How robust are estimates of the rebound effect of energy efficiency improvements? A sensitivity analysis of consumer heterogeneity and elasticities

Energy Policy 132 (2019) 1–14

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How robust are estimates of the rebound effect of energy efficiency improvements? A sensitivity analysis of consumer heterogeneity and elasticities

T

Veronika Kulmer∗, Sebastian Seebauer LIFE – Centre for Climate, Energy and Society, Joanneum Research Forschungsgesellschaft mbH, Leonhardstraße 59, 8010, Graz, Austria

ARTICLE INFO

ABSTRACT

Keywords: Economy-wide rebound Sensitivity analysis CGE model Household heterogeneity

Economy-wide rebound effects may undermine climate policies relying on energy efficiency improvements. However, available rebound estimates diverge widely. We illustrate the crucial role of model assumptions of household heterogeneity and elasticities. A computable general equilibrium model of the Austrian economy incorporates multiple household groups with heterogeneous preferences and analyzes how improving efficiency by 10% affects household fossil fuel consumption. In the base model, economy-wide rebound is 65%; different household groups show direct rebound of 8–12%; thus, economy-wide rebound is mainly advanced by indirect rebound. A sensitivity analysis using Monte Carlo simulation varies elasticities between household groups, namely substitutability between material and energy goods, and between different energy goods. In 160 simulation runs, the economy-wide rebound emerges as rather robust. By contrast, direct rebound varies widely among household groups and attains 30%, where reciprocal feedback between groups builds up. In the base model, a fossil fuel tax rate of 43% neutralizes the economy-wide rebound. When elasticities in 180 simulation runs are varied, this tax rate spans from 15% to 80%. Thus, rebound estimates and derived policy advice, such as specific rates and numbers, should be treated with great caution, unless elasticity parameters are reliable and account for heterogeneous consumer preferences.

1. Introduction Improving the energy efficiency of technologies in mobility and other consumption domains is vital to the transformation process towards a low-carbon society. Lower energy use per unit output is an attractive option to combat carbon emissions (Ringel et al., 2016; Bukarica and Tomšić, 2017); yet, the expected energy reduction, and hence reduction in carbon emissions is often not achieved. In practice, energy efficiency programs often do not realize their technologically feasible savings potential (IEA, 2014). This shortfall results from the basic economic dynamic that income freed up by efficiency gains will be re-invested in the same or other consumption domains, thereby incurring additional energy consumption (Khazzoom, 1980). The phenomenon where price responses on the market and consumer behavior undermine the projected reduction in energy use is known as rebound effect. In recent years, the direct rebound effect of certain energy efficiency technologies has received much attention from scientists and

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policymakers, but the indirect feedback effects emerging from linkages and adjustment processes between economic sectors remain under-researched (Gillingham et al., 2016; Lecca et al., 2014). This economywide rebound effect nonetheless poses a severe risk for national climate strategies relying heavily on energy efficiency improvements in order to reach a certain level of reduction in carbon emissions (OECD, 2010). The economy-wide rebound is analyzed by studies using macroeconomic models, for example input-output based, econometric simulation models, partial or general equilibrium models, which show considerable variation in their results, ranging from negative rebound effects to backfire (Allan et al., 2007; Lecca et al., 2014; Broberg et al., 2015). By means of a computable general equilibrium (CGE) model, this paper illustrates that rebound estimates depend heavily on modeling assumptions. As a consequence, in order to derive robust estimates and policy implications, models should critically address the source and reliability of their parameters.

Corresponding author. E-mail address: [email protected] (V. Kulmer).

https://doi.org/10.1016/j.enpol.2019.05.001 Received 7 November 2017; Received in revised form 29 April 2019; Accepted 2 May 2019 Available online 20 May 2019 0301-4215/ © 2019 Elsevier Ltd. All rights reserved.

2

Li et al. (2016) Lin and Du (2015) Thomas and Azevedo (2013b)

Barker et al. (2009)

CGE = computable general equilibrium. Elas = elasticity. EM = energy-material. VAE = value added-energy. KLEM = capital-labor-energy-material. E = between energy goods.

– – Systematic analysis of income elasticity: 100% to +200% 88% 30%–40% Indirect rebound: 30%–40% China China US

– – 31% (short run) to 52% (long run) Global economy

26% UK

Production sectors & households Production sectors & residential services Sectors, household Production sectors Households Barker (2007)

Econometric simulation model

Production sectors Production sectors Turner and Hanley (2011) Yu et al. (2015)

CGE model CGE model

Scotland Georgia (US)

40% (short run) to 71% (long run) Non-electricity rebound: 14% (short run) to 355% (long run) Higher than 100% −10% to +31% UK UK Households Production sectors Lecca et al. (2014) Turner (2009)

CGE model CGE model

40%–70% 30%–160% Sweden Spain CGE model CGE model Production sectors Production sectors Broberg et al. (2015) Guerra and Ferran (2010)

Non-equilibrium macroeconomic model Output distance function Econometric model Extended input-output model

–

Labor market & economic structure Capital adjustment coefficient

– –

Efficiency shock, labor market, budget constraints Efficiency shock, labor market –

Values for EM, VAE elas: 0.1 to 0.7; export elas: 2 50% reduction in EM elas Systematic analysis of VAE elas: 0.05 to 1.45 – Systematic analysis of production (0–2) and trade parameters (0–5) Values for KLEM elas: 0.4, 0.8, 1.1 Values for EM, E and KLEM elas: 0.3, 0.5, 0.7 – 30%–50% UK Production sectors

Methodology

Allan et al. (2007)

The contribution of this paper is threefold: First, we introduce multiple household groups into a CGE model of the Austrian economy and analyze rebound effects emerging from private consumption. In a base model run, we show how households with heterogeneous preferences and economic characteristics respond to a 10% exogenous efficiency improvement in the consumption of fossil fuels, and how this household response translates into economy-wide rebound. Secondly, we illustrate how strongly modeling assumptions drive the model output. In a systematic sensitivity analysis, using the Monte Carlo simulation technique, we vary elasticities of substitution within each household group to illustrate the sensitivity of the economy-wide rebound and to provide an explanation for the high variability of estimates reported in previous studies. Thirdly, our discussion of robustness addresses not just the rebound effect itself, but also the stringency

Targeted actor

1.2. Aim of the paper

Authors

Table 1 Main characteristics of recent studies analyzing the economy-wide rebound effect.

