ancona. This is split by the foreign destination for the period 2006-2011.

42

Autorità portuale di ancona, Rapporto Statistico 2011, A cura del Serv izio Promozione, Programmazione e Statistica

http://www.minoan.gr/en




Figure 9.5 Passenger traffic data for the port of Ancona for the period 2006-2011

Source: Autorità portuale di ancona, Rapporto Statistico 2011, A cura del Servizio Promozione, Programmazione e Statistica

The Port of Igoumenitsa was inaugurated in 2003 and is located in north-western Greece, in Thesprotia, in the region of Epirus. As one of the most important transport hubs in the area, the port focuses on passenger traffic, through ferry connections to domestic and foreign destinations, such as Brindisi, Bari, Ancona and Venice, while goods are transported mainly by trucks.

The port of Patras is located in the northern Peloponnese peninsula, in western Greece. The port consists of a commercial port and a passenger’s port. Almost half of the country's overall sea passenger traffic to/from foreign destinations is carried out by the port of Patras. Traffic data for the port of Patras to/from foreign destinations for the period 2001-2011 also show a considerable decrease in passenger and vehicle volumes in the last few years.43 This is illustrated in Figure 9.6.

Figure 9.6 Passenger traffic data for the port of Patras for the period 2001-2011

Source: Port Authority of Patras website, http://www.patrasport.gr/

For the Ancona - Igoumenitsa - Patras route selected for this case study, three operators have been identified: Superfast Ferries, ANEK Lines and Minoan Lines. The first two, Superfast Ferries and ANEK Lines, jointly operate this route with daily crossings in both directions throughout the year, and three additional trips per week in July, August and the

43

Port Authority of Patras website: http://www.patrasport.gr/

http://www.patrasport.gr/




beginning of September. Minoan Lines operate on this route six times a week in both directions.

9.4.2 Policy impacts

A policy leading to a carbon cost applicable for this route will have impacts on shipping operators and may potentially have impacts on customers and businesses using the shipping service. This section explores these issues.

9.4.2.1 Impacts on ship operators / owners

The costs associated with this route have been calculated for the baseline scenario as well as for each policy scenario analysed.

Table 9.16 shows the basic data inputs and assumptions used that are common for all policy scenarios. For this case study it has been assumed that the vessel type is a Ro-Ro/passenger ship, with the average characteristics of vessels used in this route.

Table 9.16 Assumptions on ship operation parameters and baseline costs on the route (2008 prices)


Route information

Distance km 926 Portworld.com and Google maps

Journey time days 1.75 Based on actual ship movements. Journey time includes the sailing time of around 24

hours plus an idle period in port

Fuel price (2030) € per tonne 772

As estimated in the IA support study (Ricardo-AEA calculations, based on the EC energy roadmap CPI scenario oil prices,

Purvin & Gertz (2009), and IMO studies). 2010 deflated to 2008.

Fuel consumption Tonnes/trip 65 Based on Notteboom et al (2010) and the distance above

Vessel emissions on this route

tCO2/trip 204 Calculated from above: one-way trip Ancona-Igoumenitsa-Patra

Emission reduction

target

% from baseline in

2030 24%

IA study output for passenger ships, corresponds to overall (fleet level)

achievement of interim target of 10% off 2005 emissions

Voyage costs for vessel (€)

Operating costs € per trip 31,332 SSS report and Ricardo-AEA calculations

Investment/financial

costs € per trip 36,040 SSS report and Ricardo-AEA calculations

Fuel costs € per trip 50,054 Calculated from above

Total costs for operating the vessel

€ per trip 117,426 Calculated from above. Costs associated with a one-way trip and idle periods in ports

between trips.

The table above shows the detailed baseline scenario costs that a single ship would be

subjected to on its voyages from Patras to Ancona, and vice versa, in 2030. It reflects a situation before any policy is introduced and is later used as the basis for policy scenario calculations.




The operators of this route have two options for complying with a policy that targets GHG emissions: paying the tax/ETS permits in full, i.e. for all carbon emissions subjected to the policy, or adopting abatement measures to reduce emissions and paying for the tax/ETS permits of the remaining emissions. The level of carbon emissions covered by EU policy on the Ancona - Igoumenitsa - Patras route is 204 tonnes of CO2 as shown in the table above.

Table 9.14 shows the 2030 carbon prices used. The projected ETS prices were based on projections provided in the EC Roadmap for moving to a competitive low carbon economy in 205044 and were used in the open ETS scenarios and the low emission tax scenario. Carbon prices in the extreme high emission tax scenario were based on the marginal abatement cost value obtained from the TIMES model for the Closed ETS policy scenario. The closed ETS carbon price would be determined in-sector, and it is difficult to provide any estimate for this. The final impact on operators will depend on how many additional permits over and above their allocation they would need to buy in order to comply or on the costs of reducing their emissions with abatement measures. In the worst case scenario, the operator would have to buy all permits at the maximum price, which is set here at the marginal abatement cost. However, in practice, complying by reducing emissions would be the most cost-effective option under closed ETS.

