Do Methodological Choices in Environmental Modeling Bias Rebound

Article pubs.acs.org/est

Do Methodological Choices in Environmental Modeling Bias Rebound Effects? A Case Study on Electric Cars David Font Vivanco,*,† Arnold Tukker,‡ and René Kemp§ †

Center for Industrial Ecology, School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut 06511, United States ‡ Institute of Environmental Sciences (CML), Leiden University, 2300 RA Leiden, The Netherlands § ICIS and UNU-MERIT, Maastricht University, 6200 MD Maastricht, The Netherlands S Supporting Information *

ABSTRACT: Improvements in resource efficiency often underperform because of rebound effects. Calculations of the size of rebound effects are subject to various types of bias, among which methodological choices have received particular attention. Modellers have primarily focused on choices related to changes in demand, however, choices related to modeling the environmental burdens from such changes have received less attention. In this study, we analyze choices in the environmental assessment methods (life cycle assessment (LCA) and hybrid LCA) and environmental input−output databases (E3IOT, Exiobase and WIOD) used as a source of bias. The analysis is done for a case study on battery electric and hydrogen cars in Europe. The results describe moderate rebound effects for both technologies in the short term. Additionally, long-run scenarios are calculated by simulating the total cost of ownership, which describe notable rebound effect sizesfrom 26 to 59% and from 18 to 28%, respectively, depending on the methodological choiceswith favorable economic conditions. Relevant sources of bias are found to be related to incomplete background systems, technology assumptions and sectorial aggregation. These findings highlight the importance of the method setup and of sensitivity analyses of choices related to environmental modeling in rebound effect assessments.

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effects”.6 Early empirical studies following the pioneering work of Khazzoom1 focused on the so-called “direct effect”, that is, the environmental consequences from the increase in the demand for a given product following an efficiency improvement in the same product. However, methodological advances in the fields of input-output analysis (IOA) as well as environmental accounting and modeling facilitated the study of the so-called “indirect effect”, which refers to the environmental consequences from increases in demand for other products (also called “respending effect”). Both direct and indirect effects make up the consumption-side microeconomic component of the rebound effect, and have been relatively well studied in the literature. As a result, rebound estimates have been calculated and their

INTRODUCTION Technological innovation aimed at improving resource efficiency often does not exploit its full potential because of the rebound effect. In short, the rebound effect can be defined as the offsetting or enhancement of environmental savings due to a series of behavioral and systemic responses to efficiency improvements.1−3 For example, fuel efficiency improvements in cars target carbon dioxide (CO2) savings, but lead to travel cost reductions and increases in real income. As a result, car drivers respond by increasing driving distances and overall consumption, which offsets the expected savings. When studying broader environmental burdens instead of energy alone, some authors speak of the “environmental rebound effect”.4 The profound implications of the rebound effect in the context of achieving environmental targets has caught the attention of both scholars and policymakers for more than three decades.5 The rebound effect concept actually encompasses multiple single effects, reason why some authors speak of “rebound © XXXX American Chemical Society

Received: April 15, 2016 Revised: September 7, 2016 Accepted: September 14, 2016

A

DOI: 10.1021/acs.est.6b01871 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

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Environmental Science & Technology

the former, four main modeling approaches are used: direct resource use and efficiency from secondary data, process-based LCA (attributional and consequential),19,20 environmentally extended input-output analysis (EEIOA)21 and hybrid LCA.22 For the calculation of the indirect effect, the use of EEIOA dominates due to the economic completeness of input-output tables (IOTs).