Study region

Economy-wide rebound Effect

Sensitivity of elasticity

The direct rebound effect, that is to say the phenomenon of demand for a good or service increasing after a technological improvement has reduced its costs, is empirically estimated to reach 20%–50% of the expected savings, depending on the consumption domain and country under investigation (e.g. Sorrell et al., 2009; Chitnis et al., 2013; IEA, 2014). The indirect rebound effect describes the subsequent change in energy use due to an increased demand for all other goods and services (Gillingham et al., 2016; Thomas and Azevedo, 2013a). The economywide rebound effect (also known as macroeconomic or aggregate/total rebound effect) emerges when the market adjustments and innovation processes after energy efficiency improvements lead to an overall increase in energy use within an economy (Gillingham et al., 2016). The economy-wide rebound effect subsumes all changes in the economy and hence accounts for direct and indirect rebound effects. Compared with the direct rebound effect, studies of the indirect and economy-wide rebound effect are less prevalent. Estimates of the economy-wide rebound effect range widely, between −10% (negative rebound effect; Yu et al., 2015) and 160% (backfire, i.e. a rebound effect that over-compensates the efficiency gain; Guerra and Ferran, 2010). This great variation is rooted in the methodological approach adopted, targeted actor, nature of the energy efficiency improvement itself, study region, assumptions on key economic characteristics (e.g. the labor market) and data used (see overview in Table 1). The wide spectrum of economy-wide rebound estimates calls for detailed sensitivity analyses to understand the reasons underlying this variation. A few studies have already undertaken valuable efforts in this regard; we return to this topic in Section 2.2 below. The majority of studies of economy-wide rebound are carried out by means of CGE models. Energy efficiency improvements simultaneously impact relative prices, wages, and economic activity; CGE models capture endogenous relative price, income and factor supply effects and hence are well suited to estimate the resulting economy-wide rebound effect (Lecca et al., 2014, Broberg et al., 2015). Most studies focus on energy efficiency improvements in production sectors. They mainly introduce an exogenous and costless energy efficiency improvement, which reduces the energy input per unit of output. Only a few studies (e.g. Lecca et al., 2014; Barker et al., 2009) specifically investigate the rebound effect of efficiency improvements in demand-side household energy use, although the general equilibrium response may differ substantially from production-side efficiency gains. These studies conceptualize households as a single, uniform market agent. However, economic impact studies of energy and climate policy (e.g. Rausch et al., 2011) underscore that (i) the specification of household demand strongly governs the direction and magnitude of impacts and (ii) household behavior is not homogenous, as assumed in most macroeconomic models, but depends on a variety of socio-economic characteristics and consumer preferences.

CGE model

Sensitivity of other parameters

1.1. Estimates of the rebound effect

– – Budget shares, fuel prices,

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Energy Policy 132 (2019) 1–14

V. Kulmer and S. Seebauer

of policies to counteract it. Taking the example of a fossil fuel tax, we study the tax rate required to neutralize the rebound effect and how this tax rate is influenced by key model parameters. This paper thus provides a critical reflection on how taking into account the full variation of model parameters renders CGE model outcomes so unspecific that their practical use becomes questionable, regarding the quantification of impacts as well as specific policy advice derived from these impacts. How arbitrary elasticities shape model outcomes is common economics textbook knowledge (Antimiani et al., 2015; McKitrick, 1998; Shoven and Whalley, 1992). Of course, many CGE modelers use their models to understand underlying dynamics rather than focus on explicit numerical estimates. However, they rely on the structure of the model and specification of parameters, and many CGE modeling approaches do not give their assumptions the full consideration they merit. In the next section, we discuss factors leading to variability in model estimates: household heterogeneity and elasticities of substitution. While our critique also holds for CGE applications in other fields, the present paper focuses on direct and economy-wide rebound effects of energy efficiency improvements in households.

they find crucial impacts of elasticity parameters throughout. In limited sensitivity analyses, Broberg et al. (2015), Turner and Hanley (2011) and Allan et al. (2007) show that the extent of the economy-wide rebound effect of energy efficiency improvements depends on key elasticities in the production function. For instance, Turner and Hanley (2011) show how the economy-wide rebound effect increases with rising values for the capital-labor-energy-material (KLEM) elasticity of substitution in sector-specific production functions. Lecca et al. (2014) find that higher elasticity of substitution in demand for energy or nonenergy material goods slightly increases the direct rebound effect. Although some studies vary certain parameter assumptions (e.g. double or half the default value), there is a considerable lack of structured, systematic efforts to assess the sensitivity of the rebound effect and the parameters driving it. A valuable exception is presented by Turner (2009), who studies the influence of elasticities of substitution in trade and production functions on the non-electricity rebound effect (similar to an indirect rebound effect). She shows that if trade and production are highly interchangeable, the non-electricity rebound effect is quite high and may even overcompensate for the initial efficiency gain. Turner reports a wide range of rebound effects, ranging from negative rebound to multiple backfire. Not surprisingly, she concludes that the rebound effect is highly sensitive to model specifications and to the quality of calibration data; thus, care should be taken when interpreting model results and deriving policy recommendations. Guerra and Ferran (2010) also undertake a detailed sensitivity analysis and find that the elasticity of substitution in production functions between value-added and energy is a key driver of the economy-wide rebound effect. If substitution between value-added and energy is highly elastic, backfire results. All these studies show that in CGE models, the elasticity of substitution strongly governs the economy-wide response to a specific shock. They conclude that there is considerable lack of empirical estimates as well as high-quality data for the specification of production and demand functions in CGE models. Efforts have been made to estimate Armington trade elasticities, which determine the degree of substitution between domestic and imported goods (Hertel et al., 2007; Hillberry and Hummels, 2013; Antimiani et al., 2015), as well as elasticity of substitution in production functions, in particular between capital and labor as well as between capital and energy (Okagawa and Ban, 2008; Koetse et al., 2008; Koesler and Schymura, 2015). However, the quality and robustness of these estimations differ considerably (Antimiani et al., 2015). Estimations of elasticities of substitution in household demand are even scarcer. Reviewing a selection of pertinent CGE models (Bosetti et al., 2006, 2015; Paltsev et al., 2005; Dimararan and McDougall, 2002; Ciscar et al., 2012) reveals that this class of elasticities mostly stems from aggregated data, is poorly supported by empirical data, and is predicated on the assumption of homogenous household preferences. Elasticity values are often extrapolated from other studies or derived from plausible assumptions. Moreover, only a few studies report the applied values of elasticity of substitution in demand functions. This overview on common model deficiencies regarding household heterogeneity and parameterization of elasticities of substitution in household demand functions underlines the need for systematic sensitivity analysis when analyzing macro-economic consequences of changes in household demand. In consequence, sensitivity analyses may improve the validity of model results and subsequent policy recommendations.

