Cost-effectiveness of greenhouse gas mitigation in transport: A review of methodological approaches and their impact

Energy Policy 39 (2011) 7776–7793

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Cost-effectiveness of greenhouse gas mitigation in transport: A review of methodological approaches and their impact Robert Kok a,b,, Jan Anne Annema a, Bert van Wee a a b

Delft University of Technology, Faculty of Technology, Policy and Management, Jaffalaan 5, 2628 BX Delft, The Netherlands Ecorys, Watermanweg 44, 3067 GG Rotterdam, The Netherlands

a r t i c l e i n f o

a b s t r a c t

Article history: Received 16 February 2011 Accepted 13 September 2011 Available online 19 October 2011

A review is given of methodological practices for ex ante cost-effectiveness analysis (CEA) of transport greenhouse gas (GHG) mitigation measures, e.g. fuel economy and CO2 standards for road vehicles in the US and EU. Besides the fundamental differences between different types of policies and abatement options which inherently result in different CEA outcomes, differences in methodological choices and assumptions are another important source of variation in CEA outcomes. Fourteen methodological issues clustered into six groups are identified on which thirty-three selected studies are systematically reviewed. The potential variation between lower and upper cost-effectiveness estimates for GHG mitigation measures in transport, resulting from different methodological choices and assumptions, lies in the order of $400 per tonne CO2-eq. The practise of using CEA for policy-making could improve considerably by clearly indicating the specific purpose of the CEA and its strengths and limitations for policy decisions. Another improvement is related to the dominant approach in transport GHG mitigation studies: the bottom-up financial technical approach which assesses isolated effects, implying considerable limitations for policy-making. A shift to welfare-economic approaches using a hybrid model has the potential to establish an improved assessment of transport GHG mitigation measures based on realistic market responses and behavioural change. & 2011 Elsevier Ltd. All rights reserved.

Keywords: Cost-effectiveness Greenhouse gas mitigation Transport

1. Introduction The transport sector is one of the major sources of carbon dioxide emissions (CO2). The transport sector is responsible for about 25% of global CO2 emissions, and road transport is responsible for 75% of the emissions from the transport sector (IEA, 2009). Between 2005 and 2050, without any new policies, the number of cars worldwide is expected to triple, to over two billion. Trucking activity will double and air travel could increase four-fold. These trends, will lead to a doubling of demand for oil and related greenhouse gas (GHG) emissions by 2050 (IEA, 2009). However, most countries and supra-national bodies like the EU want to reduce GHG emissions, not only for environmental reasons, but also for of reasons of securing the energy supply and concerns about increasing energy prices. Hence, societies and policy makers are facing a huge challenge if they desire to further decarbonize the transport sector. In meeting the above challenge, policy makers will have to make choices trading-off reductions in GHG emissions against the  Corresponding author at: Delft University of Technology, Faculty of Technology, Policy and Management, Jaffalaan 5, 2628 BX Delft, The Netherlands. Tel.: þ31 15 2788865. E-mail addresses: [email protected], [email protected] (R. Kok).

0301-4215/$ - see front matter & 2011 Elsevier Ltd. All rights reserved. doi:10.1016/j.enpol.2011.09.023

costs of making these reductions. Doing so requires comparing the cost-effectiveness of different measures. Different measures can be compared ‘‘horizontally’’, e.g. an electric vehicle versus a hydrogen fuel-cell vehicle using one methodology and set of assumptions, while one single measure can also be compared ‘‘vertically’’, e.g. by comparing the same measure across studies using different methodological approaches and assumptions. As shown in Table 1, many combinations of comparisons are possible depending on the number of studies, the number of measures and the number of methodologies and sets of assumptions. The focus in this paper is on vertical comparisons in which we analyse the impact of methodological choices and assumptions, irrespective of the fundamental differences between different measures. Horizontal comparisons between different measures are often carried out in derivative studies such as SERPEC-CC (2009) and US DoT (2010) where various non-primary studies with various diverging approaches and assumptions are combined into a new synthesis report. Policy makers may base their decisions or prioritization of measures on such synthesis reports, or make these comparisons themselves based on various studies. In both cases, this practice involves the risk of combining cost-effectiveness estimates based on fundamentally different approaches or considerably diverging assumptions and may undermine sound policy making. Meaningful comparisons of the costs and

R. Kok et al. / Energy Policy 39 (2011) 7776–7793

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Table 1 Scope of comparisons. One study One measure

Multiple studies Multiple measures

One measure

Multiple measures

One methodology and set of assumptions N/A Horizontal—within one study N/A Horizontal—across studies Multiple methodologies and sets of assumptions Vertical—within one study Vertical—across measures Vertical—across studies Vertical—across measures and studies N/A: Not applicable because no comparison is possible or not of interest.

effectiveness of measures require a consistent and appropriate methodology to estimate these costs and benefits. However, there is no one methodology and the methodologies used for estimating the costs and benefits of measures vary widely, making it difficult to make these comparisons in any meaningful manner. In addition, such comparisons are complicated by the specifics of the different transport modes, being road, rail, ships and aircraft, each using a range of technologies, and because CO2 emissions depend not only on the technology, but also on the way it is used, and other travel behaviour choices. From a societal point of view it is important to gain more insight in methodological issues related to transport GHG cost-effectiveness estimates because they may help to better select the ‘right’ policy instruments to stimulate the ‘right’ abatement options (Anable and Bristow, 2007; Kampman et al., 2006; TRB, 2009). Various authors have reviewed GHG mitigation measures and the costs involved. Most of these studies focus on differences in modelling approaches, being top-down or bottom-up (Van Vuuren et al., 2009; Wilson and Swisher, 1993), analyse economy-wide abatement potentials (Akimoto et al., 2010; Barker et al., 2006), or compare the cost-effectiveness of GHG mitigation across sectors (Smokers et al., 2009). To the best of our knowledge no systematic review of cost-effectiveness estimates and methodologies used has been carried out specifically for the transport sector. In this paper we aim to fill this scientific gap by gaining better understanding of the prevailing methodological approaches and assumptions and their potential impact on cost-effectiveness outcomes. In this article, mitigation ‘measures’ refer to both policy instruments and abatement options. Abatement options can be technical, e.g. an electric vehicle, or non-technical, e.g. a shift from car to public transport. In the current study, we aim to provide a comprehensive but non-exhaustive review of methodological practices for ex ante anticipated cost-effectiveness estimates specifically for transport GHG mitigation measures. We focus on three questions: (1) What are the key methodological differences in cost-effectiveness analysis (CEA) for transport GHG mitigation? (2) What is the potential impact of the choice of a method on the resulting estimates of cost-effectiveness? (3) How could the future practise of CEA be improved?

The remaining part of this article is organized as follows. Section 2 describes the selection of literature for the review. Section 3 provides a conceptual framework which is used to systematically review the literature on the methodologies used for a CEA. A judgement on the appropriateness or justification of methodological choices is not included in this section but is part of Section 5. Section 4 gives examples of the possible impact of key methodological choices on the outcome of the CEA. Finally, Section 5 outlines the main conclusions, and discusses what we learned from reviewing the literature. Customizing CEA for the transport sector outlines opportunities for further research.

2. Literature selection The review of the literature is based on the literature in English. This review includes:

 Peer-reviewed academic papers published in journals or as conference proceedings.

 Research documents prepared for research programs, e.g.



 

Transportation Research Board (TRB), Framework Programme of the European Community (EC-FP), National Energy Research centres. Government reports and other publications prepared by Government agencies, e.g. US Department of Transportation (DOT), US Environmental Protection Agency (EPA), UK Department for Transport (DfT). Internet sites that are credible sources such as a government agency or think-tank, e.g. European Environment Agency, Resources for the Future. Research reports from private and non-profit organizations, e.g. policy impact studies, which are generally carried out on behalf of government bodies such as the European Commission.