The resulting cost of carbon for each policy scenario for this route and the % increase in total costs are also given in the table below. It is assumed that the ship does not take up any abatement measures, and pays all relevant carbon charges as appropriate.

Table 9.17 Carbon related assumptions and costs to shipping operator in 2030

Carbon prices EUR/tCO2

Cost of carbon (€) per journey

% Increase in total costs (€)

per journey

Closed ETS (free allocation) 516 Up to 25,560* Up to 22%*

Open ETS (free allocation) 36 1,783 2%

Open ETS (full auctioning) 36 7,351 6%

Emission tax – low 36 7,351 6%

Emission tax – high 516 105,309 92%

Target-based compensation fund 36 7,351 6%


36 7,351 6%

* Assumes that all permits are bought at a price equivalent to the marginal abatement cost of abating the last tonne of CO2 required to meet the emission reduction target. In practice, the total carbon cos ts would be much lower than this.

As seen above, the policy options, would lead to an increase in the cost of shipping for operators ranging from 2% and 6% under most scenarios. Operators will be faced with two choices: to pass on this increase in cost to their customers, taking the risk that the demand for the service may decrease, which could also lead to a reduced frequency of crossings; or to absorb any additional costs, leading to a reduction in profit.

9.4.2.2 Impacts on customers

In order to estimate the possible impacts from each policy option on customers using this sea route, the ability of operators to pass costs through has been considered.

44

Reference fossil fuel prices, fragmented action scenario: http://eur-

ex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2011:0288:FIN:EN:PDF

http://eur-ex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2011:0288:FIN:EN:PDF

http://eur-ex.europa.eu/LexUriServ/LexUriServ.do?uri=SEC:2011:0288:FIN:EN:PDF




Table 9.18 Assumptions on prices to consumers (2008 prices)


Ticket fares (low season)

€ per one way trip

€300 - 335 Current prices as shown on operator’s

website (rounded), deflated to 2008, based on a one way trip for two passengers staying in a four-bed inside cabin and one

car. No discounts have been considered, which would be applicable in reality, e.g. for return bookings.

Ticket fares (middle season)


€380 - 415

Ticket fares (high season)


€460 – 505

The demand for this service is considered price sensitive, as an increase in sea fare prices may lead to modal shift to road. Four scenarios have been considered, assuming a 25%, 50%, 75% and 100% pass through of costs to customers and assuming that in 2030 the use of the ferry line will be high, i.e. 80% occupancy. The impact of the policy on sea fares is shown in Table 9.19 below. It should be noted that fare rates used in this analysis do not take into account likely increases in the prices of tickets as a result of future increases in maritime fuel costs due to the provisions of the revised MARPOL Annex VI regulation of the IMO.

Table 9.19 – Summary of policy impacts on passenger sea fares in 2030 (2008 prices)

Variable Sea fare

assuming a 25% pass through (€)



Sea fare assuming a

100% pass through (€)

ETS closed

316-532 333-560 349-587 365-615


301-507 302-509 303-511 305-513


305-513 309-521 314-529 319-537

Emissions tax (low)

305-513 309-521 314-529 319-537


367-618 435-731 502-845 569-958

Target-based

compensation fund

305-513 309-521 314-529 319-537

Contribution-

based compensation fund

305-513 309-521 314-529 319-537

Based on the above, the impacts of the key policy options on the prices of tickets could range from an additional €1 to €19 in the low season and an additional €2 to €32 in the high season. The response of consumers to these increases will depend on the price elasticity of their demand.

9.4.3 Conclusions

The aim of this case study was to highlight the risk of the policy options to the competitiveness of ferry transport, in this case between Italy and Greece.

As presented above, it is expected that all options will, to various degrees, result in an increase in the price of ferry tickets. This will result either in a decrease in ferry operators’ profitability (as they absorb the price increase) or an increase in costs for consumers.




Table 9.20 Cost of transport in 2030 under each policy option and route (2008 prices)

Road route that uses the

MoS service Road transport only

Closed ETS (free allocation) 866 - 1338

1175 - 1037 (one driver)

1796 - 1604(two drivers)

Open ETS (free allocation) 836 - 1155

Open ETS (full auctioning) 843 - 1198

Emission tax – low 843 - 1198

Emission tax – high 964 - 1953

Target-based compensation fund 843 - 1198


843 - 1198

As can been seen from the table, in all scenarios except the high tax, sea transport on this route is likely to remain considerably cheaper than transport by road. In fact it is likely that the differential may increase even more than indicated above as additional charges may be imposed on road freight transport in the future. Indeed, this may happen quite soon as the introduction of legislation in France that will impose a CO2 tax on trucks using the road network is currently being considered. According to the operator of the Nantes-Gijon MoS, this could increase the costs of driving from Nantes to Gijón by between €30 and €35. If the additional costs associated with a policy on maritime GHG emissions are within the same range, this may counteract any potential for modal shift.

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