2. Crucial CGE model assumptions when estimating the rebound effect 2.1. Household heterogeneity Macroeconomic models, in particular CGE models, generally view consumers as homogenous and model them as a uniform, representative agent. However, policies, global shocks, and other events impact households differently, depending on their income level, expenditure pattern, average age, residential environment, and other socio-economic characteristics (van Ruijven et al., 2015). Regarding energy consumption, the ability of households to switch to more efficient, less carbon-intensive technologies presumably depends on factors such as the infrastructure available at their place of living (e.g., public transport network, e-vehicle charging stations and district heating grid; Wolf and Seebauer, 2014), their access to market information (e.g., level of education and affinity to technology; Steinhorst et al., 2015) and their financial capacity to invest in alternative products (e.g., income; Chitnis et al., 2013), as well as limitations stemming from their way of life (e.g., employment or needs of children and elderly family members; Maxwell et al., 2011). As different household groups have different capabilities to adapt their consumer choices, a model that did not account for household heterogeneity would oversimplify real-world dynamics and, presumably, generate results with restricted validity. In development economics and trade policy research, country-level CGE models often include some degree of household heterogeneity. Typically, household groups are distinguished by income (Savard, 2010; Robilliard et al., 2008; Bertola et al., 2006) and/or level of urbanization (Krey, 2014; Wong and Kulmer, 2012). Factors driving the demand responses of households to policies or shocks (e.g. price elasticities by household group) are, however, ignored. To account for variability in household responses, therefore, we incorporate various household groups in our CGE model. Supply of factor endowments (capital and labor), consumer preferences and elasticity of substitution are specified separately for each household group (Melnikov et al., 2012, van Ruijven et al., 2015); thus, outcomes for consumption, income and welfare are generated for each household group. Household heterogeneity is intrinsically linked to elasticities of substitution: As argued above, elasticities may vary by households’ capabilities to adapt their consumer choices.

3. Definition of the rebound effect

2.2. Elasticities of substitution

The rebound effect is an umbrella term for a variety of economic mechanisms that counteract energy savings from improved efficiency (Sorrell, 2014). Definitions and calculations of the rebound effect vary in the literature in regard to physical quantities versus monetary values, reference to expected savings, etc. In this study, we define the direct

Sensitivity analysis may indicate the modeling error resulting from a lack of reliable elasticity estimates or from inconclusive empirical evidence. Only a few studies conduct sensitivity analyses (see Table 1); 3

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rebound effect as the difference between the proportionate change in actual household energy use and the proportionate change expected from the energy efficiency improvement (Saunders, 2000; Lecca et al., 2014). We are, thereby, using the most common and recent definition of the rebound effect (Broberg et al., 2015; Lecca et al., 2014; Gillingham et al., 2016). We define the direct rebound effect in household consumption (RD) as:

RD = 1

ECactual ECpotential

finite number of groups, where each group is endowed with capital, and skilled and unskilled labor. The r household groups represent different lifestyles and types of consumers. The r-th household group receives its income from varying sources: by providing the primary factors capital, and skilled and unskilled labor, as well as by receiving governmental transfers. In the nesting structure of consumption, the following CES substitution possibilities are assumed: At the top level, the materialtransport composite and the energy composite trade off. The energy composite is specified by a trade-off between energy goods, wherein coal, oil, gas and electricity are substitutes, while in the materialtransport composite, material inputs and passenger transport trade off. Each household group chooses consumption and savings to maximize its utility, subject to budget constraint (see Figure A-2), i.e., within each household group, the income spent for consumption and savings equals the sum of factor endowment and transfers. With regard to the public household governmental revenues are generated by taxing intermediate inputs, exports, factors of production, and demand-side consumption goods. The government's budget constraint requires that all net tax revenues are balanced by expenditure and transfers. Governmental revenue is assumed to be a flexible residual, while all tax rates are fixed. In general, domestic output is apportioned between domestic consumption and exports through a constant elasticity of transformation (CET) function. Domestic consumption comprises demand for public and private goods, investment and intermediate demand. With regard to imports, we follow Armington (1969) and assume product heterogeneity between domestic goods and imports through using a CES function. Regarding the external balance, since Austria mainly trades within the European Monetary Union, the real currency exchange rate (indexed to the model numéraire) is fixed, while foreign savings and the trade balance are flexible.

100

where ECactual is the actual percentage change in household energy consumption due to the improved energy efficiency as calculated by the model, and ECpotential is the potential saving in energy consumption originally expected from the efficiency measure. The economy-wide rebound effect (RE) of an energy efficiency improvement in consumer products and services describes the impact on energy use across the entire economy, combining both consumption and production, and is formulated as:

RE = 1

ETactual ETpotential

100

where ETactual denotes the actual change in total energy use and the term ETpotential describes the potential change in total energy use. Thereby , describes the initial share of household energy consumption EC in total energy use ( = ET ; [0,1]). Thus, ETpotential can be expressed as ECpotential . For both definitions (RD and RE), a value of 100 would imply a complete rebound effect resulting in zero net energy savings, while a value of 0 implies that the intended reduction is fully achieved, and a rebound effect does not occur. For instance, if the energy efficiency improvement is expected to reduce energy use by ten percent but in practice the energy use decreases by just five percent, then the rebound effect amounts to 50%. In case the actual change is higher than the potential change in energy use, negative rebound results. Negative rebound is mathematically possible, but has so far not been observed empirically. We hence preclude negative rebound effects and follow pertinent literature with the rebound effect being between 0 and 100.