We have focused on sources providing ex-ante assessments of measures to reduce GHG emissions from transport. A long list comprising over one hundred potentially useful sources is consolidated to a set of thirty-three sources that are analysed (Tables 2 and 3). The criteria for excluding studies was that they do not provide quantitative estimates of cost-effectiveness, or only focus on the emissions abatement potential without looking at the costs. In addition, studies based on earlier research, and having limited methodological originality in addition to its underlying sources, are excluded to prevent double counting. Many studies include multiple estimates of cost-effectiveness each estimate corresponding to a different measure or input parameter value. In such cases, the generic methodology used for all measures is analysed. Table 2 gives an overview of peer-reviewed academic literature, Table 3 of non-academic literature. In case it is not clear if a study is peerreviewed it is included in Table 3. This way we determined whether or not either of the two sources reveals more consistency and fewer differences in methodological choices (Table 4).

3. Review of methodological choices In CEA, the costs of implementing a measure are expressed per unit of effectiveness, and effectiveness being defined as the effectiveness in reaching one specific policy goal. In this article, the policy goal is defined as reducing, or avoiding GHG or CO2 equivalent (CO2-eq) emissions. The resulting cost-effectiveness estimate is expressed as a monetary value per unit of CO2-eq reduction, often in US$ or h per tonne CO2-eq avoided. Since different studies may employ different currencies and constant prices, all prices in this paper are converted to US$2009 prices.

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Table 2 Selection of peer-reviewed academic literature. Authors (date)

Impact assessment exercise

Affiliated organizations

Scope of study

Corbett et al. (2009)

The effectiveness and costs of speed reductions on emissions from international shipping Recent advances in automotive technology and the cost-effectiveness of fuel economy improvement Costs and benefits of automotive fuel economy improvement: a partial analysis Outlook for advanced biofuels

University of Delaware, Rochester Institute of Technology

International shipping

American Council for an Energy-Efficient Economy and University of Michigan

Passenger cars, USA

Oak Ridge National Laboratory, Energy and Environmental Analysis Utrecht University

Passenger cars, light trucks, USA Biofuels in transport, Netherlands Road transport fuels—power trains, USA

Decicco and Ross (1996)

Greene and Duleep (1993) Hamelinck (2004) Lutsey and Sperling (2009)

NRC (National Research Council) (2002)a Parry (2007) Rabl et al. (2007) Rubin et al. (1992)

Tippichai et al. (2009)

Van den Brink and Annema (2004) Wright and Fulton (2005)

a

Prioritizing Climate Change Mitigation Alternatives: Comparing Transportation Technologies to Options in Other Sectors Effectiveness and Impact of Corporate Average Fuel Economy (CAFE) Standards Are the costs of reducing greenhouse gases from passenger vehicles negative? Costs of carbon dioxide abatement in the United States Realistic mitigation options for global warming Introduction of a Sectoral Approach to Transport Sector for Post-2012 Climate Regime—A Preliminary Analysis Using Marginal Abatement Cost Curves Look before you leap! The necessity of shortterm CO2 emission reduction in transport Climate Change Mitigation and Transport in Developing Nations

ITS, UC Davis

Board on Energy and Environmental Systems, Transportation Research Board, National Research Council Resources for the Future Ecole des Mines, Paris and University of Colorado Carnegie Mellon University, Pittsburgh

Nihon University Chiba, Japan

Netherlands Environmental Assessment Agency (RIVM/PBL) University College London, London, UK

Road vehicles, USA

Passenger vehicle emissions, USA Road vehicles, USA Road vehicles, aircraft and demand management, USA Transport sector as a whole, Japan, EU15, USA, Russia, Canada Road transport fuels—power trains, world Surface transport, developing nations

This study was updated in NRC (National Research Council) (2011).

We have used the World Bank Consumer Price index based on IMF data as a deflator. In addition, we have used the OECD exchange rates 2009-average to convert local currencies to US$. A conceptual framework is used to determine the scope of the methodological review (see Fig. 1). The framework is based on Grubb et al. (1993), Halsnaes et al. (1998), Smokers et al. (2009) and Wang (1997). The methodological differences in a CEA relate to six key issues: scope (A), cost perspective (B); cost approach (C); type of measure (D); key assumptions (E); and the costeffectiveness calculation (F). Hereafter, we elaborate on each of these six methodological issues. At the end of this section all methodological choices for each study according to the above framework are presented in two overview tables (Tables 5 and 6). Finally, Table 7 shows which methodological choices are dominant in transport GHG mitigation studies.

Emissions in this stage are collectively known as ‘‘upstream’’, ‘‘indirect’’, ‘‘well-to-pump’’ or ‘‘well-to-tank’’ (WTT) emissions. The second stage is the energy use during vehicle operation, also known as ‘‘downstream’’, ‘‘direct exhaust’’, ‘‘pump-to-wheels’’ or ‘‘tank-towheels’’ (TTW) emissions. ‘‘Well-to-wheels’’ (WTW) emissions include both stages of the lifecycle of a transport fuel. Emissions from the production or decommissioning of transport infrastructure, vehicles or technologies are not included in this review. Looking at the two stages of the fuel lifecycle, over forty percent of the studies consider WTW emissions, one-third consider only direct exhaust emissions and one-fifth do not specify, or report clearly on this matter. Studies that do consider WTT emissions in addition to TTW emissions generally add 15–21% of the direct emissions to arrive at the WTW emissions for conventional fuels like gasoline and diesel (AEA, 2008; Lutsey, 2008; Proost and De Ceuster, 2006; TNO, 2006).

3.1. Scope of the CEA (A in Tables 5–7) 3.1.1. Types of GHG emissions The first issue concerns the types of GHG emissions taken into account in the analysis. About one-third of the studies look at CO2 emissions only as a GHG, while two-third of the studies also take other non-CO2 GHGs like CH4, N2O and occasionally HFC emissions into account. Overall GHG emissions are calculated by expressing emissions of other GHG emissions in CO2 equivalent emissions using global warming potentials (GWP). 3.1.2. Lifecycle stages of the energy chain The second issue relates to the lifecycle emissions of transport fuels which includes different stages. The first stage is feedstock extraction and distribution to fuel production and distribution.

3.1.3. Type approval (TA) or real world (RW) emissions A third issue found in many studies is the difference between a CEA assuming energy-efficiencies or emissions based on a standardized test under laboratory conditions or based on conditions that are representative for real-world energy use of transport activity. In road transport this difference is called the ‘fuel economy shortfall’ of vehicles under real world (RW) on-road driving conditions (Inhaber, 1982). The fuel economy of cars is often determined in the type approval (TA) procedures using a standardized driving cycle.1 Real world driving conditions often 1 Note that fuel economy is typically used in the US, e.g. miles per gallon (MPG), while in the EU fuel consumption (the inverse of fuel economy) is more common, e.g. liter per 100 km or grams of CO2 emissions per km.

R. Kok et al. / Energy Policy 39 (2011) 7776–7793

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Table 3 Selection of non-academic literature. Authors (date)

Impact assessment exercise

Affiliated organizations

Scope of study

AEA (2001)

Economic Evaluation of Sectoral Emission Reduction Objectives for Climate Change—Economic Evaluation of Emissions Reductions in the Transport Sector of the EU: Bottom-up analysis(updated) Impacts of regulatory options to reduce CO2 emissions from cars, in particular on car manufacturers Building a supply-side marginal abatement cost curve (MACC) for the UK transport sector Technical support for European action to reducing Greenhouse Gas Emissions from international maritime transport

AEA Technology, ECOFYS, NTUA

Passenger cars and freight trucks, EU15

AEA Technology, Association ASPEN, CE Delft, TNO, Oko-Institut

Passenger cars, EU27

UK Committee on Climate Change (CCC)

Road transport fuels—power trains, UK International maritime transport

AEA (2008)

CCC (2008) CE Delft (2009)

Defra (2007)

Synthesis of Climate Change Policy Appraisals

DfT (2009)