varying sizes have fuelled an empirical debate over the importance of the rebound effect that still remains. The size of microeconomic rebound effects is influenced by the specific products or sectors under study, the geographical scope as well as sources of bias introduced by methodological choices, among other. From these, the latter have received particular attention in the literature. A number of methodological choices have been found to have an important impact on the size of rebound effects. For example, the type of household demand model used,7−9 the household income levels considered,10 the savings ratio applied11 and the inclusion of capital costs.12 While these sources of bias relate to modeling demand changes, modeling the environmental burdens from such changes can introduce additional sources of bias. However, choices in environmental modeling have been far less studied in rebound effect studies. The most common observation on choices in environmental modeling as a source of bias relates to the adoption of a life cycle perspective. In this regard, several authors13−15 argue that the indirect effect is generally small in the context of energy rebound due to the fact that direct energy consumption (fuel and electricity) makes up a small share of total consumer expenditure. However, other authors7,16 point out that the consideration of the embodied energy of products (energy used during the life cycle of products), which makes up from one-third to two-thirds of total consumer expenditure, can notably increase indirect rebound estimates. Font Vivanco and van der Voet17 expand on this by describing systematic larger rebound sizes from those studies applying life cycle assessment (LCA). Furthermore, Font Vivanco and colleagues8 observe that the indirect rebound can be particularly large for environmental pressures other than energy, which can be easily studied via LCA, owing to uneven distributions of embodied pressures in consumer expenditure (e.g., the case of phosphate, which is significantly present in food and drinks production but to a less degree in other products). Nevertheless, there is an existing knowledge gap in the literature regarding other sources of bias, such as the various approaches to calculate the potential environmental savings or the modeling choices behind the environmental impacts from additional consumer expenditure. The main aim of this article is to assess the effect of methodological choices in environmental modeling as a source of bias in microeconomic rebound effect models. As a case study, we analyze CO2 savings from electric passenger car technologies, full-battery electric (FBE) and hydrogen fuel cell (HFC) cars, due to their expected role in climate change mitigation strategies.18 More specific research questions are • Do choices in the environmental assessment methods and environmental input-output databases cause bias in rebound effect models? • Can rebound effects from the use of FBE and HFC cars significantly offset expected CO2 savings? And if so, at which time-scale?

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CASE STUDY DESIGN, METHODS AND SOURCES OF DATA The Introduction of Electric Cars in the European Union. The case study focuses on the introduction of full battery electric (FBE) and hydrogen fuel cell (HFC) passenger cars in the EU27. The year of study is 2020, because it is expected that from this year on both FBE and HFC will achieve relevant market shares.49 The alternative technology for both FBE and HFC corresponds to the internal combustion engine (ICE) technology. The technical description of the FBE, HFC, and ICE cars is based on life cycle inventory (LCI) data for a common generic passenger car glider with the corresponding powertrains (see Supporting Information S1 for the complete physical inventory). The data for the glider as well as the FBE and ICE powertrains is based on the data set provided by Hawkins and colleagues,50 whereas the HFC powertrain data is based on Bartolozzi and colleagues.51 It merits noting that both data sets contain firsttier data on the production, use and end-of-life (EoL) stages, that is, only direct use of materials and energy. The common glider pertains to the lower-medium or compact cars segment, which currently has the largest market share in Europe.52 The powertrains are based on current commercial technologies. Specifically, a mix of diesel and gasoline engines for the ICE car (according to the market diffusion described by the European Commission53 for the year 2020, namely a 51% diesel diffusion), a first generation LiFePO4-based electric battery for the FBE car and a standard hydrogen-based fuel cell engine for the HFC car. The three powertrain technologies will be assessed on the basis of a common lifetime travel demand (see Direct and Indirect Effects As Changes in Demand). Definition of the Environmental Rebound Effect. The microeconomic environmental rebound effect (ERE), expressed in a given environmental burden e and for a time t, can be decomposed into direct (EREdir) and indirect (EREind) effects as ,t EREe , t = EREedir,t + EREeind

(1)

Furthermore, each single effect can be decomposed again into a demand and an environmental or technology effect. The demand effect relates to the changes in demand due to changes in real income, whereas the technology effect is associated with the environmental burdens associated with each unit of additional demand. Thus, EREdir and EREind can be expressed as e,t t e,t EREdir = Δddir, pbp

,t EREeind =

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∑

(2)

t e,t Δd ind, ibi

(3)

s = 1,..., n

REVIEW OF MICROECONOMIC REBOUND EFFECT STUDIES Table 1 shows a review of microeconomic rebound effect studies (direct plus indirect effects). We distinguish between the environmental models used to calculate both the environmental savings and the direct effect in terms of environmental pressures (generally calculated using the same model), and the indirect effect, also in terms of environmental pressures. With respect to

With: e,t Δr t = Δddir, p +

∑ s = 1,..., n

t Δd ind, i

(4)

where Δddir denotes the change in demand for a given electric powertrain technology p and Δdind denotes the change in demand for a consumption group i (both in monetary terms), b refers to the environmental burdens per unit of demand, B


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Environmental Science & Technology Table 1. Review of Microeconomic Rebound Effect Studies and Environmental Models Applied environmental assessment models applied

study

year 24

scope

environmental savings and direct effect

sector studied

indirect effect 25

Lenzen and Dey Briceno and colleagues26

2002 2004

Australia Norway

food and heating transport

Alfredsson27

2004

Sweden

Takase and colleagues28

2005

Japan

Brännlund and colleagues30 Thiesen and colleagues31

2007

Sweden

2008

Denmark

food, travel and utilities transport, electricity and food transport and energy services food

Girod32 Mizobuchi12

2008 2008

Switzerland Japan

Ornetzeder and colleagues34 Weidema and colleagues35 Kratena and Wüger36

2008

Austria

general consumption transport and energy services transport

2008

EU27

food

2010

Austria

Druckman and colleagues11 Tukker and colleagues38 Murray7

2011

UK

2011 2013

EU27 Australia

Thomas and Azevedo40

2013

U.S.