4.2. Calibration and data The CGE model is calibrated to the Social Accounting Matrix (SAM) of Austria, which comprises 45 production sectors, three primary factors and six household groups. The SAM is derived from the Austrian input-output table of 2009 (Statistics Austria, 2015). Values for elasticities of substitution in production sectors are taken from Okagawa and Ban (2008) and Koesler and Schymura (2015), who provide a comprehensive sectoral coverage for industrialized countries such as Austria. Armington trade elasticities stem from Dimaranan and McDougall (2002). Private consumption in the SAM is split into six household groups representing different lifestyles of energy and mobility behavior. In consequence, consumption patterns, sources of income and other socio-economic characteristics vary between the groups. The six household groups are calibrated using the Austrian Household Budget Survey (HBS) of 2005.1 The Austrian input-output table, and by extension the SAM employed in the present study, builds on the HBS to describe final demand in more detail. Both input-output table and HBS conform to each other (deviations in income and consumption expenditure amount to less than ± 8%; these deviations mainly emerge from the input-output table drawing on additional data sources besides the HBS). In order to form household groups characterized by high homogeneity within groups and high heterogeneity between groups, we use four indicators that successfully predict energy consumption and travel behavior (Boarnet and Crane, 2001; Giuliano and Dargay, 2006; Brounen et al., 2012): households with or without a car, at least one

4. Methodological approach 4.1. CGE model We apply a comparative-static, multi-sector, open economy, single country CGE model of the Austrian economy. The model extends that of Kulmer (2013) by incorporating multiple household groups while abstracting from the recursive dynamic application. The model description provided here is restricted to a non-technical summary of key characteristics, focusing on the implementation of household heterogeneity; detailed information on nesting structure and economic sectors, as well as an algebraic description of the core model, can be found in the Appendix and in Kulmer (2013). The model comprises 45 sectors, divided into energy sectors, nonenergy sectors, material sectors and passenger transport. On the production side, firms minimize costs of producing output subject to a nested constant elasticity of substitution (CES) function that describes the price-dependent use of factors and intermediate inputs (see Figure A-1; all figures and tables available in the Appendix are indicated by separate numbering with the prefix A). Several levels specify the substitution possibilities in domestic production of sector i between the primary factors (capital, skilled and unskilled labor), intermediate energy (e.g. fossil fuels) and material inputs (e.g. textiles, office machinery). Closures are obtained assuming that factors (capital, and skilled and unskilled labor) are mobile across sectors and fully employed. Following Melnikov et al. (2012), heterogeneous consumers with varying CES utilities are considered: The whole population is split into a

1 The national input-output tables from 2007 to 2010 provided by Statistics Austria use the HBS 2005 to specify final demand. The Austrian Household Budget Survey is conducted every five years. A comparison with HBS 2010 shows no significant differences in household consumption patterns such as expenditure shares and income. The six household groups may thus be considered representative and stable over time.

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Table 2 Description of household groups. Car in the household

HG1 HG2 HG3 HG4 HG5 HG6

Yes Yes No Yes Yes No

Region type

Urban Rural Urban and Rural Rural Urban Urban and Rural

Children in the household

No Yes Yes & No No Yes Yes & No

Employment status

Active and Active and Active Active and Active and Inactive

inactive inactive inactive inactive

Share of the Austrian population

15% 32% 5% 10% 23% 15%

Elasticity of substitution in the base model σefhh

σetm

0.54 0.45 0.5 0.6 0.58 0.4

0.4 0.3 0.35 0.3 0.4 0.25

HG = household group. σefhh = elasticity of substitution between energy goods. σetm = elasticity of substitution between energy and non-energy goods.

household member in gainful employment or none employed, households with or without children, and households living in urban or rural regions. After aggregating household groups with nearly identical expenditure patterns and income, six distinct household groups remain. Table 2 summarizes the characteristics of each household group; Table A-1 compares how much these groups spend in selected economic sectors. Households with children (i.e., the groups HG2 and HG5) spend a higher share of their total expenditures for energy than households without children (HG1, HG4). The expenditure share for mobility is higher among households in rural (HG2, HG4) than in urban areas (HG1, HG5). Households without a car (HG3, HG6) spend a larger part of their income on public transport than households with a car (all other HGs). Households in active employment (HG3) have a higher income and therefore consume more than inactive households (HG6). Following specifications of similar CGE models (Bosetti et al., 2006, 2015; Paltsev et al., 2005; Lecca et al., 2014), the values for the elasticity of substitution between energy goods (σefhh) in the base model are assumed to be 0.40 to 0.60, while the top-level elasticity of substitution between energy and non-energy material goods (σetm) is assumed to be 0.25 to 0.40. Details of the specification and its underlying rationale are given in the Appendix.

based energy use per unit consumption expenditure is reduced by 10% from the energy-efficiency improvement, while this change is matched by an equal change in consumption expenditure of all other goods, given their initial distribution. Total consumption expenditure hence remains unchanged. 4.4. Analytical strategy First, a base model run analyzes the magnitude of rebound resulting from a 10% improvement in fossil fuel efficiency. Both (i) the direct rebound effect within each household group and (ii) the economy-wide rebound effect, reflecting all direct and indirect linkages in the economy, are discussed. This serves to illustrate the impact channels of the economy-wide rebound effect and how household heterogeneity affects the adjustment processes in these channels. The base model run is compared to a benchmark scenario the CGE model is calibrated to, illustrating the status quo of the Austrian economy. Secondly, we investigate the robustness of the estimated rebound effects and their sensitivity to two crucial parameters in household demand: elasticity of substitution between energy and non-energy goods (top-level nesting), and between the energy goods coal, oil, gas and electricity. Gillingham et al. (2016) point out that the rebound effect of energy efficiency improvements in private consumption is largely driven by the magnitude of substitution elasticities in the household utility function. In particular, they highlight how the elasticity of substitution between goods and energy services via the channel of inter-sectoral allocation, and the energy elasticity of substitution via macroeconomic relative price responses, govern the economy-wide rebound effect. Using Monte Carlo techniques, we conduct 80 simulation runs for each elasticity parameter. This is sufficient for convergence into a clear picture; running more than 80 simulations would be possible but does not yield different results. Random number generator functions using the linear congruential method produce a series of elasticity values from a uniform distribution covering an interval from 0.1 to 1.2 (Gentle, 2003). This interval draws on two assumptions: (i) While a value of 0 implies Leontief-type fixed proportions, 0.1 reflects a lower bound of substitutability. (ii) In the CGE model literature, 0.7 is the highest reported elasticity of substitution between energy goods in household demand; the respective maximum elasticity for energy and non-energy material goods is 0.4. The value of 1.2 is selected as upper bound, since it greatly exceeds these reported values. Given the lack of reliable, empirically based elasticities, the chosen distributional interval is, on the one hand, wide enough for the purposes of our sensitivity analysis, and on the other, narrow enough to represent the Austrian context. In each simulation run, elasticity parameters are randomly generated at the household group level; thus, within the same simulation run, some groups may feature higher and others lower elasticities, by comparison with each other and their elasticities in other simulation runs and the base model run. All other model parameters are kept as in the base model run. Similarly, the particular elasticity of substitution not targeted in a specific simulation run is held constant during this run; thereby, we analyze how the economy-wide rebound