Impact Assessment of the Carbon Reduction Strategy for Transport, Low Carbon Transport: A Greener Future Synthesis of the cost benefit analysis conducted as part of the Energy Review ‘the energy challenge’ Synthesis of the analysis for the energy white paper Final Rulemaking to Establish Light-Duty Vehicle Greenhouse Gas Emission Standards and Corporate Average Fuel Economy Standards. Regulatory Impact Analysis Biofuels for transport, an international perspective Service contract to carry out economic analysis and business impact assessment of CO2 emissions reduction measures in the automotive sector Cost-effectiveness of greenhouse gas reductions in various sectors Well-to-Wheels analysis of future automotive fuels and power trains in the European context Technical Options for Abating Road Traffic Impacts—Comparative study of fuel cell vehicles and vehicles with internal combustion engines Costs and potentials of Greenhouse gas abatement in Germany Reducing US greenhouse gas emissions: How much at what cost? Roads toward a low carbon future: Reducing CO2 emissions from passenger vehicles in the global road transportation system Final Regulatory Impact Analysis, Corporate Average Fuel Economy for MY 2012–MY 2016 Passenger Cars and Light Trucks Service contract for the further development and application of the TREMOVE transport model-Lot 3: Part 3 Policy runs Review and analysis of the reduction potential and costs of technological and other measures to reduce CO2-emissions from passenger cars Transportation’s role in reducing US Greenhouse Gas emissions—Volume 1: Synthesis report Service Contract in Support of the Impact Assessment of Various Policy Scenarios to Reduce CO2 Emissions from Passenger Cars (Task B)

DTI (2006, 2007)

EPA (2010)

IEA (2004) IEEP (2005)

INFRAS (2006) JEC (2007) Kolke (1999)

McKinsey & Company (2007a) McKinsey & Company (2007b) McKinsey & Company (2009) NHTSA (2010)

Proost and De Ceuster (2006) TNO (2006)

US DoT (2010) ZEW (2006)

deviate from conditions in the standardized driving cycle. Over 40% of the studies in this literature review look at real-world conditions, 30% look at standardized laboratory conditions, while about 35% do not clarify which approach was adopted.

3.1.4. Geographical scope The last issue relates to the geographical scope which is important as different countries have different transport systems

CE Delft, DLR, Fearnley Consultants, Nature Associates, Manchester Metropolitan ¨ ko University, MARINTEK, Norton Rose, O Institut Department for Environment, Food and Rural Affairs (Defra) UK Department for Transport

Passenger cars, fuels, speed limits, smart choices, UK Road and rail transport, UK

UK Department for Trade and Industry, Ricardo consultants

Passenger cars, transport fuels, UK

United States Environmental Protection Agency

Light-duty vehicles, USA

International Energy Agency & OECD

Biofuels in transport, USA, EU and Brazil Passenger cars, EU15

IEEP, TNO CAIR

INFRAS, IFEU, IVL, TNO, TU-Graz JEC (JRC-EUCAR-CONCAWE) German Federal Environmental Agency (UBA) McKinsey & Company, BDI Initiativ McKinsey & Company, The conference board McKinsey & Company

US Department of Transportation, National Highway Traffic Safety Administration (NHTSA) KU Leuven, TML

Road transport (passenger, freight), EU25 Road transport fuels—power trains, EU27 Road transport fuels—power trains, Germany Road, rail and air transport and fuels, Germany Road transport and fuels, USA Road vehicles, fuels, mode shift and pricing, world regions Passenger cars and light trucks Passenger cars, EU15þ 4 new member states

TNO, IEEP, LAT

Passenger cars, EU15

US Department of Transportation

All modes, fuels, system and demand management, USA Passenger cars, EU25

Centre for European Economic Research (ZEW) Mannheim, B&D Forecast

with different characteristics, e.g. leading to different baseline vehicles to which measures are assumed to be applied as well as different baseline situations against which options are compared. Furthermore, consumers may have different preferences regarding measures and different tax distortions may apply. As shown earlier in Tables 2 and 3, about 70% of the studies focus on the US, the EU or selected countries within the EU, whereas 30% take a worldwide view or consider multiple regions or countries.

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Table 4 Classification of measures and their impact. Impact

Measure (M) Policy instrument (P)

Supply side (S): changing performance characteristics (costs, CO2, ancillary) Demand side (D): market response/ behavioural change

Abatement option (A)

Soft (1) e.g. eco-driving campaign

Financial (2) e.g. road charging

e.g. lower costs

e.g. implementation of car charging infrastructure

e.g. better driving style, changed perception

Regulatory (3) e.g. CO2 emission standard

e.g. implementation of fuel efficient technologies e.g. less km driven, shift to fuel e.g. shift to fuel efficient efficient cars, changing mode cars

Technical (T) (4) e.g. fuel cell

Non-technical (N) (5) e.g. modal shift, eco-driving

e.g. incremental costs, lower emissions e.g. changing mode or technology choice

e.g. lower costs of maintenance e.g. shift from car to public transport use

A) Scope of cost-effectiveness analysis (CO2-only or more GHG’s; lifecycle stages; estimated type approval or real-world emissions; geographical scope) C) Abatement costing approach (bottom-up, topdown or hybrid; financialtechnical or welfare economic)

E) Key assumptions (baseline; prices; discount rate; scale and learning effects; ancillary effects; interdependencies; rebound and feedback effects)

Cost-effectiveness estimate

B) Cost perspective (society; government; user)

D) Type of measures (policy instrument; technical abatement option; non-technical abatement option F) Cost-effectiveness calculation (cumulated lifetime annualized lifetime or market penetration effects; one-year or multi-year reference effects; average or marginal costs)

Fig. 1. Framework for reviewing the methodological differences in a CEA.

3.2. Cost perspective (B in Tables 5–7) In the literature, two important cost perspectives for the analysis of costs and benefits related to GHG mitigation measures are prevalent. Some studies map social costs and benefits which reflect all costs and benefits to society. Studies adopting this social cost perspective sometimes limit their analysis to financial-technical costs and benefits, excluding taxes and external costs and benefits. Others employ a welfare-economic analysis of costs and benefits including taxes and existing tax distortions and include a selection of external costs and benefits. Other studies focus on private costs which include only the monetary, or internal, costs and benefits faced by the private sector or end-user. Studies adopting this private cost perspective include taxes and exclude external costs. Studies adopting a private cost perspective often apply shorter payback periods and higher discount rates, or rates of return, for the investment made. Most of the studies (60%) employ a social cost perspective. About 10% calculate the cost-effectiveness from a private perspective and the remaining 30% of the studies present both perspectives. None of the studies use the government cost perspective focusing on policy implementation costs and effects on government tax revenues. 3.3. Abatement costing approach (C in Tables 5–7) In the review we identify an ‘economic’ top-down approach, an ‘engineering’ bottom-up approach and a hybrid approach

bridging the two. These approaches are fundamentally different in how they consider the behavioural impacts of measures, and in their level of detail. Conventional top-down approaches typically rely on historical real-world behaviour of markets to estimate aggregate economic relationships and model technological change, see, for example, Tippichai et al. (2009). This aggregate view of the economy enables the analyst to include multiple sectors, markets and countries or regions. The estimated relationships and parameters are assumed to reveal actual consumer preferences and implicitly incorporate changes in consumer surplus, as well as reflect the heterogeneity of real-world financial cost conditions, including private discount rates, hidden costs and possible rebound effects (Jaccard et al., 2003). Hence, top-down approaches employ an economic welfare costing approach and assess real market responses to policy measures. However, the level of detail in specifying the sector structure and technologies is limited. For example, top-down models do not distinguish different modes, mode choice, numbers of trips by mode, travel conditions and travel time or specific vehicle and fuel technologies, see, for example, Tippichai et al. (2009). Another limitation is that the existing consumer preferences only reflect variation in the existing transport system while consumer responses to structural changes or breakthrough innovations, e.g. affordable electric vehicles, are not included. Bottom-up approaches on the other hand identify individual abatement options in order to estimate how changes in the

Table 5 Overview of methodological choices in peer-reviewed academic literature. Authors (date)

Authors (date)

Corbett et al. (2009) Decicco and Ross (1996) Greene and Duleep (1993) Hamelinck (2004) Lutsey and Sperling (2009) NRC (2002) Parry (2007) Rabl et al. (2007) Rubin et al. (1992) Tippichai et al. (2009) Van den Brink and Annema (2004) Wright and Fulton (2005)

B

C

D

CO2 CO2  eq/ WTW/lifecycle GHG emissions

TTW/direct emissions

RW: real TA: type approval Public— Private— Financialworld/on-road /test cycle societal investor technical (direct effects)

X

X

X X

X

X

X

X

X

X

X

X X X

X X X X

X

X X ?

? X ? ?

X

X

X

?

? ?

?