Cellura and colleagues42

2013

Italy

transport and energy services transport, heating and food food transport and lighting transport and electricity energy services

44

Chitnis and colleagues Lin and Liu45 and Lin and colleagues46 Chitnis and colleagues9

2013 2013

UK China

2014

UK

Font Vivanco and colleagues8 Font Vivanco and colleagues47 Chitnis and Sorrell48

2014

EU27

heating and lighting transport and energy services transport, heating, lighting and food transport

2015

EU27

transport

2015

UK

transport, lighting and heating

EEIOA (life cycle) based on Lenzen attributional LCA EEIOA (life cycle) based on Statistics Norway national accounts attributional LCA EEIOA (only production) based on Statistics Sweden national accounts EEIOA (life cycle) based on the WIO model29 not reported

not reported

45−123% 8−75% 7−300% 17−125% 120−175%

consequential LCA

EEIOA (life cycle) based on Statistics Denmark national accounts attributional LCA attributional LCA EEIOA (life cycle) based on Japan national accounts and Nakamura and Otoma33 attributional LCA EEIOA (life cycle) based on Statistics Austria and Eurostat’s NAMEA hybrid attributional LCA EEIOA (life cycle) based on Eurostat’s NAMEA direct energy use and energy direct energy use and energy efficiency databases efficiency databases MR-EEIOA (life cycle) based on the SELMA model37 EEIOA (life cycle) based on the E3IOT model EEIOA (life cycle) based on Lenzen et al.39

size of the rebound effect (%)

38

206−1,600% 50% 12−38% −14% −1,275 to −32% 37−86% 7−51% 0−1% 4−24%

EEIOA (life cycle) based on the EIO-LCA model41

7−25%

attributional LCA

1−14%

EEIOA (life cycle) based on Cellura et al.43 MR-EEIOA (life cycle) based on the SELMA model37 EEIOA (life cycle) based on China national accounts

5−15% 24−37%

MR-EEIOA (life cycle) based on the SELMA model37

5−106%

attributional LCA

−200.000−51%

EEIOA (life cycle) based on the E3IOT model38 attributional LCA EEIOA (life cycle) based on the E3IOT model38 MR-EEIOA (life cycle) based on the SELMA model37

−1,957−610% 46−63%

a

Partly based on Chitnis and colleagues23 and Font Vivanco and van der Voet.17 LCA: life cycle assessment; EEIOA: environmentally extended input-output analysis; MR: multiregional.

n equals the total number of consumption groups and Δr corresponds to the total change in real income due to the shift from an ICE to an electric powertrain. Substituting eqs 2 and 3 in eq 1, the environmental rebound effect can be expressed as t e,t EREe , t = Δddir, pbp +

∑

with

t e,t Δd ind, ibi

s = 1,..., n

(7)

PSe , t = (datbae , t ) − (dptbpe, t )

(8)

(5)

where PS denotes the potential or engineered environmental savings that a given electric powertrain can achieve with respect to its ICE alternative a, AS denotes the actual savings achieved once the environmental rebound effect is considered and d denotes the travel demand for a given powertrain. By combining eqs 5, 6, and 7, the environmental rebound effect can be defined as

Moreover, the environmental rebound effect can be expressed as a percentage of the environmental savings that are “taken back” as8 ⎛ ASe , t ⎞ %EREe , t = ⎜1 − ⎟ × 100 ⎝ |PSe , t | ⎠

ASe , t = PSe , t − (PSe , t + EREe , t )

(6)

t e,t t e,t ⎛ ⎞ [[datbae , t − dptbpe, t ] − [[datbae , t − dptbpe, t ] + [Δddir, pbp + ∑s = 1,..., n Δd ind, ibi ]]] ⎟ × 100 %EREe , t = ⎜⎜1 − ⎟ |[datbae , t − dptbpe, t ]| ⎝ ⎠

C

(9)


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real income (Δrr) by each MBS for each consumption group i as