4.3. Energy efficiency scenario assumptions Among the few studies analyzing the economy-wide rebound effect of energy efficiency improvements in households, it is common practice to assume an exogenous, costless increase in energy efficiency (Lecca et al., 2014; Thomas and Azevedo, 2013a; Freire-González, 2011, Barker , 2007). This zero-cost breakthrough approach introduces a shift in consumption within the energy composite from fossil fuel based energy goods to non-energy goods by matching a relative decrease in household consumption of fossil fuel energy goods (coal, oil and gas) with an equal relative increase in all other consumption goods (this specification follows Lecca et al., 2014). The resulting consumer and market responses are hence a pure rebound effect, since only those responses are measured that happen in consequence of the energy efficiency improvement. This approach thus provides clear guidance on how isolated changes in energy efficiency would change energy use (Gillingham et al., 2016). The present study mirrors this approach, assuming a 10%2 improvement in fossil fuel efficiency for each of the six household groups, thus shifting household consumption towards non-fossil energy carriers (electricity) and material goods produced with less fossil fuel input. In terms of model specification, an exogenous parameter is introduced to the demand function of private households, similar to the AEEI parameter in production functions (Löschel, 2002; Kemfert, and Welsch, 2000). This parameter reflects that fossil-fuel 2

The 10% stringency level of the efficiency improvement presents an upper bound in previous studies analyzing the economy-wide rebound effect (see the references listed in Table 1), but judging by the performance of energy efficiency technologies already established on the market, 10% seems a rather conservative assumption. 5

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effect is influenced by elasticity of substitution in household demand – a facet neglected by many macroeconomic studies. Thirdly, we address the stringency of fossil fuel tax required to offset the economy-wide rebound effect. This is implemented in the model as an additional tax on fossil fuel use (oil, gas and coal) in household demand that applies equally to all household groups. The fuel tax is taken as an exemplary fiscal policy measure, since it is the most direct instrument to counteract the rebound effect, frequently applied in practice and easy to implement in a CGE model (Freire-González and Puig-Ventosa, 2015). Fuel tax revenue is assumed to be spent in the same proportions across all sectors as other governmental revenues; this precludes, for the purposes of modeling clarity, any policy mix such as welfare transfers to disadvantaged population segments hit most severely by the fuel tax (see Section 6). Again, we show how this tax rate is influenced by key elasticity parameters in the demand of households with heterogeneous consumption patterns. Fiscal policies impact the relative price of the good or service directly. Hence, how the economywide implications of a fuel tax unfold depends on the elasticities specified in the household demand functions. Similar Monte Carlo simulations to the above identify the sensitivity of the neutralizing fuel tax rate. Each simulation run (90 runs per elasticity parameter) applies random values for the targeted elasticity of substitution and the level of fuel tax, while holding all other parameters at their base value, in order to identify combinations of both parameters that are able to offset the economy-wide rebound effect (RT = 0, with a tolerance of ± 5%); thereby, we shed light on the question of reliability and practicability of policy recommendations.3

estimate of the economy-wide rebound effect lies within the range of recent studies (Lecca et al., 2014; Broberg et al., 2015; Yu et al., 2015) and shows no indication of backfire as in Turner and Hanley (2011). Comparing the economy-wide rebound effect with the direct rebound effect indicates that the indirect rebound channel dominates. Direct rebound builds up through indirect linkages between sectors and affects the entire economy. The shift in household expenditure stimulates production in several sectors, such as food, agriculture, transport, electricity and financial services (with an output increase of between +0.01% and +0.03%). Some of these sectors, such as financial services, have relatively small fuel content; others, however, such as electricity, agriculture and transport, are characterized by relatively high fuel intensity, and hence trigger indirect rebound channels. Taken together, the base model run reflects results found in other studies: modest absolute effects on the economy, and economy-wide rebound exceeding direct rebound. Systematic variation of elasticities however paints a much more ambiguous picture. 5.2. Sensitivity of the direct and the economy-wide rebound effect The sensitivity analysis first reveals that the direct rebound in household energy consumption is more strongly affected by the energy elasticity of substitution than by the top-level elasticity between energy and non-energy material goods. This result is not surprising, since the energy nest (comprising gas, oil and coal, and electricity) is the most proximate avenue of substitution in the assumed nesting structure (Figure A-1). Introducing different values for energy elasticity in the Monte Carlo simulation runs lets the direct rebound range across all household groups between 2% and 30% (green whiskers in Fig. 1). In contrast, simulating different top-level elasticities implies a smaller range, between 6% and 18% (black whiskers in Fig. 1). Secondly, results confirm that the heterogeneous characteristics of households matter. The size of the direct rebound varies among the six household groups, regarding the effects observed in the base model as well as the median effects and range resulting from the simulation runs (shown as yellow crosses, grey crosses and whiskers in Fig. 1, respectively). Fig. 2 illustrates on the example of HG4 the direct rebound in respect to the energy elasticities of HG4 and HG5. In case of homogeneous households, HG4's direct rebound effects obtained in the simulation runs, as indicated by the dots in Fig. 2, would continuously increase along the diagonal from the lower left corner to the upper right corner. Instead, the asymmetric clusters of simulation runs with high direct rebound (as in the upper left quadrant and lower right quadrant in Fig. 2) emphasize the relevance of household heterogeneity. The possible bandwidth of the direct rebound effect across all simulation runs and over all household groups is illustrated in Figure A-2. Thirdly, the particular composition of elasticity values across household groups drives the magnitude of the rebound. Direct rebound may reach up to 30%, if a simulation run allocates elasticity values in such a way to the respective household groups that reciprocal feedback effects build up between groups. For instance, if high-income households, who spend more for energy goods and contribute a higher share to total consumption, are assigned high energy elasticity values, they impose more substantial changes on the economy. This higher stimulus influences all other household groups to adjust their respective consumption bundles. Those individual adjustment processes accumulate to more substantial economy-wide effects. Again, Fig. 2 exemplifies this dynamic by juxtaposing the energy elasticities in household groups with contrasting consumption possibilities (rural, low-income households HG4 versus urban, high-income households HG5). The direct rebound effect in HG4 increases in cases where HG5 also shows a high degree of substitutability. Fourthly, as already indicated in the base model run (see Section 5.1), our findings show a positive, almost linear relationship between the energy elasticity of substitution and the direct rebound effect (see Figure A-2). A large number of substitution possibilities between energy