X X

X X

X

X

Welfareeconomic (wider effects)

X

Hybrid Policy— Policy— NonTechnical technology

X X

X

X

X

X

X X

X X

X

X X

X X ? ? ?

? ? ?

X X X X X

?

?

X

X

X

X

?

?

X

X

X

X

E

X X

Bottom- Topup down

X

X

X X

X

X X X X

X

X X

X X X

X

X X X

F Ancillary costs/ benefits incl.

Marginal abatement cost

Average abatement cost

Cumulated lifetime effects

Timing of implementation, timing of accounting for effects

World oil price Discount rate (%) per barrel during time frame of effects ($2009)

Rebound effects included

?, ? 1990–2005, 2010

26 45

? 5

X X

1990–2001, 1990– 2020/30 2000–2030, 2030 2010–2025, 2030,

$29–100

3/6/10

X

37–82 $79–64

10 7

X X

X

X

X

1999–2013, 1999– 2030 ?, 2004 ?, 2016 1989, 1989 2013–2020, 2020 2010–2030, ?

$36

0/12

? 43 ? ? 31–44

– 5 3/6/10 ? 4

?, ?

?

0

X

X X X

X

Market penetration effects

One-year future timeframe

X

X X

X X

X X X X X

Annualized lifetime effects

X

X

X

X X

X

X X X

X X X

X X

X

Multi-year future timeframe

R. Kok et al. / Energy Policy 39 (2011) 7776–7793

Corbett et al. (2009) Decicco and Ross (1996) Greene and Duleep (1993) Hamelinck (2004) Lutsey and Sperling (2009) NRC (2002) Parry (2007) Rabl et al. (2007) Rubin et al. (1992) Tippichai et al. (2009) Van den Brink and Annema (2004) Wright and Fulton (2005)

A

X

X X X X X X

7781

7782

Table 6 Overview of methodological choices between non-academic literature. Authors (date)

A

B

CO2 CO2  eq/ GHG

Authors (date)

X X X X

X X

X X

X X X

? ? ?

RW: real TTW/ world/ondirect emissions road

TA: type approval / test cycle

Public— Private— Financialsocietal investor technical (direct effects)

X X X ? ? ? X

X X

X

X X

X

X X ? ? X ?

X X X

X

? X

X

X X X X

Welfareeconomic (wider effects)

Bottomup

Topdown

Hybrid Policy— Policy— technical nontechnical

X

X X X X X X X X X X

X

X X X

X X X

X X X X X X X X X X X X X X

X

X

X X X X X X X X X X

X

X

X

?

X X ?

X

?

?

X

X

X

X

?

?

X

X

X

X

X

X

X

X

X X

X X X

X X X

X ? X

X

X X X X X X X X X X X

? ? X

X X X X X

D

X

X X

X

X

X X X

?

E

X X

X X

X X

X X

X

X X X

X X X

X X X

X

X

X

F Rebound effects included

Marginal Ancillary abatement costs/ benefits incl. cost

Timing of implementation, timing of accounting for effects

World oil price per barrel during time frame of effects ($2009)

Discount rate (%)

AEA (2001) AEA (2008) CCC (2008)

2000–2010, 2010 2002–2020, 2012;2020 2010–2020, 2020

45 ? 47–155

CE Delft (2009) Defra (2007) DfT (2009) DTI (2006, 2007) EPA (2010)

2007–2030, 2030

47–140

4 4 3.5/5.4/ 7.1 4/9/14

2008–2020, 2010 2010–2020, 2020 2010–2020, 2020

? 80 22–79

3.5 3.5 3.5

X

X X

2012–2016, 2012–2050

94–123c

3/7

X

X

Average abatement cost

X X X

?

X ? X

X X X

X

X

X X X X

One-year future timeframe

Annualized lifetime effects

X X X X

Market penetration effects

Cumulated lifetime effects

Multi-year future timeframe

X X X X

X

X

R. Kok et al. / Energy Policy 39 (2011) 7776–7793

AEA (2001) AEA (2008) CCC (2008) CE Delft (2009) Defra (2007) DfT (2009) DTI (2006, 2007) EPA (2010) IEA (2004) IEEP (2005) INFRAS (2006)a JEC (2007) Kolke (1999) McKinsey & Company (2007a) McKinsey & Company (2007b) McKinsey & Company (2009) NHTSA (2010) Proost and De Ceuster (2006) TNO (2006) US DoT (2010)b ZEW (2006)

WTW/ lifecycle emissions

C

2002–2010, 2002;2010 2008–2012, 2012 2008–2012, 2010–2020

30–37 58 46–50

12 0/5 4

X X

2002–2010, 2010–2020 1998–2005, 2005 2006–2020, 2020

40–81 ? 57

8 0 4

X X X

X X

2005–2030, 2005–2030

65

7

X

X

2010–2030, 2010–2030

66

?

X

2012–2016, 2012–2016

94–123c

3/7

X

X

X

2008–2012, 2020

50

4

X

X

X

2008–2012, 2012 2010–2050, 2010–2050

40–120 ?

4 7

X

2008–2012, 2010–2020

46–50

4

X

X

X

X

X

X X

X X

X X X

X

X X X

X

X

X X

X

X

X

X X

X

X

X X

X

X

X X

a The analysis strongly depends on results from TNO (2006), AEA (2001) and ZEW (2006). The methodological review in this table refers to additional methodological choices by INFRAS based on TREMOVE calculations from ZEW (2006). b CEA methodology is original. However, the study is based on various other sources potentially using different assumptions. c Values for 2015 and 2030 based on AEO (2010). Reference projection for World oil prices.

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IEA (2004) IEEP (2005) INFRAS (2006)a JEC (2007) Kolke (1999) McKinsey & Company (2007a) McKinsey & Company (2007b) McKinsey & Company (2009) NHTSA (2010) Proost and De Ceuster (2006) TNO (2006) US DoT (2010)b ZEW (2006)

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Table 7 Percentage shares (%) of methodological choices for all selected studies. Methodology/ assumptions

B

C

D

CO2 (%)

CO2-eq/ WTW / lifecycle GHG emissions (%) (%)

TTW /direct emissions (%)

RW: real world/onroad (%)

TA: type approval /test cycle (%)

Public – societal (%)

Private – investor (%)

Financialtechnical (direct effects) (%)

Welfareeconomic (wider effects) (%)

Bottom- Topup (%) down (%)

Hybrid (%)

Policy Technology (%)

Policy Behaviour (%)

36

58

36

42

21

61

12

76

15

85

9

70

6

Methodology/ assumptions

Selected approach/ specified approach Multiple approaches used Not specified or unclear approach Total

42

6

6

0

0

27

9

0

24

0

21

36

0

0

0

0

100

100

100

100

100

100

100

E

F

Oil price per Discount barrel (%) rate (%)

Rebound effects Ancillary costs/ included (%) benefits incl. (%)

Marginal abatement cost (%)

Cumulated Average abatement cost lifetime effects (%) (%)

Annualized lifetime effects (%)

Market penetration effects (%)

One-year future timeframe (%)

Multi-year future timeframe (%)

76

85

33

27

67

24

18

27

76

21

–

–

–

–

9

9

–

24

15

67

73

0

3

3

100

100

100

100

100

100

100

42

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Selected approach/ specified approach Multiple approaches used Not specified or unclear approach Total

A

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energy efficiency, fuel type, infrastructure, or land use practices might lead to different levels of GHG emissions. Bottom-up models often underestimate the costs of GHG mitigation because they assume market shares of abatement options based on their financial costs, e.g. investment and operating costs. It is not recognized that abatement options, such as electric vehicles, are not always perfect substitutes for the baseline vehicles and technologies and may have hidden costs, resulting in consumer resistance. Another reason is that the assumed market shares of abatement options are often based on the social costs excluding taxes. This treatment of consumer decision-making does not recognize that abatement options often have greater costs compared to their social costs due to private payback periods and discount rates, and accounting of taxes in retail prices. As high tax levels exist in transport, including taxes in the analysis may also lead to lower costs due to greater fuel cost savings from the private cost perspective. Furthermore, costs may also be overestimated since behavioural responses to changes in the baseline transport system, which is not within the scope of bottom-up models, could also lead to lower costs for GHG mitigation. For example, incremental costs for new vehicles or higher fuel prices due to a carbon tax may lead to less demand for transport, more efficient driving or modal shift. Hence, bottom-up approaches employ a financial-technical costing approach, include a lot of technological detail, but do not assess real market responses to measures. Hybrid models, as depicted in Fig. 2, aim to combine technological explicitness, e.g. with respect to number of trips, modes, fuels, vehicle technologies, vehicle size classes, fuel use, as in bottom-up approaches, and behavioural-economic realism, e.g. welfare-economic costs and market responses, as in top-down approaches. Approximately 85% of the studies in this review employ a bottom-up approach and use the financial-technical costing approach. Three studies use a hybrid model being the TREMOVE model (Proost and De Ceuster, 2006) and two studies employ a more conventional top-down approach on a relatively aggregated sector level (Tippichai et al., 2009; Parry, 2007).