Following eq 9, the demand effect is captured by the terms Δddir and Δdind whereas the technology effect is captured by the terms bp, ba and bi. In the next section, the approaches followed to account for these terms are further explained. Direct and Indirect Effects As Changes in Demand. To approach the changes in total demand resulting from the respending of the changes in real income, a microeconometric model that combines individual estimations for the direct and indirect effects is used.54,55 In the context of transport, a popular approach to estimate the changes in demand for the new electric powertrain, or the direct effect, is based on the own transport price elasticities of transport demand as56,57 t t t Δddir, p = − (d p[ηPT (T )[1 − sp]])

t Δd ind =

t Δrr t = (dat − dpt) − Δddir, p

t dpt = tdptptd, p

(12)

(14)

where d is the travel demand in monetary terms for a given electric powertrain p and its corresponding alternative a. An important consideration of this approach is that all additional real income is assumed to be spent and thus there are no savings. This is justified by the fact that, since a life cycle perspective is adopted, all additional income will be eventually de spent, even though the purchasing power of the income saved will fluctuate according to the evolution of both inflation and interest rates. Data on final consumption expenditures and price indices for the 1995−2012 period and for the EU27 Member States by COICOP 2 digit classification (12 categories) has been obtained from Eurostat.61 Travel demand in monetary terms for the ICE powertrain have been obtained following eq 12 using the same travel demand data as the FBE and HFC technologies (12 500 vkm/year) and a unitary price of 0,217 euro per vkm from M&C.49 The background data and the complete results from the AIDS model can be found in Supporting Information S3. Simulation of Future Scenarios. The TCO of electric cars is expected to decrease notably in the future due to, among other, economies of scale, rising fuel prices and infrastructure change.49 It is therefore valuable to assess whether future changes in the TCO will have a relevant impact on both the rebound size and its variability (difference between the maximum and the minimum size). In this sense, we define three scenarios for the years 2020, 2030, and 2050 by estimating how the costs related to purchase, maintenance, fuel and infrastructure would develop in the future. The underlying cost data is based on M&C.49 In a first scenario (S1), we use such costs in combination with existing average subsidies in Europe as described in Supporting Information S2. In a second scenario (S2), we include additional assumptions about subsidies and taxes as proposed by M&C49 in order to accelerate the diffusion of electric cars. Concretely, we assume a subsidy of 6,000 € per electric car as already implemented in several Member States as well as fuel taxes, respectively for gasoline and diesel, of 0.655 €/liter and 0.470 €/liter. It merits noting that other variables such as physical inventories and technology assumptions will remain unaltered. Accordingly, the conclusions that can be drawn from this simple exercise are limited to the impact of changes in the TCO alone. Environmental Savings and the Environmental Direct Effect. Following eq 9, the term b captures the environmental burdens per unit of demand used for the calculation of the environmental savings and the environmental direct effect. As seen in the review of rebound studies presented in Review of Microeconomic Rebound Effect Studies, this value can be calculated through a variety of environmental models. To capture such diversity, two popular and essentially different approaches will be used: attributional life cycle assessment (LCA) as well as input-output-based hybrid LCA. Following, the two approaches are briefly described. Attributional Life Cycle Assessment. Following the ISO 14040:2006 series on LCA,62 we first define the scope of the analysis. The functional unit corresponds to the life cycle vkm of the predefined FBE, HFC and ICE cars, namely 150 000 vkm or 12 500 vkm/year. The system boundaries include the production,

cpt (11)

(13)

with

(10)

cat

Δrr t MBSi

s = 1,..., n

with spt =

∑

where ηPT(T) is the own transport price elasticity of transport demand, expressed as the percentage change in quantity demanded in response to a one percent change in price, d is the total travel demand for a given electric powertrain p in monetary terms, s denotes the savings coefficient of using a given electric powertrain, c represents the total cost of ownership (TCO), td is the travel demand in demand units (vehicle-km (vkm)]) and ptd is the unitary price of travel demand. Data on travel demand for the FBE and HFC powertrains has been obtained from Hawkins and colleagues50 and Bartolozzi and colleagues51 and both correspond to 150,000 vkm during the car’s life cycle and 12 500 vkm per year. The unitary price and the TCO values for Europe and all powertrains corresponding to purchase, maintenance, fuel and infrastructure costs have been obtained for the year 2020 from McKinsey and Company (M&C).49 In addition, average subsidies per vehicle for electric cars in Europe from ARF/M&C58 have been included in the TCO calculations. The annualized TCO per vehicle has been calculated in 2709, 2942, and 3375 euros for ICE, FBE, and HFC cars, respectively. With regard to the unitary price of travel demand, it corresponds to 0.235 and 0.270 euro per vkm for FBE and HFC cars, respectively. A long-term own price elasticity of transport demand for passenger cars in Europe has been obtained from de Jong and Gunn,59 with a value of −0.26. Complete data sets and assumptions for the TCO calculations can be found in Supporting Information S2. To approach the indirect effect, we first estimate how will an additional unit of real income be spent by calculating the marginal budget shares (MBS) for each possible consumption group i. To calculate the MBS, we apply a model based on the linear specification of an Almost Ideal Demand System (AIDS), a popular demand system model developed by Deaton and Muellbauer60 and applied for rebound effect modeling in a number of studies, including Brännlund and colleagues30 and Mizobuchi.12 The AIDS model presents a number of characteristics that, in combination, makes it more advantageous to competing models.60,48 The description of the AIDS model is presented in Supporting Information S3. Once the MBS are estimated, the indirect effect in monetary terms can be calculated by multiplying the remaining change in D