5. Results 5.1. Base: rebound effect of a 10% efficiency improvement in fossil fuel consumption The demand shock of a 10% efficiency improvement in fossil fuel consumption boosts the economy slightly; Table 3 summarizes the changes in key economic variables relative to the benchmark scenario: Total output and gross domestic product (GDP) rise. There is also a positive stimulus on the labor market and wages for skilled and unskilled labor increase. Consequently, the consumption level in all household groups rises. Increase in consumption varies slightly between household groups, between 0.12% and 0.17%. Fossil fuel consumption in all household groups falls considerably by 8.9%–9.3%. Thus, the direct rebound effect is quite small and amounts to between 8.0% and 12.5%, depending on the household group (yellow crosses in Fig. 1): Households with a lower elasticity of substitution between energy goods (coal, oil, gas and electricity) show a higher reduction in fossil fuel consumption, thus a lower direct rebound effect, than households with a higher elasticity. Contrastingly, in HG5, an easier substitution between energy goods (σefhh = 0.58) and a relatively high income coincides at the highest rebound effect among all household groups. This finding already underlines the importance of sensitivity analysis of key elasticity parameters within heterogeneous households. Generally, the impacts of efficiency gains in household fossil fuel consumption on the economy as a whole are rather small. Leaving aside linkages and adjustment processes between economic sectors, total fossil fuel demand should fall by 0.72%. However, when accounting for shifts in consumption patterns and changing relative prices in the economy, the impact on the economy-wide fossil fuel output decreases by just 0.25%. This results in an economy-wide rebound effect of fuel efficiency improvements in households of 65.2%. The size of the economy-wide rebound effect is quite stable, even when assuming much higher fossil fuel efficiency improvement rates of 20%–30%. Our 3 In order to ensure valid comparison the same variable is chosen as numeraire in all base and simulation runs.

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Table 3 %-Change in key economic variables resulting from the 10% efficiency improvement in fossil fuel consumption in private households relative to the benchmark scenario. %-change

%-change

Gross domestic product

+0.11%

Real wages

Fossil fuel output Electricity output Economy-wide rebound

−0.25% +0.04% 65.2%

Household consumption Household fossil fuel consumption Direct rebound a

Skilled labor +0.03% Unskilled labor +0.06% +0.12% to +0.17% −8.9% to −9.3% 8.0%–12.5%

a: Fig. 1 shows the direct rebound for each household group in the base model run.

Fig. 1. Range of direct rebound with respect to energy elasticity of substitution and top-level elasticity of substitution, by household group. HG = household group. En = energy elasticity of substitution. Tl = top-level elasticity of substitution. Base = rebound estimate in the base model run. Median = median rebound estimate from simulation runs. Whiskers show the minimum and maximum effects observed in the simulations runs.

goods implies considerably higher direct rebound effects. Regarding the top-level elasticity between energy and non-energy material goods, the interrelation between substitution possibilities and the direct rebound effect is less pronounced. Furthermore, sensitivity analysis provides information about the robustness of the economy-wide rebound effect. The base model run

yields an economy-wide rebound effect of 65.2%. The Monte Carlo simulation runs imply a certain variability in the economy-wide rebound; however, with a range from 60% to 73%, it is not intensely sensitive. In an analogous way to direct rebound, the more easily households can substitute between energy goods, the higher the economy-wide rebound effect. The influence of the top-level elasticity between energy 7

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Fig. 2. Direct rebound of HG4 in respect to the energy elasticities of HG4 and HG5. HG = household group. Each dot refers to a specific simulation run with a particular combination of HG4 and HG5 energy elasticities. The numbers in the dots state the direct rebound effect in % observed in this specific simulation run. The size of the dots corresponds to the magnitude of the rebound effect.

Fig. 3. Range of economy-wide rebound with respect to energy elasticity of substitution and top-level elasticity of substitution. Base = rebound estimate in the base model run. Median = median rebound estimate from simulation runs. Whiskers show the minimum and maximum effects observed in the simulations runs.

and non-energy material goods is weaker by comparison (as shown by the shorter black whisker in Fig. 3). The minimum economy-wide rebound observed (60%) is generated in a simulation run where an energy elasticity lower than 0.3 is assigned to all household groups, whereas in the maximum case (73%), all household groups are assigned energy elasticity values higher than 1.0. Comparing the range in the direct rebound (2%–30%) to the range in the economy-wide rebound (60%–73%) again illustrates the dominant impact of the elasticity of substitution between energy goods on the size of the rebound effect. Direct rebound is directly affected by this elasticity; thus, the range is greater. Regarding the economy-wide rebound, the impact of this elasticity is diluted in inter-sectoral linkages and adjustments; thus, the range is smaller. Bearing in mind that the economy-wide rebound of 65% is already quite high, the range obtained in the simulation runs brings the economy-wide rebound even closer to the point where energy efficiency improvements are ineffective or even backfire. This underlines the importance of robust estimates of crucial parameters as well as careful interpretation of results. Summarizing, the simulation runs highlight that accounting for household heterogeneity leads to non-linear and reciprocal effects in how rebound appears in particular household groups.

5.3. Sensitivity of the fossil fuel tax to offset the economy-wide rebound effect Concluding our sensitivity analysis, we address the uncertainty that arises when translating economic model output into policy design. We identify the stringency of fossil fuel tax needed to neutralize the economy-wide rebound effect in our scenario of a 10% efficiency improvement in the fossil fuel consumption of private households. Note, though, that this modeling exercise only serves to illustrate the crucial role of elasticity assumptions and does not address the manifold challenges of actually putting a fossil fuel tax into practice, such as enforcement, opportunity costs, and similar. A critical discussion of distributive impacts of fuel or CO2 taxes on households and possible implementation channels is provided in Callan et al. (2009), Feng et al. (2010) and Rosas-Flores et al. (2017). In the base model run, a tax rate of 43% neutralizes the economywide rebound effect. Across all Monte Carlo simulation runs, determining the tax rate required to offset the particular economy-wide rebound for each simulation run, the tax rate spans between 15% and 80% (see whiskers in Fig. 4). This wide range makes it difficult to derive specific policy advice from the model output. Like the sensitivity results above, we find that the tax rate which 8

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Fig. 4. Range of fossil fuel tax rate which offsets the economy-wide rebound with respect to the energy elasticity of substitution. Base = fuel tax rate in the base model run. Median = median fuel tax rate from simulation runs. Whiskers show the minimum and maximum rates observed in the simulations runs.