3.4. Type of measures and impacts (D in Tables 5–7) In the cost-effectiveness studies reviewed four combinations of ‘measures’ and their impact are evaluated, see Table 4. Measures comprise policy instruments, being soft, financial and regulatory, and abatement options, being technical and nontechnical. The difference is that in case of policy instruments the CEA is based on behavioural responses to a real policy

Fig. 2. Energy-economy model typologies (Horne et al., 2005).

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instrument while in case of abatement options the analyst carries out a ‘what-if’ analysis. ‘What’ are the impacts ‘if’ people use, for example, electric vehicles or more public transport? The impacts could take place at the supply side of the transport system, e.g. vehicles, fuels, and infrastructure, or at the demand side, e.g. users of transport services. The policy instrument determines to what extent an abatement option is adopted as compared to the baseline, while an abatement option determines how much GHG emissions are abated by applying this option. The product of these two results in the total effect of a combination of a policy instrument and abatement option. About 70% of the reviewed studies focus on the combination of Policy-Technical measures or only Technical abatement options and consider only Supply side impacts (P-T-S or T-S in Table 4). About 50% emphasize the combination of Policy-Non-technical measures or only Non-technical abatement options and consider only Demand side impacts (P-N-D and N-D). The last 25% look at both Policy-Technical and Policy-Non-technical measures of which only the ones employing a top-down or hybrid approach consider both supply and demand side impacts. 3.5. Key assumptions (E in Tables 5–7) Many important assumptions have to be made in order to carry out a CEA. Some are partly determined by the abatement costing approach or cost perspective, while others are determined by convention. These key assumptions primarily relate to the baseline assumptions with respect to the reference scenario of the system under study, base year constant prices and currency used, discount rate, evolution of energy prices, e.g. oil and fuel, assumed lifetime of the policy or abatement option and time frame of their implementation, cost development as a function of scale and learning effects, treatment of ancillary effects, e.g. air pollution, health, safety, congestion, treatment of interdependencies between different measures, possible rebound effects of energy efficiency improvements and finally possible wider feedback effects. Roughly 75–85% of the studies report on the assumed oil, energy or fuel prices, and discount rates. However, it was often not straightforward to find the key assumptions and many studies refer to for instance oil price scenarios from other sources. Furthermore, in less than 30% of the studies ancillary effects are quantitatively estimated and only one-third of the studies take rebound effects of policies and options to improve fuel-efficiency into account. 3.6. Cost-effectiveness calculation (F in Tables 5–7) Three key methodological differences are found in this literature review regarding the cost-effectiveness calculation formulae. First, in some studies cumulative or annualized lifetime effects, or anticipated market penetration effects are calculated. Formulas to calculate the cumulated and annualized lifetime effects are shown in (1) and (2), respectively. These two approaches calculate the cost-effectiveness based on an assumed lifetime and intensity of use of the abatement options, but do not consider to what extent the options have been adopted in the market. Conversely, a cost-effectiveness estimation based on anticipated market penetration effects looks at the active installed/adopted abatement options in one year or in a longer time frame. Subsequently, the difference between costs and benefits, and GHG emissions between the scenario with/without those abatement options in the market is used to calculate cost-effectiveness. It is often unclear how the incremental costs of adopting an abatement option is incorporated in the generalized price, e.g. linear annualiation, life time and discount rate, whereas the avoided emissions beyond the specific time frame are not

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counted. The literature review shows that over 40% of the studies calculate cumulative lifetime effects of abatement options (1), while the annualized lifetime (2) and market penetration calculation is used in the remainder of the studies. L P

ðBt C t Þ I ð1 þ dÞt INPV L t¼0 Costeffectiveness ¼ ¼ Emission reductionL Emission reductionL

ð1Þ L

Cost-effectiveness ¼

ð1 þ dÞ d I  ð1 ann:ðBCÞ IAN ann:ðBCÞ þ dÞL 1 ¼ ann: emission reduction ann: emission reduction

ð2Þ where I is the initial investment, NPV is the net present value of technology/policy impacts, L is the lifetime of technology/policy, t is the time in years of the technology/policy being evaluated, Bt is the benefit impacts of technology/policy in year t, Ct is the cost impacts of technology/policy in year t, d is the discount rate, AN is the annuity, ann. stands for the annual. Second, one could calculate a cost-effectiveness estimate that refers to either only one, or alternatively multiple future reference years. More than 75% of the studies base their cost-effectiveness calculation on one single future reference year, e.g., lifetime effects of a hybrid vehicle in 2012, or market penetration effects of 120 g CO2/km regulated cars in 2020 as in Proost and De Ceuster (2006). About 20% use a multiyear time frame for the cost-effectiveness calculation, either based on volume-weighted average lifetime effects over multiple years, e.g. McKinsey & Company, 2007b, or market penetration effects in multiple years, e.g. market penetration of 120 g CO2/km regulated cars between 2010 and 2020 as in ZEW (2006). The last 5% of the studies is not clear about the reference year(s) of the effects. Finally, one could calculate the average or marginal costeffectiveness. Average cost is defined as total incremental cost of multiple options or multiple percentage-points improvement of energy efficiency, divided by the total CO2 reduction. Marginal cost is defined as the incremental cost of a specific chosen option or the last percentage-point improvement of energy efficiency, divided by the specific CO2 reduction achieved by applying that option. We found that about 70% provide marginal costs, 20% provide average costs and 10% provide both estimates. 3.7. Overview About 90% of the studies focus specifically on road transport, of which the majority considers only passenger car transport rather than freight transport as well. Two studies focus specifically on biofuels and another two studies focus specifically on international shipping. Air and rail transport are hardly studied. Tables 5 and 6 present an overview of the methodological choices of the studies, showing a remarkable heterogeneity. (a) The analysis strongly depends on results from TNO (2006), AEA (2001) and ZEW (2006). The methodological review in this table refers to additional methodological choices by INFRAS based on TREMOVE calculations from ZEW (2006) (b) CEA methodology is original. However, the study is based on various other sources potentially using different assumptions. Although the peer-reviewed academic literature and nonacademic literature are presented separately in Tables 5 and 6, neither of the sets show to have more consistency or less differences in methodological choices. Table 7 shows an overview of percentage-shares of difference methodological choices found in this literature review.