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are grouped according to the activity classification of the EEIOT (classification). Third, the production structure (flows related to the production stage) of the new activity is included in the interindustry flow matrix (Z) and the flows of the original activity (that which includes passenger cars) are recalculated by subtracting the monetary flows from the new activity (disaggregation). Fourth, the final demand vector is constructed using production and use data to account for regional differences in the context of MR-EEIO databases. Lastly, emissions factors, direct emissions from combustion and emissions from EoL are added using process-based LCA. An overview of the approach is shown in Figure 1, and further details and sources of data are presented in Supporting Information S5. Once a new technical coefficient matrix is calculated, CO2 emissions can be calculated using standard EEIOA73 based on the Leontief model74 as

operation and EoL stages of the cars. Second, during the inventory analysis data on processes both from the foreground and the background systems are collected. The foreground system is built from the physical LCI data provided by Hawkins and colleagues50 (see Supporting Information S1), while the background system corresponds to Ecoinvent 2.2 processes,63 following the concordances provided by the authors. The information from both systems is then integrated following the matrix notation approach.22 Allocation methods were not applied in the foreground system, as all the processes included are monofunctional. Allocation in the background system was applied following Ecoinvent default allocation factors.63 The environmental indicator chosen is CO2 emissions, and thus no further impact assessment is required. In mathematical notation, an LCA model can be described as64 → ⎯t gp⃗ e∨, t a = Bpe ,∨t a Ã −1∼ yp ∨ a

e p⃗ e ,∨t a = F e , t (I − At )−1yp⃗ t ∨ a

(15)

Where e ⃗ is an m × m vector of CO2 emissions (e) for each economic activity m, F is an m × m emission factor matrix describing the direct CO2 emissions generated by each economic activity, I is an m × m identity matrix, A is an mxm technical coefficient matrix containing the interindustry input multipliers and y⃗ is an m × 1 final demand vector describing the final demand for each activity. Similarly to eq 16, the terms bp and ba can be calculated as

Where g⃗ is a n × 1 vector describing the amount of CO2 emissions for each economic output n, B is an n × n environmental intervention matrix describing the amount of direct CO2 emissions per economic output, Ã is a n × n technology matrix → ⎯ y representing the physical flows relations between processes and ∼ is a n × 1 vector representing the final demand for economic outputs. From eq 15, the terms bp and ba can be obtained as bpe ,∨t a =

∑k = 1,..., n gp⃗ e∨, t a → ⎯t

∑k = 1,..., n ∼ yp ∨ a

(17)

bpe ,∨t a (16)

=

∑k = 1,..., m e p⃗ e ,∨t a ∑k = 1,..., m yp⃗ t ∨ a

(18)

The Environmental Indirect Effect. The environmental pressures associated with the indirect effect are generally calculated via EEIOA (see Table 1). While the basic derivation from the Leontief model used in EEIOA is consistently followed by scholars, the EEIOTs necessary to carry out such analysis can be constructed in a variety of ways. For instance, the choice of a transformation model to construct symmetric IOTs from supply and use tables,75 the resolution of the economic activities, the degree of detail in the description of national economies and the sources and methods to calculate the environmental accounts. The multiple combinations in terms of methodological choices have led to the construction of various EEIOTs, which are used for a manifold of purposes including rebound effect modeling. To account for the impact of choosing a specific database for EEIOA in the context of rebound effect modeling, three popular EEIOTs have been chosen: the E3IOT,76 the WIOD,77 and the Exiobase78 databases. A short description of each database is presented in Supporting Information S6. It merits noting that these databases have differing base years, and that these do not match with the year of the case study, 2020. However, no corrections have been made due to the exploratory nature of this study. It also merits noting that, because car transport is included in the general transport categories of EEIOTs, the indirect effect may be slightly overestimated, as the additional expenditure (and environmental impact) of car transport should be accounted for in the direct effect alone.