compensates the economy-wide rebound is chiefly driven by the elasticity of substitution between energy goods. A 15% tax rate suffices, if all household groups switch easily between energy goods (elasticity values between 0.9 and 1.1). Under this parameter setting, households substitute fossil fuels with electricity in order to avoid the pressure from the tax, which increases the relative price of fossil fuel compared to the benchmark scenario. In contrast, a tax rate of 80% or more has to be applied if households are severely restricted in shifting between energy goods (elasticity values between 0.1 and 0.3 across all household groups).

range of the direct rebound effect is connected to heterogeneity between household groups. The direct rebound effect of a particular household group not only depends on its own demand specification (e.g., elasticities, expenditure in various consumption domains and income) but may be boosted if the reactions of other household groups shift energy prices. Within unfavorable constellations of elasticities between household groups, reciprocal feedback effects may occur. This underlines the relevance of considering multiple households, with diverse market and relative price responses, as well as diverging consumer preferences. Moreover, detailing household groups may facilitate the transfer of macroeconomic models to other contexts, either spatially to other countries or temporally to future population structures. In both cases, if the demand specification of each household group remains unchanged but these groups constitute segments of the overall population of varying size, adjusting the relative weights in expenditure shares may suffice for model transfer. The high sensitivity of model outcomes to assumed elasticity values strongly compromise the usefulness of specific policy advice. In the base model run, a fossil fuel tax rate of 43% applied across all households neutralizes the economy-wide rebound effect of 65%. However, we find that the tax rate in the simulation runs varies between 15% and 80%. Such an extreme range is neither practical nor suitable information for policymakers, who normally call for precise projections of policy impacts. Unless based on reliable estimates of elasticity parameters, a tax rate determined by modeling will most likely miss its objective of neutralizing rebound and bring about unwanted economywide side effects (e.g., a drastic fall in GDP and output of economic sectors, or only negligible reductions in carbon emissions). In such a case of unreliable a-priori policy assessment, the tax rate would then have to be gradually shifted towards a more appropriate level, incurring additional opportunity costs of reiterating the policy discourse, approximating the optimal stringency level through monitoring ongoing rebound effects, constantly revising administrative processes, etc. The example of a fossil fuel tax or any similar fiscal measure illustrates that sound policy strategies need to consider the concepts analyzed in the present study. First, since the indirect rebound channel is the driving force of the economy-wide rebound effect, the neutralizing tax rate, which directly affects fuel consumption, has to be relatively high to achieve its purpose. Secondly, to be effective, a fuel tax requires high substitutability between energy goods, so that consumers are able to shift from the taxed good (e.g., oil) to an untaxed good (e.g., electricity). If substitutability is weak, the tax rate has to be set so high that it exceeds political feasibility and public acceptance. Thirdly, the substitutability question becomes even more problematic when a uniform tax rate is applied across all household groups. Our household heterogeneity results indicate that effects on group-specific welfare depend on each group's elasticity of substitution between energy goods. Take the example of a household that spends a large share of its expenditures for fuel, but cannot shift to untaxed energy goods because it is not affluent enough to invest in or access other energy technologies: This household

6. Conclusions and policy implications This paper illustrates that when investigating rebound effects with the help of CGE or similar macroeconomic models, the insights obtained depend on how household heterogeneity and elasticities of substitution are addressed. To this end, we conduct a sensitivity analysis using Monte Carlo simulation of the impacts of a 10% efficiency improvement in the fossil fuel consumption of Austrian households. We find a substantial economy-wide rebound effect of 65% but a comparatively weak direct rebound effect of 8%–12%. This underlines that indirect linkages between economic sectors provide a more relevant impact channel for rebound, and that we need to develop a better understanding of these processes in order to devise strategies to prevent or combat rebound. The importance of the indirect rebound channel is also observed by Barker, (2007) and Lecca et al. (2014). In the present study, the changing relative prices and the shift in household consumption towards non-fossil fuel goods stimulate production in economic sectors that rely heavily on fossil fuels, in particular agriculture, food and transport. From a policy perspective, this result calls for smart policy mixes including regulations and market-based instruments, targeting energy use across various areas of consumption and production. Examples for such cross-sectoral measures are emission trading schemes including household consumption, economy-wide CO2 tax or nationwide CO2 standards in products and services. Still, our model results offer no more than a preliminary picture of the economic dynamics underlying the rebound effect; how adjustments between sectors gradually unfold over time clearly merits more detailed analysis in future studies. Monte Carlo simulation runs show that the size of the economywide rebound effect is less sensitive to model specification (i.e., varies across a smaller range) than direct rebound. The size of both rebound effects may be traced back to the elasticity of substitution between the energy goods coal, oil, gas and electricity. The influence of this particular elasticity is not surprising, since our scenario directly affects fossil fuel consumption, and we assume substitutability between all energy goods (coal, oil, gas and electricity) in the energy nest. Consequently, due to the energy efficiency improvement relative prices change and as the price of fossil fuels falls, an immediate shift from electricity towards the fossil fuels takes place. The Monte Carlo simulation runs furthermore highlight how the 9

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faces large welfare impacts, as it cannot avoid the additional costs of the tax. In addition, the tax fails to achieve the original policy intention of reducing this household's fuel consumption. Governmental transfers or other supporting measures thus seem necessary to cushion detrimental impacts of a fossil fuel tax on disadvantaged population segments. For instance, tax revenues could be redistributed as direct payments to less affluent households with limited substitution possibilities. Future research could follow up on this question of equity and tax efficiency and analyze group-specific welfare impacts. Taken as a whole, this study confirms that the specification of household demand ultimately drives modeling results and derived policy recommendations; thus, we strongly advocate robust and reliable empirical estimates for elasticity parameters, as well as accounting for heterogeneous consumer preferences. We recommend using Monte Carlo simulation for detailed checks of robustness. Future studies should strive to obtain empirical elasticity estimates from household surveys of accomplished adopters of energy efficiency technologies, such as electric vehicles or retrofitting of buildings. This might help to pre-estimate the magnitude of the rebound effect and to provide solid and robust recommendations on the stringency level of policy options which might neutralize rebound. Nevertheless, surveying households includes its own limitations: Surveys need to address specific, narrow consumer technologies, which must then be extrapolated to the entire energy sector. Moreover, estimating reallocation of consumption and the resulting rebound effects requires careful disentangling of observed energy savings from factors operating at the same time such as background consumption trends, changes in households’ living conditions and context, or exaggerated initial expectations for energy savings. At the very least, a household survey may provide insights on the relative size of elasticity parameters between household groups. All the same, we are very aware that obtaining more realistic elasticity parameters is a far more challenging endeavor than simply conducting a survey, and would welcome additional approaches addressing this important topic. Our call for an empirical foundation to model assumptions also