4. Impacts of different methodological choices on costeffectiveness In this section we aim to answer the second research question: what is the potential impact of methodological choices on the cost-effectiveness estimates? Since the selected studies provide a vast amount of CEA results, we limit this section to a few illustrative examples of potential impact. Key areas for which examples are selected are (1) the abatement costing approach, types of measures and cost perspective since these are the most fundamental methodological differences, (2) scope and calculation formula since Tables 5 and 6 show many different choices made in studies which is often confusing and (3) key assumptions since these seem highly influential in the literature. The examples do not pretend to show the maximum impacts available in literature but rather highlight the order of magnitude of impacts from different methodological choices and indicate the direction of impacts in terms of cost-effectiveness estimates becoming more or less favourable. Since fuel economy standards and CO2 regulation for cars are the most widely discussed and most thoroughly researched topics, we will mainly focus on these topics. 4.1. Scope of emissions The first example is derived from the impact assessment of CO2 regulation for cars in the EU towards a 120 gCO2/km target in 2012 (Proost and De Ceuster, 2006). Fig. 3 shows the impact of excluding indirect WTT emissions in the cost-effectiveness calculation. If avoided WTT emissions, being a benefit directly linked to implementing the measure, are not counted, the same societal costs are divided by a smaller amount of CO2 savings, thus increasing the costs per tonne. Regarding the use of emissions measured under laboratorylike test conditions ((TA) or adjusted real-world (RW) emissions, most studies adopt a constant factor to arrive at RW emissions ranging from 15% to 25% of the TA emissions (AEA, 2008; Decicco and Ross, 1996; Lutsey, 2008; Proost and De Ceuster, 2006; TNO, 2006). This implies that if an abatement option could bring about 1 gCO2/km reduction in TA-emissions, these studies assume for instance 1.25 gCO2/km reduction in RW-emissions. Therefore studies based on TA values result in a total CO2 saving of 0.21 tonne per g/km reduction in TA value (over a 13 year lifetime and 16,000 km mileage per year: 13  16,000  1  10  6). The total CO2 saving in studies assuming RW emissions would be 13  16,000  1.25  10  6 ¼0.26 tonne per g/km reduction in TA

Cost-effectiveness in $(2009) per tonne CO eq

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500

400

300

200

Proost & De Ceuster (2006): Welfare effects (incl. taxes), market penetration effects in 2020, WTW -RW Proost & De Ceuster (2006): Welfare effects (incl. taxes), market penetration effects in 2020, TTW -RW

100

0 135 gCO2/km

130 gCO2/km

125 gCO2/km

120 gCO2/km

Fig. 3. Impact of scope: excluding WTT emissions, based on Proost and De Ceuster (2006).

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Cost-effectiveness in $(2009) per tonne CO -eq WTW-RW

400 Proost & De Ceuster (2006): Hybrid approach, welfare effects (incl. taxes), market penetration effects in 2015

320 Proost & De Ceuster (2006): Hybrid approach, welfare effects (incl. taxes), market penetration effects in 2020

240 ZEW (2006): Hybrid approach, welfare effects (incl. taxes), market penetration effects 2010-2020

160

INFRAS (2006): Hybrid approach, welfare effects (excl. taxes), market penetration effects 2010-2020

80

0 135 gCO2/km

TNO (2006): Bottom-up approach, financialtechnical effects (excl. taxes), lifetime effects in 2012

130 gCO2/km

125 gCO2/km

120 gCO2/km

Fig. 4. Impact of costing approach and formula: cost-effectiveness towards the 120 gCO2/km regulation for cars in the EU.

value. The RW/TA factor does not only results in greater CO2 savings but also fuel cost savings, implying different CEA results between studies which differ only in assuming TA or RW values. 4.2. Abatement costing approach and calculation formula We compare the results of a bottom-up financial-technical analysis with results from hybrid welfare-economic analyses using different assumptions related to taxes and time frame. Again we use the CEA studies of type-approval CO2 regulations for new cars in the EU. Five cost-effectiveness estimates from four different sources are plotted in Fig. 4 for the scenario moving from 140 gCO2/km level in 2008 to 120 gCO2/km in 2012. Proost and De Ceuster (2006), ZEW (2006) and INFRAS (2006) are all based on the results of model runs with the hybrid transport model TREMOVE. TREMOVE employs a welfare-economic costing approach which means that taxes are relevant and consumers and producers will respond to changes in the generalized price caused by incremental costs for vehicle purchase, for compliance with the new CO2 target, and changes in the fuel efficiency of cars. It is assumed that the loss in government tax revenues is compensated by an increase in general taxes, which has a negative impact on the overall welfare. However, INFRAS (2006) calculates the cost-effectiveness based on the TREMOVE runs ‘corrected’ for tax impacts. While the results from Proost and De Ceuster (2006) and ZEW (2006) are already from a social cost perspective, INFRAS (2006) appears to intent to make these results more comparable to TNO (2006) where taxes are excluded in the financial-technical analysis from the social cost perspective. However, since existing tax distortions in transport are highly relevant to consumer and producer utility levels in such a welfare-economic approach, and changes in utility levels are not necessarily equal to changes in government tax revenues, the approach in INFRAS (2006) seems inappropriate. Another difference is found between Proost and De Ceuster (2006) who calculate the cost-effectiveness for one single year, being 2015 or 2020, while ZEW (2006) and INFRAS (2006) use a multiyear time frame, from 2010 to 2020. Finally, TNO (2006) employs the bottom-up financial-technical costing approach, excluding taxes from the analysis and calculating cumulative lifetime impacts of vehicles meeting the CO2 target in 2012. By comparing ZEW (2006) and INFRAS (2006) one can conclude that excluding taxes from the analysis, in this case, results in a worse cost-effectiveness estimate (the tax-related increase in consumer surplus, mainly from fuel cost savings, is larger than the decrease in government tax revenues). The

maximum dispersion between these studies to achieve the same emission standard 120 gCO2/km is a gap of $162 per tonne CO2  eq ($223 versus $385) between ZEW (2006) and Proost and De Ceuster (2006). We have found similar methodological differences and impacts in two more recent US regulatory impact analyses supporting the final rulemaking establishing new CAFE2 and GHG emissions standards for passenger cars and light trucks from 2012 to 2016 (EPA, 2010; NHTSA, 2010). Table 8 shows three cost items being the vehicle costs for achieving compliance with the new CAFE/GHG standards, the fuel savings at pre-tax fuel prices and fuel savings at post-tax fuel prices. The columns show different calculation formulae for CEA. Lifetime effects are shown for model years 2012–2016, the annual market penetration effects are shown for calendar years 2020 and 2050, and finally the cumulated annual effects are shown for the total 2012–2050 time frame. The results indicate that the cost-effectiveness, including only the incremental compliance costs, varies from 21 to 103 $ per tonne CO2-eq depending on the calculation formula and discount rate. The cost-effectiveness including also pre-tax fuel savings varies from 52 to  311 $ per tonne CO2-eq, and the cost-effectiveness including post-tax fuel savings varies from  61 to  342 $ per tonne CO2-eq. An additional impact on cost-effectiveness may result from the difference between cumulated and annualized lifetime effects. Davidson et al. (2007) show that this difference could lead to a 30% difference in cost-effectiveness, which is related to the discount rate used to annualize the initial investment. Fig. 5 shows the potential impact from using marginal or average abatement costs (MAC or AAC) and includes the AAC curve for EU emissions regulation for cars, the same as in Fig. 4 (TNO, 2006). In addition, a MAC curve is plotted with 5 g CO2/km steps taken from INFRAS (2006) and we have constructed a MAC curve based on 1 g CO2/km steps. Based on this figure one can see that reaching the 120 gCO2/km target costs on average $339 per tonne CO2, but moving from 121 to 120 g CO2/km has a marginal cost of $507 per tonne CO2. 4.3. Cost perspective Fig. 6 shows the impact of cost perspective choice on costeffectiveness estimates of transport GHG mitigation measures in 2 Corporate Average Fuel Economy (CAFE) is the sales weighted average fuel economy, expressed in miles per gallon (mpg), of a manufacturer’s fleet of passenger cars or light trucks, manufactured for sale in the United States, for any given model year.

N/A ¼Not available. NPV¼ Net Present Value. d¼ discount rate.

a The cumulated CO2-eq reduction between 2012 and 2050 are estimated by the authors using a linear approach for the annual reduction from zero in 2012 to 506 Million ton CO2-eq in 2050 (2050  2012)  0.5  506 ¼9612 Million ton CO2-eq.

21  52  61 37  129  148 56  136 N/A CEA results $per tonne CO2-eq incremental costs only $per tonne CO2-eq incremental costs and fuel savings excl. taxes $per tonne CO2-eq incremental costs and fuel savings incl. taxes

56  94 N/A

103  133  169

39  311  342

198,544 696,033 782,029 9,612 357,993 1,599,449 1,783,961 9,612 19,621 177,165 192,791 506 16,102 36,944 42,532 156 53,418 143,843 N/A 962 53,418 184,512 N/A 962 CEA variable Incremental/compliance costs ($2009 millions) Fuel savings excl. taxes ($2009 millions) Fuel savings incl. taxes ($2009 millions) CO2-eq WTW reductiona

Table 8 Impact of taxes, discount rates and time horizon on CEA outcomes. Source: Based on EPA (2010).