Total values for bp and ba and contribution analysis by economic process can be found in Supporting Information S4. Input-Output-Based Hybrid Life Cycle Assessment. Departing from the same physical LCI as the foreground system, an alternative approach to process-based LCA is to use this information to disaggregate a given economic activity from an EEIOT, and calculate the CO2 emissions in an input-output (IO) framework. This approach follows the model III formulated by Joshi,65 which was partially based on previous works such as those from Gibbons and colleagues,66 and later applied in various studies such as that from Lenzen.67 This approach introduces a number of advantages with respect to process-based LCA, such as avoiding traditional cutoff issues in LCA (that is, the exclusion of background processes from the product system) and the simplification of the inventory analysis stage, so that the background system need not to be constructed from process databases.68,69 However, it also incorporates traditional issues within IOA, especially related to the high level of aggregation of activity classifications, aggregation errors, fluctuations due to the use of monetary values and incompleteness of environmental statistics.70 Our approach departs from the work of Joshi,65 but rather than modifying the technical coefficients as suggested by the author, we modify the interindustry flows from a symmetrical IOT. This approach allows to easily recalculate the technical coefficients of the original activity and avoids issues related to working with coefficients.71 In short, our approach follows five general steps; First, the physical LCI (Z̃ ) of a new activity (electric and ICE cars) is converted from physical (p) to monetary (m) units by means of production and trade statistics (monetarisation). Concretely, we build concordances between Ecoinvent and the NACE rev.2 classifications, and derive unit values (€ per physical unit) from the Prodcom database.72 Second, the flows

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RESULTS Rebound Effect Size and Variability. The size of the environmental rebound effect according to the multiple methodological choices defined is shown in Table 2. On average, the results describe, respectively for FBE and HFC cars, a moderate negative rebound effect (also known as conservation rebound effect, as it leads to increased environmental savings79) E


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Figure 1. Graphical overview of the input-output-based hybrid life cycle assessment approach. LCI: life cycle inventory; IOT: input-output table; #: physical units; $: monetary units.

Table 2. Size of the Rebound Effect (Expressed as a % of CO2 Savings Taken Back) For Full Battery Electric and Hydrogen Fuel Cell Cars method (environmental savings and direct rebound effect)

attributional process-based LCA

input−output-based hybrid LCA

EEIO database (indirect rebound effect)

E3IOT

Exiobase

WIOD

E3IOT

Exiobase

WIOD

full battery electric car • direct effect • indirect effect hydrogen fuel cell car • direct effect • indirect effect

−9% −3% −6% −12% −1% −11%

−11% −3% −8% −14% −1% −13%

−11% −3% −8% −14% −1% −13%

−9% −4% −5% −10% −1% −9%

−5% −1% −4% −11% −1% −10%

−5% 0% −5% −12% −1% −11%

of about −8% and −12%. The sign and the size relates mostly to a modest increase in the TCO with respect to the ICE alternative. These results contrast with those from Font Vivanco and colleagues,8 which described much larger sizes for the same technologies using the global warming potential and greenhouse gases indicators (−681 to −416% and −282 to −183%, respectively). However, the authors studied specific luxury car models, while, in this study, average car models have been assessed. The differences in TCO − much higher for luxury models− can thus largely explain such discrepancy. In this study, the change in TCO for FBE and HFC is respectively 9% and 25%, while in Font Vivanco et al. it corresponds to 70% and 68%. Because the car models assessed in this study represent average models, our results are also expected to be more realistic when generalized for electric vehicles. Furthermore, in agreement with Font Vivanco and colleagues8 for the case of electric cars and some of the rebound literature,23,27,30 the rebound effect is mostly driven by the indirect effect. The indirect effect is responsible, respectively, for an average of 76% and 86% of the total size. The maximum variability observed in the results is, respectively for FBE and HFC cars, 58% and 30%. This degree of variability is comparable with that introduced by choices in the demand effect. For instance, Murray7 found that choices in the

functional form of the expenditure elasticities introduced a maximum variability of 50% in the rebound size. Also, Druckman and colleagues11 simulated the sensitivity of the rebound size by using various realistic savings ratios, estimating a maximum variability of 35%. Moreover, in order to study which methodological choicemethod or database used has a larger effect on total variability, we have applied a two-way analysis of variance (ANOVA) test (see Supporting Information S7). While only statistically significant at α = 0.05 in the case of HFC, the results describe for both FBE and HFC a larger effect of the choice of method. Sources of Bias. In the following we analyze in more detail how the differences between methods and databases can influence the size of the rebound effect. To this end, we first focus on the models and databases used in the calculation of the environmental savings and the direct effect. We compare the CO2 emissions associated with each of the processes involved in the construction and use stages (the EOL stage has been modeled using the LCA approach for all vehicle technologies and is thus omitted). We use the FBE car technology as an example, as sources of bias would be of similar nature across technologies. From the 184 processes in the physical inventory (as described in Ecoinvent v2.2, as the initial source of data), we select the five processes which display a greater difference in CO2 emissions F