extends to the Monte Carlo approach. In the present study, we applied a uniform distribution when generating random elasticity values, for lack of empirical data. However, other distribution shapes for elasticity values (e.g., normal distribution) or different shapes for each household group might be more realistic. Again, future empirical research could provide guidance on which distribution shapes and parameters to enter into a Monte Carlo sensitivity analysis. In a broader context, our critique of CGE modeling assumptions extends beyond the issue of household demand investigated here to the production-side impacts of energy efficiency improvements. Changes in production sectors cause more severe and quite different changes throughout the economy than do changes in household demand. But as in modeling household demand, the magnitude, and even direction, of macroeconomic impacts arising from changes in production sectors depend on key parameters, such as trade elasticities and assumptions about the labor market and international trade, as well as elasticities of substitution in production functions (Antimiani et al., 2015; Landis, 2012; Beckman et al., 2011; Hertel et al., 2007). A production-side perspective could also expand on our scenario of an exogenous, costless energy efficiency improvement. Receiving the efficiency improvement free of charge provides households with a windfall increase in available budget. Rebound inevitably follows, when this additional budget is spent on other goods and services. In contrast, when modeling an endogenous efficiency improvement, households have to pay upfront investment costs. By restricting the available household budget, this should buffer short-term rebound until the investment has depreciated in value. We would thus welcome further studies on economy-wide rebound effects that also address the question of sensitivity and parameterization of CGE production functions. Acknowledgements This research received financial support from the Austrian Climate and Energy Fund and was carried out within the Austrian Climate Research Program (funding no. B464789).

Appendix CGE Model structure Behavior of firms Figure A-1 describes the several levels that specify the substitution possibilities in domestic production of sector i between the primary factors, intermediate energy and material inputs. In the upper nest, material goods trade off with an energy-value added composite ( mkle ). Within the latter, energy inputs and value added trade off. The value added is specified by a trade-off between capital and the labor aggregate, where skilled and unskilled labor trade off. Within the energy-composite, energy goods eff governs the substitution between energy goods (coal, oil, electricity, gas).

Fig. ure A-1. Nesting structure of domestic production in the CGE model.where between the material aggregate and the energy-value-added composite between energy aggregate and value-added composite eff among energy inputs in the energy aggregate lk between labor and capital in the value-added composite su between skilled labor and unskilled labor in the labor composite ma among non-energy, material inputs in the material aggregate mkle

elk

10

denotes the following production sector substitution elasticities:

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Behavior of private households

Fig. ure A-2. Nesting structure of household consumption in the CGE model where

denotes the following production sector substitution elasticities:

among energy goods in the energy aggregate between consumption and savings in final demand etm between energy and the material-transport composite thh between passenger transport and the material composite (other consumption of material goods) mhh among consumption goods in the material aggregate effhh cs

Values for the elasticity of substitution between energy goods (σefhh) and between energy and non-energy goods (σetm) are derived from the literature. A detailed review of a selection of pertinent CGE models (Bosetti et al., 2006, 2015; Paltsev et al., 2005, Dimararan and McDougall, 2002; Ciscar et al., 2012, Koesler et al., 2015) reveals that only a few studies report the applied values of elasticity of substitution in demand functions. Bosetti et al. (2006, 2015) and Paltsev et al. (2005) report a top-level elasticity of 0.5 and 0.7 respectively (the elasticities do not differ between countries in the multi-regional model structure). The elasticity of substitution between energy goods in household demand varies between 0.2 and 0.4 (Bosetti et al., 2006, 2015; Paltsev et al., 2005). We use the values drawn from the literature as guidance and distinguish between the household groups on the basis of plausible judgment and specific findings for Austria (Kletzan-Salmanig et al., 2009): (i) Urban areas offer a larger set of consumption possibilities than rural areas and hence more flexibility. (ii) Households in urban areas have a higher average income than households in rural areas and hence substitute more easily between products and (iii) households with lower income and no car access are the least flexible regarding their consumption possibilities. Table A-1

Specification of household groups: expenditure shares in selected sectors

HG1 HG2 HG3 HG4 HG5 HG6

Energy exp.

MPT exp.

PT exp.

3% 4% 4% 4% 6% 6%

15% 20% 6% 18% 18% 4%

3% 2% 5% 3% 1% 3%

HG = household group. Exp. = expenditure. MPT = motorized passenger transport. PT = public transport. Mean differences in expenditure shares between household groups are statistically significant (ANOVAs, p < .01).

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CGE model results

Fig. ure A-3. Bandwidth of the direct rebound effect across all Monte Carlo runs and household groups with respect to the energy elasticity of substitution.

List of sectors Table A.2

List of economic sectors. Sector

Description

AGR FORR CT PTC MINN FOOD TEXT COC CHEM GUM GLA MET METP MACH OFF EN MANU VEH VPO OVEH FLIE RG ELY CONT SM SB RS WS SMV STW GST RET PT OTR SHIP AIR TSER COM INS FIN

Products of agriculture, hunting and related services Products of forestry and fishing Coal and lignite; peat Crude petroleum and gas Other mining and quarrying products Food products, beverages and tobacco Textiles, leather products, wearing apparel Refined petroleum products, oil and other fuels Chemicals, chemical products and man-made fibres Rubber and plastic products Other non-metallic mineral products Basic metals Fabricated metal products, except machinery and equipment Machinery and equipment n.e.c. Office machinery and computers Electrical machinery and apparatus n.e.c. Manufacturing Passenger car Other transport equipment Other motor vehicles Furniture; other manufactured goods n.e.c. Secondary raw materials Electrical energy, steam and hot water Construction work Sale of motor vehicles < 3.5 tons Sale of motor vehicles > 3.5 tons Repairing services Whole sale of motor vehicles and supplies Sale of motor vehicles and supplies Sale of two-wheelers Services of gas station Retail Public transport Land transport; transport via pipeline services Water transport services Air transport services Supporting and auxiliary transport services Post and telecommunication services Insurance and pension funding services Financial intermediation services

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Table A.2 (continued) Sector

Description

RD COSER PUS ROSI PRS MPT

Research and development services Other business services Public services Rest of other services Private services Motorized passenger transport

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