Lifetime effects model years 2012–2016, d ¼ 3%

Lifetime effects model years 2012–2016, d ¼ 7%

Annual market penetration effect in 2020

Annual market penetration effect in 2050

NPV of effects in calendar years 2012–2050, d ¼7%

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NPV of effects in calendar years 2012–2050, d ¼ 3%

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the UK (CCC, 2008). Although higher discount rates, being 5.4% and 7.05% instead of 3.5%, are used in the private cost perspective compared to the social perspective, due to high fuel duties in the UK, measures become much more cost-effective in the private cost perspective than in the social cost perspective. The average difference between the two cost perspectives is $438 per tonne CO2. Also the ranking of options changes depending on the cost perspective. For example, biofuels are the least favourable option from a private perspective, but not from the social perspective.

4.4. Key assumptions: technology, oil prices, discount rates and ancillary effects To illustrate the potential impact of different choices regarding key assumptions we have constructed cost-effectiveness estimates based on a cost–benefit analysis of fuel economy improvement for cars in the US by Greene and Duleep (1993). Greene and Duleep introduced three sets of key assumptions affecting the costs and benefits, being an intermediate, unfavourable, and favourable set of assumptions. The parameters include the discount rate, time horizon of effects, rebound elasticity, world oil price elasticity, energy security costs, and road safety costs. Fig. 7 shows the cost-effectiveness estimates for each of the three sets of assumptions. The blue cost-effectiveness estimates include only direct vehicle costs and fuel savings, whereas the red costeffectiveness estimates include various ancillary effects. The most important ancillary effects are cost savings from lower oil prices due to less demand in the US, reduced wealth transfer from US consumers to oil producers and energy security benefits, e.g. less storage costs for strategic reserves to reduce potential economic costs of supply disruptions. Air pollutant emissions, road safety and the consumer surplus comprise only a minor part of the ancillary effects. Note that although the CEA outcomes are widely different, all cases result in a net benefit or no regret. The reason why all these results from the US show a net benefit as compared to net costs for the EU situation in Fig. 4 is because of the geographical scope, the corresponding baseline vehicles to which abatement options are compared, and the stringency of technology assumptions and time frames being considered. A 2002 baseline vehicle in the EU includes already highly costeffective ‘low-hanging fruit’ as compared to a 1989 US baseline vehicle. We now illustrate the impact of technology assumptions in Fig. 8. In Greene and Duleep (1993) three alternative fuel economy levels for 2001 are considered, ranging from 32.9 to 36.5 miles per gallon (MPG) for cars. The fuel economy level 2 as shown in Fig. 7 is based on moderate technology improvement, resulting in 35.0 MPG in 2001. However, one could also consider the maximum use of proven fuel economy technology, like fuel economy level 3 in Greene and Duleep (1993), or even more advanced technologies that may not yet be proven. Fig. 8 shows the impact of moving from moderate to maximum proven technology improvement on the estimated cost-effectiveness. Although the fuel economy level 3 will save more fuel and reduce more GHG emissions, the cost-effectiveness is reduced by about $100 per tonne CO2. The following example shows the potential impact of different oil price assumptions. Fig. 9 shows four oil price assumptions which are used in the cost-effectiveness estimate of type approval CO2 regulation for cars in the EU (TNO, 2006). The $58 per barrel of oil scenario was already shown in Figs. 4 and 5. The difference between the lowest and the highest oil price assumption is approximately $80/barrel and results in a gap of $163 per tonne CO2  eq WTW RW emissions.

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Cost-effectiveness in $(2009) per tonne CO -eq WTW RW

600 MAC curve (1 gCO2/km steps) in 2012 - derived from TNO(2006)

500 INFRAS (2006): Bottom-up approach, MAC curve (5 gCO2/km steps) in 2012 - derived from TNO (2006)

400 300

AAC curve in 2012 - derived from TNO(2006)

200 TNO (2006): Bottom-up approach, financial-technical effects (excl. taxes), lifetime effects in 2012

100 0 0

5

10

15

20

GHG reduction (gCO2-eq per km) beyond 140 gCO2/km (2008/9 ref. veh.) Fig. 5. Impact of formula: average and marginal abatement costs towards the 120 gCO2/km regulation for cars in the EU.

PH/EV Hybrid Hybrid rigid HGV rigid HGV Stop-start medium PH/EV EV small car large car car Biofuels <7.5t van >7.5t Cost-effectiveness in $(2009) per tonne CO

Cost-effectiveness in $(2009) per tonne CO

400

Moderate technology improvement (level 2)

200

0

-200

-400

Maximum technology improvement (level 3)

50 0 -50 -100 -150 -200 -250 -300 -350

-600

Intermediate assumptions, no ancillary effects Intermediate assumptions, with ancillary effects

Fig. 8. Impact of technology assumptions: cost-effectiveness estimates for fuel economy improvements for cars in the US, derived from Greene and Duleep (1993).

-800 Social costs Private costs -1000

5. Conclusions and discussion

Fig. 6. Impact of cost perspective: cost-effectiveness estimates from a social and private perspective, derived from CCC (2008).

intermediate assumptions

unfavourable assumptions

favourable assumptions

Cost-effectiveness in $(2009) per tonne CO

0 -100 -200 -300 -400 -500

Scenario: Fuel economy level 2 (moderate technology improvement), no ancillary effects Scenario: Fuel economy level 2 (moderate technology improvement), with ancillary effects

-600 -700 -800

Fig. 7. Impact of ancillary effects and various key assumptions: cost-effectiveness estimates for fuel economy improvements for cars in the US, derived from Greene and Duleep (1993).

5.1. Conclusion 5.1.1. Key methodological choices and differences in CEA Besides the fundamental differences between different types of policies and abatement options which inherently result in different cost-effectiveness outcomes, different methodological choices and sets of assumptions are another important source of variation in CEA results. Fourteen important methodological issues clustered into six groups are identified on which thirtythree selected studies are systematically reviewed. Many studies make different methodological choices and assumptions, which are often not clearly clarified. This makes it difficult to,what we call, vertically compare cost-effectiveness estimates of transport GHG mitigation measures across studies unless all underlying methodologies and sets of assumptions are analysed in detail. Researchers cannot assume that policy-makers, and probably also their advisors, have the time and the knowledge to carry out additional analyses to make results of studies comparable, while making them comparable is needed if such studies aim to provide relevant information for policy choices. The lack of consistency between cost-effectiveness studies might easily result in misinterpretations, confusion, and misinformed decision-making.

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Cost-effectiveness in $(2009) per tonne CO2 e WTW RW

400 350 300 250 Oil price: $ 40 per bbl 200

Oil price: $ 58 per bbl

150

Oil price: $ 81 per bbl

100

Oil price: $ 120 per bbl

50 0 135 gCO2/km in 2012

130 gCO2/km in 2012

125 gCO2/km in 2012

120 gCO2/km in 2012

Fig. 9. Impact of oil price assumptions: cost-effectiveness estimates for European regulation of CO2 emissions from cars for four oil price scenarios, based on TNO (2006).