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Figure 2. Comparison of the five processes in the construction and use of a full battery electric car which display a greater difference in CO2 emissions (in kg) between models and databases.

in eq 6). The same outcome is to be expected when using databases in which the environmental intensity of certain products is underreported. For instance, by applying overoptimistic technology assumptions (e.g Ecoinvent v2.2, as per our example) and/or too aggregated economic classifications which include less environmentally intensive activities (e.g., Exiobase, as per our example). Second, we analyze potential sources of bias in the calculation of the indirect effect by calculating the maximum difference in CO2 emissions between IO databases for each expenditure category (see Figure 3). The largest difference comes from housing and household utilities, where the E3IOT database shows a notably lower carbon intensity than the WIOD and Exiobase databases. This can be partially explained again by aggregation issues when building the concordances to the COICOP classification, as well as by how the E3IOT database was built. Thus, final demand data and carbon intensity coefficients from this database could be respectively under and overestimated, leading to lower carbon intensity and therefore higher actual carbon savings and lower rebound effect. Issues of similar nature are to be expected in the rest of expenditure categories. In general terms, the WIOD database yields the highest CO2 intensity values, followed by Exiobase and E3IOT. This differences are consistent with the literature, as Moran and Wood82 also found that the WIOD database systematically yielded higher CO2 intensities than Exiobase. Simulation of Future Scenarios. The results of the simulation of future scenarios in terms of the rebound size based on changes in the TCO from all combinations of methods and databases are shown in Figure 4. The results of the scenario S1 (existing subsidies) show that, while the TCO of both FBE and HFC is on a par with the ICE alternative by the year 2020, both electric technologies become notably cheaper by the year 2050. As a result, the rebound effect reverses its sign and reaches a size of respectively 10% and 5%. The variability of the rebound size increases as well and reaches a maximum value of, respectively for FBE and HFC cars, 136% and 47%. Regarding the scenario S2 (optimiztic subsidies), both the use of FBE and HFC cars becomes cheaper by the year 2020, which causes a positive rebound effect of respectively 23% and 4% on average.

(difference between the lowest and the largest value) between models and databases (see Figure 2). Relatively lower emissions mean that a given model may underestimate emissions from the studied system, and thus environmental savings can be enhanced and the direct effect underestimated, thus reducing the rebound effect. The opposite would hold true for relatively higher emissions. Almost 80% of the total difference comes from two single processes: electricity used during the use stage and steel used in the construction of the body and doors. For both processes, Hybrid LCA (HLCA) in combination with the E3IOT database shows the largest emissions. A plausible explanation for this is the fact that this database is relatively outdated (and thus reflects older technology levels such as a lower diffusion of renewable energies) and is based on a number of technology assumptions rather than actual economic and environmental accounts (e.g., by forcing European production structures from a few countries and emissions on U.S. data).80 For electricity, conventional LCA shows larger emissions than the other hybrid LCA models. This can be partly explained by the fact that the correspondent economic sectors include both production and trade of electricity, which would lower the emission intensity (emissions per economic output) as trade services require less energy inputs. Aggregation issues are a common source of bias in the IO literature.81 Regarding the production of steel, Hybrid LCA models in combination with the Exiobase and WIOD databases describe larger emissions than conventional LCA. This can be partly attributed to the fact that background processes in LCA may be omitted due to incomplete reporting.22 Also, the LCA model uses regional production data for Europe, whereas HLCA models based on MRIOT (WIOD and Exiobase databases) are able to account for steel production from other non-EU countries, such as China, where aspects such as technology levels, environmental regulation and the energy mix can lead to higher carbon intensity. In summary, considerable sources of bias in the calculation of the environmental savings and the direct effect can be related to incomplete background systems, technology assumptions and sectorial aggregation. Therefore, models in which some background emissions are omitted (e.g., LCA) can underestimate the rebound effect, as this would increase the actual environmental savings achieved (numerator G


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Figure 3. Comparison of expenditure categories in terms of differences in CO2 emissions (in kg) between input-output databases from the indirect effect from full battery electric cars.

Figure 4. Rebound effect size according to scenarios S1 and S2 for full battery electric and hydrogen fuel cell cars.

novel rebound effect estimates for full battery electric (FBE) and hydrogen fuel cell (HFC) cars in Europe. The selected methodological choices are found to influence the rebound size importantly, with a maximum variability across models exceeding 130% in some cases. Between the two types of choices studied in this article, the selection of method (LCA or Hybrid LCA) leads to higher variability of results than the selection of EEIOT databases. Through a detailed analysis of the results, three potentially relevant sources of bias are identified: incomplete background systems, technology assumptions and sectorial aggregation. In this sense, models that omit some background emissions (e.g., LCA) or databases in which the environmental intensity of certain products/sectors is underreported, for instance by applying overoptimistic technology assumptions and/or too aggregated economic classifications, can notably underestimate the rebound effect. For both FBE and HFC cars, and in the absence of additional subsidies and taxes, we found a moderate negative rebound effect. This means that the predicted CO2 savings would be enhanced as the higher costs of FBE and HFC cars reduce consumers’ effective income and so total consumption. The results are robust across data sources and modeling approaches but not robust over time.

By the year 2050, the rebound effect reaches an average size of respectively 44% and 24%. Thus, even assuming very optimiztic assumptions on the levels of subsidies and taxes to favor the diffusion of electric cars, these sizes are in line with the results from the literature for rebound effects in the transport sector. For example, Thomas and Azevedo83 calculated a rebound size for gasoline efficiency improvements in the US and for CO2 emissions of about 20%. Also, Chitnis and Sorrell48 estimated a rebound effect for vehicle fuel improvements in the UK in terms of greenhouse gas (GHG) emissions of 46%. Lastly, during the entire studied period, the variability of the rebound size is respectively about 130% and 50%. A variability higher than 100% would entail that the rebound size could be notably over or underestimated.

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DISCUSSION In this study, we assess to which extent methodological choices in environmental modeling influence environmental rebound effect estimates. For the case of CO2 emissions, we apply two LCA-based modeling approaches (attributional process-based LCA and IO-based hybrid LCA) in combination with three EEIO databases (E3IOT, Exiobase, and WIOD) to calculate H


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Environmental Science & Technology Notes

The results of simulations show that, with presumably decreasing costs of ownership, both FBE and HFC would be cost-effective by the year 2050, resulting in a positive yet modest rebound effect. With optimiztic assumptions on subsidies and taxes, both technologies show a notable positive rebound effect of 44% and 24%, respectively. While such sizes mean that reasonable environmental savings are still achieved even with very favorable conditions, the high variability found calls for caution in interpreting this and other rebound effect results. The findings of this study are relevant in a number of ways and to various audiences. First, it highlights the importance of choices in the environmental models used for rebound analysis, in particular the choice of method, a topic which has received little to no attention within the specialized literature. Also, the importance of sensitivity analyses in order to control for potential sources of bias from such choices. Second, while several studies have focused on the variability introduced by the choice of method84−88 and the EEIO database,82,87,89 the combined study of methods and databases as a source of bias is largely unexplored. This makes our findings not only relevant for rebound effect modeling, but also in the broader context of environmental modeling. Third, it provides novel rebound estimates for electric cars under current and future scenarios, a topic of high interest in light of their expected diffusion,18 yet again largely unexplored. Lastly, this study is not without its own limitations, which calls for further analysis. For instance, the methodological choices tested could be expanded to include additional sources of bias, such as characterization factors and emission timings, and allocation methods in particular. The latter has been found to bias importantly results in the context of LCA.90 Furthermore, several assumptions have been used in our calculations due to lack of data, for instance in the IO-based hybrid LCA approach (e.g., proportional production outputs) and the respending model, which assumes consumption patterns for the economy as a whole. This study has not considered the effect of the reallocation of money from the consumers to the taxpayers via subsidies, which could counteract the rebound effect to some extent. While this paper has focused on CO2 emissions alone due to data availability, additional and more comprehensive indicators would shed light on the trade-offs between environmental pressures. These could include water pollution from resource extractions related to the manufacture of electric batteries and the effect of methane production for generating hydrogen in terms of GHG emissions. Within environmental assessment methods, additional methods could be included in future research, such as integrated hybrid LCA and streamlined and consequential LCA. Similar exercises focusing on other sectors and environmental burdens would also shed light on whether the sources of bias and variability ranges found in this study can be generalized or are rather case-dependent.

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The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This research has been undertaken within the framework of the Environmental Macro Indicators of Innovation (EMInInn) project, a collaborative project funded through the EU’s Seventh Framework Programme for Research (FP7) (grant agreement no. 283002). We thank Ester van der Voet, Hai-Xiang Lin, João Dias Rodrigues and three anonymous reviewers for their comments.

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