Therefore, we see a strong need to further improve the comparability of GHG mitigation studies in transport. 5.1.2. Potential impact of methodological choices Changing one methodological choice or assumption, e.g. as shown in Fig. 8 and 9, may already lead to a different CEA outcome of about $100–200 per tonne CO2-eq, while changing multiple methodological choices or a whole set of assumptions, e.g. as shown in Fig. 7, may lead to even larger variation. Based on the quantitative analysis of all thirty-three studies in this review on which we based the examples in Section 4,we consider that the potential variation between lower and upper cost-effectiveness estimates for GHG mitigation measures in transport, resulting from different methodological choices and assumptions, is within the order of $400 per tonne CO2-eq. Even one single methodological choice could potentially change the sign of the abatement costs, e.g. positive or negative cost-effectiveness in Fig. 6. The differences in outcomes are mainly driven by differences in abatement costing approaches, calculation formulae, scope issues, the cost perspective, and key assumptions. 5.1.3. Improving future CEA Related to our third research question -How could the future practise of CEA be improved?- we conclude the practise of using CEA for policy-making could improve considerably by clearly indicating the specific purpose of the CEA and its strengths and limitations for policy decisions., see further discussion below. Furthermore, this paper shows that in making cost effectiveness overviews of transport GHG mitigation measures for policymakers, CEA specialists should not mix cost effectiveness results of studies which differ in methodological approaches because, by doing so, the ranking of measures would be highly based on arbitrary methodological choices. 5.2. Discussion 5.2.1. Making the purpose of the CEA clear There are several ways which can be used to distinguish the specific purpose that the outcome of a CEA serves. In Table 9 we propose to be especially clear in the purpose of the kind of measures studied in the CEA: is the cost-effectiveness of a policy or a set of policies studied or is an abatement option or set of abatement options assessed (the columns in Table 9). It is also important to make clear which cost perspective is chosen (the rows in Table 9). Example 1 in Table 9 refers to utility functions in hybrid models such as TREMOVE. Example 2 consequently assesses the welfare costs to society based on real market

Table 9 CEA purpose: type of measure and cost perspective. Cost perspective

Private/market

Public/society

Government

Type of measure Policy (wider impacts)

Abatement option (isolated impacts)

(1) e.g. consumer response to higher costs for transport services due to a CO2 standard (2) e.g. welfareeconomic costs to society of introducing a CO2 standard (3) e.g. revenue impact of fiscal stimulation of A and B label cars

(4) e.g. option analysis/ technology assessment using private discount rate and incl. taxes (5) e.g. option analysis, technology assessment using social discount rate and excl. taxes (6) e.g. revenue impact of fiscal stimulation of electric vehicle over their lifetime

responses as described in example 1, see for example Proost and De Ceuster (2006). Examples 3 and 4 refer to the isolated assessment of abatement options from a private or public perspective see, for example, CCC (2008) in Fig. 6. Examples 5 and 6 refer to the potential impact of measures on government revenues. Example 5 would assess the real market response in terms of changes in sales, usage etc., whereas example 6 would assess the lifetime effects of one type of vehicle. Examples 1 and 4 should be used to analyse how stringent or strong the stimulation of an abatement option should be and what the financial and CO2 effects are when the abatement option is used. Example 2 should be used to assess the societal costs, benefits and social cost-effectiveness of policy measures promoting the adoption of abatement options.

5.2.2. Limitations of the bottom-up financial technical approach We note considerable limitations of the usefulness for policymaking of the dominant approach in transport GHG mitigation studies: the bottom-up financial technical approach, e.g. in AEA (2001), Lutsey and Sperling (2009), McKinsey & Company (2007b) and TNO (2006). Of course, the different abatement costing approaches all have advantages and disadvantages which inherently result in differences in policy relevance. Top-down approaches are most suitable to address long-run multi-sector policy goals, like CO2 stabilization scenarios (Halsnaes et al., 1998) or highly aggregated policy implementation levels like a carbon tax or carbon trading system. The welfare-economic costing approach results in relatively realistic responses of

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markets to policy measures. However, since this approach is so highly aggregated, it provides limited insight into the prioritizing of individual short-run policies and options (Morthorst, 1994). Bottom-up approaches with their detailed appraisal of individual options are most suitable to address short-run sector specific options. Though, due to the isolated and financial-technical appraisal of individual abatement options, mostly technical, it provides limited insight in how markets react to those options in terms of uptake rates or behavioural responses. Another disadvantage of bottom-up approaches using a social cost perspective is that they are often not suitable to address regional differences in existing tax distortions. Top-down or hybrid approaches do inherently address tax distortions and real market responses based on historic consumer preferences. Hybrid approaches combine the advantages of the former two (Grubb, 1993). Hybrid models have the capability of appraising policies instruments as a means of inducing an earlier or wider adoption of GHG abatement options by changing economic incentives, the regulatory environment, consumer preferences, or removing barriers such as market imperfections. Bottom-up approaches focus on the supply side of measures, while hybrid approaches consider the interaction between both supply and demand, being changing utility levels and consumer choices in response to imposed measures. One of the reason why EPA (2010) has not used any hybrid approaches like consumer vehicle choice models is that in practice, EPA finds that the state of the art of these models is not yet settled. Nevertheless, we consider a bottom-up financial-technical approach alone not to be sufficient. A welfare-economic approach using a hybrid model using the output from the bottom-up analysis as an input has in our view the potential to an improved assessment of transport GHG mitigation measures based on realistic market responses and behavioural change. 5.2.3. Differences in methodological choices become more important in future Although different methodological choices are nowadays highly relevant to CEA outcomes, several trends highlight that even larger impacts of methodological choices can be expected in the future. One example is the increasing use of alternative fuels and electrification of transport. In those cases the indirect WTT emissions and non-CO2 GHGs will become increasingly more important (TML, 2008). Additionally, also the fuel economy shortfall issue—the difference between fuel efficiency real-world and test values—might become even larger in the future. Dutch data (TNO, 2010) show that especially for fuel-efficient cars, e.g. below 130 g CO2 per km, the difference between real-world and testvalue CO2 emissions is high: with real-world values sometimes dozens percentage points higher than the values measured in the type-approval standardized tests. The conceptual framework presented in Fig. 1 might help researchers and policy makers to perform CEA’s more consistently and better interpret the methodological differences, which lead to different CEA outcomes. 5.2.4. Further research Based on our review we identify three areas for further research. First, policies to induce behavioural change are clearly underrepresented in transport GHG mitigation studies because they are often referred to as difficult to quantify. In cases where they are included, it is often unclear what methodology or assumptions were used. CfIT (2007) conclude that the lack of emphasis on measures to encourage behavioural change represents a significant missed opportunity. UK-ERC (2009) concludes there is untapped potential for carbon reduction from altering consumer behaviour. This may for instance relate to purchasing fewer and more efficient cars, make fewer trips, change

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destinations, switch mode and use cars more efficiently. More research is needed to quantify the costs and benefits of policies that induce behavioural change, not only in road transport, but all modes. This means that methodological limitations (e.g. consumer preference dynamics, non-financial barriers) of existing hybrid approaches and welfare-economic cost assessments need to be improved. Second, as we propose to move from the dominant financialtechnical approach to a welfare-economic approach in transport GHG mitigation studies, more research is needed in this field to overcome the current weaknesses or lack of confidence in this approach, e.g. as mentioned in EPA(2010).We recommend exploring the feasibility of making an in-depth comparative assessment of modelling tools, methodologies and assumptions used for the impact assessment of transport GHG mitigation measures. Some researchers for instance argue that cars are partly a positional good: status is derived from relative differences between people. This discussion indicates that positional consumption only changes relative needs resulting in a zero sum game from a societal perspective. This means that policies aimed at changing certain attributes of cars, e.g. size, horse power, of the entire fleet might lead too very limited loss of welfare to the society (Davidson and Van Essen, 2009; Verhoef and Van Wee, 2000). Another welfare-economic uncertainty relates to the difference between ex-ante anticipated loss of welfare, based on historical preferences, and ex post revealed welfare losses. Consumer preference dynamics mean that the willingness to pay for certain options may change in time or adapt to a new situation, which results in a lower loss of welfare than anticipated, see for example ‘the neighbor’ effect in Mau et al. (2008). In general we recommend doing more research into ex-post analysis van GHG mitigation in transport. Ex-post evaluation of technology costs may help for instance to determine reliable scale and learning curves of technical abatement options. Third, another area to improve we want to highlight is the selection and quantification of ancillary effects. Although ancillary impacts are often very uncertain, various authors stress the importance of considering ancillary impacts, which could comprise up to 100% of the direct abatement costs (Stern Review, 2006). This also holds for taking into account rebound effects beyond direct rebound related to increased demand/usage (Greening et al., 2000). This means more research is needed into rebound effects related to changes in automotive attributes (e.g. size, horsepower, and safety) and possible congestion effects (Hymel et al., 2010). Finally, since the transport sector involves many distortionary taxes more research is recommended on including pre-existing tax distortions and interactions with the broader fiscal system, also known as tax-interaction effects and revenue-recycling effects. Parry et al. (1999) highlights the importance of distortion factors on welfare effects from carbon abatement policies. For transport GHG abatement policies Parry (2007) shows a dispersion of marginal abatement costs of $1000 per tonne of carbon, equal to about $273 per tonne of CO2, depending on the treatment of externalities and fiscal-interaction effects.

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